speed of learning and price multiples

7/29/2019 Speed of Learning and Price Multiples

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The Speed of Learning about Firms Profitability and their Price Multiples: A

Global Perspective

PANKAJ K. JAIN AND UDOMSAK WONGCHOTI*

Abstract

This paper is an application of learning models in valuation of stocks listed on 52

international stock exchanges. We present direct evidence of declining analyst forecast errors

and return volatility with advancement in a firms age in the context of a learning model.

Consistent with a relatively new theory of learning and unlike the traditional focus of asset

pricing models on the discount rate in denominator, we find support for the notion that this

reduction in uncertainty about a firms prospects affects the expected value of cash flows in the

numerator of the valuation equation. This results in an inverse and convex relation between a

firms age and its market-to-book ratio. The valuation effects of learning are pervasive over time

and across countries, and are stronger for firms that do not pay dividends. We also find that the

speed of learning about firms profitability and its impact on valuation varies in economies with

diverse market designs and legal frameworks. Forecast errors are lower with strict enforcement

of laws prohibiting insider trading, higher feasibility of short selling, and dominance of local

versus foreign investors. These features also increase the learning speed and fuel quicker

achievement of long run equilibrium valuations.

JEL Classification Codes: G12, G14, G15

Key Words: Market to book ratio

______________________

* Jain is from Fogelman College of Business and Economics, The University of Memphis, USA and Wongchoti isfrom Massey University, Palmerston North, New Zealand. Please send correspondence to Pankaj Jain, FCBE 425,University of Memphis, Memphis, TN 38111, Phone (901) 678 3810, Fax (901) 678 0839, Email:

[email protected] or [email protected]. We are grateful to Michael Pagano, Ian Cooper, Henk

Berkman, John G Powell, Geoff Jones, Ben Jacobsen, and seminar participants at the European Finance Association

Annual Meeting 2008 (Athens, Greece), University of Mississippi, Southern Methodist University, and OldDominion Universityfor comments and suggestions. All errors are our responsibility.


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The Speed of Learning about Firms Profitability and their Price Multiples: A

Global Perspective

Abstract

This paper is an application of learning models in valuation of stocks listed on 52

international stock exchanges. We present direct evidence of declining analyst forecast errors

and return volatility with advancement in a firms age in the context of a learning model.

Consistent with a relatively new theory of learning and unlike the traditional focus of asset

pricing models on the discount rate in denominator, we find support for the notion that this

reduction in uncertainty about a firms prospects affects the expected value of cash flows in the

numerator of the valuation equation. This results in an inverse and convex relation between a

firms age and its market-to-book ratio. The valuation effects of learning are pervasive over time

and across countries, and are stronger for firms that do not pay dividends. We also find that the

speed of learning about firms profitability and its impact on valuation varies in economies with

diverse market designs and legal frameworks. Forecast errors are lower with strict enforcement

of laws prohibiting insider trading, higher feasibility of short selling, and dominance of local

versus foreign investors. These features also increase the learning speed and fuel quicker

achievement of long run equilibrium valuations.


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Capital markets play an important role in the economic development of any country. Well

functioning markets ensure that both corporations and investors pay or receive fair prices for

their securities. This equilibrium assures that valuable projects are financed and negative present

value projects are rejected. In this framework, valuation of equity securities involves discounting

the profits (dividends, earnings, or cash flows) the stock brings to the stockholder in the

foreseeable future, and a final value upon disposition.

Much of the financial research, including the seminal CAPM and Fama-French (1995) 3-

factor model, is focused on the discount rate and its equity risk premium component. Formulas

for arriving at the profitability of a firm are in place as well. The calculations, however, depend

heavily on accurate forecasts of the firms revenues and expenses. Forecasting the future demand

for a firms products and its future competitive position accurately is indeed a big challenge. Bulk

of the finance literature implicitly assumes that since the negative errors in forecasting may be

offset by an equal amount of positive errors, such errors may be inconsequential in valuing

stocks at the portfolio level.

Only recently, Pastor and Veronesi (2003) suggest that the uncertainty about future

profitability and forecasting errors, even if symmetric around zero, affect asset prices and

valuations because of convexity in asset pricing formula. The gains from a positive surprise in

firms earnings growth asymmetrically outweigh the losses from a negative surprise of the same

magnitude. They predict that the ratio of market value to book value (M/B) declines with firms

age by assuming that investors learn about its expected future profitability with the

advancements in a firms age.

