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U N I V E R S I TAT I S O U L U E N S I SACTAG

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eli Kortela

OULU 2006

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Heli Kortela

THE PREDICTIVE POWER OF FINANCIAL MARKETSESSAYS ON THE RELATIONSHIP BETWEENTHE STOCK MARKET AND REALECONOMIC ACTIVITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION,DEPARTMENT OF ECONOMICS,UNIVERSITY OF OULU

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A C T A U N I V E R S I T A T I S O U L U E N S I SG O e c o n o m i c a 2 5

HELI KORTELA

THE PREDICTIVE POWER OF FINANCIAL MARKETSEssays on the relationship between the stock market and real economic activity

Academic dissertation to be presented, with the assent ofthe Faculty of Economics and Business Administration ofthe University of Oulu, for public defence in AuditoriumTA105, Linnanmaa, on December 2nd, 2006, at 12 noon

OULUN YLIOPISTO, OULU 2006

Copyright © 2006Acta Univ. Oul. G 25, 2006

Supervised byProfessor Juha Junttila

Reviewed byProfessor Johan KnifProfessor Juuso Vataja

ISBN 951-42-8307-4 (Paperback)ISBN 951-42-8308-2 (PDF) http://herkules.oulu.fi/isbn9514283082/ISSN 1455-2647 (Printed)ISSN 1796-2269 (Online) http://herkules.oulu.fi/issn14552647/

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OULU UNIVERSITY PRESSOULU 2006

Kortela, Heli, The predictive power of financial markets. Essays on the relationshipbetween the stock market and real economic activityFaculty of Economics and Business Administration, Department of Economics, University of Oulu,P.O.Box 4600, FI-90014 University of Oulu, Finland Acta Univ. Oul. G 25, 2006Oulu, Finland

AbstractThis thesis investigates whether stock returns can help forecast macroeconomic activity. Futureearnings and dividends and current stock prices should contain information about the future state offirms and the consumption possibilities of consumers. These activities are linked to aggregateeconomic development and, hence, the stock markets should improve economic forecasting.

We review the theoretical points that justify the importance of stock markets in economicforecasting. Recent literature on the stochastic discount factor in asset pricing and the real businesscycle models has approached this connection. We try to show that the direction between financialmarkets and macroeconomy could be from stock markets to real economy.

We empirically test the forecasting ability of stock markets with respect to macroeconomy. Theunexpected part of stock return can be revealed with economic tracking portfolios (ETP), which areconstructed so that the unexpected portion of the portfolio return has the maximum correlation withrevisions to expectations of the target variable. ETP's track how investors revise their expectationsabout relevant macroeconomic variables. The results show that specific stock portfolios track futurechanges in macroeconomic variables well.

In the previous literature, stock returns have been connected to the business cycle. This connectionis analysed by explaining stock returns with total factor productivity (TFP) as a factor. TFP ismeasured by corporate innovation variable, i.e. the change in a firm's gross profit margin unexplainedby changes in firm's capital and labour. The TFP variable performs quite nicely in explaining stockreturns and it can be related to stock market momentum. Next, the aim is to investigate the forecastingpower of stock returns together with the TFP factor. Even though in our results the TFP contains noinformation relevant for economic forecasting, the stock returns continue to perform well.

Keywords: business cycle, economic growth, financial markets, forecasting,macroeconomy, stock returns

Acknowledgements

This project began in the fall of 2000 and has benefited from many people along the way. My deepest gratitude belongs to Professor Juha Junttila, the personal supervisor of this dissertation. He has improved my work with insightful and encouraging comments, and dealt with patience the great geographical distance we had during the second half of my time as a researcher. Professors Rauli Svento and Mikko Puhakka have affected to my motivation towards Ph.D. studies and the complementation of my thesis. I also would like to acknowledge Professors Juha-Pekka Kallunki and Jukka Perttunen, of the encouragement at the early stages of my Ph.D. student years.

The final version of this dissertation has improved by the comments of my two official examiners, Professors Juuso Vataja and Johan Knif. I’m also very grateful to all the persons who commented my work and presentations at the workshops of The Finnish Doctoral Program in Economics and the various international congresses I have attended to. All the remaining errors are solely mine.

Finnish Doctoral Program in Economics (FDPE), Yrjö Jansson Foundation, Finnish Cultural Foundation and The Research Foundation of OKO Bank are gratefully acknowledged for their financial support. Professor Markku Rahiala has provided help with statistical issues. I wish to express my thanks also to Dr. Seppo Eriksson for the editorial advice, and to the whole of the first floor for the practical issues. All my colleagues in economics and accounting departments, you have made this path soooo much smoother to walk on.

I would also like to mention the highly important role of my friends in providing amusement and pleasant moments but also shoulders to cry on and lots of therapeutic sessions during these years. My inlaws, Marjatta and Urpo, have given me (besides a residence) lots of love and support in Oulu. My mother Hilkka and father Seppo endlessly believe in me: their role in my life is invaluable and I cannot thank you enough. Tuomas and Tiina, thank you for always being there. Last but not least, Antti, you challenge me like no one else and I wouldn’t change your love for anything.

Karhusuolla, Espoossa 13.11.2006 Heli Kortela

List of figures

Fig. 1. Time series graphs of the means of CI variables by country.................................50 Fig. 2. Actual and predicted values of growth rates of GDP and PCE with one

period forecasting horizon...................................................................................101

List of tables

Table 1. List of countries analysed from 1993:1 to 2003:4 and the number of firms used in corporate innovation (CI) calculations by country and in the whole data..................................................................................................45

Table 2. Descriptive statistics of the main variables for the whole data........................46 Table 3. Descriptive statistics of the different CI variables ...........................................48 Table 4. Correlations between different variables of interest within the whole

data set .............................................................................................................55 Table 5. Asset pricing tests on firm-specific excess returns using corporate

innovation (CI) as a factor in CAPM...............................................................59 Table 6. Asset pricing tests on firm-specific excess returns with corporate

innovation spread and momentum spread as additional factors in the Fama-French 3-factor model ...........................................................................60

Table 7. Regression tests on the relationship between corporate innovation (CI) and momentum from the whole data........................................................66

Table 8. Tests on the relationship between CI and R&D expenditure ...........................69 Table 9. Asset pricing tests using aggregated R&D expenditure and R&D

spread as factors in alternative asset pricing specifications .............................70 Table 10. The country-specific autocorrelation coefficients of differently

calculated corporate innovations .....................................................................77 Table 11. List of the countries included in the data.........................................................86 Table 12. Descriptive statistics for the main variables. ...................................................87 Table 13. Unit root test statistics using Phillips & Perron (1988) test by country...........89 Table 14. Correlation coefficients of the variables to be used in macroeconomic

forecasting regressions ....................................................................................91 Table 15. The in-sample results of corporate innovation and industry stock

returns forecasting quarterly GDP and PCE growth rates with the whole data sample and by country.................................................................93

Table 16. The in-sample results of finding the best model to forecast quarterly GDP and PCE growth rates with whole sample and by country.....................96

Table 17. The results of recursive estimation: RMSE’s of two models used to forecast quarterly GDP and PCE growth rates by country...............................98

List of the original articles

The thesis includes the following separate and published study,. Heli Kortela (formerly Kinnunen) is the corresponding author in the article and she has participated wholly in the making of the article. The idea for the study was originally planned by Juha Junttila. Heli Kortela implemented the plan, carried out the calculations and was the main writer of the first version of the study. Juha Junttila provided the data, helped with the RATS design in empirical implementation and participated in the writing of the final, published version of the study.

I Junttila J & Kinnunen H (2004) Economic Tracking Portfolios in an IT-intensive Stock Market. Quarterly Review of Economics and Finance 44, 601–623.

Contents

Abstract Acknowledgements List of figures List of tables List of the original articles Contents 1 Introduction ................................................................................................................... 15

1.1 Background and the purpose of the study...............................................................15 1.2 Theoretical foundations on the connection between stock market and real

economy: a literature review .................................................................................17 1.2.1 Literature on the stochastic discount factor .....................................................17 1.2.2 Rational valuation formula and the theoretical background of

economic tracking portfolios ...........................................................................21 1.2.3 Economic growth models, the technology shock and the stock market .........24

1.3 Main empirical findings..........................................................................................28 1.3.1 Chapter 2: Total factor productivity, corporate innovation and

momentum in international asset returns.........................................................28 1.3.2 Chapter 3: The role of corporate innovation and stock returns in

predicting macroeconomy ...............................................................................30 1.3.3 The original article: The performance of economic tracking portfolios

in an IT-intensive stock market........................................................................31 References ........................................................................................................................ 33 2 Total factor productivity, corporate innovation and momentum in international

asset returns................................................................................................................... 36 2.1 Introduction ............................................................................................................36 2.2 Literature review ....................................................................................................38

2.2.1 Corporate innovation as a proxy for total factor productivity and its role in asset returns..........................................................................................38

2.2.2 Corporate innovation and momentum in international asset returns ..............42 2.3 Data ........................................................................................................................44 2.4 Results on the role of corporate innovation in asset pricing ...................................46

2.4.1 Description of corporate innovation ................................................................46 2.4.2 Corporate innovation in explaining stock returns............................................56

2.5 Relationship of corporate innovation with momentum and research & development expenditure.......................................................................................65

2.5.1 Corporate innovation and momentum .............................................................65 2.5.2 Corporate innovation and R&D expenditures .................................................68

2.6 Conclusions ............................................................................................................73 References ........................................................................................................................ 75 Appendix .......................................................................................................................... 77 3 The role of corporate innovation and stock returns in predicting

macroeconomy.............................................................................................................. 80 3.1 Introduction ............................................................................................................80 3.2 Corporate innovation and stock returns: empirical implementation .......................82 3.3 Data ........................................................................................................................85 3.4 Results on the relationship between CI, stock returns and macroeconomy............87 3.5 Conclusions and future research...........................................................................108

References Original articles

1 Introduction

1.1 Background and the purpose of the study

Predicting macroeconomic activity has generously provided its share of challenges for the researchers and policy makers. Recently, the interest in economic forecasting has turned to the highly sensitive financial market for the purpose of finding more accurate and timely information about real economic development. Especially the connection between stock markets and real economy has inspired researchers, since previous literature documents no consensus for the reasons, direction or the channels of influence of this connection. Intuitively, the stock market should provide valuable information about macroeconomy. Already the fundamental valuation principle of equity states that current stock prices should reflect the discounted values of expected future cash flows. The strength of the economy partly determines the magnitudes of these cash flows. Hence, stock markets should be considered as an important aspect in economic modelling and especially, in macroeconomic forecasting.

Among the pioneering theoretical foundations for the analysis of the connection between the stock market and macroeconomy has been the Arbitrage Pricing Theory (see originally Ross 1976 & Chen et al. 1986), which addresses the question of whether the risk associated with particular macro variables is reflected in expected asset returns. A closely related analysis is that of the consumption based Capital Asset Pricing Model (see e.g. Sharpe 1964, Breeden 1979 & Breeden et al. 1989), which concentrates on a single macroeconomic influence, i.e. the growth of aggregate consumption. An important paper on stock return forecastability is that of Lettau & Ludvigson (2001), which shows that the ratio of consumption to wealth – a pure macroeconomic variable – forecasts stock returns. An example of the newest development could be Piazzesi et al. (2004): their model generates return predictability from a housing wealth/consumption ratio that forms an addition to the macro variables list known to forecast returns.

Traditionally, the direction of influence in the relationship between stock market and economic activity has been from the macroeconomy to the stock market, so researchers have mainly considered the effect of macroeconomic events on stock prices. Recently, the interest in this area has shifted towards the information content of asset returns concerning the future values of macroeconomic variables. This shift has been apparent especially in the research departments of central banks (see the work by Hayes 2001 &

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Junttila 2002), which essentially try to build models to explain and predict economic development. Furthermore, the central pieces of dynamic macroeconomic theory are the forces that determine the allocation of consumption and investment across time and states of nature. Asset markets can be considered to be the mechanism that does all this equating as asset returns are “the price line that draws together the marginal rates of substitution and marginal rates of transformation”, in the words of Cochrane (2005). Also, due to the fact that most macroeconomic time series cannot be explained and predicted accurately by their own history or by other macroeconomic variables, researchers have turned to other markets in order to find the relevant information variables.

It has already been documented by a number of authors (e.g. Estrella & Hardouvelis 1991, Davis & Fagan 1997, Estrella & Mishkin 1998 and Ang et al. 2004) that price variables that forecast asset returns can also forecast economic activity. The purpose of this thesis is to investigate whether stock prices, and more specifically stock returns, can help forecast macroeconomic development. The intuition behind the predictive power of stock markets with respect to macroeconomy stems from the fact that the stock markets are forward-looking markets: the current stock price discounts future earnings and dividends which on their part should contain information about the future state of a firm and the consumption possibilities of a consumer. Firm’s and consumer’s activities are directly linked to aggregate real economic development and hence the stock markets should provide a fruitful ground for exploring new ways to improve economic forecasting. Although stock markets are said to be speculative in nature (and we do not want to deny that), it is possible and also highly likely that some part of the stock return is based on fundamental factors that are linked to the real economy, and this part is of interest to us. Hence, the goal here is to conduct preliminary investigations for finding the essential part of the stock market information that can predict macroeconomy.

First, we review the theoretical points that can be used to justify the importance of financial market data in economic modelling and forecasting, focusing especially on the performance of stock market. Although there is no clear-cut and complete theory of how and why stock markets should be considered as an indicator for predicting real economic activity, recent literature on e.g. stochastic discount factor (SDF) in asset pricing and real business cycle models has approached this interesting issue. We try to find the points that specifically indicate that the connection and direction between financial markets and macroeconomy could be from stock markets to real economy.

Secondly, we test the forecasting ability of stock markets with respect to macroeconomy. Especially interesting is the unexpected part of stock return as well as the part of the stock return that possibly is related to the business cycle. The unexpected part of the stock return can be revealed with economic tracking portfolios (ETP), which are constructed so that the unexpected portion of the portfolio return has the maximum correlation with revisions to expectations of the target variable. The returns on an ETP track how investors revise their expectations about relevant macroeconomic variables period by period. The part of the stock return that is related to the business cycle will be analysed by constructing an asset pricing framework that uses total factor productivity (TFP) (a well known business cycle variable) as a factor. This framework is then turned around in order to investigate the forecasting power of stock returns together with the TFP factor.

17

The structure of the thesis is the following. Later in this chapter, the theoretical foundations for the connection between the stock market and macroeconomy are reviewed through the stochastic discount factor (SDF) literature, and the rational valuation formula of asset pricing and the real business cycle models, which are closely related to SDF. In chapter 2, we use a new measure for total factor productivity called corporate innovation (CI) (see Vassalou & Apedjinou 2004), which is build from firm-specific factors. Asset pricing tests on stock portfolios are conducted using corporate innovation as an additional factor and utilising a large international data set. Furthermore, the relationship between corporate innovation and asset price momentum1 is examined. In chapter 3, again using a large international data set, the information content of stock returns and corporate innovation variable regarding macroeconomic forecasting is tested in order to investigate the type of information the stock market potentially possesses. The aim of the original article included in this thesis is to find economic tracking portfolios based on stock market data that can forecast macroeconomic variables, concentrating on the information of the volatile and hence very interesting technology stocks. Information technology oriented Finnish data are used in the empirical analysis of the original article.

1.2 Theoretical foundations on the connection between stock market and real economy: a literature review

1.2.1 Literature on the stochastic discount factor

One way to look at the link between financial markets and macroeconomic variables is through literature on stochastic discount factors.2 Investors must decide how much to save and how much to consume, and what portfolio of assets to hold. The marginal utility loss of consuming a little less today and buying a little more of the asset should equal the marginal utility gain of consuming a little more of the asset’s payoff tomorrow. It directly follows that the asset’s price should equal the expected discounted value of its payoff, using the investor’s marginal utility, i.e. the stochastic discount factor, to discount the payoff. An important implication of this is that risk corrections in asset prices should be driven by the covariance of asset payoffs with marginal utility, and hence, with consumption.

A more general representation equates the discount factor with the growth in the marginal value of wealth (see e.g. Cochrane 2005), which measures “hunger” – marginal, not total utility. To make the ideas behind the stochastic discount factor literature operational, one needs some procedure to measure the growth in the marginal value of wealth. Traditional theories of finance, i.e. CAPM, ICAPM and APT, measure “hunger” by the behaviour of large portfolios of assets. Multifactor models, like Fama & French (1992, 1993, 1995, 1996) three-factor model use returns of multiple portfolios to measure 1 Momentum in asset markets refers to the stylized fact that past winner stocks seem to perform well also in the future and past losers tend to continue to perform badly. 2 See for example the work by Cochrane & Hansen (1992), Campbell & Cochrane (1999, 2000), Cochrane (1991, 1994, 1999, 2001, 2005), Campbell (2003) and Chen & Ludvingson (2004).

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the marginal value of wealth. Literature connecting financial markets to the real economy asks what are the real economic determinants of marginal value of wealth. As Cochrane (2005) points out, the marginal value of wealth can equal the marginal utility of consumption. Assets must offer high returns if they pay off well in good times and pay off badly in bad times when measured by aggregate consumption growth. The economic understanding of the stock market must be based on the idea that people fear that stock will fall in bad times – and at some point we must be able to measure bad times by people’s decision to cut back on consumption.

Cochrane (2001) starts by figuring out the value of uncertain cash flows, that is, value of payoffs in asset markets. If you buy a stock today, the payoff next period ( 1+tx ) is the stock price ( 1+tp ) plus dividend ( 1+td ), i.e. 111 +++ += ttt dpx . Here 1+tx is a random variable - it is not known to investor exactly, but he can assess the probabilities of possible outcomes. The value of this payoff can be found by asking what it is worth to a typical investor. Investors have a utility function defined over the current and future values of consumption,

[ ])()(),( 11 ++ += ttttt cuEcuccU ρ , (1.1)

where tc denotes consumption at date t and tE is an expectations operator. This periodic utility function captures the fundamental desire for more consumption,

rather than a desire for such intermediate objectives as mean and variance of portfolio returns. The utility function is increasing, thus capturing the desire for more consumption, and strictly concave, reflecting the declining marginal value of additional consumption. This captures investor’s impatience and his aversion to risk. Discounting the future by ρ captures impatience and hence, ρ is called the subjective discount factor.

If the investor can freely buy or sell as much of the payoff 1+tx as he wishes at price tp , how much will he choose to buy or sell? Denote by w the original consumption level

and by ξ the amount of the asset he chooses to buy. Then, his problem is

{ } [ ])()(max 1++ ttt cuEcu ρξ (1.2)

s.t. ξttt pwc −= and ξ111 +++ += ttt xwc . Substituting the constraints into the objective function and setting the derivative with respect to ξ equal to zero, one obtains the Euler equation (the first-order conditions) for an optimal consumption and portfolio choice,

[ ]11 )(')(' ++= ttttt xcuEcup ρ , or (1.3)

⎥⎦

⎤⎢⎣

⎡= +

+1

1

)(')('

tt

ttt x

cucuEp ρ , (1.4)

where u’ is the first derivative of u.

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Equation (1.3) expresses the standard marginal condition for an optimum: )(' tt cup is the loss in utility if the investor buys another unit of the asset, and [ ]11 )(' ++ ttt xcuE ρ is the increase in discounted utility he obtains from the extra payoff at t + 1. In other words, the investor continues to buy or sell the asset until the marginal loss equals the marginal gain. Equation (1.4) is the central asset pricing formula. Given the payoff and the investor’s consumption choice, it tells you what market price to expect. Its economic content is simply the first-order conditions for optimal consumption and portfolio formation.

Now, as Cochrane (2001) shows, one can conveniently break up the equation (1.4) by defining a stochastic discount factor 1+tm as

)(')(' 1

1t

tt cu

cum +

+ ≡ ρ . (1.5)

Then, the basic pricing formula (1.4) can simply be expressed as

)( 11 ++= tttt xmEp . (1.6)

The term stochastic discount factor refers to the fact that 1+tm generalises the standard discount factor. Equation (1.6) says something deep: one can incorporate all risk corrections by defining a single stochastic discount factor – the same for each asset – and putting it inside the expectations. The correlation between the random components of the common discount factor 1+tm and the asset-specific payoff 1+tx generates asset-specific corrections.

The factor 1+tm is also often called the marginal rate of substitution of consumption today and tomorrow, based on equation (1.5). In that equation, 1+tm is the rate at which investor is willing to substitute consumption at time t + 1 for consumption at time t (see a derivation of a consumption-based asset pricing model in Campbell & Cochrane 1999).

1+tm can also be referred to as the marginal rate of transformation, or the return on investment, as in Cochrane’s (1991) production-based model. In that model, the stochastic discount factor is inferred from data on investment through a production function (analogously to the consumption and utility function model described here).

Cochrane & Hansen (1992), among others, characterise the properties of stochastic discount factors that are consistent with the behaviour of asset market payoffs and prices. As long as there are no arbitrage opportunities3 in the market, we can always interpret asset market data through the frictionless market paradigm4 and these models can be conveniently understood by characterising the stochastic discount factors through which

3 The principle of no arbitrage implies that alternative ways of constructing the same payoff must have the same cost or price, as long as there is a nontrivial, nonnegative portfolio payoff. The opportunities to make risk-free profits on an arbitrarily large scale do not exist. This also implies that each portfolio payoff must have a unique price. 4 A frictionless market has no arbitrage opportunities and thus, obeys the law of one price. Investors/consumers can purchase a claim to a linear combination of any two security market payoffs by simply purchasing the corresponding linear combination of the securities. Thus, asset pricing in a frictionless market is a linear pricing functional that maps the space of asset payoffs into prices.

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such models generate asset price predictions. In general terms, exact linear factor pricing models imply stochastic discount factors that are linear combinations of the underlying collection of ‘factors’ that are closely linked to economic fluctuations.

Cochrane (2001, 2005) emphasises the fact that covariance of a payoff with the discount factor, not payoff’s variance, determines its riskiness. Investor cares about the volatility of consumption. He does not care about the volatility of his individual assets or of his portfolio, if he can keep up a steady consumption stream. The stochastic discount model implies that assets whose returns covary positively with consumption make consumption more volatile, and so must promise higher expected returns to induce investors to hold them. Conversely, assets that covary negatively with consumption, such as insurance, can offer expected rates of return that are lower than the risk-free rate, or even negative (net) expected returns.

In equations (1.6) or (1.3) one has not used most of the usual assumptions made in the asset pricing literature. Complete markets or a representative investor are not assumed. They are used if one wants to use aggregate consumption data in marginal utility )(' tcu , or other specifications or simplifications of the model. Nothing has been said about the payoff or the return distributions, in particular it has not been assumed that returns are normally distributed or that utility is quadratic. Cochrane (2001) utilises a power utility example, but the requirements of time- and state-separable utility function (e.g. habit persistence, see an example in Campbell & Cochrane 1999) are not necessary. They may be used, but they complicate the relation between the discount factor and real variables. One does not assume that investors have no non-marketable human capital or no outside sources of income. The first-order conditions for purchase of an asset relative to consumption hold no matter what else is included in the budget constraint (contrary to e.g. the CAPM).

An important aspect of much work in finance is whether markets are rational and efficient, or not. Within the interests of this study, the only content to the rationality questions is whether the “hunger” apparent in asset prices reflects macroeconomic conditions perfectly. According to Cochrane (2005), markets can only be said to be rational if high average returns correspond to poor performance in bad times in the real economy. If there are no arbitrage opportunities, markets can be found irrational only if it can be proved that the periods of “hunger” mirrored in asset prices have no connection to the real economy. Already Fama & Schwert (1977) investigated this issue and their core finding indicates that expected returns vary over time, and are correlated with business cycles (high in bad times, low in good times).

So far in this representation, nothing has been said about where the joint statistical properties of the payoff 1+tx and the marginal utility 1+tm or consumption 1+tc come from. Nothing has been said about the fundamental exogenous shocks that drive the economy either. Cochrane (2001) shows that, in most cases, equations (1.3), (1.4) and (1.5) do not require any assumptions on exogeneity or endogeneity, or general equilibrium. Equation (1.6) is a condition that must hold for any asset, for any production technology. It is tempting to interpret that )( 11 ++ ttt xmE determines tp as in many applications, but equation (1.3) can also be written in the following form:

[ ]ttttt pxcuEcu /)(')(' 11 ++= ρ . (1.7)

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One can think of this equation as determining today’s consumption given asset prices and payoffs, rather than determining today’s asset price in terms of consumption and payoffs. Thinking about the basic Euler equation in this way leads to permanent-income model of macroeconomy that takes asset return process as exogenous and studies (endogenous) consumption decisions.

In Cochrane’s (2001) representation investor’s utility is directly linked to his consumption behaviour. General equilibrium models deliver equilibrium decision rules

,...),( ttt iyfc = linking consumption to other variables, such as income, investment ( ti ), output ( ty ), etc. Substituting the decision rules in the consumption-based model, one can link asset prices to other macroeconomic aggregates. Thus, according to above intuition, asset prices should be able to explain and predict other (future) macroeconomic variables as well.

1.2.2 Rational valuation formula and the theoretical background of economic tracking portfolios

The main research frameworks have concentrated in examining the relationship between stock market and macroeconomy based on a presumption that the direction of indicator power - loosely speaking, causality – goes from macroeconomic variables to financial market. First of all, following Junttila (2002), the appropriate term could be “direction of forecasting ability” since it would be absurd to assume that for example changes in the stock market returns could actually ’cause’ changes in the future values of real activity or inflation. Based on the following theoretical reasoning, we can make the fundamental assumption that the possibilities of macroeconomic risks are priced in the financial market and this affects both observed and expected prices and returns. It follows from this that changes in the current and past values of financial market variables might indicate (not cause) also the development of future values of macroeconomic variables.

Secondly, for example Lee (1992) using the vector autoregressive specification for studying the ‘causal’ relationships and dynamic interactions among asset returns, inflation and real activity in US data shows that the stock returns appear Granger-causally prior and help explain real activity. On the other hand, he finds that stock returns help explain only little variation in inflation, whereas interest rates do a much better job. The main point is, though, that there is some information in stock returns that can help predict future changes in macroeconomic variables: they may not ‘cause’ the changes in economic activity but they surely contain some innovation type of information about economic development.