The goal of this study is to enhance our understanding of the learning process and its

valuation implications along several dimensions. First, we establish the validity of the existence


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of a learning process and declining uncertainties by directly testing for any changes in analyst

forecast errors with advancements in a firms age. This is an important link in the learning theory

not yet tested explicitly. Second, we present pervasive international evidence of the valuation

implications of the learning theory from firms listed in 52 stock exchanges around the world. Our

third contribution deals with a subtle extension of the concept of speed of learning beyond firm

specific characteristics. We do confirm the differences in the learning process for dividend

paying and non-dividend paying firms, in international samples. We then extend that logic to

demonstrate differences in the learning process and its valuation implications in economies with

diverse market designs and legal frameworks. In our panel data analysis, the effects of market

restructuring are particularly interesting to analyze. There have been some significant changes in

the financial environment in recent years. Examples include stricter enforcement of laws

prohibiting insider trading laws, ever changing restrictions on short-selling constraints, and

increased involvement of sophisticated foreign institutional traders in global markets. Important

empirical questions arise as a result. Does a systematic pattern of learning exist in all markets?

Does the speed of learning change with significant variations in financial market regulations and

foreign investor participation? We develop these hypothesis and provide a related literature

review in section I of the paper.

We then address these issues empirically in a cross-country setting by analyzing a

universe of 22,858 international firms and its various subsets based on data availability in

Datastream international and I/BE/S international. Data sources are sample characteristics are

shown in section II. In that section, we also present the results of our data analysis. Both analyst

forecast errors and return volatility decline with advancement of firms age. This is a direct

evidence of a learning curve for analysts and stock market investors in the global markets.


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Consistent with the asset pricing model based on learning, M/B ratio declines with the resolution

of uncertainty with advancement in firms age. The valuation effects of learning are pervasive

over time and across countries, and are stronger for firms that do not pay dividends. We also find

that the speed of learning about firms profitability and its impact on valuation varies in

economies with diverse market designs and legal frameworks. Forecast errors are lower with

strict enforcement of laws prohibiting insider trading, higher feasibility of short selling, and

dominance of local versus foreign investors. These features also increase the learning speed and

fuel quicker achievement of long run equilibrium valuations. However, some features such as

short selling introduce additional noise in emerging markets that outweigh the benefits of

speedier learning about negative news.

We summarize our findings and discuss some potentially fruitful directions for further

research in section III.

I. Testable Hypotheses

H10: The Learning Process:There is a reduction in uncertainty about the profitability of a

firm with advancement in its age:

0 1

where 2represents the uncertainty about profitability. New firms tend to possess high degree of

uncertainty about their product demand, revenue stream, operational cost, cash flow and

profitability. These uncertainties can manifest themselves in higher analyst forecast errors in the

early year of the firms existence. With the passage of time, such uncertainties resolve as the firm

goes through the concrete implementation of its business plans. The analysts can also forecast

the firms profitability more accurately with the advancement in firms age with more data


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availability on actual historic performance of the firms and its financial results. Markov and

Tamayo (2006) and Linnainmaa and Torous (2009) develop models to explain predictability of

analyst forecast errors based on a learning process, although their focus is on separating learning

from irrationality at the analyst level. We conduct original and direct cross-country tests of

aggregate learning by investigating the relation between median analyst forecast error from

I/B/E/S international and firm age.

Valuation Model that includes the Numerator Effect of Uncertainty: The learning curve

in the financial markets around the world affects firms valuations if the effects of uncertainty

are explicitly modeled. Pastor and Veronesi (2003) predict higher M/B ratios for younger firms

due to greater uncertainty about their future profitability. The genesis of this relationship is the

convexity in the following valuation equation:

])2/exp[(]}){exp[( 2 TrgTrgEB

M+== (2)

whereM/B stands for market to book ratio, E{.} is the expectations operator, g is the growth rate,

ris the stochastic discount factor, Tcan be interpreted as the time after which firm is not

expected to grow at an abnormal rate, exp stands for exponential, It is a mathematical property of

this equation that M/B increases in 2

because of the convex relationship between growth rate

and valuation resulting from the convexity of compounding. Innovation is good and innovative

firms are valued highly even when their profitability is highly uncertain. The absolute wealth

increases associated with growth rates one unit about average dominate the absolute wealth

decrease associated with growth rates one unit below average. With learning, such uncertainties

reduce over time.


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H20: Valuation Implication of the Learning Curve: Thus, the theoretical prediction about the

effect of learning on valuations is that, ceteris paribus, M/B declines with the advancement in a

firms age:

/

0

/

0 3

Previous literature has confirmed this relationship for U.S. stocks. We present

comprehensive tests of this hypothesis in a global setting.