The empirical results from stochastic discount factor models imply that stock returns should incorporate valuable information about future economic development in the form of e.g. production, consumption and income variables. For example, the stochastic discount factor in Campbell & Cochrane’s (1999) consumption-based model is strongly influenced by innovations in consumption. This is exactly what the economic tracking portfolios (ETP’s) measure, the news elements that stock returns contain about future economic developments. Moreover, in equation (1.6) the stochastic discount factor 1+tm , linking tomorrow’s payoffs to today’s asset price, depends on investor’s utility that in

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turn is directly related to consumption today and in the future. As there are no assumptions about exogeneity and endogeneity, one can naturally think that asset prices incorporate information about utility now and utility tomorrow. It follows that the asset prices should be able to explain and predict investor’s future utility, and, in turn, investor’s consumption.

The theoretical background of economic tracking portfolios goes back to the rational valuation formula of stock prices, which simply states that the current stock price reflects the sum of future expected dividends. To be more precise, consider the famous static Gordon’s (1962) growth model that gives the fundamental price of an equity, f

tp , as

[ ] )()1/(11

itti

ift dErp +

∞

=∑ += , (1.8)

where the key elements are expectations on future dividends, itt dE + , and the discount factor r which may be time varying and as such dependent on macroeconomic conditions.

As Fama (1990) and Lee (1992) noted, expected variations in real activity are not the only source of variations in stock returns in standard valuation models. The possible sources can be categorized as follows (see also Binswanger 1999 and Junttila 2002):

− shocks to expected cash flows, − shocks to discount rates, and − predictable return variation stemming from the time varying discount rate used for the

pricing of expected cash flows in the basic pricing formula given above.

Hence, the realized stock return could be decomposed into three parts, the risk-free rate, the risk premium and the unanticipated shocks to stock returns. As Junttila (2002) points out, especially the third source in variations of stock returns is assumed to be connected to the risk-free rate and the risk premium, which are supposed to reflect current business conditions. As already mentioned, most of the previous literature has concentrated on the forecasting direction from macroeconomy to the financial markets, but using similar arguments for the connection between macroeconomy and the financial markets, the analytical framework can be turned around to analyse the forecasting power of financial market variables. This is possible in the economic tracking portfolio analysis.

The ETP analysis for tracking macroeconomic news starts by constructing portfolios with unexpected returns that are maximally correlated with unexpected components of macroeconomic (state) variables. Specifically, the target variable is “news” about yt+k, where yt+k is a macroeconomic variable such as the inflation rate in period t + k, and k denotes the forecast horizon. News is defined as innovations in expectations about yt+k with notation )()()( 1 kttkttktt yEyEyE +−++ −≡Δ , where EΔ describes changes in expectations. The tracking portfolio returns are obtained from equation tt bRr = , where

tR is a column vector of chosen asset returns from the end of period t – 1 to the end of period t and b is a row vector of portfolio weights. Unexpected returns are actual returns minus expected returns, i.e. )(~

1 tttt RERR −−≡ . The portfolio weights are chosen so that tR~ is maximally correlated with )( ktt yE +Δ . One can show the connection between future values of macroeconomic variables and

current stock returns through the growth model that is restated in dynamic form using the

23

Campbell’s (1991) variance decomposition. According to Campbell (1991), the unexpected excess return on equity is

∑∑∑∞

=++

∞

=++

∞

=+++++ −−Δ−=−

01

11

01111 ))((

jjt

j

j

fjt

j

jjt

jttttt erdEEeEe κκκ (1.9)

where te is the log excess return on equity, tdΔ is the change in log real dividends, ftr is

the log real risk-free interest rate and κ is a linearization parameter, which is a little less than unity. Investors will enjoy a positive unexpected excess return if expected dividend growth is revised upwards, or if expected risk-free real interest rate and/or expected future excess equity returns are revised downwards. Revisions to these components of equity valuation are likely to be related to changes in expectations of macroeconomic variables of interest. Hence, the innovations of future values of macroeconomic factors are reflected in unexpected changes in equity returns.

When we decompose the target variable into expectations and expectation errors, we will see the above implications more clearly (see also Lamont 2001, Hayes 2001 and Junttila 2002). For any target variable ty , its realised value at time t + k can be expressed as a sum of the previous period’s conditional expectation plus a one-period forecast error,

kt+ε , hence

ktktktkt yEy ++−++ += ε1 . (1.10)

Correspondingly, the conditional expectation at t + k can be rewritten as a sum of the conditional expectation at t + k – 2 plus the change in the expectation between the two periods, yielding

ktktktktktktkt yEEyEy ++−+−++−++ +−+= ε)( 212 . (1.11)

Backward reduction to time t – 1 results in an expression

ktjkt

k

jjktkttkt yEEyEy +−−+

=−++−+ −+= ∑ )( 1

01 , (1.12)

where ktktkt yyE +++ = . The second term on the right hand side of (1.12) is the sum of k + 1 one-period expectations revisions, and since expectations are revised only when news appear in the market, we assume that these are (iid) shocks. A tracking portfolio observes the first of these expectations revisions, ( kttt yEE +−− )1 , which is distinct when we rewrite equation (1.12) as

kttktttkttkt yEEyEy ++−+−+ +−+= ,11 )( ξ , (1.13)

where ∑=

++ ≡k

jjtktt

1, εξ and ktjtjtjt yEE +−+++ −≡ )( 1ε .

24

Thus, the target variable is now the sum of the conditional expectation at time t – 1, the revision to this expectation between t – 1 and t, and the sum of k one-period future expectations revisions.

The logic used in backward reduction (from equation (1.11) to equation (1.12)) is not quite clear-cut. There seems to be a little contradiction between the dynamics and statistics in the expectations window: the expectations are reduced in time in order to find the change in expectations between time t – 1 and t, whereas the target variable yt+k is not reduced but stays attached to the same point in time. When expectations are broken down to monthly changes, one should consider changing the target variable in time also. This can be done by a rolling estimation procedure. A question now emerges: could it be possible to capture the real growth in the target variable this way? One downside to this approach is that one could not forecast several periods ahead, we could only look at the next period’s values of target variable. And the point in the ETP analysis used in this thesis is to find out if the current stock returns (at time t) incorporate the change in expectations about yt+k between time t – 1 and t, k being the forecasting horizon of more than one period, and whether this information can be used to forecast future values yt+k.

An economic tracking portfolio connects the change in expectations of yt+k between time t – 1 and t to the unexpected returns on a portfolio of assets, that is

ttkttt RayEE η+=− +−~)( 1 (1.14)

where tR~ is a vector of unexpected (log) returns on N base assets, a is a 1×N vector of portfolio weights and tη is a tracking error.

As can be seen, the left-hand side of (1.14) is in general unobservable but as we shall demonstrate in the original article, in the spirit of Lamont (2001), the estimation and testing of the ETP weights can be conducted in terms of the observable actual future value yt+k. In addition to showing how the ETP weights can be constructed from observable variables, we will conduct empirical tests on Finnish data in order to see if it is possible to find some information about macroeconomic conditions through economic tracking portfolios.

1.2.3 Economic growth models, the technology shock and the stock market

Business cycle models assume that in utility maximization households equate intertemporal marginal rates of substitution with intertemporal marginal rates of transformation, as emphasized by Cochrane & Hansen (1992). Under the complete market hypothesis, asset returns offer a direct measure of these margins, and so they should provide an excellent guide to further development of business cycle models. Indeed, many current economic growth models have succeeded in accounting for the behaviour of asset prices in addition to accounting for business cycle fluctuations (see Danthine & Donaldson 1993, Jermann 1998, Lettau & Uhlig 1997 and 2000, Tallarini 1998 and Boldrin et al. 2001). In this thesis, the standard real business cycle model

25

(RBC) is also taken as one possible starting point in evaluating the forecasting properties of asset returns (especially stock returns) with respect to macroeconomy.

Consider a real business cycle model based on a representative agent framework5: there are no frictions or transaction costs and for simplicity, money and government are abstracted. The agent has a neoclassical production technology6, i.e.

),( tttt lkFAy = , (1.15)

where ty is output and the inputs to production are knowledge or technology ( tA ) (or as referred frequently in the literature, total factor productivity shock, TFP), physical capital ( tk ), and labour ( tl ). There are two resource constraints that agents face. Output is divided between two uses, consumption ( tc ) and investment ( ti ), i.e. ttt icy += , and total endowment of time ( th ) to labour ( tl ) and leisure ( tn ), i.e. ttt nlh += . Investment increases the stock of capital and capital depreciates at the rate δ .

The representative agent maximizes the expected value of her utility

⎥⎦

⎤⎢⎣

⎡= ∑

∞

=++

0),(max

jjtjt

jt ncuEU ρ , 10 << β , (1.16)

where )(•u is the instantaneous utility function of the representative agent, ρ is the discount factor and E is the expectations operator.

Because all agents are identical, one can solve for the equilibrium quantities and prices by solving the agent’s optimisation problem. Expectations are assumed rational, agents know the probability distribution generating tA and all markets clear. Thus, maximising the utility function (1.16) subject to constraints given by production function (1.15) and the resource limitations provides a set of first-order conditions that characterise market equilibrium. In order to obtain a specific solution to this RBC model, we need to make further assumptions and give explicit forms to the utility and production functions. We assume that capital appreciates fully within a single period (i.e. 1=δ ), utility is log-linear (equation (1.17)) and the production function is Cobb-Douglas (equation (1.18)):

)1log()1(log),( tttt lclcu −−+= θθ (1.17)

αα −= 1tttt klAy , (1.18)

where 0>θ . Now it is possible to solve for the time paths of the three choice variables, tc , tk and tl . Under these assumptions the utility function ensures that the income and

substitution effects of a wage change cancel each other, so that the solution for labour is

5 Here, the general representation of the RBC model follows loosely Romer (2001) and Barro & Sala-i-Martin (2003), and the more profound reviews of Stadler (1994) and Danthine & Donaldson (1993). 6 A production function is neoclassical if the following properties are satisfied: (1) constant returns to scale, (2) positive and diminishing returns to private inputs, and (3) Inada-conditions: the marginal product of capital (or labour) approaches infinity as capital (or labour) goes to zero and approaches zero as capital (or labour) goes to infinity. (Barro & Sala-i-Martin 2004).

26

constant.7 Using the method of undetermined coefficients8, one can solve for the values of consumption and capital stock:

ααρα −−−= 1])1(1[ tttt klAc (1.19)

ααρα −+ −= 1

1 )1( tttt klAk . (1.20)

By assumption, the effects of the level of technology die away slowly.9 Capital accumulates gradually (the shock translates into a change in the capital stock in the future) and then slowly returns to normal. The net result of the movements in tA , tk and

tl is that output increases in the period of the shock and then gradually returns to normal. Consumption responds less and more slowly than output (investment is more volatile than consumption) to the technology shock.

In growth accounting models, as in this one too, capital is considered an exogenous factor and thus, technology shock may understate the importance of productivity change in stimulating the output growth. In a dynamic context, there exists a feedback mechanism between TFP change and capital (see equation (1.20)). TFP captures the effect of a technical change on increased output per person, but additional output per person usually will lead to increased savings and investment and, hence, to a rise in the capital-labour ratio. The Solow residual correctly measures the shift in production possibilities but does not capture the induced effects of technology on growth (see a discussion in Schreyer & Pilat 2001, Sargent & Rodriquez 2001 and Hulten 2000) – or as we shall call the ‘indirect’ effects of TFP on growth.

This is where asset prices, in our case especially the stock prices, come into the picture. Canova & De Nicolo (1995) study the properties of a general equilibrium, multicountry model of the business cycle with special attention paid to the relationship between stock returns and real activity.10 The structural model is a three-country model with three consumption goods and each country specializing in the production of one good. It incorporates a labour-augmenting Hicks-neutral deterministic technological progress with technological shocks and shocks to a stochastic government expenditure as

7 Most of the technology innovations are highly persistent, raising the real wage permanently. This is unlikely to elicit a large labour supply response, because the substitution effect of the higher wage is likely to be offset by the income effect. This effect on the constancy of labour is modelled by the log-linear utility function. (Stadler 1994). 8 For a linear non-homogeneous ordinary differential equation with constant coefficients, one can guess the trial solution of the non-homogeneous term, plug it in and then solve for the unknown coefficients to obtain a particular solution. (Barro & Sala-i-Martin 2004). 9 Most innovations in technology are regarded as highly persistent, or even permanent, and this causes the effects of a technology shock to diminish slowly from the economy. 10 See also a nice representation of the relationship between traditional and adjusted Solow residual and asset returns in Lee (2004) that uses production-based asset pricing models and real business cycle (RBC) models. As the investment returns are exposed to the aggregate productivity shock in the RBC models and the investment returns equal the asset returns in the production-side asset pricing models, if the two models are combined, the link between the aggregate productivity shock and the asset returns can be found. The results in Lee (2004) show that the measure of productivity shock – ’adjusted Solow’s residual’, taking variable capital utilization into account – exhibits dynamic effects on the asset returns.

27

two possible sources of disturbances. Preferences and technologies are allowed to differ across countries and foreign capital goods are used in the production of domestic capital goods.

Canova & De Nicolo (1995) consider two types of equities, unlevered and levered11. The dividends accrued to stockholders are, respectively,

))1(( 11

hgthhgtg

hgththththt kkvlwyd δ−−−−= +∑ , (1.21)

[ ]111

2 ,0max hhhtht qqd Φ−= + , (1.22)

where hgtv is the price of the investment good g in terms of good h, hΦ is a parameter regulating financial leverage, ∑

==

T

thth qTq

1

1 )/1( , while rhtq are equity prices for asset

r = 1, 2 which can be found using

[ ]rht

rht

t

tt

rht dq

uuEq 11

1

''

+++ += ρ . (1.23)

This equation is exactly Campbell & Cochrane’s (1999) consumption-based asset pricing model12 with additional information about the specific form of the marginal utility as a function of consumption and leisure (see equation (1.6) in section 1.2.1).

According to Canova & De Nicolo (1995), the effect of technology shock on domestic equity prices depends on two contrasting factors. First, because firm’s output increases, firm’s earnings increase and part of this increase will be distributed to shareholders in the form of increased dividend payments. If shocks are persistent, future cash flows accruing to shareholders are expected to increase and this increases equity prices. The other part of the increase in earnings will finance new investments and unless installation costs are large, investment is likely to increase in order to take advantage of the higher productivity of capital. This lessens the probability that dividends increase. On the other hand, investing usually means that future output increases and this increases future earnings and thus future dividends. Secondly, the technology shocks influence consumption and working hour decisions of agents and therefore the pricing kernels in the asset pricing equations. For highly autocorrelated shocks, the pricing kernel is likely to decrease and this depresses the asset prices.

The next period’s capital also affects this period’s dividend payments (see equation (1.21)), which in turn will be taken into account in stock prices and returns. And as is

11 The first equity is a claim that pays out each period the residual value of the output after outlays for investments, installation costs (the costs of installing, or moving, intermediate capital good from the location it is produced to country where it will be used) and factor payments to labour have been made. Hence, its value is simply the discounted stream of factor payments to capital owners (unlevered equity). The second equity obtains because corporations may use bonds in addition to equity financing. One period risky bonds promise to pay a fraction hΦ of the average firm value in country h over all possible states next period or the future value of the firm, whichever is lower. The payoff of the equity is the residual value of stream of factor payments to capital owners after bond payments have been made (financially levered equity). 12 See also Cochrane (2001 and 2005).

28

apparent from equation (1.23), the stock price reflects future stock prices and future dividends. We have just discussed that output through firm’s earnings and consumption through the pricing kernel are present in stock prices. If the indirect effects of technology shocks on capital13 show up in output and consumption in the (near) future (see equations (1.18) and (1.19)), stock prices reflect these upcoming changes. Thus, to account for all the effects of a technology shock in output and consumption, one needs to consider the information content of stock prices. Since we are talking about indirect effects, the changes in output and consumption can be quite small and the sensitive stock market is very likely to incorporate these changes.

From the basis of our theoretical discussion on economic growth models and stock markets, in chapter 2 of this thesis we test empirically the risks of total factor productivity in the stock markets, i.e. investigate whether stocks can be explained by a total factor productivity component. We use a recently developed variable called corporate innovation14 as the measure of TFP. Furthermore, in chapter 3, we combine once again the stock market and real economy by attempting to forecast economic development with stock returns and total factor productivity variable, i.e. corporate innovation. The interest in chapter 3 is especially on the possible joint effects of stock returns and a TFP shock on real economy and the similar/differing information content of both.

1.3 Main empirical findings

1.3.1 Chapter 2: Total factor productivity, corporate innovation and momentum in international asset returns

In the first empirical essay, we examine a measure of total factor productivity (TFP) called corporate innovation (CI) in asset pricing framework and as an explanation to international momentum phenomenon. According to e.g. real business cycle literature, a TFP should have an effect on the stock market, because the information about a TFP shock is gradually revealed to the market. The effects of increased dividend payments of firms and the consumption decisions of agents are likely to appear sooner in the asset prices than the impact of new, potentially profitable investments of firms and working hour decisions of consumers. This introduces return continuation to asset markets and hence connects a TFP shock to momentum.

Corporate innovation as a measure of TFP, first defined by Vassalou & Apedjinou (2004), considers all the other effects except those of capital and labour on total output. We measure CI as the component of a firm’s change in gross profit margin (GPM) no explained by the growth in capital and labour it has in place. At a firm level, it captures the contribution of of non-labour, non-capital production factors to a firm’s gross profits. 13 As we already have discussed, a TFP captures the effects of a technological change on increased output, but increased output per person can lead to a change in the capital-labour ratio, and this effect will not be captured by TFP. 14 See the work by Vassalou & Apedjinou (2004).

29

Because it is firm specific, it may be considered to represent intangible assets such as research and development (R&D) expenditure, which are closely related to technology shock15. At an aggregate level, the measure of CI is equivalent to a scaled TFP variable.

Corporate innovation is a scaled TFP variable and can be calculated in various ways. First, we examine the differently calculated CI variables. Secondly, we address the question of a TFP shock having effects on stock market by building asset pricing regressions with corporate innovation as a factor. We focus on the momentum phenomenon by using momentum portfolios (see Jegadeesh & Titman 1993) as target assets and by adding a momentum factor in the regression analysis. Furthermore, we consider the relationship between CI and R&D expenditures by building simple regression equations between the two variables.

The data set consists of a quarterly panel data across 18 OECD member countries from January 1993 to December 2003 representing all the available data after transformations. Compustat Global Vantage provides the data on the CI variable, stock returns and the aggregate R&D expenditure, and the short-term interest rate data is from OECD. The large number of countries slightly restricted the time period available to the analysis, but considering the panel estimations used in several previous studies on momentum16, we decided to have as many different countries in our data as possible.

The results show that the nature of corporate innovation is dependent on the way it is calculated, namely as a residual in a regression of gross profit margin (GPM) on capital and labour (in the spirit of Solow 1956 residual and following Vassalou & Apedjinou 2004) or as an actual measure of the difference in GPM and capital and labour. An aggregate corporate innovation variable seems to explain returns in addition to Fama & French (1993) factors and momentum factor of Jegadeesh & Titman (1993) in some countries, whereas in others its significance vanishes.

We are able to find some evidence that corporate innovation may be a partial explanation of momentum. The results from regressions using the market portfolio and the momentum factor are highly similar to the results using the market factor and corporate innovation. Furthermore, the momentum factor becomes statistically non-significant when the CI factor is added to the regression. CI factor also improves the pricing models of momentum portfolios, especially well when analysing the momentum spread (returns on portfolio of high past returns minus returns on portfolio of low past returns). All these results are in line with Vassalou & Apedjinou (2004). In a preliminary analysis, it is hard to find differences between the performance of a 2-factor model with corporate innovation factor and a 2-factor model with aggregate R&D expenditure factor. This may be an indication that the corporate innovation captures to some extent similar kind of information as R&D expenditure, indicating also that it may capture the effects of partly unobservable intangible assets.

15 In many occasions, the total factor productivity shock has been interpreted as representing a technology shock in the economy. 16 See the work by e.g. Jegadeesh & Titman (1993 and 2001), Conrad & Kaul (1998) and Vassalou & Apedjinou (2004).

30

1.3.2 Chapter 3: The role of corporate innovation and stock returns in predicting macroeconomy

Corporate innovation (CI) is a proxy for total factor productivity (TFP) that is calculated from firm-specific data (see Vassalou & Apedjinou 2004). Previously, it has been shown to explain stock markets. This chapter investigates empirically the theoretical implications of real business cycle models that a TFP shock and stock markets should be able to explain and predict economic behaviour measured by aggregate output and consumption. Positive technology shocks – that can be measured with corporate innovation variable – affect firm’s stock prices through increased output which affects expected future cash flows and through consumer’s consumption and working decisions that show up in asset pricing kernels. Hence, both the TFP shock (measured by CI) and stock returns should be able to forecast future economic activity. We evaluate whether the TFP shock can solely and together with stock market information forecast future consumption and output growth.

The international data used in this analysis is the same as in chapter 2 with few additional restrictions. Hence, the stock market data and all variables needed in the calculation of corporate innovation are from Compustat Global Vantage, and the OECD database is the source for macroeconomic variables. The time period is from February 1994 to April 2004, representing the period for which data for all variables are available. We choose 14 OECD member countries to be included in the analysis. The interest here is on the growth effects of technological change, which is measured by corporate innovation that is abstracted from firm-level changes in capital, labour and gross profit margin. This is why we focus on growth rates instead of levels in our analysis. Also, excess stock returns have turned out to be good economic forecasters and their ‘innovation capturing’ nature seems to be the key in predicting economic growth.

Disappointedly, a TFP shock measured as aggregated corporate innovation has no predictive power towards either consumption or output growth. This may be due to the measurement problems that are associated with the empirical calculation of CI. Also, the forecasts are calculated only one quarter ahead which may be too short of a time period for the changes to show up in economic growth variables. A refreshing exception in the results is Japan where CI variable quite strongly contributes to the predicting of Japanese output and consumption growth. This result may be explained by the strong role of technology in Japanese society, which implies that the TFP shock is likely to spread throughout the economy much faster and with stronger presence. Also, the specific ‘work-appreciating’ culture is likely to advance the shock’s influence: labour force improvements can be more visible and measurable.

Once again stock returns proved to be an informative source regarding economic forecasting. They predicted both target macroeconomic variables, but especially gross domestic product growth, remarkably well, even outperforming well-known business cycle variables interest rates and spreads, which were introduced to the regression models as control variables. Hence, conclusions from the results indicate that stock markets should definitely be taken seriously in economic forecasting, since they capture valuable information regarding real economic developments. The result that the TFP shock has no predictive power over macroeconomy, but can still explain stock prices (the results in

31

chapter 2) implies that the information in stock markets incorporates important factors (containing similar information than a TFP shock and hence making the TFP shock itself unnecessary) that otherwise would be excluded from economic forecasting because of their statistically non-significant nature.

1.3.3 The original article: The performance of economic tracking portfolios in an IT-intensive stock market

In the original article by Junttila & Kinnunen (2004), we first of all show how to empirically estimate an economic tracking portfolio. Secondly, we use the ETP approach for forecasting future values of macroeconomic variables in the information technology -intensive Finnish stock market. Lately, the volatility in the stock prices of technology firms has gained a lot of attention – and not only for the possible speculative behaviour of the market – and it is interesting to explore if the large fluctuations in stock prices contain any information about fundamental, macroeconomic sources. The Finnish stock market is quite heavy on information technology (IT) and electronics industries, partly due to the strong role of one telecommunications company, namely Nokia, and this is the reason why we concentrate our analysis on the Finnish stock market.

The main focus here is the macroeconomic forecasting power of industry portfolios, especially the so-called new economy stocks. Due to larger fluctuations in the new economy industry stock prices (relative to more traditional Finnish industries), one could expect the unexpected returns also to be large and hence, potentially contain some information about future real economy. Because we emphasize the forecasting ability of stock returns, we focus on the out-of-sample performance of the investigated regression models. Furthermore, the ETP framework focuses especially on the unexpected part of asset returns and their relation to changes in target macroeconomic variables.

The monthly Finnish data set runs from February 1991 to June 1999. The target variables are industrial production growth, private consumption growth, inflation, and output growth. These represent the basic macroeconomic indicators and are forecasted three different horizons ahead. The starting set of base asset consists of 16 industry portfolios and a market portfolio. However, based on the results from the whole data set, the number of base assets was limited to three groups of industry portfolios, namely basic group (metal and wood industries), new economy group (IT, multibusiness and other services industries) and finance group (banking & finance and finance & investments industries). In addition, we used the dividend yield and the term spread of interest rates as control variables.

The results confirm the recently obtained conclusions in Lamont (2001), Hayes (2001) and Junttila (2002) that ETPs contain relevant information about macroeconomic variables. When compared to the market portfolio, some industry portfolios seem to forecast the future macroeconomy better than others, but the need for more accurate portfolio formation than simply the market portfolio is clear. The tracking power of industry portfolios differs with respect to different macroeconomic variables and different forecasting horizons. In addition, our results give strong support for the use of industry portfolios in out-of-sample forecasting in the ETP framework. The out-of-sample

32

forecasting results are even stronger than in-sample results, especially with private consumption.

The ‘new economy’ stock returns have clear and continuous forecasting power for future inflation and changes in industrial production (and with lesser extent for output growth too) in the Finnish economy at short horizons. The basic industries seem to forecast output growth, at least in-sample. Overall, the number of base assets seems to affect quite strongly to our ETP analysis, indicating that our a priori selection of only few industry portfolios is a step on the right direction. This encourages the search for more defined, detailed and even smaller asset portfolios than the market portfolio, when it comes to economic forecasting.

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Lamont OA (2001) Economic tracking portfolios. Journal of Econometrics 105(1), 161 – 184. Lee B-S (1992) Causal relations among stock returns, interest rates, real activity, and inflation.

Journal of Finance 47, 1591 – 1603. Lee J-J (2004) The adjusted Solow residual and asset returns. Working paper, Towson University.

Submitted.

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Lettau M & Ludvigson S (2001) Consumption, aggregate wealth and expected stock returns. Journal of Finance 56, 815 – 849.

Lettau M & Uhlig H (1997) Preferences, consumption smoothing and risk premia. CEPR Discussion Paper No 1678.

Lettau M & Uhlig H (2000) Can habit formation be reconciled with business cycle facts?. Review of Economic Dynamics 3, 1, 79 – 99.

Piazzesi M, Schneider M & Tuzel S (2004) Housing, consumption and asset pricing. Manuscript, University of Chigago, NYU and UCLA.

Romer D (2001) Advanced Macroeconomics. 2nd edition, McGraw-Hill, New York. Ross S (1976) The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory 13, 341

– 361. Sargent TC & Rodriguez ER (2001) Labour or Total Factor Productivity: Do We Need to Choose?.

Working Paper 2001-4, Department of Finance, Canada. Schreyer P & Pilat D (2001) Measuring productivity. OECD Economic Studies No. 33, 2001/II. Sharpe WF (1964) Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of

Risk. Journal of Finance 19, 425 – 442. Solow RM (1956) A Contribution to the Theory of Economic Growth. Quarterly Journal of

Economics 70, 65 – 94. Solow RM (1957) Technical Change and the Aggregate Production Function. Review of

Economics and Statistics 39, 312 – 320. Stadler GW (1994) Real Business Cycles. Journal of Economic Literature 32, 1750 – 1783. Tallarini TD Jr. (2000) Risk-sensitive real business cycles. Journal of Monetary Economics 45, 507

– 532. Vassalou M & Apedjinou K (2004) Corporate innovation, Price Momentum and Equity Returns.

Working Paper, Columbia University. Submitted.

2 Total factor productivity, corporate innovation and momentum in international asset returns

2.1 Introduction

According to modern theoretical models (see e.g. Canova & De Nicolo 1995, Jermann 1998, Hulten 2000, Tallarini 2000 and Lee 2004), total factor productivity (TFP) should affect asset prices and returns. This is not surprising since TFP is a well-known business cycle variable and asset returns have been shown to react differently in good and bad states of the economy (see Fama & French 1977). The TFP shock influences through various channels:

− through firm’s increased output which eventually shows up in expected dividends and this is discounted in stock prices;

− through consumption decisions of the agents which also affect working hour/leisure decisions, and which end up in stock prices via pricing kernels of asset prices; and

− through the change in capital/labour ratio which is assumed constant in traditional real business cycle models, but the effects of which can be assumed to be seen with the help of stock market data (see the theoretical discussion in chapter 1, section 1.2.3).

One problem, though, associated with TFP is its measurement: it is difficult to identify and measure candidate technology shocks in the actual economy.

This chapter investigates one measure of technology shock build from firm-level variables using a large international data set. The measure of technology shock – or total factor productivity (TFP) as implied by theory – used here is corporate innovation (CI), which is a change in a firm’s gross profit margin (GPM) not explained by the capital and labour the firm has in its use, where GPM refers to the difference between firm’s sales and the cost of the goods it sells. Corporate innovation is a scaled TFP component of a firm and can be calculated in various ways, all closely related to each other. The analysis of corporate innovation starts by examining the differently calculated CI’s. Next, corporate innovation factor is used in stock pricing tests in order to examine whether it can explain international excess stock returns.

Due to the channels of impact of TFP on asset prices, the information about a TFP shock is gradually revealed to the market. This introduces return continuation to

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theoretical models and, hence, connects a TFP shock to momentum phenomenon. Vassalou & Apedjinou (2004) connect corporate innovation to momentum as a necessary condition for the momentum to exist in the US stock market. This essay extends their analysis by introducing corporate innovation to international stock markets in the analysis of momentum. Furthermore, some of the production factors captured by corporate innovation can simply be intangible assets such as research and development (R&D) expenditure, or licensing and patents. Since previous literature lacks empirical tests on the relationship between corporate innovation and R&D, this study attempts to preliminarily analyse this connection, too.

A large international set of 18 OECD member countries from time period 1993:1 to 2003:12 is used in the quarterly panel data analysis. A detailed firm-level data set is essential for the calculation of corporate innovation and also for the availability of the firm-level stock returns, and this is provided by Compustat Global Vantage database. Instead of prices and levels, we use (excess) stock returns and changes in the respective values of the variables to assess the “news” elements of the variables analysed.

The results show that the performance of corporate innovation is at least partly dependent on the way it is calculated. One can compute CI as an actual difference between GPM and capital and labour, or one can build a regression model of GPM on capital and labour and use the resulting parameter estimates to calculate the CI (hence, computed in the spirit of Solow 1956, 1957 residual). In most cases, the ‘generated’ CI seems to have worked better. Various asset pricing tests on both individual and industry returns show that CI can explain excess stock returns rather nicely in addition to the market factor, the Fama & French (1993) size and book-to-market factors and momentum factor of Jegadeesh & Titman (1993). However, it is possible to find some differences between countries and for some countries’ stock returns the CI factor seemed non-significant.

The relationship between corporate innovation and momentum gains some support. CI factors improve the pricing models of momentum portfolios especially well when analysing the momentum spread, i.e. returns on portfolio of high past returns minus returns on portfolio of low past returns. Moreover, corporate innovation – even though quite closely related to R&D expenditure, especially at the aggregate level – seems to capture more than just the measurable R&D expenditure, indicating that it might reflect the effects of other factors, for example the harder-to-measure intangible assets.

This chapter is organized as follows. Section 2.2.1 guides through the building of corporate innovation in empirical applications. Section 2.2.2 addresses the connection between corporate innovation and momentum in the view of the previous literature. Section 2.3 describes the data and sections 2.4 and 2.5 report the results of the empirical analysis. Section 2.4.1 analyses differently calculated CI factors and section 2.4.2 presents the results from several asset pricing tests. Section 2.5.1 presents the results from analysing the connection between CI and momentum. Section 2.5.2 discusses the relationship between CI and R&D expenditures and, finally, section 2.6 concludes.

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2.2 Literature review

2.2.1 Corporate innovation as a proxy for total factor productivity and its role in asset returns

One of the challenges in economics and finance is the lack of structural models that can answer questions concerning what induces financial variables to lead real activity and through what channels. In chapter 1, we reviewed some existing literature on the subject, including the work of Kydland & Prescott (1982), Long & Plosser (1983), Danthine & Donaldson (1993), Canova & De Nicolo (1995), Jermann (1998), Hulten (2000), Tallarini (2000), Horvath (2000) and Lee (2004). Canova & De Nicolo (1995) examine the relationship between asset returns and real activity within a general equilibrium model. The disturbances in their model are considered to be

− exogenous government expenditure shocks; and − exogenous technology disturbances, as in the standard real business cycle (RBC)

literature.

Within the framework of this chapter, more interesting shocks are the technology disturbances that can be considered as total factor productivity shocks according to some RBC models. Briefly, when technology shocks drive the business cycle, dividend yields are less correlated with gross national product (GNP) because the effect of disturbances on dividends and on expected future payoffs of the assets is tempered by the change in investments occurring in response to the changes in the productivity of capital. Hence, a TFP shock affects stock returns through other channels (e.g. the discount factor or the covariance between future payoffs and consumption). If we can measure the technology – or TFP – shock correctly, we should find that it affects stock prices and returns.

As can be seen from chapter 1, in Canova & De Nicolo (1995), equities are priced using a basic consumption-based model (see e.g. Cochrane 1991, 2001) with marginal utility of consumption of the representative agent and future payoffs constructing of future dividends and equity prices. In the one-good models, technology shocks can affect domestic equity prices through two contrasting ways. First, because output increases, firms’ earnings increase. This increase is used to finance new investments and to pay dividends to shareholders. If the technology shocks are persistent, future cash flows accruing to shareholders are expected to increase and this increases equity prices. The technology shock also influences consumption and working hour decisions of agents and therefore the pricing kernels in the asset pricing equations. For highly autocorrelated shocks, the pricing kernel is likely to decrease and this depresses asset prices. In the three-good model of Canova & De Nicolo (1995) the additional features are related to installation costs and unlevered and levered equity17. If installation costs are non-

17 There are two types of equities: one is a claim which pays out each period the residual value of the output after outlays for investments, installation costs and factor payments to labour; the value of this is simply the discounted stream of factor payments to capital owners (this is called unlevered equity). The second type of equity stems from the possibility of corporations to use bonds in addition to equity financing. The payoff of the

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negligible and technology disturbances drive the business cycle, investment will be less procyclical and as a consequence, dividend payments will be more volatile and procyclical; consumption volatility is also likely to increase and this increases the volatility of the discount factor in the asset pricing equations. From stockholders’ perspective, leverage is a cost similar to labour or installation costs and this makes levered equities riskier than unlevered equities; this is likely to increase the volatility and the procyclicality of dividends when technology shocks drive the cycle. All this indicates that the TFP shock has an effect on asset prices.

The criticism18 that attacks the real business cycle models is often related to the technology shock being the only source of business cycle variation. The objections include the facts that

i it is difficult to identify candidate technology shocks in the actual economy19, and ii the postulated technology shocks need to be highly persistent.

This study uses a measure of technology shock build from firm-level variables. Corporate innovation variable can be calculated in a way that is closely related to the calculation of the Solow (1957) residual with a difference that the residuals are firm-specific, not aggregate. Thus, we do not concentrate on building a theoretical model for the differences in growth behaviour of countries, or justify the source of technology shocks; we just empirically investigate whether a technology shock variable measured at firm-level can explain stock returns across countries.

The scaled TFP measure, corporate innovation, is the change in a firm’s gross profit margin (GPM) not explained by the capital and labour the firm has in its use. GPM is defined as the difference between a firm’s sales and the cost of the goods it sells. From this definition it is apparent that CI does not need to be positive (sales can be smaller than the costs). Just like in the case of TFP, it can take any value.

When deriving CI, we adopt the standard Cobb-Douglas production technology (following Vassalou & Apedjinou 2004). Hence, a firm’s output is given by

ααitititit LKAY −= 1 , (2.1)

where itY denotes the ith firm’s value of output at time t, itK is the firm’s capital stock (in levels) used for the production of ty , itL is the labour input (in levels) in the production process and itA is the total factor productivity at time t. The exponents α and ( α−1 ) denote the shares of labour and capital, respectively.

In a competitive labour market and assuming the absence of intermediate goods in the production function, the gross profit margin of the firm i is

ilititit MPLYGPM −= , (2.2)

second kind of equity is the residual value of stream of factor payments to capital owners after bond payments have been made (this is called financially levered equity). 18 See e.g. Danthine & Donaldson (1993). 19 Most frequently cited illustrations, oil shocks, are not technology fluctuations but actually factor price changes.

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where ilMP is the marginal product of labour given by

11 −−= ααα itititil LKAMP . (2.3)

Hence,

111 −−− −= αααα α itititititititit LKALLKAGPM

ααα itititit LKAA −−= 1)(

aititit LKA αα −−= 1)1( (2.4)

According to equation (2.4), a firm’s gross profit margin at time t is a function of the firm’s capital and labour at time t, and the part itA)1( α− , which is called corporate innovation in the terms of Vassalou & Apedjinou (2004). We can see now that the CI is a scaled itA , which corresponds to the TFP of the firm.

Now, Vassalou & Apedjinou (2004) build an empirical estimate of the corporate innovation term CI at time t. The starting point is the following regression equation

ititiitiiit lbkbbgpm ζ+++= 210 , (2.5)

where ( )1log −= ititit GPMGPMgpm is the change in the ith firm’s log GPM from time t to time t - 1, ( )1log −= ititit KKk is the change in log capital stock from time t to time t - 1 for firm i, and ( )1log −= ititit LLl is the change for firm i in the log labour employed from time t to time t - 1.

Corporate innovation (CI) is then given by

)ˆˆ( 21 itiitiitit lbkbgpmCI +−= , (2.6)

where 1ib ja 2ib are the ordinary least squares (OLS) estimates of 1ib and 2ib respectively, i.e. the factor shares of capital and labour. It can be noted that the computation of CI used in Vassalou & Apedjinou (2004) is very similar to that of TFP or Solow residual (see Solow 1956, 1957).20

In this theoretical derivation of CI, it can be noted that capital is assumed constant and hence, it does not enter equation (2.2).21 A more traditional – and intuitively more realistic – approach is to include capital in the profit equation and this naturally would alter the theoretical specifications used here to derive CI. Lee (2004) indeed shows that constant capital utilization is just a modified version of a more general model and furthermore, capital utilization greatly affects the pro-cyclical behaviour of productivity 20 As noted in Vassalou & Apedjinou (2004), some assumptions of the original derivation in Solow (1957) do not hold in this application. The estimation of corporate innovation is simply in the spirit of Solow residual. 21 I thank Jouko Vilmunen from Bank of Finland for pointing this out to me.

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series. Also, the assumption of capital and labour being constant in time could be relaxed; hence, one could utilize a more general production function instead of the specific Cobb-Douglas. It, however, may be the case that in empirical applications the production function indeed reduces to the Cobb-Douglas form and thus, the utilization of it in this context is justified. Furthermore, as can be seen later, the assumption of constant capital does not affect the empirical construction of CI, so as for now, we follow Vassalou & Apedjinou (2004) in our theoretical part of CI.

Furthermore, it is highly likely that CI captures the price changes occurring due to the technological change in the constant term of equation (2.5) through term α . α measures the share of labour in the production function and through the analysis of labour productivity one could obtain more insight to the behaviour of the constant 0ib .22 The variation in the constant term in empirical application could be a sign of the effect of a TFP shock on labour productivity and through that on the prices of the firm’s products. The variation in constant term, however, can also capture other effects. For example, large fraction of the theoretical growth models (see a review in Barro & Sala-i-Martin 2004) suggest that the quality of labour can change according to e.g. improvement in education and the ratio of labour and leisure. This will also be picked up by 0ib .

Empirically, a quite natural way of calculating corporate innovation would be to just deduct capital and labour from GPM, hence obtain an actual CI. This would, of course, not be in the spirit of Solow, but basically should capture same influences.23 Another alternative way to obtain an empirical measure for CI is to exclude constant from the CI regression (equation 2.5). As already discussed, the role of the constant can be quite important in generating CI. One goal of this chapter is to build corporate innovation using all three possible ways and to compare them and their performance in asset pricing. In addition to using firm-specific CI’s, we follow Vassalou & Apedjinou (2004) and build an aggregate measure of corporate innovation, ACI. First, GPM, capital stock and labour are aggregated across all firms. Then the growth rates of these variables are built over the past quarter and finally an aggregate CI factor is constructed. As already mentioned, this ACI factor is a scaled TFP, naturally with the difference that it is computed using only publicly traded firms instead of all firms in the economy.

Corporate innovation can be related to intangible assets, such as research and development (R&D) expenses or licensing and patents. Most previous papers focus on how intangible assets should be treated according to different accounting practices. One study that considers the effects of intangible assets on equity returns is that of Chan, Lakonishok & Sougiannis (2001), who examine whether stock prices fully value firms’ intangible assets, specifically R&D expenditures on US data. Results provide little evidence for the link between R&D spending and future stock returns: average stock return of firms that do R&D is comparable to the return on stocks for firms with no R&D. This seems to be consistent with the notion that the market price on average incorporates fully the benefits of R&D spending. The focus of this chapter and that of Vassalou &

22 See e.g. Hall (1990), who notes that Solow’s residual fails to fulfil the invariance property assumption: hence, shifts in product demand and labour supplies have an effect on the residual. 23 The growth accounting literature that utilizes regression estimation in their empirical applications suggests several problems associated with the calculations (see e.g. Schreyer & Pilat 2001). Hence, the actual CI is build in order to compare the performance of actual and generated CI’s.

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Apedjinou (2004) is different as we examine the effects that non-capital and non-labour production factors have on firms’ gross profits and its expected returns.

Accounting principles around the world differ with respect to the way firms report intangible assets in their financial statements and some intangibles, such as brand names, cannot be reported at all. CI could be interpreted as capturing all the intangible assets, hence including also the ones that otherwise are hard to identify. However, Vassalou & Apedjinou (2004) claim that CI is much more general than any particular intangible asset category: it can be regarded as the return on capital for a particular firm, to which intangible assets simply contribute (positively or negatively). Nevertheless, Vassalou & Apedjinou (2004) lack the tests that show the existing or non-existing relationship between CI and intangible assets. Empirically, it would be interesting to examine the relationship between CI and intangible assets using dataset from an area where accounting standards require firms to include detailed information about their intangible assets in financial statements. However, this is quite difficult using an international framework. Hence, in this study, measurable R&D expenses are used as a benchmark for corporate innovation in order to evaluate the information that CI captures.

2.2.2 Corporate innovation and momentum in international asset returns

Due to the channels of impact on asset prices, the information about a TFP shock is gradually revealed to the market. This introduces return continuation to the model and hence connects TFP shock to the momentum phenomenon. The effects of increased dividend payments of firms and the consumption decisions of agents are likely to appear sooner in the asset prices than the impact of new, potentially profitable investments of firms and worked hour decisions of consumers. This intuition supports the empirical literature documenting that momentum profits are a medium-term phenomenon24. Firms report about increased dividends shortly – but not immediately – after the TFP shock and agents can modify their consumption behaviour also within a shorter time period. It takes a while longer for the new investments to be chosen and implemented (and also to be informed about to the public) and for the consumers to change their worked hours - leisure relation.

An extensive literature has documented that average stock returns are related to stock’s past performance. The profitability of these so called momentum strategies, first documented by Jegadeesh & Titman (1993), remain the only CAPM-related anomaly unexplained by the Fama-French three-factor model (Fama & French 1996). There can be found several possible explanations for the momentum in previous literature, among them data mining and behavioural patterns (e.g. Jegadeesh & Titman 2001) and risk (e.g. Conrad & Kaul 1998). Recently, there have emerged some risk-based explanations for the Fama-French three-factor model (see e.g. Liew & Vassalou 2000, Lettau & Ludvigson 2001, Vassalou 2003, Li et al. 2003 and Vassalou & Xing 2004). These explanations connect the Fama-French factors size and book-to-market to macroeconomic variables 24 In this chapter, medium-term period refers to time period of one to three years.

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and the business cycle. Interestingly, momentum strategies have also been related to business cycle variables for example in Chordia & Shivakumar (2002), Griffin et al. (2003) and Vassalou & Apedjinou (2004).

Rouwenhorst (1998) document that returns on European momentum portfolios are significantly correlated with relative strength strategies in the US. This correlation potentially reflects a priced momentum factor that is common across markets. Intuitively, we can match this result with the goal of this chapter. CI measures TFP shock that can be e.g. a change in technology. In the modern world and financial markets, news and innovations travel fast, hence businesses and industries across the world adapt rapidly to new technology. If momentum reflects the effects of CI, we would expect there to be a common priced momentum factor in international asset returns.

Despite the evidence reporting a negligible role for industries in understanding financial markets (e.g. Heston & Rouwenhorst 1994 and Griffin & Karolyi 1998), industry-level studies are motivated because firms within industries tend to be highly correlated, at least within a country. Their behaviour related to corporate finance is similar, they face the same regulatory environment and, most importantly from the point of view of this study, they are similarly sensitive to macroeconomic shocks. In the momentum framework, Moskowitz & Grinblatt (1999) document a strong and prevalent momentum effect in industry components of US stock returns. Using a model with common macroeconomic variables, Chordia & Shivakumar (2002) suggest that the source of profitability associated with momentum payoffs is related to the business cycle.25

Using the aggregate corporate innovation (ACI) abstracted from US data, Vassalou & Apedjinou (2004) examine whether CI represents a risk factor in equity returns and whether the momentum factor shares any priced information with ACI. First of all, they conclude that ACI receives a positive risk premium in the cross-section of equity returns. Stocks with high sensitivity to CI offer higher expected returns, probably because investors are averse to variations in CI and hence, they require a premium to hold stocks with large exposures to this variable. Secondly, comparing the 6-month/6-month momentum strategy and 6-month/6-month CI-based strategy26, their results show that losers are typically firms with negative CI’s whereas winners are the firms with highest average CI among momentum portfolios. CI seems to enhance the profitability of a firm and therefore high CI firms are the most profitable ones. Furthermore, results indicate that what differentiates winners from losers is unrelated to their capital and labour growth. Corporate innovation seems to be a necessary condition for the momentum to

25 Chordia & Shivakumar (2002) rely on prior studies (e.g. Chen et al. 1986) and use lagged values of the value-weighted market dividend yield, default spread, term spread and a yield on three-month T-bill as macroeconomic variables, some of which are well-known business cycle variables. 26 For testing the pricing of the corporate innovation variable, Vassalou & Apedjinou (2004) construct a returns-based CI variable. In particular, they compute CI for all stocks in the sample using growth rates in GMP, capital and labour over the past two quarters (6 months). Then they rank stocks on the basis of their CI and create 10 portfolios of which they form a zero-investment portfolio that is long on the decile with the highest CI stocks and short on the decile with the lowest CI stocks. The holding period for these portfolios is 6 months. Similarly, they construct the 6-month/6-month momentum portfolio following Jegadeesh & Titman (1993): it is a zero-investment portfolio that is long on the decile with highest past 6-month returns (winners) and short on the decile with lowest past 6-month returns (losers).

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exist. The performance of momentum strategy is dependent on going long on stocks with high levels of CI.

Not all studies support the link between momentum and macroeconomy. Griffin et al. (2003) investigate the relationship between momentum returns and macroeconomic risk on a global basis and analyse whether this international evidence is consistent with risk-based or behavioural models of momentum. They document that momentum portfolio profits only weakly co-move among 40 countries and are not related for example to Chen et al. (1986) macroeconomic factors. Furthermore, the forecasting model proposed by Chordia & Shivakumar (2002) has very low explanatory power for momentum profits when applied to the international data. When investigating the model in detail with the US data, positive profits can be found both in good and bad business cycle states indicating that momentum profits are not a reward for priced business cycle risk.

2.3 Data

We use quarterly panel data across several countries.27 The time period is from 1993:1 to 2003:4, which represents the period for which data for all variables and all countries are available. Compustat Global Vantage data is used to compute the CI variable, stock returns and the aggregate R&D expenditures. We choose 18 OECD member countries to our analysis (see Table 1 for a list of the countries and Table 2 for descriptive statistics of the main interesting variables in this analysis).

The computation of CI for each firm in each country involves measures of gross profit margin, capital and labour. The GPM is calculated as the Compustat item “Sales” minus Compustat item “Operating expense”28. Capital is measured as Compustat item “Property, plant and equipment” and labour is measured as Compustat item “Number of employees”29. All the variables needed for the different calculations of CI are measured as changes in logs in their respective values. We calculate the quarterly stock returns from Compustat monthly stock price data30. All the price data are first transformed into returns by taking changes in the logs of the respective monthly prices. The quarterly return is then calculated as a simple arithmetic mean of the monthly returns. This indicates that we assume that all the possible information in stock returns – the information they contain about the performance of the firm and about the aggregate economic activity – will build

27 Our approach differs from Vassalou & Apedjinou (2004) in that they use cross sectional analysis from one country, namely the US. 28 Vassalou & Apedjinou (2004) used Compustat item “Cost of goods sold” instead of “Operating expenses”, but their analysis covered only US data. The problem with using “Cost of goods sold” on international data is that the item is constructed differently in different countries depending on the accounting and financial statement systems of the country. Thus, “Cost of goods sold” does not measure same things whereas “Operating expenses” seem to be identically constructed across countries. Notice, that “Operating expenses” measures other operational expenses and, hence, excludes capital and labour. 29 Once again we are forced to use a related measure instead of the most realistic one in our analysis: Compustat items ”Labour and related expenses”, ”Wages and salaries” and ”Wages other” which would probably be better and more accurate measures of labour in our analysis, differ across countries and furthermore are not computed in all the countries included in the analysis. Thus, we use “Number of employees” as a proxy for labour. 30 Some firms have multiple stock price series and we chose the longest one to represent the stock price.

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up gradually during the quarter. All the returns are in excess of short-term interest rates (3-month interest rate) obtained from OECD Source database. The R&D expenditure is measured as Compustat item “R&D expenditure”.

Table 1. List of countries analysed from 1993:1 to 2003:4 and the number of firms used in corporate innovation (CI) calculations by country and in the whole data

Country Abbreviation Min no Max no Australia AUS 120 800 Austria AUT 40 248 Belgium BEL 56 296 Canada CAN 376 752 Switzerland CHE 256 620 Germany DEU 540 2 396 Denmark DNK 132 444 Spain ESP 88 412 Finland FIN 116 392 France FRA 520 2 028 Great-Britain GBR 1 268 3 264 Ireland IRL 36 160 Italy ITA 16 672 Japan JPN 1 164 12 744 Netherlands NLD 204 556 Norway NOR 28 404 Sweden SWE 152 916 United States USA 7 112 10 176 All countries ALL 15 036 36 536 Abbreviations of the countries’ names as defined here are used throughout the chapter. Min no and max no refer to the minimum and maximum number of firms available for the construction of corporate innovation (CI) by country and for the data as a whole (ALL), throughout the time period covered here.

Some of the items required (namely variables needed for the calculation or measurement of gross profit margin, capital and labour) are reported only on annual basis. In order to obtain quarterly observations, we convert the annual data by simply assigning the annual observation of the year for the quarters of that year.

The number of firms varies greatly by country and this naturally may affect the final results: the interpretation of the results is harder for the countries with only few firms to build corporate innovation from. In general, both firm-specific and aggregated gross profit margins are on average negative, which indicates that the corporate innovation measures also can be more on the negative side. As can be seen from Table 2, this is indeed the case for the generated CI. The mean and variance of actual CI is quite different to that of generated CI. This can produce different results depending on which CI measure is used in the analysis. Next we turn to the more specific description of the construction of CI’s.

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Table 2. Descriptive statistics of the main variables for the whole data

Variable Mean STD Min Max Capital -1,911 1,881 -12,067 14,359 Labour -2,102 1,775 -10,292 10,130 GPM -1,070 1,718 -35,453 9,866 Aggreg. capital -2,408 1,869 -6,692 3,333 Aggreg. labour -2,402 2,074 -6,695 5,086 Aggreg. GPM -1,989 1,703 -7,850 3,773 Excess returns -4,891 2,447 -15,178 6,023 Excess market ret -5,375 2,478 -14,077 4,794 Size 0,556 1,930 -10,786 15,647 Book-to-market -0,382 1,451 -15,062 14,671 R&D -1,632 1,605 -9,301 10,140 Aggreg. R&D -1,955 1,852 -7,327 5,656 Actual CI (ci) 2,943 2,897 -34,387 16,437 Gen. CI (cigen) -0,021 1,489 -35,317 9,185 Gen. CI (cigenw) -0,008 1,486 -35,321 9,148 Aggreg. actual CI (ACI) 2,821 2,602 -5,782 10,124 Aggreg. cigen (GCI) -0,311 1,091 -4,593 2,319 Aggreg. cigenw (GICI) 0,031 0,946 -3,597 2,075 Mean, standard deviation (STD), minimum (Min) and maximum (Max) values of variables are reported. All variables are expressed as changes in the logs of their respective values. Capital, labour and gross profit margin(GPM) are used in the calculations of corporate innovation (CI). CI’s are also calculated as aggregates and for this calculation aggregated capital, labour and GPM are needed. Aggregation is done within countries. Firm-specific returns and market returns are in excess over 3-month interest rate. Size is measured as market value of a firm. Aggregated R&D is calculated also by country. Actual CI is formed as follows: actual CI = GPM –capital – labour, generated GCI is formed using equations (2.5) and (2.6), and generated GICI is formed using also the equations except that equation (2.5) excludes constant from the analysis. Aggregated values of different CI variables are calculated by using aggregated capital, labour and GPM in the equations mentioned above.