Speed of Learning: We formulate the concept of speed of learning for a given change in

the learning environment (E) as the change in the rate of reduction of uncertainty in analyst in

each year of a firms life as follows:

4

We examine three specific learning environment variables. The first variable relates to

the laws prohibiting insider trading. Stricter enforcement of such laws could either slow the

learning process or speed it up depending on the trade-off between internal and external sources

of information. On the one hand such laws eliminate arguably the most informed participants

(insiders) from affecting the learning grounds. This effect can slow the learning process. On the

other hand, the persistence of insider trading poses puts everyone else at a relative disadvantage;

a disincentive for equity research analysts and expert investors that could drive them away from

spending efforts towards learning about firms profitability. Empirical analysis is necessary to

determine which of these two effects dominate. We divide the firms years into those before and

those after the first enforcement of insider trading law in each country. The cut-off year for each

country is taken from Bhattacharya and Daouk (2002) and is reproduced in Appendix 1.


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The second aspect of the learning environment is the feasibility of short-selling in a

market. The ability to short-sell is an important tool for arbitrageurs or analysts to exploit both

positive and negative news about a firm. Without this tool, there are limited incentives to search

for negative information about stocks that one does not own. As a result, we conjecture that the

feasibility of short-selling helps sharpen up the learning process. We divide the sample into

markets where short selling is feasible versus those where it is infeasible. This information is

obtained from Charoenrook and Daouk (2005). They study the impact of short selling constraints

on the cost of equity (the discounting rate in asset pricing models, which is a denominator effect)

whereas our focus on reduction in uncertainty of the cash flows through learning (a numerator

effect). Short selling facility is likely to enhance the learning process by rewarding both positive

and negative findings about the prospects of a stock.

The third aspect of learning environment that we analyze is the intensity of involvement

of foreign institutional traders in a countrys stock markets. Foreign investors can speed up

learning process because they can bring more sophisticated research skills into the country.

However, the involvement of foreign institutions is also associated with factors that can

introduce a lot of noise in the valuation process and slow down learning. Such factors include

capital flight, language barriers, and deviation of systemic or idiosyncratic foreign factors from

domestic factors. Empirical investigation can help us understand whether foreign institutions add

more speed or more noise to the learning process. Of the 52 countries in our sample, only 40

countries have foreign institutional trading data available in the Plexus database.1

Thus, a

reduced sample is analyzed for this set of regressions. We divide the total dollar volume of all

trades undertaken by foreign institutions in a country by the total market capitalization of that

1 The details of Plexus dataset are described in Chiyachantana et al. (2004).


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countrys stock market. Based on the median of this measure we divide the sample into countries

with high versus low foreign institution involvement.

These arguments lead us to the following hypothesis which we test by comparing the rate

of decrease in analyst forecast errors over time, across markets which possess stricter versus

weaker learning environment.

H30:Learning Environment: Various aspects of the learning environment affect the speed

learning by the investors.

We test this hypothesis separately for each aspect of the learning environment. For

example, the tests for the effect of short selling compares the rate of decrease in analyst errors

over time in markets with feasible short selling with the decrease in markets with restricted short

selling.

Following Corollary 4 about dividend paying versus non-dividend paying firms in Pastor

and Veronesi (2003), we generalize the valuation implications of speed of learning for other firm

characteristics and market design. The valuation effects of a change in the speed of learning

resulting from a given change in the learning environment (E) is measured as the change in the

slope of M/B ratio in each year of a firms life as follows:

/

5

We test the valuation implications of differential learning environments by separately estimating

the coefficients of the learning model for the three aspects of stricter versus weaker enforcement

of insider trading regulation, short selling feasibility, and involvement of foreign institutional

investors.

II. Data and Empirical results


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Our main data sources are I/B/E/S International and Datastream International. We obtain

data item forecast period (FPEDATS), stock ticker symbol (TICKER) mean analyst forecasted

EPS (MEANEST), number of analysts or forecasters (NUMEST) for the stock for the given

period, and actual EPS (data ACTUAL) for each firm from I/B/E/S summary file for 12,453 US

firms and 21,271 international firms for each available year and then focus on the period from

1981 to 2004. We obtain market to book ratio (data item mnemonic MTBV), dividend per share

(DPS), total assets (DWTA), return on equity (DWRE), long term debt (account item 321), stock

return index (RI), and stock price (P) for 22,858 international firms from Datastream

International. We verify the accuracy of this historical data by comparing it with Compustat

Global datasets and Yahoo Finance for one company in each country. The next important item

we need is the age of each firm. Direct information on this variable is not available in any

traditional dataset. Therefore, we follow Fama and French (2001) and Pastor and Veronesi

(2003) and use the year of first appearance of any variable for a firm in the dataset as its year of

birth.2

A. Evidence of learning process in a univariate setting

Table 1 provides preliminarily evidence consistent with Hypothesis 1 along two dimensions.