2.4 Results on the role of corporate innovation in asset pricing

2.4.1 Description of corporate innovation

The first goal of this chapter is to build the firm-specific CI-variables. There are basically two ways to build the CI-variable. First, as presented previously in this chapter, one can use regression analysis (see equation (2.5)) and create a Solow inspired generated CI from the parameter estimates of that regression (as in equation (2.6)). One question emerges from the estimation of CI, namely the inclusion of the constant term in the calculus. We test the validity of the constant term by testing if it can be excluded from the analysis. If the constant does affect the parameter estimates and cannot be excluded from the regression, it can be considered as representing for example the general technological

47

development in an economy. It can also be interpreted to capture some country-specific factors that are not included in the analysis, e.g. the price changes discussed earlier.

Another way to build the CI-variable is simply to subtract capital and labour from gross profit margin and hence, obtain an actual (in the sense that it is not generated) CI-variable. Naturally, this way of calculating does not make a distinction between the effects of labour and capital and e.g. the effects the constant term captures. Actual CI is a rough and simple measure that is used to give some benchmark to the generated CI. The GPM, capital and labour used in the above calculations are all one-quarter changes in logs of their respective values. As already mentioned, CI can take both positive and negative values.

The different ways of constructing CI results in three CI variables: the actual CI (ci), generated CI with a constant included in the CI-regression (see equations (2.5) and (2.6)) (cigen), and generated CI with a constant excluded from the CI-regression (cigenw). Table 3 reports some basic statistics of the CI variables and Figure 1 shows the time series graphs of the means of CI variables by country.

48

Table 3. Descriptive statistics of the different CI variables

ci cigen cigenw F-test Country Mean STD Mean STD Mean STD p-value AUS 2,143 3,437 0,213 1,470 0,151 1,473 0,000 AUT 3,073 2,841 -0,144 1,500 -0,051 1,501 0,014 BEL 2,500 2,784 -0,343 1,525 -0,138 1,535 0,000 CAN 2,390 2,798 -0,141 1,389 -0,066 1,390 0,000 CHE 3,218 2,662 -0,108 1,429 -0,034 1,429 0,003 DEU 2,673 3,010 0,103 1,476 0,052 1,477 0,000 DNK 3,022 2,794 -0,098 1,516 -0,035 1,517 0,028 ESP 2,774 2,795 -0,430 1,413 -0,154 1,427 0,000 FIN 2,919 2,694 -0,178 1,405 -0,061 1,407 0,000 FRA 2,608 2,854 -0,118 1,420 -0,053 1,421 0,000 GBR 2,699 2,683 -0,014 1,444 -0,006 1,444 0,304 IRL 2,391 2,439 -0,207 1,240 -0,080 1,244 0,000 ITA 2,823 2,864 -0,033 1,417 -0,014 1,417 0,326 JPN 3,779 3,156 0,112 1,626 0,038 1,626 0,000 NLD 2,552 2,555 -0,007 1,255 -0,003 1,255 0,808 NOR 2,316 2,865 -0,052 1,451 -0,026 1,451 0,211 SWE 2,487 2,892 0,016 1,447 0,008 1,447 0,539 USA 2,648 2,651 -0,088 1,431 -0,034 1,432 0,000 ALL 2,943 2,897 -0,021 1,489 -0,008 1,486 0,000 The means (mean) and standard deviations (std) of differently calculated CI’s by country and within the whole data (ALL). The actual CI (ci) is calculated simply by deducting capital and labour from GPM. The generatedCI (cigen) is calculated using a regression ititiitiiit lbkbbgpm ζ+++= 210 , where itk = capital for

firm i at time t, itl = labour for firm i at time t,, ib ’s are the regression coefficients and itζ are the error terms.

Hence, cigen is )ˆˆ( 21 itiitiitit lbkbgpmCI +−= , where ib are the parameter values of the regression

mentioned above. cigenw reports the generated CI that was calculated without the constant term in theregression. Capital, labour and gross profit margin (GPM) that are needed in the construction of CI aremeasured as 1-quarter changes in logs in the respective values. The null of constant 0ib is tested using an F-test

and the p-values from this test are reported in the last column, where statistically non-significant values at 5 % level are highlighted.

As can be noted, the actual CI is very different from the two generated CI’s with both larger mean and standard deviation. Furthermore, most of the values of generated CI’s are negative contrary to actual CI, which is positive throughout the whole sample. This is partly due to the fact that the changes in capital and labour have been (sometimes) negative during the time period in question.31 Negative actual CI indicates that the changes in capital and labour have been greater than the changes in GPM, and if both capital and labour have been negative and are deducted from GPM, the negative signs

31 The time period studied here includes both a hyper technological boom and a severe recession that shook especially the industrialized countries, of which this sample mostly consists of. This may affect the calculation of generated CI.

49

outdo each other, hence, resulting in a positive actual CI. This is all the information that the actual CI accounts for. When building generated CI’s, the regression coefficients in equations 2.5 and 2.6 define the importance of capital and labour in GPM, but leave room also for other influences (e.g. in the form of constant term and in the weights the potentially small coefficients place on capital and labour). Ineffective behaviour of firms, i.e. not using optimally the capital and labour they have, can be an explanation for the differences between actual and generated CI’s. The actual CI remains positive, but when taking the weights of capital and labour into account, the effect is opposite. The generated CI’s, one with constant in the regression, the other without, seem to be quite similar.

There can be found minor differences between countries with respect to all CI’s. One notable fact is that for few countries, namely Australia, Denmark, Japan and Sweden, the generated CI’s are positive while for all the other countries the variables obtain negative values.

Which of the CI’s should be preferred in our analysis? It is quite difficult to rationalize beforehand why one is better than the other. Perhaps one reason could be that even though CI is meant to be a wide measure of all the other things affecting firm’s output besides capital and labour, one should specify other variables to be used in the construction. This is important, since in the modern world firms are build of much more than just capital and labour – the problem associated with this is the difficulty of measurement (see the earlier discussion of intangible assets). From this point of view, the usage of a regression analysis in the construction CI (resulting in a generated CI) should be preferred, since it weights the influence of capital and labour to GPM and hence, leaves room for other influences too. How to measure the other influences is a question worth of another paper and, hence, will no be discussed further here.

50

Fig. 1. Time series graphs of the means of CI variables by country

We present graphs from example countries, which in this case are Australia (AUS), Canada (CAN), Finland (FIN), Germany (DEU), Great Britain (GBR), Japan (JPN), Spain (ESP) and USA. ci refers to the actual CI, cigen to the generated CI and cigenw to the ‘without constant’ generated CI.

51

Fig. 1 (continued)

52

Fig. 1 (continued)

53

Fig. 1 (continued)

54

Because the numbers in Table 3 indicate the similarity in size and in sign of the two generated CI’s and that the constant term can be excluded from the construction of CI in only five of 18 countries32, from now on for the most part, the analysis in this chapter concentrates on actual corporate innovation (ci) and with constant in regression generated corporate innovation (cigen)33. What needs to be noticed, though, is the fact that depending on the country the generated CI cannot be calculated the same way for the optimal results within each country.

In order to proxy TFP with corporate innovation, CI needs to be somewhat persistent in time. This question is investigated with autocorrelation tests and the results are reported in the Appendix in Table 10 for the whole data. The tests conducted for the whole data set in Panel A and also the country-specific tests34 show some autocorrelation in actual and generated CI from lag 5 up to lag 12, the correlation being higher for the actual CI. The correlation coefficients are not very high, but the persistence is shown clearly through the continuation of the autocorrelation up to 12 lags. The average CI’s of the portfolios formed on the basis of sorting stocks by their CI35 for the whole data show even stronger autocorrelation in the form of higher correlation coefficients (in Panel B). When using the generated CI, the autocorrelations are always positive and persist up to lag 12, and when using actual CI, the autocorrelations persist up to lag 8, however, changing from positive to negative from lag 3 to lag 4. The autocorrelations for the deviations of CI’s from the mean CI’s of the three CI-based portfolios in Panel C are quite persistent and almost always positive (only few countries form exception) (see the results in Vassalou & Apedjinou 2004). Due to this persistency in the time series, we have used Newey and West (1987) covariance matrix and a truncation lag of 12 in all the following analyses.

Table 4 reports the correlation coefficients between variables of interest for the whole data. Despite analysing firm-specific data, we aggregate different CI’s across the firms. In order to obtain aggregated CI-variables, the changes in logs in GPM, capital and labour are aggregated by country and then differently calculated CI’s are built. First of all, the differently calculated CI’s (ci, cigen and cigenw, and their aggregated versions ACI, AGCI and AGICI) are not highly correlated among each other. Individual CI’s seem to share about half of their information with each other (coefficients are both 0,519)36, while the aggregated CI’s have even smaller correlation coefficients 0,289 and 0,424. All the CI variables seem to be quite weakly correlated with the individual stock returns and the market returns, the highest correlation coefficient being only 0,232. This may be first indication that the CI variables tested in this chapter are not very informative factors in asset pricing models.

32 Namely, the constant can be excluded from the CI-regression in Great Britain, Italy, Netherlands, Norway and Sweden. 33 The results remain qualitatively the same when using without constant generated corporate innovation (cigenw) and are, hence, not reported here, but are available from the author upon request. 34 The country-specific autocorrelation tests are not reported here due to the lack of space. 35 The stocks in this sample were sorted by the firm’s corporate innovation factor and were divided into high CI, medium CI and low CI portfolios in the spirit of Fama & French (1995, 1996) size and book-to-market portfolios. We discuss the formation in more detail later on. 36 As could already be noted from the investigations in Tables 2 and 3, this indicates that the two individual CI series are very close to each other.

55

Table 4. Correlations between different variables of interest within the whole data set

Panel A. Pearson’s correlation coefficients for individual CI’s ci cigen cigenw ci 1,000 . . cigen 0,519 1,000 . cigenw 0,519 0,998 1,000 re 0,134 0,117 0,092 MKT 0,133 0,040 0,017 RD -0,347 0,115 0,116 Panel B. Pearson's correlation coefficients for aggregated CI's ACI AGCI AGICI ACI 1,000 . . AGCI 0,289 1,000 . AGICI 0,424 0,918 1,000 re 0,106 0,232 0,160 MKT 0,013 0,197 0,090 ARD -0,617 0,233 0,161 Description of notation: ci = actual CI, cigen = generated CI with constant included in the regression, cigenw = generated CI with constant excluded from the regression, ACI = aggregated actual CI, AGCI = aggregatedgenerated CI with constant in the regression, AGICI = aggregated generated CI without constant in theregression, re = firm-specific return, MKT = market return, RD = firm-specific R&D expenditure and ARD = aggregated R&D expenditure. All variables are expressed as changes in logarithms of their respective values.Both firm-specific returns and market returns are expressed as excess over short-term interest rate (3-month interest rate).

More interesting correlations can be found between different CI variables and R&D expenditures. Aggregated CI’s and aggregated R&D have stronger relationship than individual CI’s and individual R&D, which is quite expected. Firms differ greatly even within industries and let alone countries: some firms simply do R&D and some don’t and since CI is supposed to measure more than just R&D, the relationship cannot be very strong at an individual firm level. The correlation coefficient between aggregate actual CI (ACI) is high but (surprisingly) negative. The aggregate generated CI’s (AGCI and AGICI) obtain rather modest, though positive, correlation coefficients. All this suggests that CI captures more than R&D, perhaps the immeasurable intangible assets, perhaps some other factors.

What could explain the negative correlation between actual CI and R&D expenditures? This is quite interesting since one aspect of the paper is to investigate if CI captures similar information to research & development expenditures. This perhaps indicates that CI is indeed a much wider measure of influences on firm’s output than intangible assets or R&D expenses. It may even capture the influence of for example brands or people’s images of firms, hence, the speculative component of the firm value in the markets. Also, research & development can be a risky and uncertain business area and only some (or in the worst case, nothing at all) of the R&D expenses generate profit to the firm (and the time horizon for the income to appear can be long compared to the R&D expenses that occur presently). As already discussed previously, the generated CI’s

56

put weights on the influence of capital and labour, and if their effect is small, this is captured better than when using actual CI.

2.4.2 Corporate innovation in explaining stock returns

The next stage is to test whether corporate innovation can explain stock returns. As the theory lacks a model that gives basis for the aggregated corporate innovation as a risk factor, we choose to add ACI to a CAPM specification, generating therefore a 2-factor model37. An aggregate ACI measure is used to capture the TFP shock and firm-specific target variables are utilized in a regression model of the form

ittitiiit ACIMKTRE εδβα +++= , (2.7)

where itRE is the excess return38 of a firm i at time t, tMKT is the excess return on a market portfolio at time t, tACI is the aggregate corporate innovation calculated from each firm i at time t, itε are the error terms of the asset pricing equation and iα is the constant term and iβ and iδ are the parameter estimates of the regression.

Another popular factor model used in e.g. momentum analysis is the Fama-French 3-factor model using the size and book-to-market factors in addition to the market factor39. We construct a country-specific return-based CI spread by building it similarly to Fama & French (1993) SMB and HML factors: first, stock returns are sorted by their CI’s and ‘high CI’ and ‘low CI’ portfolios are constructed. The portfolio HLCI is now created by subtracting the average return of ‘low CI’ portfolio from the average return of ‘high CI’ portfolio.40 Finally, a 4-factor model is build using the market factor, HML and SMB factors and our HLCI factor. More specifically, the regression equation is of the form

37 The chosen specification can be justified with reference to Merton’s (1973) intertemporal CAPM (ICAPM). Since ACI is a proxy for TFP and TFP is a well-known business cycle variable that affects the investment opportunity set, ACI is bound to do the same. See a more detailed discussion in Vassalou & Apedjinou (2004). Furthermore, as we take a risk-based view on the momentum phenomenon, we follow Fama & French (1998). They also add their relative distress factor (Fama & French, 1993) to one-state-variable ICAPM (or two-factor APT) using international data. The CI factor is similar business-cycle factor to the relative distress factor and thus it can be regarded as a priced risk in asset markets. 38 All the returns are expressed in excess over risk-free interest rate that is approximated by a short-term interest rate. This is done in order to concentrate on the sole information content of stock market: interest rates are well known variables to forecast aggregate economy and their influence is excluded from stock market by analysing excess returns. 39 The quarterly market returns are calculated as averages of monthly market returns. Firm size is captured by its market value and book-to-market information is simply the firm’s book value divided by its market value. Firm size and book-to-market variables are measured as changes in logs of their respective values in all regressions. All information is obtained from the Compustat data files. 40 In this paper, we sort stocks by CI and create three portfolios using 25% and 75% fractiles. Thus, stocks that have CI smaller than the 25% fractile form the ‘low’ portfolio, stocks with CI between the 25% and 75% fractiles form the ‘medium’ portfolio and stocks with CI larger than 75% fractile form ‘high’ portfolio. The HLCI portfolio is now simply the average return of the ‘high’ minus the average return of the ‘low’. FF-factors SMB and HML are also constructed using 25% and 75% fractiles, otherwise they are build as in Fama & French (1993).

57

ittitititiiit HLCIHMLSMBMKTRE εδϕφβα +++++= , (2.8)

where itRE is the excess return of a firm i at time t, tMKT is the excess return on a market portfolio at time t, tSMB is the size factor in FF-model at time t, tHML is the book-to-market factor in FF-model at time t, tHLCI is the CI-based return variable at time t, itε are the error terms of the pricing equation. The asset pricing equations (2.7) and (2.8) are run for the whole data41 and for each country independently.

Since one aim of this chapter is to address the question of corporate innovation in relation to momentum, we build a momentum spread to be used in the asset pricing regressions. We rank stocks on the basis of their past 2-quarter returns and construct 3 portfolios, once again using 25% and 75% fractiles in building the portfolios. The variable MOM is the return on a portfolio where the average return of the portfolio with the lowest past 2-quarter returns (losers) is deducted from the average return of the portfolio with the highest past 2-quarter returns (winners). MOM is either added to the asset pricing regressions generating a 5-factor model or it is used to replace HLCI factor in the equation (2.8).

Notice that when the 2-factor and 4-factor models are estimated for the whole data (ALL in Tables 5 and 6) the excess market returns and the aggregated corporate innovation are country specific. This makes them part of the idiosyncratic component of the risk and hence cannot be regarded as general risk factors.42

The results43 of the regressions of the 2-factor model are reported in Table 5, Panels A and B for individual asset returns44. Both actual CI and generated CI were used as factors in separate regressions. In Table 5, both CI variables are statistically significant almost in all cases. The negative parameter estimate for actual CI is a little surprising, since investors want to hedge against CI because CI can be considered as a state variable affecting the investment opportunity set, and this would result in a positive parameter estimate. The results using generated CI’s are more mixed, since the expected sign of the parameter estimate would be positive, and this is the case only in about half of the countries. All this indicates that the CI variable contains additional information (additional to the market factor) about the returns on stocks. And actually, the result supports the view that CI captures such information that can be – at least partly – regarded as uncertain and risky from firm’s perspective and may not result in positive profit for the firm (such as part of R&D expenses, brand names and the image of the firm).

The constants of the regressions are close to zero only in few cases – namely in Australia and Italy and for the dataset as a whole – which implies that the market factor and CI factor leave room for other factors in explaining excess stock returns in this 41 The world market index is not considered as a part of the model when the model is estimated for the whole data. 42 I thank my examiners for pointing this out to me. 43 The residual diagnostics for the models are also reported in the tables. DW denotes the Durbin & Watson (1971) residual autocorrelation statistics and ARCH denotes residual heteroscedasticity measured as in White (1980). 44 All the pricing regressions were also conducted on industry stock returns, since the previous literature usually tests Fama & French (1993) factors in the context of pricing asset portfolios and not individual assets. Due to the lack of space and since they remain qualitatively the same, these results are not reported here.

58

dataset. The results here support the conclusions in Vassalou & Apedjinou (2004) that ACI is a priced risk factor. Surprisingly, the results here are very similar to Vassalou & Apedjinou (2004) when using the actual CI instead of the generated CI (which is what they used).

Table 6 reports the results from the 4-factor regression model for individual returns. In Panels A and B, both the actual and generated CI are in most countries significantly different from zero, thus contributing to the model. The generated CI in Panel B is non-significant in Austria, Switzerland, Germany, Denmark, Great Britain and Sweden. Hence, once again the performance of CI in asset pricing regressions is dependent on the way we calculate the CI factor and on the country. In Panel E are the results of regressions using market portfolio and momentum factor MOM, and they are very similar to the results using the CI factor. In this respect, the results indicate that corporate innovation and momentum may be related. More information can be obtained from Panels C and D, where the CI variables are included in the regressions together with MOM. And indeed, especially with generated CI, the results show that CI and momentum contain some of the same information: MOM factor becomes non-significant when HLGCI is added to the regression in five countries. But once again, the results vary greatly from country to country.

The residual diagnostics are reported in Tables 5 and 6. They show residual autocorrelation for about half of the countries and no residual autocorrelation for the other half (DW statistic should be around 1,5 – 2,5 if there model is free from autocorrelation in error terms). Conditional heteroscedasticity in error terms seems to be present in most of the countries45.

45 Due to the lack of space, we do not report the P-values of the tests, but they are available from the author upon request.

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508

1,43

9 1,

425

1,52

2 1,

419

1,60

4 1,

348

1,62

2 1,

490

1,77

2 1,

511

1,40

0 1,

348

1,06

4 A

RC

H

2897

,9

77,2

9 4,

10

8,45

74

,27

33,5

0 30

6,23

33,3

4 49

,44

9,54

29

,12

404,

9026

,45

82,3

5 20

9,74

48,5

6 79

,28

104,

4516

72,8

RE =

exc

ess

firm

-spe

cific

ret

urn,

MK

T =

exce

ss m

arke

t re

turn

, AC

I =

aggr

egat

ed a

ctua

l C

I, AG

CI

= ag

greg

ated

gen

erat

ed C

I. P-

valu

es a

re r

epor

ted

in p

aren

thes

is.

Stat

istic

ally

non

-sig

nific

ant

valu

es a

t 5

% l

evel

are

hig

hlig

hted

. Th

e m

odel

fit

stat

istic

rep

orte

d is

the

adj

uste

d R

2 . D

W d

enot

es D

urbi

n-W

atso

n st

atis

tic f

or r

esid

ual

auto

corr

elat

ion

and

AR

CH

mea

sure

s res

idua

l het

eros

ceda

stic

ity.

60 Ta

ble

6. A

sset

pri

cing

test

s on

firm

-spe

cific

exc

ess

retu

rns

with

cor

pora

te in

nova

tion

spre

ad a

nd m

omen

tum

spr

ead

as a

dditi

onal

fact

ors

in th

e Fa

ma-

Fren

ch 3

-fact

or m

odel

Pane

l A. F

F 3-

fact

or m

odel

+ a

ctua

l CI s

prea

d:

itt

tt

tit

HLC

IH

ML

SMB

MK

TRE

εδ

ϕφ

βα

++

++

+=

A

LL

AU

S A

UT

BEL

C

AN

C

HE

DEU

D

NK

ES

P FI

N

FRA

G

BR

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

-0,5

13

-1,0

72

-3,2

94-2

,948

-0,1

22-1

,509

-4,2

75-1

,690

-0,7

99-3

,352

-1,8

98

-1,7

34-1

,438

-0,7

69-2

,071

-2,0

19-5

,564

-1,0

76-2

,259

(0

,000

) (0

,000

) (0

,000

)(0

,000

)(0

,199

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)β

0,78

2 0,

690

0,35

0 0,

410

0,78

9 0,

518

0,12

6 0,

654

0,83

6 0,

389

0,62

8 0,

705

0,71

7 0,

843

-0,0

070,

559

0,26

7 0,

746

0,62

2

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,1

93)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

φ 0,

339

-0,0

11

0,28

0 -0

,047

-0,1

090,

146

-0,2

040,

707

0,90

8 0,

854

0,51

7 0,

599

0,41

8 0,

492

0,15

3 0,

402

-0,3

480,

098

2,40

4

(0,0

00)

(0,8

95)

(0,0

00)

(0,5

49)

(0,1

62)

(0,0

41)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,1

11)

(0,0

00)

ϕ 0,

349

0,29

3 0,

021

0,20

2 0,

670

0,22

8 1,

157

0,44

9 0,

435

-0,3

460,

269

-0,6

36-0

,134

0,22

9 -0

,219

0,22

3 -0

,246

0,44

9 0,

613

(0

,000

) (0

,000

) (0

,664

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)δ

0,43

9 0,

020

-0,1

33-0

,375

0,65

0 -0

,002

-0,2

39-0

,183

-0,1

320,

564

-0,1

73

1,50

4 0,

424

-0,6

010,

276

-0,4

480,

364

0,28

1 -0

,251

(0

,000

) (0

,805

) (0

,120

)(0

,000

)(0

,000

)(0

,974

)(0

,000

)(0

,035

)(0

,059

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)A

dj.R

2 0,

677

0,23

3 0,

113

0,23

4 0,

520

0,29

5 0,

144

0,33

9 0,

740

0,27

1 0,

352

0,36

8 0,

447

0,80

5 0,

008

0,23

7 0,

269

0,47

1 0,

554

DW

1,

077

1,34

3 1,

604

1,49

9 1,

549

1,52

7 1,

552

1,50

0 1,

617

1,29

7 1,

648

1,23

4 1,

772

1,55

0 1,

772

1,55

4 1,

367

1,38

0 1,

192

AR

CH

35

50,1

90

,20

26,0

0 25

,39

112,

3310

2,45

154,

9044

,61

49,9

2 76

,12

160,

26

999,

8418

,51

96,9

7 10

26,8

155,

7211

6,16

118,

7627

71,6

61 Ta

ble

6 (c

ontin

ued)

Pa

nel B

. FF

3-fa

ctor

mod

el +

gen

erat

ed C

I spr

ead:

it

tt

tt

itH

LGC

IH

ML

SMB

MK

TRE

εδ

ϕφ

βα

++

++

+=

A

LL

AU

S A

UT

BEL

C

AN

C

HE

DEU

D

NK

ES

P FI

N

FRA

G

BR

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

-0,3

80

-1,4

66

-3,1

82-3

,053

-0,7

20-1

,533

-4,2

44-1

,677

-0,6

31-2

,413

-1,8

65

-1,8

08-1

,591

-0,4

83-2

,163

-1,8

76-5

,478

-1,2

60-2

,201

(0

,000

) (0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)β

0,78

5 0,

675

0,36

0 0,

422

0,73

0 0,

516

0,13

1 0,

660

0,85

4 0,

542

0,62

6 0,

707

0,71

4 0,

843

-0,0

260,

554

0,27

2 0,

731

0,61

8

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

φ 0,

467

0,05

7 0,

312

-0,2

64-0

,090

0,14

7 -0

,244

0,67

6 0,

938

1,18

4 0,

713

1,00

2 0,

479

0,79

3 0,

187

0,39

9 -0

,204

0,09

9 2,

368

(0

,000

) (0

,468

) (0

,000

)(0

,001

)(0

,255

)(0

,032

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,027

)(0

,109

)(0

,000

)ϕ

0,38

5 0,

255

0,01

0 -0

,087

0,74

4 0,

226

1,08

1 0,

382

0,50

0 -0

,298

0,17

6 -0

,224

-0,0

830,

394

-0,2

300,

140

-0,1

260,

467

0,54

1

(0,0

00)

(0,0

00)

(0,8

26)

(0,1

12)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,1

02)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

16)

(0,0

00)

(0,0

00)