First, we investigate whether there is a long-run declining trend of analyst forecast errors over

time. Analyst forecast errors is computed as the absolute difference between mean year-end

forecast and actual year-end EPS, divided by the absolute actual EPS. Panel A shows that

median analyst forecast error is 20.69% for 1 year old US firms and 11.42% for 20 year old US

2 In Datastream, annual price tends to be the variable that becomes available first. As a result, the definition of age in

our study is based primarily on the first year the annual price is available. This information is used to allocate the

firm to an age category in our panel dataset. We start this process from the year 1969 which is the first year ofavailability of price data in Datastream for UK firms. Our sample ends in the year 2004 implying that the maximum

age that any firm can attain in our sample is 36 years. Book value data becomes available in Datastream only from

the year 1981, which practically becomes the beginning date for all of our empirical analyses based on M/B ratio.


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firms.3

The difference of 9.27% between the two median errors is statistically significant at 1%

level and can be interpreted as the cumulative amount of learning. This finding verifies an

important but untested assumption implicit in the Pastor and Veronesi (2003) learning model that

investor learn about firms profitability with advancement in its age. In Panel B, we establish that

the learning process is omnipresent in the worldwide sample of firms. The cumulative magnitude

of learning is 9.08% in the international sample is very similar to the US sample although in

international firms in any given age group have larger errors than the US firms.

[Insert Table 1 about here]

Another dimension of uncertainty about the firms profitability is its return volatility. We

compute return volatility as the standard deviation of monthly returns of each stock in each year

of observation. Panel C of Table 1 shows that return volatility declines with advancement is a

firms age. Median return volatility is 11.89% for 1 year old firms and 7.75% for 20 year old

firms. The difference between the two groups is 4.13%, which is statistically significant at the

1% level.

B.Country-wise analysis of the impact of the learning process on firms price multiples

The value implication of the learning process is declining M/B ratio with advancement in

a firms age, as stated in our second hypothesis, Table presents the median M/B ratio in 52

countries for firms in ages ranging from one to 10 ten years. Ratios for US firms, shown in bold

font, are reproduced from the Pastor and Veronesi (2003) article. Our international evidence is

consistent with the patterns found in their study and serve to establish the pervasive global

3 The forecast errors are low in the IPO year of the firms, jump up in year 2 and then decline monotonically giving

the error curve a hump-shape. Reasons for low errors in the IPO year could include the fact the costs in the projectbuild-up stage (when there are usually no revenues) could be easier to forecast than the combination of revenues and

costs in the subsequent years. The legal consequence of erroneous predictions in the IPO prospectus are also more

severe than the analyst forecast errors in subsequent errors.


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application of valuation implications of the learning model. For the overall sample, median M/B

of 1 year old firms is 1.99 compared to M/B of 1.27 for 10 year old firms. The difference of 0.72

between the two groups reported in the second last column is statistically significant at the 1%

level. We present country-wise data for each of the 52 countries. The countries are grouped into

developed and emerging markets based on the classification obtained from Morgan Stanley

Capital Internationals website at mscidata.com. The direction of the change in M/B is consistent

with the learning model in 46 out of the 52 countries or 88% of our sample and the difference is

statistically significant in 43 countries or 83% of the sample countries. The countries with some

of the biggest learning effects include Netherlands, Japan, and France among the developed

markets and Egypt, Thailand, and Morocco among the emerging markets. We corroborate these

country-wise findings on the valuation implications of the learning process in multivariate

settings in the following sections.


C. Multivariate analysis of the valuation implications of the learning model

Next we perform a international panel data regressions to confirm the significance of the

inverse relationship between M/B and firms age controlling for other factors that are known to

affect M/B4.:

log(M/B)i,t = a + b.AGEi,t + c.DDi,t + d.LEVi,t + e.SIZEi,t + f.ROEi,t + g.ROE(1)i,t

+ h.ROE(2)i,t + i.ROE(3)i,t + j.RET(1)i,t + k.RET(2)i,t + l.RET(3)i,t+ m.RET(3)i,t + i,t (6)

where i = 1- N, N is the # of firms in each year t. AGE is defined as - 1/ (1+ Firms Age) as this

specification captures the convexity in the relationship between M/B and firm age in accordance

with Pastor and Veronesis (2003) learning model. DD is the dividend dummy with value 1 for

4 For all regression analyses, we winsorize observations with any variable at the 1st and 99th percentile, to arrive at

the final sample. Firms which were born in the year 2002 onwards are also excluded as we need three years of data

for future ROE and annual stock returns.


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dividend paying firm and 0 otherwise. LEV is the debt ratio. SIZE is the natural log of the firms

total asset. Pastor and Veronesi (2003) utilize the Bayesian updating technique in their learning

model to predict the relationship between expected profitability (positive) and expected future

stock returns (negative) and Market-to-book ratio. In our regression, ROE is the return on equity

and regressed up to three years following year t. RET is future annual stock return up to three

years from current period. The standard errors are clustered by firm to take into account residual

dependence created by firm effect, as suggested by Petersen (2009).5

The results are provided in

Table 3.


The global prevalence of inverse relationship between M/B ratio and the firms age

implied by the learning model is confirmed in the regression framework where the coefficient on

age is -0.81 with a t-statistics of -17.22, which makes it highly significant at the 1% level. The

magnitude of the coefficient compares well with the benchmark for NYSE stocks during 1962 to

2000 analyzed by Pastor and Veronesi (2003). They report the AGE coefficient of -0.71 which

translates into a economically significant 12.5 percent difference in valuation between the one

and two years old firm. The coefficients on control variables are in the expected direction. There

is a positive relation between future growth in profitability and the M/B ratio implying that

investors pay a higher price for firms with higher growth prospects (g). Future stock returns are

negatively correlated with current M/B ratio implying that investors are willing to accept a lower

equity premium (r) by paying a higher stock price today for the high M/B stocks. It is important

5 In line with Pastor and Veronesi (2003), we also perform Fama and Macbeth(1973) regression to deal withpotential time effect in our study and arrive at the same conclusion as clustered standard errors method reported in

the Tables. The Fama and Macbeth(1973) results are not presented for brevity but are available upon request.


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to note that the effect of uncertainty of profitability (2), measured through firm age, survives in

these regressions after controlling for these other important determinants of M/B ratio.

We also divide our sample into developed and emerging markets. The coefficient on age

is more negative in the emerging markets. One interpretation of this finding is that the lower

quality of disclosures in emerging markets increases the uncertainties about the cash flows of the

new companies. Therefore, investors have to learn more from the companys actual cash flows

than from the financial projections. The other interesting insight includes a stronger preference

for dividend payments in emerging markets. M/B ratio is 0.14 times higher for dividend paying

firms in emerging market relative to non-dividend paying firms. On the contrary, M/B ratio is

lower by 0.16 times for dividend paying firms in developed market relative to non-dividend

paying firms in those markets. Results in all panels of table 3 are consistent with our second

hypothesis.

D. Dividend vs Non-dividend paying firms

Dividend payments have two opposite effects on the M/B ratio in the context of learning.

The positive effect of dividend payments is a quicker reduction in the uncertainty about the cash

flows that investors receive from younger firms. A bird in hand is better than two in the bush.

The company can lose undistributed profits in future but it cannot reclaim distributed dividends

because of the limited liability feature. Moreover, managers tend to smooth dividend payments

over time. Thus, dividend policy can release information to the outside investors about

managements expectations of future profitability. These uncertainty dampening effects of

dividend payment would increase the correlation due to a higher speed of learning. However, the

opposite effect in the context of learning is that dividend payments reduce the firms growth rate

(Pastor and Veronesi (2003)) and thus convexity. Reduced uncertainty also lowers convexity and


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correlations of dividend paying firms. The net result of these two effects is an empirical issue

which we examine in Table 4.


An extended version of regression equation (6) is used to capture the incremental effect

of dividend payments on the learning process. We now have an interaction term between AGE

and dividend dummy, in addition to the other variables discussed previously. Overall, dividend

payment has an important role in determining the strength of the relationship between M/B and

the firms age. In developed markets, dividend payments weaken the convexity of the M/B to

age relationship, consistent with Pastor and Versoni (2003) prediction of lower growth rate of

dividend paying firms.The coefficient on the interaction term of age times dividend dummy is

positive 0.56 and statistically significant in Panel A for the entire sample and 0.60 and

statistically significant in Panel B for the developed markets. However, in emerging markets

dividend payments do not significantly affect the learning phenomenon. The interaction term has

an insignificant coefficient of 0.079 with a t-statistics of 0.43.It is possible that uncertainty

reduction effects of dividends in the emerging markets offset the growth dampening effect.

E. Analyst forecast errors and the learning environment

We now merge the firm-specific and country-specific information from the various data

sources i.e. I/B/E/S, Datastream International, and learning environment values for each country

for each year. The purpose of this exercise is to understand the incremental effects of each

variable and also how a change in a given feature of the learning environment affects the

learning process and its valuation implications. Only those firm-year or country-year

observations which are present in all three data sources are included.


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We test if the mean analyst error is related to a firms age. Table 5 presents several variations of

regression results all of which point to an inverse relationship between analyst errors and firms

age. Each observation represents a firm-year. Dependent variable is the analyst forecast error

throughout this table. Results in Panel A are based on all countries. The first row uses only firm

age as the explanatory variables; the second row adds control variables commonly used in

analyst forecast literature such as Thomas (2002); and the third row adds control variables from

the learning model. Results for sub-samples based on developed market are in Panel B, emerging

markets in Panel C, and just the U.S. in Panel D. The coefficient on age is negative and

statistically significant in each row. This finding is consistent with the learning curve stated in

the first hypothesis. The coefficients on the control variables are generally consistent with prior

literature and can be interpreted as follows. Firm size matters; the bigger the firm, the smaller are

the errors. Consistent with previous studies, analyst forecast errors decline with the number of

analysts, whereas they increase with leverage and return volatility. With all these control

variables included in the regression, the negative relationship between analyst forecast errors and

firm age is still significant at the 1% level. Our findings represent the first large scale worldwide

evidence of the existence of a learning curve for the equity analysts.