δ -0

,464

0,

996

-0,0

770,

400

0,20

6 0,

032

0,11

1 -0

,043

-0,3

98-0

,619

-0,2

83

-0,0

580,

212

-0,7

380,

116

-0,4

33-0

,154

0,13

7 -0

,089

(0

,000

) (0

,000

) (0

,236

)(0

,000

)(0

,001

)(0

,645

)(0

,096

)(0

,505

)(0

,000

)(0

,000

)(0

,000

) (0

,494

)(0

,010

)(0

,000

)(0

,000

)(0

,000

)(0

,049

)(0

,059

)(0

,001

)A

dj.R

2 0,

678

0,27

0 0,

112

0,23

4 0,

514

0,29

5 0,

141

0,33

7 0,

745

0,28

4 0,

353

0,35

6 0,

435

0,80

5 0,

008

0,23

8 0,

258

0,46

7 0,

554

DW

1,

079

1,41

1 1,

603

1,50

4 1,

528

1,52

7 1,

546

1,49

7 1,

650

1,32

1 1,

650

1,21

2 1,

740

1,54

7 1,

770

1,55

8 1,

349

1,36

8 1,

191

AR

CH

37

72,3

58

,72

28,8

9 26

,98

111,

09

82,2

7 21

0,55

42

,37

33,3

4 70

,76

174,

91

1152

,5

23,1

6 10

4,87

95

3,04

15

4,39

11

1,65

12

4,28

25

44,8

62 Ta

ble

6 (c

ontin

ued)

Pa

nel C

. FF

3-fa

ctor

mod

el +

act

ual C

I spr

ead

+ m

omen

tum

spre

ad:

itt

tt

tt

itM

OM

HLC

IH

ML

SMB

MK

TRE

ελ

δϕ

φβ

α+

++

++

+=

A

LL

AU

S A

UT

BEL

C

AN

C

HE

DEU

D

NK

ES

P FI

N

FRA

G

BR

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

-0,7

70

-1,4

13

-3,7

04-3

,450

-0,3

38-1

,705

-4,1

13-1

,520

-0,4

53-3

,031

-1,6

96

-1,8

41-1

,411

-0,1

74-2

,245

-1,6

68-5

,182

-1,4

45-3

,232

(0

,000

) (0

,000

) (0

,000

)(0

,000

)(0

,001

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,064

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)β

0,78

3 0,

689

0,34

1 0,

383

0,78

5 0,

513

0,13

0 0,

633

0,84

7 0,

386

0,64

2 0,

697

0,71

8 0,

873

0,01

7 0,

577

0,29

6 0,

718

0,62

1

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

01)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

φ 0,

271

0,03

3 0,

222

-0,0

47-0

,107

0,03

7 -0

,142

0,75

9 0,

899

0,83

7 0,

547

0,42

7 0,

407

0,40

1 -0

,131

0,44

7 -0

,280

0,14

8 2,

010

(0

,000

) (0

,687

) (0

,000

)(0

,545

)(0

,170

)(0

,637

)(0

,003

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,004

)(0

,018

)(0

,000

)ϕ

0,41

6 0,

342

0,00

2 0,

264

0,70

6 0,

338

1,19

1 0,

463

0,44

4 -0

,393

0,24

2 -0

,611

-0,1

400,

139

0,01

6 0,

210

-0,3

220,

378

0,79

6

(0,0

00)

(0,0

00)

(0,9

60)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

08)

(0,0

03)

(0,2

72)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

δ 0,

520

0,06

2 -0

,142

-0,5

020,

699

0,14

9 -0

,303

-0,2

15-0

,249

0,56

9 -0

,175

1,

693

0,42

9 -0

,556

0,54

1 -0

,436

0,35

1 0,

356

0,71

0

(0,0

00)

(0,4

39)

(0,1

07)

(0,0

00)

(0,0

00)

(0,2

61)

(0,0

00)

(0,0

13)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

λ 0,

242

0,24

6 0,

252

0,26

2 0,

169

-0,0

71-0

,120

-0,2

17-0

,252

-0,2

86-0

,109

0,

103

-0,0

16-0

,360

0,32

3 -0

,198

-0,1

540,

167

0,97

8

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

01)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,7

62)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

17)

(0,0

03)

(0,0

00)

Adj

.R2

0,68

1 0,

237

0,12

4 0,

244

0,52

3 0,

297

0,14

6 0,

348

0,74

3 0,

276

0,35

3 0,

369

0,44

6 0,

812

0,02

3 0,

243

0,27

1 0,

454

0,59

4 D

W

1,09

0 1,

351

1,61

6 1,

520

1,55

7 1,

532

1,55

5 1,

530

1,63

8 1,

316

1,65

1 1,

236

1,77

2 1,

599

1,80

0 1,

553

1,37

5 1,

388

1,30

8 A

RC

H

3382

,1

92,3

5 27

,94

31,3

4 13

8,27

129,

2021

3,80

52,2

5 52

,52

83,2

5 18

1,49

10

62,4

25,5

9 11

1,89

1032

,015

7,60

117,

2115

9,94

2270

,0

63 Ta

ble

6 (c

ontin

ued)

Pa

nel D

. FF

3-fa

ctor

mod

el +

gen

erat

ed C

I spr

ead

+ m

omen

tum

spre

ad:

itt

tt

tt

itM

OM

HLG

CI

HM

LSM

BM

KT

REε

λδ

ϕφ

βα

++

++

++

=

A

LL

AU

S A

UT

BEL

C

AN

C

HE

DEU

D

NK

ES

P FI

N

FRA

G

BR

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

-0,6

13

-1,6

17

-3,5

52-3

,140

-0,8

63-1

,699

-4,1

65-1

,432

-0,5

15-2

,232

-1,7

30

-1,7

77-1

,596

0,09

9 -2

,268

-1,5

48-4

,988

-1,3

14-3

,135

(0

,000

) (0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,283

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)β

0,78

7 0,

674

0,35

9 0,

418

0,72

3 0,

509

0,13

1 0,

634

0,85

3 0,

534

0,63

7 0,

709

0,71

4 0,

873

0,00

2 0,

570

0,30

5 0,

717

0,63

7

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,6

84)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

φ 0,

418

0,07

6 0,

265

-0,2

69-0

,095

0,07

0 -0

,191

0,75

0 0,

915

1,16

2 0,

711

1,03

1 0,

481

0,68

3 0,

140

0,44

0 -0

,106

0,13

4 2,

098

(0

,000

) (0

,339

) (0

,000

)(0

,001

)(0

,227

)(0

,334

)(0

,003

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,274

)(0

,034

)(0

,000

)ϕ

0,45

9 0,

281

-0,0

07-0

,072

0,77

6 0,

328

1,08

9 0,

413

0,48

3 -0

,324

0,15

6 -0

,245

-0,0

820,

292

-0,0

250,

130

-0,2

300,

471

0,88

5

(0,0

00)

(0,0

00)

(0,8

88)

(0,1

98)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,1

23)

(0,0

00)

(0,0

81)

(0,0

01)

(0,0

00)

(0,0

00)

(0,0

00)

δ -0

,530

0,

964

-0,1

370,

382

0,12

2 0,

002

0,05

7 -0

,224

-0,3

54-0

,590

-0,2

50

-0,0

390,

211

-0,6

93-0

,205

-0,4

24-0

,203

0,11

2 -0

,076

(0

,000

) (0

,000

) (0

,041

)(0

,000

)(0

,072

)(0

,977

)(0

,431

)(0

,001

)(0

,000

)(0

,000

)(0

,000

) (0

,651

)(0

,014

)(0

,000

)(0

,000

)(0

,000

)(0

,011

)(0

,131

)(0

,003

)λ

0,23

8 0,

107

0,27

3 0,

060

0,11

5 0,

133

-0,0

48-0

,251

-0,1

18-0

,190

-0,0

76

-0,0

320,

003

-0,3

640,

313

-0,1

88-0

,212

-0,0

190,

907

(0

,000

) (0

,055

) (0

,000

)(0

,240

)(0

,000

)(0

,001

)(0

,069

)(0

,000

)(0

,010

)(0

,001

)(0

,008

) (0

,041

)(0

,955

)(0

,000

)(0

,000

)(0

,000

)(0

,001

)(0

,686

)(0

,000

)A

dj.R

2 0,

681

0,27

1 0,

125

0,23

5 0,

515

0,29

6 0,

141

0,35

0 0,

746

0,28

6 0,

353

0,35

6 0,

435

0,81

2 0,

021

0,24

5 0,

263

0,44

8 0,

592

DW

1,

092

1,41

2 1,

618

1,50

5 1,

532

1,53

1 1,

547

1,53

3 1,

655

1,33

4 1,

651

1,21

2 1,

740

1,59

8 1,

794

1,55

7 1,

361

1,37

4 1,

302

AR

CH

39

18,9

74

,06

31,6

5 34

,08

163,

8086

,14

251,

5651

,67

41,1

9 96

,83

203,

45

1323

,231

,37

114,

2710

11,8

148,

5311

0,39

142,

5826

13,5

64 Ta

ble

6 (c

ontin

ued)

Pa

nel E

. FF

3-fa

ctor

mod

el +

mom

entu

m sp

read

: it

tt

tt

itM

OM

HM

LSM

BM

KT

REε

λϕ

φβ

α+

++

++

=

A

LL

AU

S A

UT

BEL

C

AN

C

HE

DEU

D

NK

ES

P FI

N

FRA

G

BR

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

-0,6

95

-1,4

55

-3,6

92-2

,973

-0,8

46-1

,697

-4,1

44-1

,524

-0,6

04-2

,769

-1,7

13

-1,7

89-1

,644

0,02

1 -2

,317

-1,7

66-4

,961

-1,1

79-3

,179

(0

,000

) (0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,821

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)(0

,000

)β

0,79

4 0,

684

0,34

0 0,

430

0,72

1 0,

509

0,13

1 0,

641

0,82

9 0,

461

0,64

6 0,

708

0,69

9 0,

893

-0,0

090,

563

0,32

3 0,

733

0,63

5

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

64)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

φ 0,

272

0,03

8 0,

234

-0,1

86-0

,105

0,07

0 -0

,158

0,71

7 0,

852

1,01

0 0,

554

1,01

2 0,

555

0,36

2 0,

065

0,50

6 -0

,162

0,13

8 2,

106

(0

,000

) (0

,641

) (0

,000

)(0

,015

)(0

,181

)(0

,334

)(0

,001

)(0

,000

)(0

,000

)(0

,000

)(0

,000

) (0

,000

)(0

,000

)(0

,000

)(0

,016

)(0

,000

)(0

,086

)(0

,029

)(0

,000

)ϕ

0,49

3 0,

352

-0,0

120,

113

0,78

4 0,

328

1,09

2 0,

377

0,39

4 -0

,403

0,14

6 -0

,238

-0,0

980,

168

-0,0

390,

081

-0,2

350,

462

0,90

5

(0,0

00)

(0,0

00)

(0,7

94)

(0,0

13)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

63)

(0,0

00)

(0,0

05)

(0,0

29)

(0,0

00)

(0,0

00)

(0,0

00)

λ 0,

200

0,24

0 0,

251

0,12

2 0,

133

0,13

3 -0

,057

-0,2

09-0

,206

-0,2

66-0

,107

-0

,033

0,03

9 -0

,382

0,28

3 -0

,224

-0,1

79-0

,021

0,90

7

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

15)

(0,0

00)

(0,0

01)

(0,0

20)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

35)

(0,4

55)

(0,0

00)

(0,0

00)

(0,0

00)

(0,0

06)

(0,6

56)

(0,0

00)

Adj

.R2

0,67

8 0,

237

0,12

3 0,

222

0,51

5 0,

297

0,14

1 0,

347

0,74

2 0,

263

0,35

2 0,

356

0,43

2 0,

807

0,02

0 0,

232

0,26

0 0,

448

0,59

2 D

W

1,08

3 1,

351

1,61

3 1,

477

1,53

1 1,

531

1,54

7 1,

525

1,63

0 1,

293

1,64

7 1,

212

1,73

1 1,

557

1,79

3 1,

531

1,35

5 1,

373

1,30

1 A

RC

H

3020

,7

78,1

9 20

,70

27,5

3 13

8,68

83,9

4 17

9,85

41,6

6 46

,14

72,1

4 16

4,09

10

73,7

24,6

4 12

1,95

980,

6114

9,05

107,

9013

0,38

1847

,8FF

refe

rs to

the

Fam

a-Fr

ench

3-f

acto

r mod

el. C

orpo

rate

inno

vatio

n sp

read

(diff

eren

t CI-

fact

ors)

and

mom

entu

m s

prea

d (M

OM

) hav

e be

en c

alcu

late

d as

follo

ws:

(1) s

tock

s ar

e so

rted

by d

iffer

ent C

I’s

and

past

2-q

uarte

r ret

urns

, (2)

sto

ck a

re d

ivid

ed in

to p

ortfo

lios

with

hig

h C

I’s,

high

pas

t ret

urns

, low

CI’

s an

d lo

w p

ast r

etur

ns u

sing

qua

rtile

s an

d (3

) HLC

I-sp

read

and

MO

M-s

prea

d ar

e ca

lcul

ated

as

‘hig

h’-p

ortfo

lio m

inus

‘low

’-po

rtfol

io. H

LCI r

efer

s to

por

tfolio

cal

cula

ted

from

the

basi

s of

act

ual C

I and

HLG

CI

refe

rs to

por

tfolio

cal

cula

ted

from

the

basi

s of

gen

erat

ed C

I (w

ith c

onst

ant i

n re

gres

sion

). Fa

ma-

Fren

ch fa

ctor

s SM

B a

nd H

ML

are

calc

ulat

ed b

y so

rting

sto

cks

on s

ize

and

book

-to-m

arke

t, fo

rmin

g ‘h

igh’

and

‘lo

w’

portf

olio

s us

ing

quar

tiles

and

ded

uctin

g ‘lo

w’

from

‘hi

gh’.

Stat

istic

ally

non

-sig

nific

ant

para

met

er v

alue

s at

5 %

lev

el a

re

high

light

ed.

The

mod

el f

it st

atis

tic r

epor

ted

is t

he a

djus

ted

R2 .

DW

den

otes

Dur

bin-

Wat

son

stat

istic

for

res

idua

l au

toco

rrel

atio

n an

d A

RC

H m

easu

res

resi

dual

he

tero

sced

astic

ity.

65

2.5 Relationship of corporate innovation with momentum and research & development expenditure

2.5.1 Corporate innovation and momentum

For further analysis of the connection between momentum and corporate innovation, we examine the regressions of momentum portfolios and CI portfolios on different asset pricing specifications. Individual returns are divided into portfolios separately by firm’s CI values and by their past returns, forming three CI-based and three past returns –based portfolios: high CI/high momentum, medium CI/medium momentum and low CI/low momentum. In addition, we use HLCI, HLGCI and MOM portfolio returns as benchmarks. The asset pricing specifications used are CAPM, FF-model and 4-factor model using SMB, HML and either HLCI/HLGCI or MOM as factors.

Table 7 provides evidence46 on the relationship between corporate innovation and momentum. In Panel A, the performance of the 4-factor model (judged by the statistically significant loadings on MOM) with respect to CAPM and FF-model is slightly better when testing low, medium and high CI portfolios (actual CI sorted LCI, MEDCI, HCI and generated CI sorted LGCI, MEDGCI and HGCI). But the constants of the regressions are all statistically significant, indicating possibly that some additional variables are needed to price stocks accordingly. The coefficients of MOM are smaller than of the other factors and negative in the case of HLCI. This similar positive/negative switch between different CI variables is apparent also from Panel B in the form of positive coefficients on HLCI and negative coefficients on HLGCI in the regressions of three momentum portfolios.

As previously described in the momentum literature, the MOM portfolio returns are hard to explain with CAPM and FF-model. In Panel B, the results seem to indicate that the inclusion of the CI factor can improve the model; the loadings on HLCI and HLGCI are statistically significant. When using the 4-factor model with actual CI to explain high past returns portfolio (HMOM), the constant term becomes statistically non-significant. This is in line with the results of Vassalou & Apedjinou (2004) since they also report that the presence of HLCI results in a non-significant constant for momentum portfolios. This is quite a strong indication of support for CI being at least partially rational explanation for the momentum. However, our measure of HLCI is build on the actual CI and as can be seen from Table 7, Panel B, the regression using generated CI that is calculated exactly as in Vassalou & Apedjinou (2004) – HLGCI – produces statistically significant constant. The results seem to partly depend on the way we calculate CI, and in some countries the results are more encouraging than in others, but with respect to the relationship between CI and momentum, our results are similar to those in Vassalou & Apedjinou (2004).

46 This evidence is preliminary as it is conducted only on the whole data set, not by countries. Hence, some caution should be used in viewing these results. Also, as can be noted, there can be found autocorrelation and heteroscedasticity in the errors of the models.

66 Ta

ble

7. R

egre

ssio

n te

sts o

n th

e re

latio

nshi

p be

twee

n co

rpor

ate

inno

vatio

n (C

I) a

nd m

omen

tum

from

the

who

le d

ata

Pane

l A. R

egre

ssio

ns o

f CI-

portf

olio

s and

CI s

prea

ds o

n al

tern

ativ

e m

odel

s

tt

itM

KT

REε

βα

++

=

tt

tt

itH

ML

SMB

MK

TRE

εϕ

φβ

α+

++

+=

α β

Adj

. R2

DW

A

RC

H

α β

φ ϕ

Adj

. R2

DW

A

RC

H

LCI

-0,5

98

0,77

5 0,

815

0,65

7 85

90,7

5 -0

,519

0,

776

0,35

3 0,

322

0,82

3 0,

661

1265

7,6

MED

CI

-0,6

12

0,79

5 0,

823

0,66

4 70

83,3

2 -0

,489

0,

794

0,30

5 0,

419

0,83

3 0,

667

9100

,28

HC

I -0

,606

0,

801

0,81

5 0,

641

7314

,51

-0,4

53

0,80

0 0,

326

0,50

1 0,

828

0,64

4 90

28,0

2 LG

CI

-0,7

55

0,80

2 0,

812

0,65

6 76

78,2

4 -0

,587

0,

799

0,20

1 0,

499

0,82

4 0,

656

9727

,16

MED

GC

I -0

,615

0,

791

0,82

5 0,

667

7261

,27

-0,5

02

0,79

1 0,

301

0,39

2 0,

834

0,66

9 99

43,7

9 H

GC

I -0

,455

0,

782

0,81

2 0,

635

7194

,73

-0,3

62

0,78

5 0,

503

0,40

8 0,

825

0,64

3 10

164,

4 H

LCI

-0,0

08

0,02

6 0,

071

0,85

9 78

28,3

8 0,

066

0,02

3 -0

,027

0,

180

0,16

9 0,

924

1004

8,0

HLG

CI

0,29

9 -0

,020

0,

039

0,76

4 21

5,08

0,

225

-0,0

14

0,30

2 -0

,091

0,

125

0,78

6 72

59,6

5

tt

tt

tit

MO

MH

ML

SMB

MK

TRE

ελ

ϕφ

βα

++

++

+=

α

β φ

ϕ λ

Adj

. R2

DW

A

RC

H

LCI

-0,7

82

0,78

0 0,

278

0,40

8 0,

254

0,82

8 0,

683

1407

8,7

MED

CI

-0,6

89

0,79

7 0,

254

0,48

0 0,

189

0,83

7 0,

689

1075

2,5

HC

I -0

,638

0,

802

0,28

1 0,

556

0,17

3 0,

831

0,66

4 11

049,

8

LG

CI

-0,7

77

0,80

1 0,

153

0,55

6 0,

178

0,82

7 0,

678

1099

7,3

MED

GC

I -0

,697

0,

793

0,25

3 0,

451

0,18

3 0,

838

0,69

0 11

560,

4

H

GC

I -0

,623

0,

788

0,42

9 0,

493

0,25

1 0,

830

0,66

5 12

303,

8

H

LCI

0,14

5 0,

022

0,00

3 0,

148

-0,0

81

0,19

5 0,

947

1249

1,6

HLG

CI

0,15

4 -0

,013

0,

276

-0,0

63

0,07

3 0,

145

0,79

8 10

271,

3

67 Ta

ble

7 (c

ontin

ued)

Pane

l B. R

egre

ssio

ns o

f mom

entu

m p

ortfo

lios a

nd m

omen

tum

spre

ad o

n al

tern

ativ

e m

odel

s

tt

itM

KT

REε

βα

++

=

tt

tt

itH

ML

SMB

MK

TRE

εϕ

φβ

α+

++

+=

α β

Adj

. R2

DW

A

RC

H

α β

φ ϕ

Adj

. R2

DW

A

RC

H

LMO

M

-1,1

23

0,79

5 0,

812

0,67

2 57

98,0

3 -0

,937

0,

790

0,13

9 0,

524

0,82

3 0,

661

8828

,74

MED

MO

M

-0,5

85

0,80

6 0,

824

0,76

5 60

60,2

3 -0

,446

0,

806

0,40

3 0,

498

0,83

9 0,

777

1248

6,4

HM

OM

0,

044

0,76

9 0,

783

0,72

9 86

89,7

0 0,

023

0,77

6 0,

534

0,12

6 0,

788

0,73

9 12

738,

3 M

OM

-0

,025

1,

177

0,01

5 1,

353

7992

,55

0,97

8 -0

,014

0,

375

-0,3

84

0,11

9 1,

367

1011

0,7

t

tt

tt

itH

LGC

IH

ML

SMB

MK

TRE

ελ

ϕφ

βα

++

++

+=

α β

φ ϕ

δ A

dj. R

2 D

W

AR

CH

LM

OM

-0

,786

0,

780

0,34

3 0,

462

-0,6

73

0,82

9 0,

654

1890

4,5

MED

MO

M

-0,3

21

0,79

8 0,

571

0,44

6 -0

,551

0,

842

0,77

7 15

503,

2

H

MO

M

0,10

5 0,

771

0,64

3 0,

093

-0,3

61

0,79

0 0,

737

1596

1,5

MO

M

0,91

2 -0

,010

0,

286

-0,3

57

0,29

1 0,

138

1,37

6 13

054,

3

tt

tt

tit

HLC

IH

ML

SMB

MK

TRE

ελ

ϕφ

βα

++

++

+=

α β

φ ϕ

δ A

dj. R

2 D

W

AR

CH

LM

OM

-0

,977

0,

776

0,15

6 0,

416

0,59

7 0,

827

0,67

6 14

515,

8

M

EDM

OM

-0

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0,

795

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7 0,

415

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8 0,

841

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9 16

837,

8

H

MO

M

0,01

0 0,

772

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089

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682,

2

M

OM

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003

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05

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147

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6 14

525,

7

LC

I ref

ers t

o th

e lo

w C

I – p

ortfo

lio b

uild

usi

ng a

ctua

l CI,

MED

CI i

s the

med

ium

CI –

portf

olio

bui

ld u

sing

act

ual C

I and

HC

I is t

he h

igh

CI –

portf

olio

bui

ld u

sing

act

ual

CI.

LGC

I, M

EDG

CI a

nd H

GC

I ref

er re

spec

tivel

y, e

xcep

t tha

t the

y ar

e bu

ilt u

sing

gen

erat

ed C

I (w

ith c

onst

ant i

nclu

ded

in th

e re

gres

sion

). H

LCI r

efer

s to

a fa

ctor

bui

ldby

ded

uctin

g LC

I fro

m H

CI a

nd H

LGC

I is

HG

CI –

LG

CI.

LMO

M re

fers

to th

e lo

w p

ast r

etur

ns p

ortfo

lio, M

EDM

OM

is th

e m

ediu

m p

ast r

etur

ns p

ortfo

lio, H

MO

M is

th

e hi

gh p

ast r

etur

ns p

ortfo

lio a

nd M

OM

is th

e m

omen

tum

spr

ead

calc

ulat

ed a

s th

e di

ffer

ence

bet

wee

n H

MO

M a

nd L

MO

M. M

KT

are

the

exce

ss m

arke

t por

tfolio

retu

rns;

SM

B a

nd H

ML.

Sta

tistic

ally

non

-sig

nific

ant p

aram

eter

val

ues

at 5

% le

vel a

re h

ighl

ight

ed. T

he m

odel

fit

stat

istic

rep

orte

d is

the

adju

sted

R2 . D

W d

enot

es

Dur

bin-

Wat

son

stat

istic

for r

esid

ual a

utoc

orre

latio

n an

d A

RC

H m

easu

res r

esid

ual h

eter

osce

dast

icity

.

68

2.5.2 Corporate innovation and R&D expenditures

One very interesting aspect that was mentioned, but not investigated in Vassalou & Apedjinou (2004), is that CI measures intangible assets on a larger scale than e.g. R&D expenditure and patents. Within our data, we had access to firm-specific R&D expenditure through Compustat. In an attempt to investigate the relationship between CI and R&D, we start by replacing ACI in the asset pricing equation by aggregate R&D expenses. We sort stocks based on their R&D expenditure, create high R&D and low R&D portfolios and build a return-based HLRD variable – R&D spread – in order to repeat some of the analysis done with the CI. We also regress the CI-based portfolios on the R&D expenses -based factor and the other way around. The assumption naturally is that if R&D expenditure behaves in the similar fashion as CI, CI may simply capture the R&D of the firms.

The results from regressions of CI on R&D and reverse are reported in Table 8. Even though the coefficients are all highly statistically significant, there seems to be quite loose link between CI and R&D, since the adjusted R2 for individual regressions are very low and the constants differ from zero. The relation seems to be strongest between the aggregated actual CI and aggregated R&D expenditure. This is expected, since actual CI is calculated straight from the measurable variables. Thus, it seems that CI (calculated by any of the alternative ways), at least at an aggregate level, can capture some information about immeasurable intangible assets - corporate innovations – but this relation is not very strong.

In order to obtain further evidence on the connection between CI and R&D expenditure, we performed the same asset pricing regressions as before, but substituting aggregate CI variable with aggregate R&D. The results are presented in Table 9. Panel A shows the results from 2-factor model using individual firm returns as test assets. The regressions seem highly similar to the regressions with CI’s (see Table 5). Naturally there are differences between countries, but for the most part, in countries were models with CI work, also the model with R&D works. Overall, it is hard to find differences between the performance of a 2-factor model with aggregate CI as a factor and a 2-factor model with aggregate R&D as a factor. Panels B and C report the results from regressions of firm-specific returns on FF-factors, MOM and R&D spread HLRD. The results here continue to be almost identical to the results using the CI spreads (see Table 6). The constants are around the same size, adjusted R2 are very similar and MOM seems to become non-significant when aggregate R&D is included in the regression only in Australia, Belgium, Italy and Sweden - roughly the same countries as in Table 6, Panel D, when using HLCI/HLGCI.

The tests show that the error variances are not the same throughout the sample and roughly in half of the countries the models suffer from autocorrelation.

69

Table 8. Tests on the relationship between CI and R&D expenditure

Panel A. Separate regressions of aggregated and individual CI’s on R&D expenditure:

tttt RDARDfactorCI εδβα +++=− )(

ACI AGCI AGICI ci cigen cigenw α 1,427 -0,044 0,197 2,359 0,231 0,236 (0,000) (0,000) (0,000) (0,000) (0,000) (0,000) β -0,789 0,139 0,083 - - - (0,000) (0,000) (0,000) δ - - - -0,605 0,108 0,109 (0,000) (0,000) (0,000) Adj. R2 0,380 0,054 0,026 0,121 0,013 0,013 DW 0,006 0,004 0,006 1,757 1,992 1,995 ARCH 642,56 3620,02 2067,71 444,64 2,99 3,37 Panel B. Separate regressions of aggregated and individual R&D on different aggregated and individual CI’s:

ttttt ciAGICIAGCIACIfactorDR ϕφδβα ++++=− )&( ttt cigenwcigen εηγ +++

ARD ARD ARD RD RD RD α -0,527 -1,832 -1,966 -0,965 -1,638 -1,639 (0,000) (0,000) (0,000) (0,000) (0,000) (0,000) β -0,481 - - - - - (0,000) δ - 0,389 - - - - (0,000) φ - - 0,311 - - - (0,000) ϕ - - - -0,199 - - (0,000) γ - - - - 0,123 - (0,000) η - - - - - 0,124 (0,000) Adj. R2 0,380 0,054 0,026 0,121 0,013 0,013 DW 0,007 0,005 0,005 1,806 1,688 1,689 ARCH 2587,88 3535,02 2548,40 397,94 258,94 252,98 ci = actual CI, cigen = generated CI with constant included in the regression, cigenw = generated CI withconstant excluded from the regression, ACI = aggregated actual CI, AGCI = aggregated generated CI withconstant in the regression, AGICI = aggregated generated CI without constant in the regression, RD = firm-specific R&D expenditure and ARD = aggregated R&D expenditure. All variables are expressed as changes inlogs. P-values are reported in parenthesis. The model fit statistic reported is the adjusted R2. DW denotes Durbin-Watson statistic for residual autocorrelation and ARCH measures residual heteroscedasticity.

70 Ta

ble

9. A

sset

pri

cing

test

s usi

ng a

ggre

gate

d R&

D e

xpen

ditu

re a

nd R

&D

spre

ad a

s fac

tors

in a

ltern

ativ

e as

set p

rici

ng sp

ecifi

catio

ns

Pane

l A. C

APM

+ a

ggre

gate

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&D

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N

FRA

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L IT

A

JPN

N

LD

NO

R

SWE

USA

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96

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-3,7

49-2

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-4,9

55-2

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ble

9 (c

ontin

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nel C

. FF

3-fa

ctor

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el +

R&

D sp

read

+ m

omen

tum

spre

ad:

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tt

tt

itM

OM

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DH

ML

SMB

MK

TRE

ελ

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α+

++

++

+=

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LL

AU

S A

UT

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C

AN

C

HE

DEU

D

NK

ES

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N

FRA

G

BR

IR

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N

LD

NO

R

SWE

USA

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99

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48-4

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00)

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00)

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717

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73

Why are there great differences between countries in the performance of corporate innovation, especially in the asset pricing framework? One reason could be the difference in R&D intensity of the countries. The higher the R&D intensity, the higher the likelihood, that stock markets price the corporate innovation factor. Another reason could relate to the structure of financial markets in different countries. The equity market is much more developed in some countries and equity is highly likely to be the source of finance for e.g. R&D the firms are doing. Thus, in countries where financial markets are dominated by banks and other institutions using debt as a financing method instead of equity financing, the level of R&D is lower. Since CI at least partly reflects intangible assets, the level of CI is also likely to be lower and, hence, it’s capabilities in asset pricing and in explaining momentum may be limited.

2.6 Conclusions

This chapter examines a proxy for total factor productivity (TFP) called corporate innovation (CI) in the international asset pricing framework. Corporate innovation is the change in firm’s gross profit margin (GPM) not explained by firm’s capital and labour. The analysis of corporate innovation is conducted using a data set of 18 OECD member countries from the time period of 1993 – 2003. The ability of corporate innovation to explain excess stock returns is examined by including CI variable to some of the most well known asset pricing models. Following Vassalou & Apedjinou (2004), the relationship between corporate innovation and price momentum is of special interest. Moreover, since corporate innovation closely relates to intangible assets of a firm, such as R&D expenditure or licensing and patents, we analyse the connection between corporate innovation and measurable R&D expenditure.

Overall, the results indicate that corporate innovation can explain stock markets in most of the countries in this sample, but the CI factor does not seem to improve dramatically the performance of the well known asset pricing models like CAPM and the Fama-French model. Hence, the performance of corporate innovation is highly dependent on the countries chosen and also on the way it is calculated, specifically as a residual on a regression model (in the spirit of the Solow residual) or as an actual variable. We find some evidence to support Vassalou & Apedjinou’s (2003) results that suggest corporate innovation to be a partial rational explanation for momentum, but this relationship is dependent on the way we calculate the CI and also on the country in question. Furthermore, it seems that CI captures at least partly the same information as R&D expenditure, indicating that CI may very well be a measure of intangible assets (which in most countries and cases are difficult to measure in practice).

This chapter has attempted to enlighten the corporate innovation variable and its relation to the stock market. Future research could concentrate more deeply on the relationship between corporate innovation and intangible assets: if data were available, it would be interesting to see if other intangible assets such as patents contribute anything to the understanding of CI. More attention should also be paid to the construction of CI and hence, on how e.g. changing the underlying assumptions of the theoretical derivation of CI would affect its’ behaviour. The analysis of labour productivity is central in this

74

area. Furthermore, a very challenging area of research emerges from connecting TFP and corporate innovation together with asset returns to aggregate output and consumption, as most economic growth models – and especially real business cycle models – indicate.

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Appendix

Table 10. The country-specific autocorrelation coefficients of differently calculated corporate innovations

Panel A. Autocorrelations for actual and generated CI ci Lags ChiSq DF Pr>ChiS Autocorrelations 6 9999,9 6 <0,0001 0,187 0,181 0,184 0,179 0,179 0,183 12 9999,9 12 <0,0001 0,185 0,176 0,176 0,18 0,177 0,178 cigen Lags ChiSq DF Pr>ChiS Autocorrelations 6 2461.93 6 <0,0001 0,037 0,042 0,042 0,037 0,035 0,04 12 4637.38 12 <0,0001 0,042 0,034 0,035 0,034 0,034 0,039

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Table 10 (continued) Panel B. Autocorrelations for the average CI's of the portfolios formed by sorting stocks by their CI's ci-low Lags ChiSq DF Pr>ChiS Autocorrelations 6 636.93 6 <0,0001 0,726 0,452 0,178 -0,097 -0,112 -0,128 12 690.84 12 <0,0001 -0,143 -0,159 -0,094 -0,028 0,037 0,102 ci-medium Lags ChiSq DF Pr>ChiS Autocorrelations 6 643.29 6 <0,0001 0,731 0,462 0,193 -0,076 -0,088 -0,1 12 679.60 12 <0,0001 -0,112 -0,124 -0,067 -0,011 0,046 0,103 ci-high Lags ChiSq DF Pr>ChiS Autocorrelations 6 645.55 6 <0,0001 0,732 0,463 0,195 -0,073 -0,088 -0,102 12 680.44 12 <0,0001 -0,117 -0,131 -0,082 -0,032 0,018 0,067 cigen-low Lags ChiSq DF Pr>ChiS Autocorrelations 6 993.39 6 <0,0001 0,801 0,601 0,402 0,202 0,166 0,13 12 1017.31 12 <0,0001 0,093 0,057 0,061 0,065 0,069 0,072 cigen-medium Lags ChiSq DF Pr>ChiS Autocorrelations 6 1195.61 6 <0,0001 0,822 0,644 0,466 0,288 0,252 0,217 12 1347.00 12 <0,0001 0,181 0,145 0,16 0,175 0,19 0,205 cigen-high Lags ChiSq DF Pr>ChiS Autocorrelations 6 987.28 6 <0,0001 0,796 0,593 0,389 0,186 0,185 0,185 12 1126.24 12 <0,0001 0,184 0,184 0,175 0,166 0,158 0,149

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Table 10 (continued) Panel C. Autocorrelations for the deviations of CI's from the mean CI's from the CI-based portfolios ci-low - ci Lags ChiSq DF Pr>ChiS Autocorrelations 6 3528,93 6 <0,0001 0,051 0,045 0,049 0,043 0,043 0,047 12 6474,64 12 <0,0001 0,049 0,04 0,039 0,044 0,041 0,041 ci-medium - ci Lags ChiSq DF Pr>ChiS Autocorrelations 6 790,1 6 <0,0001 0,027 0,02 0,024 0,018 0,019 0,022 12 1327,68 12 <0,0001 0,025 0,015 0,015 0,02 0,016 0,016 ci-high - ci Lags ChiSq DF Pr>ChiS Autocorrelations 6 2896,2 6 <0,0001 0,047 0,04 0,044 0,038 0,039 0,043 12 5283,29 12 <0,0001 0,045 0,035 0,035 0,04 0,037 0,036 cigen-low - cigen Lags ChiSq DF Pr>ChiS Autocorrelations 6 2131,1 6 <0,0001 0,034 0,039 0,039 0,034 0,032 0,037 12 3978,89 12 <0,0001 0,039 0,031 0,032 0,031 0,031 0,035 cigen-medium - cigen Lags ChiSq DF Pr>ChiS Autocorrelations 6 762,03 6 <0,0001 0,019 0,025 0,024 0,02 0,017 0,023 12 1366,96 12 <0,0001 0,025 0,017 0,018 0,017 0,017 0,021 cigen-high - cigen Lags ChiSq DF Pr>ChiS Autocorrelations 6 1593,1 6 <0,0001 0,029 0,035 0,034 0,029 0,027 0,033 12 2950,89 12 <0,0001 0,035 0,027 0,028 0,026 0,026 0,03 For the definitions of ci and cigen, see Table 3. ChiSq refers to the Chi-square test statistic for testing white noise in the time series and DF are the degrees of freedom. The ci and cigen portfolios in Panel B are formed by sorting stocks based on their individual CI’s (using both actual CI (ci) and generated CI (cigen)) and thendividing the stocks into three portfolios: high-portfolios contain the stocks with highest CI’s (25 % of the total number of stocks), low-portfolios contain the stocks with lowest CI’s (25 % of the total number of stocks) andmedium-portfolios contain the rest of the stocks (with ‘medium’ size CI’s).

3 The role of corporate innovation and stock returns in predicting macroeconomy

3.1 Introduction

This chapter draws attention to stock market and total factor productivity (TFP) as factors in explaining and predicting macroeconomy. The sensitive and forward-looking nature of stock prices and returns make them ideal candidate variables for economic forecasting. Total factor productivity measures for example technological change, human capital and ‘knowledge’, all factors that are increasingly important in modern business. This chapter aims to find out if these two factors contribute to economic forecasting, since previous chapters have documented that stock returns can predict economic activity, and TFP shock is a priced risk factor in stock markets.

According to many growth models, especially the Solow (1956, 1957) model, which is the starting point for most analyses of economic growth, a firm’s output depends on production which is a function of labour, capital and “knowledge” or TFP. TFP is often interpreted as capturing technology shocks, thus it accounts for all the other factors affecting output besides capital and labour.47 In Solow’s model, only changes in TFP have persistent growth effects making TFP a well-known business cycle variable. Also, majority of real business cycle studies postulate technology shocks as the ultimate source of variation in the economy (see e.g. Danthine & Donaldson 1993). The growth of output per worker can be decomposed into the contribution of growth of capital per worker and a remaining term, Solow residual, which represents the changes in TFP.

Financial markets commonly agree that a firm’s market value as measured by its market share and profits is much more than the combination of labour and capital the firm utilizes. Vassalou & Apedjinou (2004) refer to such non-capital and non-labour productivity factors as corporate innovation (CI). It is measured as the component of a firm’s change in gross profit margin not explained by the growth in capital and labour it has in place. At an aggregate level this measure proxies for a scaled TFP. According to

47 Following Solow (1957), technology shock or TFP shock is used as a shorthand expression for any kind of shift in the production function (e.g. slowdown, improvement in the education or in “knowledge”).

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Merton’s (1973) intertemporal capital asset pricing model (ICAPM), if TFP is a state variable48 then it affects the investment opportunity set and therefore equity returns.

Vassalou & Apedjinou (2004) show that a scaled measure of TFP – CI – is priced in the cross-section of equity returns for all US firms using data from 1967 to 2001. The results also indicate that CI can be viewed at least as a partial explanation for price momentum. By analysing international data set in chapter 2, we find that an aggregate CI seems to explain returns in some countries and that CI may be a partial explanation of momentum. Furthermore, CI captures to some extent the same information as R&D expenditures, indicating that it captures the partly unobservable intangible assets.

The structural model of Canova & De Nicolo (1995) considers equities in relation to economic growth.49 Positive technological shocks (measured by changes in TFP) increase output that increases firm’s earnings which in turn affects expected future cash flows of shareholders. They also affect consumption decisions that influence the agent’s marginal utility, which in turn shows up in equity prices through an asset pricing kernel. Hence, if the price of a stock equals the expected discounted value of future earnings, stock returns should price TFP shocks (as was empirically shown in Vassalou & Apedjinou 2004 and in chapter 2 above) and they should also contain information about contemporaneous and future developments of output and consumption (which will be tested here).

The purpose of this chapter is to evaluate whether a TFP shock measured by corporate innovation can solely and together with stock market information explain and forecast future economic activity, namely output and consumption according to general growth models. We build a corporate innovation (ICI) variable aggregated on industry-level from firm-specific information and regress it – together with the industry-level stock returns – on aggregate output and consumption measures. Our data set consists of 14 OECD member countries and the time period covers 10 years.

Surprisingly, a TFP shock measured as aggregated corporate innovation seems to have no predictive power towards either consumption growth or output growth. This may be due to the measurement problems associated with the empirical calculation of CI, or to the short forecasting horizon used in the analysis: the forecasts are calculated one quarter ahead which may be too short of a time period for the changes to show up in economic growth variables. Japan seems to form an exception: CI variable contributes strongly to predicting Japanese economic growth. Japan’s results may be explained by the strong role of technology in Japanese society, which implies that the TFP shock is likely to spread throughout the economy much faster and with stronger presence. Also, the specific ‘work-appreciating’ culture is likely to advance the shock’s influence: labour force improvements can be more visible and measurable.

However, stock returns seem to predict both target macroeconomic variables, but especially gross domestic product growth, remarkably well, even outperforming well-known business cycle variables like interest rate spread which is introduced to the regression models as a control variable. Hence, our results again indicate that stock markets should definitely be taken seriously in economic forecasting, since they capture

48 The TFP is a state variable in dynamic equilibrium macro models with representative agents, see for example Danthine & Donaldson (1993) for an excellent survey on the earlier literature and Horvath (2000) for more recent approach. 49 See a more profound representation in chapter 1, section 1.2.3.

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valuable information regarding real economic developments. The result that a TFP shock has no predictive power over macroeconomy, and the fact that it is a priced risk factor in stock prices implies that the information in stock markets incorporates important factors (like TFP shock) that otherwise would be excluded from economic forecasting.

This chapter is organized as follows. Section 3.2 briefly reviews the theoretical aspects of the connection of TFP and stock market and describes the empirically testable implications of the theoretical models using corporate innovation as a proxy for the TFP shock. Section 3.3 describes the data and addresses some troubling issues related to international macro data. Section 3.4 presents the results and finally, section 3.5 concludes.

3.2 Corporate innovation and stock returns: empirical implementation

The real business cycle models50 developed from Solow’s (1956, 1957) growth model focus on a particular kind of shock in characterizing business cycle activity, namely a Hicks-neutral technology shock, i.e. the Solow residual or the total factor productivity shock. This real shock alters the productivity of all factors of production proportionally. This way of thinking contradicts with the Keynesian view that nominal shocks to the marginal efficiency of capital are of primary importance in generating business cycles. Dejong et al. (2000) from the basis of Greenwood et al. (1988) empirically investigate a model that incorporates two kinds of shocks: a total factor productivity shock (a supply-side shock) and an investment-specific productivity shock (a demand-side shock). Their results suggest that both shocks are important in understanding post-war U.S. fluctuations, but that total factor productivity shocks are primarily responsible for beginning and ending recessions.

In this study we choose to adopt the traditional real business cycle approach due to the nature of the corporate innovation (CI) shock that we use to measure the Solow residual. The corporate innovation (which was already defined in chapter 2) builds directly from firm-specific variables i.e. from variables linked closely to production of a firm and thus it measures real shocks to the economy as opposed to nominal shocks. According to Vassalou & Apedjinou (2004) and chapter 2 above (former with U.S. data, latter with international data), corporate innovation is priced in stock returns. It can loosely be concluded that stock market - apart from its speculative nature - is based on real variables. We extend previous literature by examining the capability of corporate innovation – a real variable and a proxy for well-known business cycle variable TFP – to predict and forecast future output and consumption (together with sensitive and volatile stock returns), as is anticipated by the traditional RBC models.

The TFP shock being the only source of business cycle variation has been criticised in recent literature.51 First of all, the candidate TFP shock measures are hard to identify, and

50 See the work of Kydland & Prescott (1982), Long & Plosser (1983), Danthine & Donaldson (1993), Stadler (1994), Jermann (1988), Dejong et al. (2000), Horvath (2000), Hulten (2000) and Tallarini (2000). 51 See e.g. Danthine & Donaldson (1993).

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secondly, the measure needs to be persistent in nature. Here, we turn to a more detailed market data and build a measure of technology shock from firm-level variables. Corporate innovation variable is calculated in a way that is closely related to the calculation of the Solow residual with a difference that the residuals are firm-specific, not aggregate.52 Hence, the aim is to empirically investigate one measure of a TFP shock and its capability to explain and predict changes in output and consumption growth across countries (as is referred to in theory), not to justify the source of technology shock and certainly not to explain the differences in growth behaviour of different countries.

Corporate innovation is a change in a firm’s gross profit margin (GPM) not explained by the capital and labour the firm has in its use. GPM is defined as the difference between a firm’s sales and the cost of the goods it sells. Hence, the corporate innovation variable is given by

)ˆˆ( 21 itiitiitit lbkbgpmCI +−= . (3.1)

From the definition of CI, we can see that the CI measure could be easily interpreted as intangible assets such as research and development expenditure. Vassalou & Apedjinou (2004) claim that CI is a much more general than any particular intangible asset category. The results on the relationship between CI and research and development (R&D) expenditures in chapter 2.5.2 seem to support this. Even though aggregated R&D seems to behave very similarly to aggregated CI in asset pricing models, the correlation and regressions between CI and R&D show that additional information is needed to fully comprehend CI. CI does not capture exactly firm’s earnings either because stochastic investments are also included in the free cash flows which proxy for earnings (see more detailed representation in Vassalou & Apedjinou 2004).

The empirically testable implications of the theoretical real business cycle model can be derived as follows. The standard RBC model states that the production function tY is a function of knowledge or technology ( tA ), physical capital ( tK ), and labour ( tL ), hence

αα −= 1tttt KLAY . (3.2)

One can solve the RBC model for the values of consumption and capital stock:

ααρα −−−= 1])1(1[ tttt KLAC (3.3)

ααρα −+ −= 1

1 )1( tttt KLAK , (3.4)

52 In the previous chapter, the corporate innovation was calculated both similarly to Solow residual and as an actual variable and their performance was monitored. Despite the problems associated with generated variables like Solow residual and generated CI (i.e. when they are calculated as a result from a regression analysis), they seem to perform better than the actual CI variable. Hence, this study concentrates solely on the CI build in the spirit of the Solow residual.

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where tC denotes consumption and ρ is the discount factor. As in chapter 1, the level of technology diminishes slowly from the economy. The technology shock translates into a change in the capital stock in the future, hence, affecting capital gradually and allowing it slowly to return to normal. As equation (3.2) shows, the net result of the movements in

tA , tK and tL is an increase in output in the period of the shock, causing output also gradually to return to normal level. Since investment is more volatile than consumption, consumption responds less and more slowly than output.

The main interest here concerns the effects of technology shock on output and consumption, i.e. equations (3.2) and (3.3). Because we are interested in the growth effects of technological change we consider changes instead of levels of the target macroeconomic variables. We obtain the forward looking equations from (3.2) and (3.3) as

1111 )1( ++++ −++= tttt klay αα (3.5)

[ ] 1111 )1()1(1 ++++ −+++−−= tttt klac ααρα , (3.6)

where yt+1 = log(Yt+1/Yt), lt+1 = log(Lt+1/Lt), and at+1, kt+1 and ct+1 respectively. Now, if aggregated CI (calculated as growth rate in our empirical application) proxies

for scaled A and if we assume that contemporaneous excess stock returns may reflect the changes in future labour and capital that cause the future output and consumption to change, we can rewrite equations (3.5) and (3.6) by using aggregated CI and excess stock returns. We choose to aggregate both CI and stock returns to industry-level. Hence, we can build empirical specifications as follows:

ttt REICIy += ++ 11 and (3.7)

ttt REICIc += ++ 11 , (3.8)

where ( )ttt YYy 11 log ++ = is the growth in output from time t to time t + 1, ( )ttt CCc 11 log ++ = is the growth in consumption from time t to time t + 1, 1+tICI is the

growth rate of aggregate corporate innovation from time t to time t + 1 and tRE are the excess stock returns at time t.

In our final empirical specification we predict target macroeconomic variables at least one period ahead using contemporaneous or past values of stock returns, corporate innovation and control variables. Hence, we simply build a regression model of the following form for each country:

ktttjtkt ZREICIm +−−+ ++++= εδχβα 1)1( . (3.9)

where ktm + is the growth in target variable (either output or consumption) from period t to t + k, )1( −tjRE are the excess stock returns of industry j at time t - 1, 1−tZ are control variables (e.g. lagged values of target variables, dividend yield and interest rate spread) and 1+tε are the error terms. From the basis of the theoretical discussion earlier, we

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expect output to react more quickly to ICI and stock returns than consumption. The control variables are examined in the regression because in (3.9) they are widely recognized to forecast future economic development. Furthermore, the interest rate spread may capture the effect of the discount factor and, jhence, the inclusion of it should improve the model.53

3.3 Data

We use quarterly panel data across several countries. The time period is from 1994:2 to 2004:4, which represents the period for which data for all variables are available after data transformations. Compustat Global Vantage data are used to compute the CI variable and stock returns, and OECD database is the source for macroeconomic variables. We use an international data set and choose 14 OECD member countries to be included in our analysis because these 14 countries could provide us with the necessary data (see Table 11 for a list of countries included in the analysis and Table 12 for descriptive statistics of the main interesting variables).54

The difficulties in comparisons of multi-factor productivity or in this case TFP across countries are mainly due to the difficulties in measuring the capital stock (which differs quite substantially across countries). Naturally, economic theory assumes that capital stock includes all assets of the firm, whether physical or other. Official estimates of capital stock across countries embody a wide variety of assumed asset maturities and depreciation patterns, and additional problem concerns the deflators used to measure investment. Partly due to the problems these measurement issues cause and partly because of the large international data set, we choose to take a shortcut and utilise only a measure of physical capital stock of a firm.55 The measurement of labour, especially when using solely the number of employees as is done in this chapter, poses smaller issues in the measurement of TFP. Furthermore, this analysis concentrates on the changes in all utilized variables, hence avoiding some of the measurement and comparison problems associated for example to converting levels variables to same currency by using exchange rates or purchasing power parities.

The computation of CI for each firm in each country involves measures of gross profit margin, capital and labour. Gross profit margin is calculated as Compustat item “Sales” minus Compustat item “Operating expense”56. Capital is measured as Compustat item “Property, plant and equipment” and labour is measured as Compustat item “Number of

53 See a discussion on the usefulness of interest rates and financial spreads in Fama (1990) and Davis & Fagan (1997). 54 The empirical analysis in this chapter is conducted on the data set as a whole and separately for each country. 55 Our approach follows the mainstream literature, which is forced to do the same exactly because of the problems associated with data. 56 Vassalou & Apedjinou (2004) used Compustat item “Cost of goods sold” instead of “Operating expenses”, but their analysis covered only the US data. The problem with using “Cost of goods sold” on international data is that the item is constructed differently in different countries depending on the accounting and financial statement systems of the country. Thus, “Cost of goods sold” does not measure same things whereas “Operating expenses” seem to be identically constructed across countries. Notice that “Operating expenses” measures other operational expenses and, hence, excludes capital and labour.

86

employees”57. All the variables needed for the calculation of CI are measured as changes in logs in their respective values. The industry-level CI variable ICI is build by first aggregating GPM, capital and labour to industry level by country and then calculating ICI using regression analysis as in equations (2.5) and (2.6) in chapter 2.

Table 11. List of the countries included in the data

Country Abbreviation Australia AUS Belgium BEL Canada CAN Germany DEU Spain ESP Finland FIN France FRA Ireland IRL Italy ITA Japan JPN Netherlands NLD Norway NOR Sweden SWE United States USA All countries ALL

We calculate the quarterly stock returns from Compustat monthly stock price data58. First we transform stock prices into returns by taking changes in logs of the respective series. The industry-level quarterly return is calculated as a simple mean of the firm-level monthly returns59. All the returns are in excess of short-term interest rates (3-month interest rate) obtained from OECD Source database. The macroeconomic variables for each country obtained from OECD Source are output (nominal gross domestic product GDP, measured in market prices) and consumption (private consumption expenditure

57 Once again we are forced to use a related measure instead of the most realistic one in our analysis. Compustat items ” labour and related expenses”, ”Wages and salaries” and ”Wages other” would be better and more accurate measures of labour in our analysis. Schreyer & Pilat (2001) underline the importance of choosing hours actually worked as the variable for labour input because it bears a closer relation to the amount of productive services provided by workers than simple head count. Furthermore, Lee (2004) suggests that utilized labour (often measured as total labour hours = average hours worked times the number of workers) is the most accurate measure in growth accounting framework since it measures the proportion attributed to labour contribution, rather than the number of workers. Unfortunately, the related Compustat items differ across countries and are not computed in all the countries included in the analysis. Thus, we are forced to use “Number of employees” as a proxy for labour in our international setting. 58 Some firms have multiple stock price series and we chose the longest one to represent the stock price. 59 This indicates that we assume all the possible information in stock returns – the information they contain about the performance of the firm and about the aggregate economic activity – will build up gradually during the whole year.