Next, we turn our attention to three different features of the learning environment in each

country to analyze how they affect the learning process. These features are a history of actual

enforcement and convictions under laws prohibiting insider trading laws, feasibility of executing

short sales by investors who possess negative information but do not own the stock, and above

median involvement of sophisticated foreign institutional traders in a country. For each feature,


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we define a learning environment indictor dummy variable (E) and assign it the value of 1 for the

country-years when that feature is present and 0 if it is absent.

There is a rich cross-sectional variation as well as time-series variation in the learning

environment features across countries. At the beginning of our sample in 1981, the proportion of

firms from countries where prohibition against insider trading was enforced is 11% and this

proportion increases to 84% by 2004. The proportion of firms from countries where short selling

is feasible changes from 96% in 1981 to 72% in 2004. Although this statistic might seem odd,

but it is a manifestation of the typical regulatory response to major market crashes. Thus, not

many countries thought about restricting short sales until 1987, when a host of restrictions were

considered by the regulators. Many restrictions are removed after a significant amount of time

elapses after a major crash. Thus, short selling feasibility has a tremendous amount of cross

sectional and time series variation in our sample. Finally, the proportion of firms-years where

foreign institutional holding is above median is constant at 50% for each year by definition.

We estimate the analyst forecast error regressions similar to Table 5 again but after

adding the interactive learning environment * age variables among the explanatory variables and

report the results in Table 6. Separate regressions are estimated to assess the effect of each

learning environment variable. Negative coefficient should be interpreted as faster speed of

learning.


Enforcement of insider trading law speeds up the rate of decline of analyst forecast errors

with advancement in a firms age. Thus, the incentives for outside analysts to generate profitable

information outweigh any information losses from eliminating corporate insiders from the price

discovery process. Regulators should, therefore, enact insider trading prohibitions and


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enthusiastically prosecute insider trading cases. Feasibility of executing short sell transactions is

another market environment feature that speeds up the rate of decline of analyst forecast errors

with advancement in a firms age. This finding is consistent with our hypothesis that short selling

creates bigger incentives for learning about both positive and negative information about a firms

profitability. In contrast, the involvement of foreign institutional investors appears to generates

higher errors for older firms, which is inconsistent with the notion of a learning curve. Thus, it

appears that the noise introduced by factors such as capital flight, language barriers, or deviation

of systemic or idiosyncratic foreign factors from domestic factors outweigh the sophistication

and skill that foreign investors might bring to a stock research in a given country.

F. Valuation implications of the learning environment and the speed of learning

Finally, we estimate an incremental effect regression model similar to one proposed by He and

Ng (1998) to investigate the incremental effect of learning environments on learning speed,

which is captured by the negative and convex relationship between the rate of change in M/B

ratio (i.e., log(M/B)t - log(M/B)t-1) and the firms age :

Rate of Change of Valuation = cd0E + cd1E AGEi + cd2E DIVi + cd3E.LEVi +cd4E.SIZEi + cd5E ROEi + cd6E ROE(1)i + cd7E ROE(2)i + cd8E ROE(3)i + cd9E

RET(1)i + cd10E RET(2)i + cd11E RET(3)i + c0 + c1AGEi + c2DDi + c3LEVi +

c4SIZEi + c5ROEi + c6ROE(1)i + c7ROE(2)i + c8ROE(3)i + c9RET(1)i + c10RET(2)i +c11D RET(3)i + i

where (i = 1- N, N is the # of firms). AGE is defined as - 1/ (1+ Firms Age), which captures the

convex relationship between M/B and firm age in the Pastor and Veronesi (2003) learning

model. DIV is the dividend dummy with value 1 for dividend paying firm and 0 otherwise. LEV

is the debt ratio. SIZE is the natural log of the firms totol asset. ROE is the return on equity and

regressed up to three years following year t. RET is future annual stock return up to three years

from current period. E represents learning environment dummy variable. E equals 1 for better


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learning environment and 0 otherwise as described in the previous section. All variables of firms

in each country are measured in its own currency. There are 9,640 firms (68,034 firm-year

observations) with valid data for the whole sample, and 6,631 and 3,009 representing developed

and emerging markets respectively. All t-statistics reported in parentheses are based on clustered

standard errors as suggested by Petersen (2009). For brevity, we report only three regression

coefficients, AGE, E, and AGE*E. Panel A is based on full dataset whereas Panel B and Panel C

are based on developed market and emerging market sub-samples, respectively. Three separate

regressions are reported in each panel, one for each learning environment.