87

PCE60) and they are converted to growth rates by taking the first differences of the logarithms of the respective value index series. As instrumental variables in the macro forecasting equation we utilize the dividend yield (obtained from Compustat), and the term spread of interest rates (10 year government bond – 3-month interest rate, obtained from OECD Source).

Table 12. Descriptive statistics for the main variables.

Variable Mean STD Min Max GGDP 1,2229 1,1536 -2,8294 8,1640 GPCE 1,1699 0,8508 -2,4740 6,5387 ICI -0,0326 2,3346 -7,5194 40,7940 IRE -6,8133 2,4828 -16,3730 5,5771 DIV -1,2438 1,7270 -5,7394 6,3081 SPREAD 1,4775 1,0146 -2,6100 4,6740 Description of abbreviations: GGDP = growth rate of gross domestic product at country level, GPCE = growthrate of private consumption expenditure at country level, ICI = growth in industry level corporate innovation,IRE = excess stock returns at industry level, DIV = dividend yield at country level and SPREAD = interest rate term spread at country level. Mean, standard deviation (STD), minimum (Min) and maximum (Max) values ofthe variables are reported. All variables are expressed as changes in logs in their respective values. Generated industry-level ICI is formed using equations (2.5) and (2.6). Industry returns are in excess over 3-month interest rate. Dividend yields are aggregated within countries. Term spread is the difference between 10-year bond rate and 3-month interest rate. Total output is the growth rate of gross domestic product at country level.Consumption is the growth rate of private consumption measured at country level.

3.4 Results on the relationship between CI, stock returns and macroeconomy

At first, a small general note on CI: it should be positive when the rate of growth of the volume of gross output rises faster than the rate of growth of all combined inputs. As can be seen from Table 12 the mean of industry-level CI is only slightly on the negative side and the maximum value (40,79) is more positive than the minimum value (-7,52) is negative. In general, the efficiency of industries has not been very high during time period 1994 – 2003 for the whole data, but industries seem to differ greatly with respect to their corporate innovation.

As a first step in the analysis, the unit root tests are performed on all variables included in the analysis, both for original level series and for the growth rates (i.e. the changes in logs of the respective level series). Phillips & Perron (1988) test is used as the statistical tool. As can be seen from Table 13, Panel A, the original level series of the industry-specific variables are all quite clearly stationary time series. Equally clearly

60 Private consumption is reported only as volume index in OECD Database and to obtain a value index we have used OECD Database information for consumer price index. Resulting private consumption time series is a nominal measure.

88

Panel A shows that the target macroeconomic variables, gross domestic product (GDP) and private consumption expenditure (PCE), and the long-term (LIR) interest rates are all non-stationary. With the short-term interest rate, the unit root results vary across countries.

From Panel B we can see that the industry-level growth rates of capital (ICAP), labour (ILAB) and gross profit margin (IGPM) – the components used to build industry-level corporate innovation (ICI) – are stationary series, as is also the corporate innovation variable. The growth rates of GDP and PCE (GGDP and GPCE) are both clearly stationary, which is quite expected. The control variables – country-level dividend yield (DIV) and interest rate spread (SPREAD) – are also stationary across countries.

Why do we choose growth rates instead of levels in our analysis? First of all, the interest here is on the growth effects of technological change (i.e. the Solow residual). It is quite natural to explain economic growth with growth in other factors in the economy. The measure of technological change in this chapter, corporate innovation, is abstracted from firm-level changes in capital, labour and gross profit margin. Secondly, as has been shown in the previous chapter, excess stock returns contain relevant information about future development in aggregate economy and especially about the growth rates of aggregate macroeconomic variables. In other words, the ‘innovation capturing’ nature of stock market can potentially be utilized in assessing the economic growth. Thirdly, the time series properties of the macroeconomic variables require some differencing in order to perform a proper regression analysis61. As the results in Table 13, Panel B show, our regression analysis should not suffer about the problem of variables containing unit roots.

61 If the variables individually contain unit roots but are cointegrated, levels of the variables can be used in the regression analysis. But since we are specifically interested in changes of the variables, we have to consider the stationary properties of the variables.

89 Ta

ble

13. U

nit r

oot t

est s

tatis

tics u

sing

Phi

llips

& P

erro

n (1

988)

test

by

coun

try

Pane

l A. T

he o

rigin

al ti

me

serie

s

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

C

AP

-12,

368

-9,9

55

-15,

249

-15,

695

-8,0

06

-8,1

17

-16,

561

-6,3

10

-9,7

33

-138

,068

-10,

428

-8,2

04

-13,

079

-32,

031

(0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) LA

B

-10,

997

-9,4

48

-14,

609

-15,

517

-9,6

37

-7,5

28

-16,

609

-5,7

52

-9,5

07

-46,

885

-9,1

50

-7,5

82

-10,

448

-35,

817

(0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) G

PM

-16,

212

-15,

419

-22,

014

-19,

504

-8,1

35

-8,4

05

-22,

641

-8,4

40

-10,

717

-152

,659

-11,

102

-8,4

83

-14,

397

-33,

350

(0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) IP

RIC

E -3

,383

-1

,949

-4

,674

-3

,155

-3

,816

-3

,172

-3

,128

-4

,250

-3

,220

-5

,473

-3

,491

-2

,679

-5

,973

-4

,743

(0,0

13)

(0,3

10)

(0,0

01)

(0,0

25)

(0,0

04)

(0,0

24)

(0,0

26)

(0,0

01)

(0,0

20)

(0,0

01)

(0,0

10)

(0,0

80)

(0,0

01)

(0,0

01)

GD

P 6,

731

1,34

8 2,

183

-0,7

07

6,37

2 0,

017

2,70

7 2,

555

0,23

7 -1

,065

-0

,711

0,

725

1,54

0 3,

752

(0

,999

) (0

,999

) (0

,999

) (0

,837

) (0

,999

) (0

,956

) (0

,999

) (0

,999

) (0

,973

) (0

,724

) (0

,836

) (0

,992

) (0

,999

) (0

,999

) PC

E 4,

909

2,49

3 3,

978

-1,4

62

5,26

7 2,

570

4,35

0 2,

970

1,11

7 -0

,465

-0

,937

2,

069

2,99

6 5,

246

(0

,999

) (0

,999

) (0

,999

) (0

,546

) (0

,999

) (0

,999

) (0

,999

) (0

,999

) (0

,998

) (0

,890

) (0

,770

) (0

,999

) (0

,999

) (0

,999

) D

IVTO

T -2

0,17

5 -2

0,80

6 -2

4,08

2 -3

8,41

3 -1

6,65

1 -1

3,53

0 -6

5,96

1 -9

,596

-3

7,32

4 -1

68,5

99-1

4,59

7 -2

8,03

8 -2

3,68

9 -3

7,99

4

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

SIR

-1

,927

-3

,083

-2

,124

-4

,079

-4

,115

-4

,364

-5

,332

-6

,455

-2

,192

-3

,742

-3

,871

-2

,251

-2

,446

-1

,194

(0,3

19)

(0,0

34)

(0,2

37)

(0,0

03)

(0,0

02)

(0,0

01)

(0,0

01)

(0,0

01)

(0,2

12)

(0,0

06)

(0,0

04)

(0,1

92)

(0,1

35)

(0,6

72)

LIR

-1

,742

-1

,590

-1

,538

-1

,648

-1

,999

-2

,381

-1

,931

-2

,143

-2

,227

-1

,977

-1

,689

-2

,736

-1

,849

-1

,758

(0,4

06)

(0,4

82)

(0,5

08)

(0,4

53)

(0,2

87)

(0,1

52)

(0,3

17)

(0,2

29)

(0,2

00)

(0,2

96)

(0,4

32)

(0,0

75)

(0,3

54)

(0,3

98)

90 Ta

ble

13 (c

ontin

ued)

Pane

l B. T

he g

row

th ra

tes o

f the

var

iabl

es

A

US

BEL

C

AN

D

EU

ESP

FIN

FR

A

IRL

ITA

JP

N

NLD

N

OR

SW

E U

SA

ICA

P -5

,697

-4

,106

-3

,568

-5

,379

-3

,211

-2

,308

-3

,745

-2

,855

-4

,986

-5

,538

-2

,858

-3

,279

-3

,998

-3

,771

(0,0

01)

(0,0

02)

(0,0

08)

(0,0

01)

(0,0

21)

(0,1

71)

(0,0

04)

(0,0

53)

(0,0

01)

(0,0

01)

(0,0

53)

(0,0

18)

(0,0

02)

(0,0

04)

ILA

B

-4,8

57

-3,0

88

-4,2

69

-3,8

19

-3,3

72

-3,3

26

-3,5

45

-3,1

11

-5,6

35

-3,9

31

-2,4

55

-3,4

02

-3,4

27

-3,6

70

(0

,001

) (0

,030

) (0

,001

) (0

,004

) (0

,013

) (0

,015

) (0

,008

) (0

,028

) (0

,001

) (0

,003

) (0

,128

) (0

,012

) (0

,011

) (0

,006

) IG

PM

-6,4

75

-5,1

34

-3,6

23

-4,6

23

-3,4

21

-3,5

86

-3,7

86

-2,9

29

-5,2

60

-5,0

33

-2,4

99

-3,2

25

-3,9

77

-3,5

10

(0

,001

) (0

,001

) (0

,006

) (0

,001

) (0

,012

) (0

,007

) (0

,004

) (0

,045

) (0

,001

) (0

,001

) (0

,117

) (0

,020

) (0

,002

) (0

,009

) IC

I -4

,250

-2

,239

-4

,536

-7

,013

-4

,434

-2

,825

-4

,015

-6

,084

-3

,823

-7

,035

-3

,662

-5

,986

-4

,449

-3

,692

(0,0

01)

(0,1

93)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

57)

(0,0

02)

(0,0

01)

(0,0

04)

(0,0

01)

(0,0

06)

(0,0

01)

(0,0

01)

(0,0

05)

IRE

-13,

003

-11,

972

-10,

686

-10,

636

-7,4

38

-10,

205

-12,

604

-8,9

79

-7,5

27

-17,

077

-11,

608

-9,9

29

-9,2

61

-9,3

12

(0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) (0

,001

) G

GD

P -2

1,60

2 -1

8,42

0 -1

2,04

8 -2

1,62

9 -2

3,32

1 -1

2,95

6 -1

9,81

3 -2

5,37

6 -1

7,62

8 -1

6,16

6 -1

3,10

5 -1

9,35

7 -2

6,50

6 -1

7,69

9

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

GPC

E -2

3,79

2 -1

9,03

3 -2

0,18

6 -1

8,24

6 -2

3,36

5 -2

0,81

3 -2

5,85

4 -2

4,37

6 -1

6,52

5 -2

6,11

6 -1

5,23

5 -2

5,05

2 -2

9,68

2 -2

2,48

0

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

SPR

EAD

-7

,958

-7

,282

-7

,539

-6

,972

-9

,729

-5

,324

-1

0,43

8 -6

,269

-6

,866

-6

,130

-6

,212

-5

,720

-9

,763

-5

,349

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

DIV

-9

,351

-9

,022

-8

,999

-8

,902

-8

,314

-7

,010

-7

,778

-6

,222

-9

,527

-7

,147

-7

,477

-8

,291

-8

,977

-6

,053

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

(0,0

01)

The

orig

inal

ser

ies

in P

anel

A a

re a

ll in

leve

ls, w

here

as th

e gr

owth

ser

ies

in P

anel

B h

ave

been

tran

sfor

med

by

taki

ng lo

garit

hms

and

by d

iffer

enci

ng. G

ross

dom

estic

pr

oduc

t (G

DP

and

GG

DP)

, priv

ate

cons

umpt

ion

expe

nditu

re (

PCE

and

GPC

E), i

nter

est r

ate

spre

ad (S

PREA

D) a

nd d

ivid

end

yiel

d (D

IVTO

T an

d D

IV)

are

aggr

egat

ed to

co

untry

leve

l, st

ock

pric

es (I

PRIC

E), s

tock

retu

rns

(IR

E), c

orpo

rate

inno

vatio

n (I

CI)

, cap

ital (

CA

P an

d IC

AP)

, lab

our (

LAB

and

ILA

B) a

nd g

ross

pro

fit m

argi

n (G

PM a

nd

IGPM

) are

agg

rega

ted

to in

dust

ry le

vel.

The

Phill

ips

& P

erro

n (1

988)

uni

t roo

t tes

t has

bee

n pe

rfor

med

on

a nu

ll hy

poth

esis

of a

sin

gle

mea

n, w

hich

pro

duce

s te

st s

tatis

tic

Z-ta

u an

d th

e re

spec

tive

p-va

lues

(whi

ch a

re re

porte

d in

par

enth

esis

).

91

Table 14 reports the Pearson correlation coefficients of the variables to be used in macro (forecasting) regressions. Table 14 shows the correlation between contemporaneous values of the variables, but also one quarter lagged values of stock returns and control variables dividend yield and interest rate spread, since in equation (3.9) they are from past period. Industry-level corporate innovation (ICI) – the variable of main interest in this chapter – has very low correlation with both target macroeconomic variables and it may be an indication of potentially low forecasting power of ICI in macro regressions.

Table 14. Correlation coefficients of the variables to be used in macroeconomic forecasting regressions

GGDP GPCE ICI IRE IRE-1 DIV DIV-1 SPRE SPRE-1 GGDP 1,000 . . . . . . . . GPCE 0,520 1,000 . . . . . . . ICI 0,025 0,030 1,000 . . . . . . IRE -0,211 -0,215 -0,008 1,000 . . . . . IRE-1 -0,141 -0,199 -0,007 0,708 1,000 . . . . DIV 0,012 -0,015 0,033 -0,027 -0,014 1,000 . . . DIV-1 0,069 0,002 0,030 -0,043 -0,027 0,825 1,000 . . SPRE 0,024 -0,078 -0,038 0,256 0,275 -0,019 -0,067 1,000 . SPRE-1 0,013 -0,083 -0,038 0,205 0,256 0,023 -0,019 0,845 1,000 The values are Pearson correlation coefficients. All variables are expressed as changes in logs in their respective values. Capital, labour and gross profit margin are used in the calculations of corporate innovation (CI). Theyhave been aggregated from firm-level to industry level. Aggregation is done within countries. Generated CI is formed using equations (2.5) and (2.6). Industry returns are in excess over 3-month interest rate. Dividend yields are aggregated within countries. Term spread is the difference between 10-year bond rate and 3-month interest rate. Total output is gross domestic product at country level. Consumption is private consumptionmeasured at country level. Description of abbreviations: ICI = growth in industry level corporate innovation,IRE = excess stock returns at industry level, GGDP = growth rate of gross domestic product at country level, GPCE = growth rate of private consumption expenditure at country level, DIV = dividend yield at country leveland SPRE = interest rate term spread at country level. -1 denotes the one period lagged value of respective variable.

The industry-level stock returns (IRE and IRE-1) have higher correlation with target variables. Surprisingly, the correlation coefficients are negative. One reason may be the forward looking nature of stock market: the correlations are between contemporaneous/one period (one quarter) lagged values of stock returns and target macro variables, and hence do not consider e.g. six month lagged values (stock markets are said to forecast economic development about six months ahead). Another reason can be related to the specific time period analysed here. The developed countries in the sample and during the sample period have experienced some turbulence in economy, and the development of stock markets has also seen highs and lows with perhaps more speculative characteristics emerging than in average compared to long time series.

The dividend yield (DIV and DIV-1) and spread are almost as poorly correlated with targets as corporate innovation. The lagged values of dividend yields have higher correlation coefficients than contemporaneous values, especially with GGDP, indicating

92

perhaps that dividend yields are more closely related to macroeconomic developments than has been previously accounted for.62 The negative correlation coefficient between another business cycle variable, interest rate spread (SPRE and SPRE-1), and GPCE reflects the countercyclical nature of interest rates.

The residual diagnostics of all the in-sample regressions are reported in the tables. DW denotes the Durbin-Watson residual autocorrelation statistic and ARCH denotes residual heteroscedasticity. Due to the persistency in the time series that was tested in chapter 2, we have used Newey and West (1987) covariance matrix and a truncation lag of 12 in all the following analyses.

Table 15 shows the in-sample results from regressing country-level target macroeconomic variables on industry-level CI and one quarter lagged industry-level excess stock returns (model A) using a simple OLS panel regression. The target variables are one-quarter growth rates in GDP and PCE that are being explained (or ‘forecasted’) one, two and four periods ahead (i.e. k = 1, 2 and 4). For both target variables and for all forecasting horizons, corporate innovation ICI in general obtains very small and statistically non-significant parameter values, hence having no explanatory power at all.63 The only exception is the case of Japan with target variable being GDP, where corporate innovation is statistically significant. The one quarter lagged stock returns (IRE) seem to contain more information, especially when forecasting PCE. When forecasting GDP a little over half of the countries have stock returns performing well. When forecasting PCE stock returns have significant parameter estimates for most of the countries, especially with longer forecasting horizons. This potentially reflects the growing interest of small investors towards stock market and hence, the consumption smoothing effect the stock market can offer for them. Nonetheless, the corporate innovation performs quite disappointedly when trying to explain future target variables in-sample, as is also apparent from very low model fit statistics (adjusted R2).

Table 15 shows that only in few of the countries there is a problem with unequal variances of error terms in the models. The models are free of residual autocorrelation, though.

62 Quite a new strand of literature (see the work of Cochrane 1999, 2001 and 2004) is investigating the possibility of the stock market containing information about future developments of the economy, and the main question in that literature at the moment is the channel of the macroeconomic influences. In other words, following Campbell’s (1991) basic stock valuation formula, do these effects come from dividend yields and cash flows accruing in the future, from the discount factor, or from the co-movements of the discount factor and the stock returns. 63 This is also confirmed in regressions using solely industry-level corporate innovation (ICI) as the explanatory variable. The results are not reported here, but are available from the author upon request.

93

Tabl

e 15

. The

in-s

ampl

e re

sults

of c

orpo

rate

inno

vatio

n an

d in

dust

ry st

ock

retu

rns f

orec

astin

g qu

arte

rly

GD

P an

d PC

E gr

owth

rate

s with

th

e w

hole

dat

a sa

mpl

e an

d by

cou

ntry

Mod

el A

: k

tt

tk

tIR

EIC

Im

+−

++

++

=ε

χβ

α1

, k

= 1,

2 a

nd 4

.

Pane

l A: T

he q

uarte

rly g

row

th ra

te o

f GD

P as

targ

et v

aria

ble

ALL

A

US

BEL

C

AN

D

EU

ESP

FIN

FR

A

IRL

ITA

JP

N

NLD

N

OR

SW

E U

SA

α

0,72

6 1,

386

1,37

9 2,

120

0,55

8 1,

573

1,65

3 1,

105

1,45

0 0,

803

0,05

1 0,

961

2,83

5 1,

543

1,48

0

β

0,01

2 0,

003

0,00

2 -0

,012

-0

,008

0,

008

-0,0

07

0,00

2 0,

214

-0,0

16

0,06

9 0,

020

-0,1

39

0,00

7 0,

004

χ

-0,0

69

-0,0

15

0,06

2 0,

107

-0,0

05

-0,0

06

0,06

5 0,

031

-0,1

99

-0,0

43

-0,0

16

-0,0

36

0,13

8 0,

063

0,02

7 A

dj. R

2 0,

023

-0,0

02

0,01

7 0,

056

-0,0

05

-0,0

04

0,00

6 0,

011

0,02

1 0,

037

0,00

0 0,

001

0,01

5 0,

020

0,01

8 D

W

1,59

3 2,

060

1,96

9 1,

084

2,16

7 2,

178

1,38

8 1,

951

2,92

3 1,

770

1,45

5 1,

238

1,78

1 2,

614

1,66

4

k=1

AR

CH

57

,63

24,9

5 7,

76

30,0

0 18

,59

9,00

3,

88

18,5

6 2,

49

33,3

8 10

,92

3,02

18

,21

18,0

7 19

,48

α

0,71

4 1,

382

1,44

6 1,

960

0,72

0 1,

545

1,88

5 1,

140

2,27

8 0,

841

0,16

8 1,

183

2,00

6 1,

084

1,48

6

β

0,01

0 0,

000

-0,0

01

-0,0

47

-0,0

64

0,00

6 -0

,004

0,

001

0,14

9 -0

,010

0,

111

0,02

7 -0

,030

0,

001

0,00

8 χ

-0

,069

-0

,015

0,

075

0,08

6 0,

018

-0,0

10

0,10

2 0,

037

-0,0

79

-0,0

36

0,02

4 0,

000

0,05

0 -0

,006

0,

028

Adj

. R2

0,02

2 -0

,004

0,

029

0,03

5 0,

002

-0,0

03

0,02

5 0,

017

-0,0

03

0,02

6 0,

006

-0,0

06

-0,0

05

-0,0

06

0,01

9 D

W

1,61

4 2,

080

1,98

4 1,

089

2,16

3 2,

149

1,45

7 1,

943

2,89

0 1,

661

1,49

0 1,

338

1,85

9 2,

553

1,66

7

k=2

AR

CH

67

,47

12,7

6 3,

19

6,94

3,

08

9,62

0,

93

8,47

10

,00

29,8

6 3,

52

4,26

22

,32

24,1

3 36

,01

α

0,70

8 1,

957

1,57

2 1,

453

0,95

6 1,

859

1,71

7 1,

281

1,15

9 1,

152

0,00

5 1,

253

1,28

4 1,

537

1,40

3

β

0,01

0 -0

,001

-0

,016

-0

,043

0,

059

-0,0

16

-0,0

07

0,00

9 -0

,110

-0

,009

0,

205

0,02

8 0,

047

-0,0

04

-0,0

02

χ

-0,0

70

0,05

2 0,

095

0,01

6 0,

055

0,02

7 0,

075

0,05

6 -0

,240

0,

004

-0,0

29

0,01

2 -0

,034

0,

061

0,01

7 A

dj. R

2 0,

023

0,02

4 0,

054

-0,0

02

0,02

3 0,

014

0,01

0 0,

042

0,03

1 -0

,005

0,

026

-0,0

05

-0,0

06

0,01

9 0,

004

DW

1,

615

2,07

0 1,

981

1,02

8 2,

218

2,24

1 1,

425

1,90

3 2,

866

1,73

1 1,

505

1,43

7 1,

825

2,66

4 1,

627

k=4

AR

CH

61

,85

3,65

5,

71

7,16

6,

65

14,4

2 10

,12

3,77

17

,30

16,9

6 15

,81

4,96

10

,02

18,5

4 1,

97

94 Ta

ble

15 (c

ontin

ued)

Pa

nel B

: The

qua

rterly

gro

wth

rate

of P

CE

as ta

rget

var

iabl

e

A

LL

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

0,

736

2,22

7 1,

548

1,37

4 0,

488

1,97

0 1,

108

1,14

4 2,

062

0,81

4 0,

400

0,86

5 1,

216

1,17

7 1,

398

β

0,00

8 -0

,004

0,

006

0,00

0 0,

049

0,03

5 -0

,002

-0

,005

-0

,006

-0

,009

0,

043

-0,0

07

-0,0

08

0,01

7 0,

010

χ

-0,0

62

0,06

8 0,

092

0,01

5 -0

,020

0,

057

-0,0

04

0,03

6 -0

,026

-0

,040

0,

024

-0,0

49

-0,0

26

0,04

2 -0

,018

A

dj. R

2 0,

032

0,01

9 0,

082

-0,0

02

-0,0

01

0,04

5 -0

,008

0,

008

-0,0

08

0,04

6 -0

,001

0,

007

-0,0

02

0,01

0 0,

007

DW

1,

818

2,23

0 2,

072

1,92

9 1,

840

2,19

4 2,

355

2,40

7 2,

889

1,48

7 2,

490

1,55

6 2,

191

2,84

0 2,

116

k=1

AR

CH

39

,40

13,2

0 5,

73

11,3

3 7,

92

22,1

2 2,

72

36,1

1 2,

12

8,40

16

,63

2,70

11

,60

16,8

3 34

,16

α

0,76

2 2,

049

1,41

0 1,

510

0,61

0 1,

872

1,67

5 0,

849

3,00

4 0,

889

0,35

5 0,

776

1,03

3 1,

101

1,53

7

β

0,00

6 -0

,004

0,

005

-0,0

26

-0,0

33

0,02

1 0,

002

-0,0

06

0,08

3 -0

,003

0,

058

0,01

9 -0

,011

-0

,006

0,

002

χ

-0,0

58

0,04

8 0,

073

0,03

5 -0

,004

0,

044

0,08

9 -0

,007

0,

109

-0,0

30

0,00

9 -0

,063

-0

,046

0,

031

0,00

3 A

dj. R

2 0,

027

0,00

7 0,

050

0,00

8 -0

,005

0,

024

0,05

0 -0

,004

0,

008

0,02

5 -0

,003

0,

014

0,00

7 0,

003

-0,0

04

DW

1,

829

2,26

3 2,

009

1,90

5 1,

792

2,23

1 2,

351

2,40

3 2,

885

1,47

5 2,

492

1,46

1 2,

337

2,81

1 2,

094

k=2

AR

CH

39

,63

12,2

1 4,

00

11,1

4 7,

75

27,9

8 12

,39

23,1

9 2,

57

9,20

18

,39

11,2

6 23

,26

16,5

6 40

,20

α

0,75

2 2,

379

1,41

3 0,

916

1,02

5 1,

903

1,61

3 1,

395

1,95

3 1,

003

0,31

8 0,

816

1,20

6 1,

115

1,40

4

β

0,01

0 0,

000

-0,0

06

-0,0

24

0,05

3 -0

,016

0,

006

0,01

2 -0

,042

-0

,006

0,

110

0,02

8 0,

006

-0,0

28

0,00

6 χ

-0

,058

0,

087

0,07

6 -0

,047

0,

060

0,04

6 0,

078

0,07

0 -0

,029

-0

,015

-0

,003

-0

,055

-0

,022

0,

034

-0,0

16

Adj

. R2

0,02

9 0,

038

0,05

6 0,

019

0,02

0 0,

026

0,03

7 0,

042

-0,0

08

0,00

2 0,

001

0,00

9 -0

,004

0,

007

0,00

5 D

W

1,79

8 2,

362

1,95

3 1,

961

1,84

3 2,

188

2,35

2 2,

564

2,74

3 1,

572

2,49

7 1,

448

2,23

3 2,

780

2,11

5

k=4

AR

CH

55

,33

4,97

2,

20

14,2

4 9,

42

13,3

4 7,

40

22,7

2 16

,58

8,80

21

,36

6,24

17

,27

15,2

1 35

,35

All

para

met

er v

alue

s sta

tistic

ally

sign

ifica

nt a

t 5 %

leve

l are

hig

hlig

hted

. The

mod

el fi

t sta

tistic

repo

rted

is th

e ad

just

ed R

2 . k

tm

+ar

e th

e ta

rget

mac

roec

onom

ic v

aria

bles

, th

e gr

owth

in g

ross

dom

estic

pro

duct

(GG

DP)

and

the

grow

th in

priv

ate

cons

umpt

ion

expe

nditu

re (G

PCE)

and

the

in-s

ampl

e re

gres

sion

s us

e le

ads

of 1

, 2 a

nd 4

per

iods

in

targ

et v

aria

bles

in o

rder

to ‘f

orec

ast’

the

futu

re d

evel

opm

ent o

f GG

DP

and

GPC

E. In

dust

ry-le

vel c

orpo

rate

inno

vatio

n (I

CI)

val

ues

are

cont

empo

rane

ous

valu

es a

nd e

xces

s in

dust

ry r

etur

ns (

IRE)

are

1 p

erio

d la

gged

val

ues.