The negative coefficient on AGE variable in every panel establishes the inverse and

convex relation between M/B and firm age. The second column is simply the direct effect of a

particular type of regulation on valuation. Note that the coefficients in the second column only

capture the level of valuations and are not related to the learning model because the learning

model requires an interaction with firm age. From the overall sample used in Panel A, we

observe that all three features of the learning environment have positive coefficient. Thus, we

conclude enforcement of insider trading laws, feasibility of short selling transaction, increased

presence of foreign institutional investors, are all associated with higher stock valuations. Thus

in the valuation sense, these features are good. However, to assess the impact of these features on

the speed of learning and change in M/B, we interact firm age with learning environment

dummy. The coefficients are in the third column. Negative coefficients represent faster speed

according to the learning model. Negative coefficient suggests that enforcement of insider

trading speeds up the learning process whereas positive coefficients for short selling and foreign

investors suggest that those features actually slow the learning process. In Panel B for developed


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20

markets and Panel C for emerging markets, we see that the results are consistent with overall

results for speedier learning with enforcement of insider trading and slower learning with above

median foreign institutional investors. However, the short selling activities have opposite effects

in developed versus emerging markets. Short selling appears to speed up the learning process

only in developed markets. In emerging markets short selling is slowing the learning process

through additional noise. The overall conclusion from the table is consistent with our third

hypothesis which states that various features of learning environment matter as determinants of

the speed of learning.

G. Robustness tests

We conduct several robustness checks to verify that our findings can be generalized.

Tables are not included in the manuscript for brevity but will be available from authors or data

section of the journal website, if this facility is provided. Earlier in Tables 1 and 2, we showed

that the decline in analyst forecast errors and M/B ratio with the advancement of firms age is

pervasive across markets and countries. We also conduct year-by-year analysis and find that the

in any given year, the analyst forecast errors and M/B ratio are lower for older firms than those

for younger firms.

III. Conclusion

This paper provides pervasive global evidence consistent with an intriguing valuation

theory that takes into account a learning curve for stock market analysts and investors. The


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21

valuation model proposed by Pastor and Veronesi (2003) assumes that investors face significant

uncertainty about profitability and cash flows of young firms. Unlike the traditional focus of

asset pricing models on the denominator or discount rates, they focus on how the uncertainty

affects the expected value of cash flows in the numerator. The convex nature of valuation

equation implies that the uncertainty actually increases the M/B. However, as time passes,

investors learn about the true potential of firms profitability and resolve the uncertainty. Thus,

the model predicts that M/B is higher for younger firms than for older firms.

In this paper, we provide a comprehensive empirical analysis of this issue in a global

setting. By directly showing that analyst forecast errors decline with advancements in a firms

age, we provide an important link in the learning theory not yet tested globally in the empirical

literature. Return volatility of stocks in global, developed, and emerging markets also decreases

with a firms age. Next, we present pervasive international evidence of the valuation implications

of the learning theory from firms listed in 52 stock exchanges around the world. Over eighty

percent of the countries have statistically significant valuation changes consistent with the

learning theory. This inverse and convex relationship between M/B and firm age is statistically

significant in the regression framework after controlling for other factors known to determine the

market-to-book ratio such as future growth potential and expected equity premium. The inverse

relationship between M/B and firms age is also more striking for non-dividend paying firms.

We extend the concept of speed of learning beyond firm specific characteristics to

understand the impact of diverse market designs and legal frameworks on the learning process.

The three key learning environment features included in our analysis are stricter enforcement of

laws prohibiting insider trading laws, ever changing restrictions on short-selling constraints, and

increased involvement of sophisticated foreign institutional traders in global markets.


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22

Enforcement of insider trading law speeds up the rate of decline of analyst forecast errors with

advancement in a firms age, and firms are valued at their long run equilibrium values more

quickly. Thus, the incentives for outside analysts to generate profitable information outweigh any

information losses from eliminating corporate insiders from the price discovery process.