ALL

ref

ers

to th

e an

alys

is w

ith th

e w

hole

dat

a. k

ref

ers

to f

orec

astin

g ho

rizon

s of

1, 2

and

4 q

uarte

rs. D

W d

enot

es

Dur

bin-

Wat

son

stat

istic

for r

esid

ual a

utoc

orre

latio

n an

d A

RC

H m

easu

res r

esid

ual h

eter

osce

dast

icity

.

95

The results are reported in Table 16, Panels A and B. With only one exception, i.e. Japan in the case of GDP, k = 2 and 4, the corporate innovation is excluded from the analysis in every country. Interest rates and spreads are well-known business cycle variables, so their role is not surprising. What is surprising is that for quite a few of the countries, either one (or both) of the control variables are excluded, but stock returns remain in the model. This supports the view in this chapter that the stock market contains relevant information about future economic activity not captured by other well-known business cycle variables. The models suffer from residual heteroscedasticity but are free from residual autocorrelation.

In order to investigate the power and stability of the forecasting equations and parameters, recursive estimation techniques are used. The recursive estimation is run through the whole sample, hence starting with four observations (i.e. the observations of one year) in the first regression and gradually building up by adding each quarterly observation at a time to the analysis. Table 17 reports the root mean squared errors (RMSE’s) of the models and figure 2 draws the actual and predicted values of growth rates of GDP and PCE with one period forecasting horizon. The two models defined for the whole data regressions (models A and B) are also used in recursive regressions.

Judged by the in-sample results in Tables 15 and 16, the RMSE’s in Table 17, Panel A (which reports the results using model A) should be larger than in Panel B (which reports the results of model B). This seems indeed to be the case in most of the countries. Model B predicts better the future values of growth in GDP in all of the countries, and in only Belgium, Ireland and USA model A is better in predicting growth in PCE. Hence, in most of the countries, model B (a model without CI) exceeds model A in performance indicating that corporate innovation, at least as defined and measured in this chapter, does not contribute to the forecasting of macroeconomy. This evidence can be interpreted to be quite strong, since in some countries, model B contains only one explanatory variable – either industry stock returns, dividend yield or interest rate spread – that alone outperforms model A with corporate innovation (examples of this are Germany for GDP, k = 1, 2 and 4; Canada for PCE, k = 4; and Finland for PCE, k = 4).

Figure 2 reveals that both models A and B – but especially model B - predict remarkably well the right direction and timing of the change in target variables. Furthermore, the models seem to work especially nicely during the new millennium. Hence, lagged values of stock returns, dividend yields and interest rate spread – all important variables in Campbell’s (1991) model – can help macroeconomic forecasting. This is also the expected prediction of this chapter (see the discussion in section 3.2). The corporate innovation seems to be statistically unacceptable variable in our analysis, but by visual inspection, the predicted values of model A are not very far from the predicted values of model B. Thus, there is no extra information that the corporate innovation can offer in addition to stock returns and control variables.

96 Ta

ble

16. T

he in

-sam

ple

resu

lts o

f fin

ding

the

best

mod

el to

fore

cast

qua

rter

ly G

DP

and

PCE

grow

th r

ates

with

who

le s

ampl

e an

d by

co

untr

y

Mod

el B

: The

star

ting

mod

el is

k

tt

tt

tk

tSP

READ

DIV

IRE

ICI

m+

−−

−+

++

++

+=

εφ

δχ

βα

11

1,

k =

1, 2

and

4. V

aria

bles

with

sta

tistic

ally

non

-sig

nific

ant p

aram

eter

val

ues

are

drop

ped

out o

f th

e m

odel

one

by

one

until

all

varia

bles

in

clud

ed in

the

mod

el a

re si

gnifi

cant

. Pa

nel A

: The

qua

rterly

gro

wth

rate

of G

DP

as ta

rget

var

iabl

e

A

LL

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

0,

794

1,47

2 1,

377

2,12

1 0,

591

1,29

4 1,

715

0,96

9 2,

636

0,37

9 -0

,506

0,

969

3,70

4 1,

077

2,39

3

β

- -

- -

- -

- -

- -

- -

- -

- χ

-0

,068

-0

,030

0,

062

0,10

7 -

-0,0

27

0,15

8 -

-0,2

03

-0,0

59

- -0

,045

0,

233

0,05

8 0,

044

δ

0,04

5 0,

106

- -

- -0

,132

0,

241

0,09

1 0,

713

- 0,

120

0,14

9 -

- 0,

252

φ

- -0

,065

-

- -

-0,0

60

0,30

5 -

- 0,

248

0,56

9 0,

095

-0,2

53

0,25

5 -

Adj

. R2

0,02

7 0,

027

0,02

1 0,

058

0,00

0 0,

032

0,35

1 0,

177

0,10

6 0,

169

0,22

6 0,

020

0,03

2 0,

064

0,34

8 D

W

1,60

6 2,

209

1,96

9 1,

070

2,16

9 2,

264

2,02

2 2,

263

3,09

0 2,

056

1,99

4 1,

229

1,85

8 2,

654

2,08

2

k=1

AR

CH

12

8,85

40

,84

50,5

9 77

,43

33,3

9 33

,85

37,4

6 10

5,15

69

,60

63,4

3 14

4,17

43

,10

66,8

7 41

,74

20,9

7 α

0,

810

1,44

5 1,

080

2,21

1 0,

593

1,76

9 1,

982

1,17

7 3,

692

0,43

7 -0

,276

1,

023

3,18

7 0,

936

2,02

6

β

- -

- -

- -

- -

- -

0,10

6 -

- -

- χ

-0

,067

-0

,038

0,

060

0,10

2 -

- 0,

179

0,02

0 -

-0,0

52

- -

0,15

1 -

0,03

8 δ

0,

064

0,19

4 0,

061

- -

- 0,

209

0,07

9 0,

499

- 0,

121

- 0,

379

- 0,

150

φ

- -

0,15

2 -0

,075

-

-0,1

04

0,23

0 -0

,057

-

0,23

7 0,

422

0,09

4 -0

,238

0,

109

- A

dj. R

2 0,

031

0,06

8 0,

077

0,04

1 0,

000

0,02

1 0,

253

0,14

4 0,

039

0,15

0 0,

153

0,00

8 0,

074

0,00

5 0,

135

DW

1,

629

2,11

0 2,

095

1,10

6 2,

185

2,29

0 2,

044

2,21

3 3,

070

1,91

5 1,

785

1,37

5 1,

977

2,60

1 2,

112

k=2

AR

CH

11

6,60

35

,65

41,8

1 89

,28

41,8

5 53

,65

24,5

3 71

,39

51,9

3 81

,64

67,3

3 48

,67

62,8

3 49

,55

56,0

6

97 Ta

ble

16 (c

ontin

ued)

Pane

l A. (

cont

inue

d)

A

LL

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

0,

919

1,92

0 1,

270

2,60

8 0,

948

2,07

1 1,

346

1,06

0 2,

509

1,05

8 -0

,094

1,

020

2,19

8 1,

624

1,50

9

β

- -

- -

- -

- -

- -

0,19

8 -

- -

- χ

-0

,061

0,

061

0,08

2 0,

065

0,05

3 0,

039

0,07

6 0,

037

-0,2

01

-0,0

96

- -

- 0,

061

- δ

0,

050

-0,0

87

0,06

1 0,

099

- 0,

079

-0,0

80

0,03

6 0,

495

- -

0,14

5 0,

944

0,17

4 0,

105

φ

-0,0

61

- 0,

128

-0,3

31

- -

0,17

3 0,

077

-0,2

56

0,12

0 0,

117

0,21

8 -

- 0,

081

Adj

. R2

0,03

1 0,

039

0,09

5 0,

125

0,02

3 0,

028

0,03

5 0,

075

0,07

8 0,

130

0,02

9 0,

046

0,32

4 0,

053

0,09

1 D

W

1,62

6 2,

047

2,19

2 1,

157

2,23

6 2,

296

1,45

9 1,

965

3,00

9 1,

934

1,54

8 1,

577

2,39

1 2,

698

1,78

4

k=4

AR

CH

10

9,42

41

,84

23,7

3 43

,79

45,0

4 72

,40

36,0

4 30

,64

40,0

7 75

,79

62,1

8 46

,62

66,8

7 60

,44

39,6

4

Pane

l B: T

he q

uarte

rly g

row

th ra

te o

f PC

E as

targ

et v

aria

ble

A

LL

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

α

0,

736

2,09

1 1,

316

0,97

7 0,

801

1,74

2 1,

159

1,31

0 3,

073

0,58

5 -0

,057

0,

831

1,37

3 1,

109

2,07

5

β

- -

- -

- -

- -

- -

- -

- -

- χ

-0

,062

0,

077

0,08

8 -

- 0,

043

- 0,

030

- -0

,049

-

-0,0

62

- 0,

043

- δ

-

- -

-0,0

60

0,16

2 -0

,087

0,

075

0,05

7 0,

509

- 0,

112

0,17

5 -0

,124

-0

,132

0,

168

φ

- 0,

218

0,11

5 0,

085

- -

- -0

,101

-

0,13

4 0,

417

0,12

5 -0

,071

-

-0,0

23

Adj

. R2

0,03

1 0,

091

0,11

2 0,

014

0,03

9 0,

051

0,03

0 0,

059

0,08

3 0,

101

0,10

1 0,

035

0,03

5 0,

035

0,20

0 D

W

1,81

8 2,

311

2,14

0 1,

881

1,97

9 2,

222

2,43

4 2,

629

3,05

9 1,

556

2,81

8 1,

551

2,19

2 2,

857

2,67

4

k=1

AR

CH

14

3,98

77

,06

8,75

51

,70

82,9

9 66

,18

52,2

2 65

,40

43,0

3 40

,25

36,1

6 18

,88

50,2

1 45

,02

70,4

1

98 Ta

ble

16 (c

ontin

ued)

Pane

l B. (

cont

inue

d)

ALL

A

US

BEL

C

AN

D

EU

ESP

FIN

FR

A

IRL

ITA

JP

N

NLD

N

OR

SW

E U

SA

α

0,81

1 1,

758

1,09

8 1,

473

0,72

5 2,

194

1,81

0 1,

268

3,57

3 0,

737

0,28

2 0,

788

1,02

9 1,

102

2,40

1

β

- -

- -

- -

- -

- -

- -

- -

- χ

-0

,055

-

0,06

9 -

- 0,

062

0,10

6 -

0,10

7 -0

,035

-

-0,0

61

-0,0

46

0,03

1 0,

044

δ

- 0,

230

- 0,

088

0,20

8 0,

115

0,07

2 0,

071

0,34

5 -0

,046

0,

141

- -

- 0,

143

φ

-0,0

22

0,18

1 0,

157

- 0,

080

- -

-0,2

04

- 0,

130

0,25

2 -

- -

-0,0

89

Adj

. R2

0,02

8 0,

099

0,10

5 0,

014

0,06

2 0,

041

0,08

0 0,

111

0,04

7 0,

115

0,08

5 0,

017

0,01

1 0,

005

0,17

9 D

W

1,83

0 2,

302

2,10

7 1,

881

1,94

2 2,

258

2,43

6 2,

514

3,12

6 1,

599

2,72

2 1,

507

2,33

0 2,

816

2,66

6

k=2

AR

CH

11

5,90

24

,85

27,0

8 57

,68

52,8

2 53

,99

54,7

7 10

1,91

50

,89

36,2

7 52

,98

52,4

2 54

,07

47,0

2 49

,32

α

0,91

6 2,

428

1,11

9 1,

506

0,81

0 2,

319

1,60

5 1,

383

3,02

1 1,

210

0,14

4 0,

424

1,53

5 1,

603

1,89

6

β

0,00

9 -

- -

- -

- -

- -

- -

- -

- χ

-0

,050

0,

069

0,06

2 -

0,05

4 0,

069

0,07

7 0,

070

- -

- -0

,057

-

0,03

8 -

δ

- 0,

151

0,06

2 -

- 0,

154

- -

0,38

8 -0

,105

-

- 0,

229

0,10

9 0,

120

φ

-0,0

73

- 0,

117

-0,1

44

0,11

2 -

- -

-0,2

14

- 0,

118

0,21

8 -

-0,2

37

- A

dj. R

2 0,

036

0,06

1 0,

129

0,05

7 0,

038

0,05

9 0,

040

0,04

4 0,

057

0,17

6 0,

004

0,06

7 0,

115

0,08

2 0,

095

DW

1,

808

2,34

2 2,

192

2,13

0 1,

948

2,25

9 2,

360

2,54

0 3,

046

1,75

5 2,

543

1,55

2 2,

360

2,97

1 2,

400

k=4

AR

CH

13

7,12

26

,29

19,0

8 82

,76

40,9

7 28

,71

59,3

0 48

,40

36,0

4 51

,15

40,3

4 68

,87

48,0

3 36

,31

49,8

6 A

ll th

e pa

ram

eter

val

ues a

re st

atis

tical

ly si

gnifi

cant

at 1

0% le

vel,

at le

ast.

The

mod

el fi

t sta

tistic

repo

rted

is th

e ad

just

ed R

2 . k

tm

+ a

re th

e ta

rget

mac

roec

onom

ic v

aria

bles

, th

e gr

owth

in g

ross

dom

estic

pro

duct

(GG

DP)

and

the

grow

th in

priv

ate

cons

umpt

ion

expe

nditu

re (G

PCE)

and

the

in-s

ampl

e re

gres

sion

s us

e le

ads

of 1

, 2 a

nd 4

per

iods

in

targ

et v

aria

bles

in

orde

r to

‘fo

reca

st’

the

futu

re d

evel

opm

ent

of G

GD

P an

d G

PCE.

Ind

ustry

-leve

l co

rpor

ate

inno

vatio

ns (

ICI)

are

con

tem

pora

neou

s va

lues

and

exc

ess

indu

stry

retu

rns

(IR

E) a

re 1

per

iod

lagg

ed v

alue

s. A

LL re

fers

to th

e an

alys

is d

one

with

the

who

le d

ata.

k re

fers

to fo

reca

stin

g ho

rizon

s of

1, 2

and

4 q

uarte

rs. D

W d

enot

es

Dur

bin-

Wat

son

stat

istic

for r

esid

ual a

utoc

orre

latio

n an

d A

RC

H m

easu

res r

esid

ual h

eter

osce

dast

icity

.

99 Ta

ble

17. T

he re

sults

of r

ecur

sive

est

imat

ion:

RM

SE’s

of tw

o m

odel

s use

d to

fore

cast

qua

rter

ly G

DP

and

PCE

grow

th ra

tes b

y co

untr

y

Mod

el A

: k

tt

tk

tIR

EIC

Im

+−

++

++

=ε

χβ

α1

, k =

1, 2

and

4.

Mod

el B

: The

star

ting

mod

el is

k

tt

tt

tk

tSP

READ

DIV

IRE

ICI

m+

−−

−+

++

++

+=

εφ

δχ

βα

11

1,

k =

1, 2

and

4. V

aria

bles

with

sta

tistic

ally

non

-sig

nific

ant p

aram

eter

val

ues

are

drop

ped

out o

f th

e m

odel

one

by

one

until

all

varia

bles

in

clud

ed in

the

mod

el a

re si

gnifi

cant

(see

Tab

le 1

6 fo

r the

fina

l ‘be

st’ m

odel

s).

Pane

l A. T

he R

MSE

's of

regr

essi

ons u

sing

mod

el A

A

US

BEL

C

AN

D

EU

ESP

FIN

FR

A

IRL

ITA

JP

N

NLD

N

OR

SW

E U

SA

GG

DP

k =

1 1,

214

1,21

9 2,

285

1,33

4 1,

411

1,26

2 0,

955

4,85

9 2,

186

1,23

7 1,

142

3,27

6 2,

393

0,66

2

k =

2 1,

939

1,14

4 2,

461

1,45

2 1,

504

1,57

9 1,

225

5,43

3 2,

974

1,31

3 1,

060

3,76

6 2,

744

0,66

9

k =

4 3,

167

1,46

6 2,

758

1,28

1 0,

894

1,30

8 1,

491

3,68

3 1,

535

1,32

9 1,

321

4,37

7 1,

911

0,99

4 G

PCE

k =

1 3,

191

0,84

6 2,

988

1,47

5 1,

381

0,98

3 1,

415

2,59

2 1,

562

1,93

4 1,

132

1,54

3 2,

560

0,61

0

k =

2 3,

415

1,09

1 1,

086

0,96

5 3,

541

0,94

8 1,

783

3,12

4 2,

061

1,79

1 1,

037

2,02

2 3,

838

0,76

9

k =

4 3,

288

0,94

3 2,

699

1,60

1 2,

395

1,13

3 1,

187

4,15

7 2,

009

1,92

2 0,

948

1,72

3 2,

921

0,79

1

Pa

nel B

. The

RM

SE's

of re

gres

sion

s usi

ng m

odel

B

AU

S B

EL

CA

N

DEU

ES

P FI

N

FRA

IR

L IT

A

JPN

N

LD

NO

R

SWE

USA

G

GD

P k

= 1

0,86

4 1,

259

1,52

0 0,

722

0,75

0 1,

033

1,01

4 3,

832

1,09

4 0,

798

1,11

6 2,

402

1,46

9 0,

465

k

= 2

0,91

6 1,

194

1,50

2 0,

708

0,74

2 1,

086

1,01

6 3,

641

0,91

5 0,

854

0,95

4 2,

420

1,43

5 0,

532

k

= 4

0,78

5 1,

697

1,39

4 0,

737

0,77

2 1,

403

1,01

4 2,

399

1,04

9 0,

876

1,01

7 2,

452

1,71

9 0,

545

GPC

E k

= 1

0,98

8 1,

000

1,45

5 1,

149

0,81

5 0,

650

1,08

4 14

,760

0,

843

0,79

9 1,

020

1,04

7 2,

590

0,76

7

k =

2 1,

104

1,02

2 0,

936

1,39

1 0,

910

0,66

5 1,

720

4,67

7 0,

625

0,70

7 1,

016

1,01

0 2,

669

0,91

4

k =

4 0,

835

1,32

6 0,

780

1,31

1 0,

914

0,66

5 0,

854

13,7

44

0,66

6 0,

762

0,91

2 0,

876

1,40

3 0,

826

100 Ta

ble

17 (c

ontin

ued)

Th

e re

curs

ive

estim

atio

n is

run

thro

ugh

the

who

le s

ampl

e, s

tarti

ng o

nly

with

four

obs

erva

tions

in th

e fir

st re

gres

sion

. The

mod

el fi

t sta

tistic

repo

rted

is R

MSE

, whi

ch re

fers

to

the

root

mea

n sq

uare

d er

rors

of

the

mod

els.

kt

m+

are

the

targ

et m

acro

econ

omic

var

iabl

es, t

he g

row

th in

gro

ss d

omes

tic p

rodu

ct (

GG

DP)

and

the

grow

th in

priv

ate

cons

umpt

ion

expe

nditu

re (G

PCE)

and

the

recu

rsiv

e re

gres

sion

s us

e le

ads o

f 1, 2

and

4 p

erio

ds (k

) in

targ

et v

aria

bles

in o

rder

to ‘f

orec

ast’

the

futu

re d

evel

opm

ent o

f GG

DP

and

GPC

E. I

ndus

try-le

vel

corp

orat

e in

nova

tions

(IC

I) a

re c

onte

mpo

rane

ous

valu

esan

d ex

cess

ind

ustry

ret

urns

(IR

E) a

re o

ne p

erio

d la

gged

val

ues.

ALL

ref

ers

to t

he

anal

ysis

don

e w

ith th

e w

hole

dat

a.

101

Fig. 2. Actual and predicted values of growth rates of GDP and PCE with one period forecasting horizon

We present graphs from example countries, which in this case are Australia (AUS), Canada (CAN), Finland (FIN), Germany (DEU), Japan (JPN), Spain (ESP) and USA. gdpforeA (or pceforeA) refers to the forecasts of the model A, gdpforeD (or pceforeD) those of the model B and gdplead1 (or pcelead1) is the actual observation at time t + 1.

102

Fig. 2 (continued)

103

Fig. 2 (continued)

104

Fig. 2 (continued)

105

Fig. 2 (continued)

106

Fig. 2 (continued)

107

Fig. 2 (continued)

108

Now, why is the role of corporate innovation in our empirical analysis so small, even though the theoretical discussion would indicate a much stronger part for it? This is interesting, since the acceptance of corporate innovation would indicate for example that the role of technology in people’s (private) lives in the modern world is strong (like it is, at least in the countries analysed here) and the role of education and knowledge of your profession is highly important64 (which is a trend in developed countries like the ones in this sample). One reason for the poor performance of corporate innovation can be the way we formulate it. The calculation unfortunately includes some of the traditional measurement problems associated with total factor productivity, e.g. the scarce data on labour and capital. In addition, the regression approach we use to calculate corporate innovation has several disadvantages65 that potentially affect our results.

Furthermore, why Japan makes an exception by including corporate innovation in the ‘best’ model in some cases? This may be due to the strong role of technology in Japanese society. The TFP shock is likely to spread throughout the economy more efficiently and faster than in other, less technology-oriented countries. The labour force improvements also could be more visible and measurable in Japan because of the specific working environment and the ‘work-appreciating’ culture.

3.5 Conclusions and future research

This chapter empirically investigates the theoretical implication of real business cycle models that a technology – or total factor productivity (TFP) – shock and the stock markets contain information about future economic development. A TFP shock should have a direct effect on output and consumption and the stock market potentially captures some indirect effects of this shock. The indirect shocks can be e.g. the effects of the changing capital/labour ratio, which in itself is not accounted for in theoretical derivations, but which could show up in the sensitive stock price of a firm. Hence, both a TFP shock and stock returns should be useful in macroeconomic forecasting.

An international data set consisting of 14 OECD member countries and covering 10 years is used to analyse the growth effects of a technology shock, which is measured by Solow (1956, 1957) inspired corporate innovation, which is the change in a firm’s gross profit margin unexplained by changes in capital and labour the firm has in its use. In addition, the innovation capturing nature of stock market is considered by using excess stock returns.

Surprisingly, a TFP shock measured as corporate innovation seemed to have no predictive power towards output and consumption. The only exception in our data is

64 See the discussion in section 3.2.1 of the improvements in the labour force not accounted by the measure of changes in labour used here (ILAB). 65 The drawbacks of the regression-based estimate of corporate innovation are, for example,: (i) the changes in capital and labour cannot be regarded as exogenous with respect to variations in gross profit margin, (ii) the growth rate of capital is unlikely to correspond well to the capital stock currently utilized in production and this leads to low estimates of the contribution of capital accumulation to economic growth, and (iii) the regression framework needs to be extended from its usual form to allow for time variations in factor shares and the TFP growth rate. (Barro & Sala-i-Martin 2004).

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Japan: this result may be explained by the highly technology-oriented and “work-appreciating” Japanese culture. The poor results on the TFP shock may be due to the construction of corporate innovation, which is a result of a regression analysis. However, stock returns performed well in macroeconomic forecasting and seemed to incorporate the same information as the TFP, hence, leaving no role for the TFP measure corporate innovation. In other words, a price variable (stock return) outperformed a variable build from real firm-specific variables (corporate innovation). This encourages us to investigate more closely the stock market as a predictor of real economic activity since it seems that the stock returns withhold important information regarding future economic development that cannot be otherwise utilised (such as the hardly measurable technology shock).

Potential future research should first of all include a more profound theoretical analysis of the mechanism that connects a TFP shock and stock returns, and then again asset returns and future economic activity. Secondly, the construction of corporate innovation as a measure of a TFP shock needs more attention. One could use some other empirical way of constructing aggregate corporate innovation than a regression analysis: potential alternative could be the utilization of various index number formulae (Laspeyres, Paasche, Fisher or Törnqvist indices) (see the work by Schreyer & Pilat 2001 and OECD Manual 2001 on productivity and inputs). Thirdly, the role of labour in the channels of influence of a TFP shock seems to be important and should be looked at more carefully. And fourthly, combining an economic tracking portfolio framework with corporate innovation analysis could provide fruitful results on obtaining useful information for economic forecasting.

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Original articles

I Junttila J & Kinnunen H (2004) Economic Tracking Portfolios in an IT-intensive Stock Market. Quarterly Review of Economics and Finance 44, 601–623.

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THE PREDICTIVE POWER OF FINANCIAL MARKETSESSAYS ON THE RELATIONSHIP BETWEENTHE STOCK MARKET AND REALECONOMIC ACTIVITY

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