Regulators should, therefore, enact insider trading prohibitions and enthusiastically prosecute

insider trading cases. Feasibility of executing short sell transactions is another market

environment feature that speeds up the rate of decline of analyst forecast errors with

advancement in a firms age. This finding is consistent with our hypothesis that short selling

creates bigger incentives for learning about both positive and negative information about a firms

profitability. However, the short selling activities have opposite valuation effects in developed

versus emerging markets suggesting that this feature might be introduce more noise in emerging

markets outweighing the uncertainty resolution effects. Finally, the involvement of foreign

institutional investors appears to generate higher errors for older firms, which is inconsistent with

the notion of a learning curve. Thus, it appears that the noise introduced by factors such as

capital flight, language barriers, or deviation of systemic or idiosyncratic foreign factors from

domestic factors outweigh the sophistication and skill that foreign investors might bring to a

stock research in a given country. When domestic investors dominate, learning speed is faster

and M/B valuation ratio approaches long term equilibrium faster.

rmFuture research can explore additional determinants of the speed of learning and also

develop more advanced theoretical constructs for this concept. The results in this paper implore

that asset pricing models include the learning curve as an important factor. The learning process


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23

about firms profitability has important implications for stock valuation in countries around the

world.

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Shefrin, Hersh and Meir Statman, 1995, Making Sense of Beta, Size, and Book-to-market,

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TABLE 1. The Learning process: Declining uncertainty with advancement in firms age

We obtain mean analyst forecasted EPS and actual EPS for each firm from I/B/E/S international dataseforecast errors is computed as the absolute difference between mean forecast and actual EPS, divided b

Data are winsorized at 1st

percentile and 99th

percentile to eliminate potential outliers and input errors. T

in the database is used as a proxy for the firms year of birth for the purposes of calculating its age. Each

are grouped together. Panel A used only 12,453 US firms and shows the median analyst forecast errors group. Panel B repeats the analysis including 21,271 international firms. We obtain total monthly return

Datastream International. Return volatility is computed as the standard deviation of monthly returns of observation. Panel C presents the median return volatility for firms within each age group. Total learnin

year 1 and year 20. Asterisks indicate statistical significance at 1% level with ***.

Panel A. Declining Analyst Forecast Errors for US Firms

Firm Age in Years 1 2 3 4 5 6 7 8

Forecast Error 20.69% 29.27% 31.56% 31.79% 30.77% 27.45% 23.99% 25.00%

Firm Age in Years 11 12 13 14 15 16 17 18

Forecast Error 19.40% 19.12% 18.58% 17.32% 15.75% 16.34% 15.69% 13.63%Total Learning 9.27%***

Panel B: Global Evidence on Declining Analyst Forecast Errors


Forecast Error 23.08% 31.92% 33.33% 33.56% 34.49% 32.26% 30.83% 30.79%

Firm Age in Years 11 12 13 14 15 16 17 18

Forecast Error 23.19% 23.57% 24.64% 24.14% 22.05% 24.73% 23.19% 18.88%

Total Learning 9.08%***

Panel C: Declining Return Volatility for Firms around the World


Return Volatility 11.89% 11.32% 11.28% 11.19% 10.75% 10.64% 10.29% 9.95%

Firm Age in Years 11 12 13 14 15 16 17 18

Return Volatility 10.02% 9.89% 9.17% 9.74% 9.38% 8.59% 8.38% 9.03%Total Reduction 4.13%***


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TABLE 2. Country-wise analysis of the impact of the learning process on firms price multip

This table presents the medianmarket to book ratio (Datastream mnemonic MTBV) in 52 countries for

one to 10 ten years. The last column shows the number of firms for which Datastream has MTBV. Data

percentiles of MTBV to remove potential outliers and data entry errors. Sample period ranges from 198

appearance of a firm is used as a proxy for the firms years of birth for the purposes of calculating its agshown in bold font, are reproduced from the Pastor and Veronesi (2003) article. Cumulative effect of le

as the difference between year 1 M/B and year 10 M/B. We indicate statistical significance of the differ

levels with ***, **, *, respectively.

Age 1 2 3 4 5 6 7 8 9 10

Cum

effec

learn

valua

All firms 1.99 1.79 1.62 1.55 1.43 1.43 1.41 1.37 1.30 1.27 0

Panel A: Developed markets

Australia 1.68 1.55 1.38 1.40 1.20 1.48 1.43 1.56 1.36 1.33 0

Austria 1.49 1.31 1.12 1.23 1.30 1.27 0.98 1.03 0.98 0.97 0

Belgium 1.62 1.30 1.18 1.24 1.15 1.18 1.05 1.18 0.97 1.03 0

Canada 1.54 1.56 1.49 1.55 1.50 1.50 1.61 1.71 1.60 1.56 -0

Denmark 1.22 1.28 1.07 1.13 0.99 1.14 1.08 1.01 0.96 0.99 0

Finland 1.50 1.23 1.22 1.31 1.35 1.33 1.3 1.22 1.22 1.39 0

France 2.29 1.90 1.60 1.40 1.40 1.30 1.19 1.09 1.16 1.23

Germany 2.37 1.95 1.55 1.32 1.33 1.69 1.90 2.10 2.11 1.86 0

Hong Kong 1.71 1.40 1.20 1.08 0.90 0.87 0.87 0.75 0.81 0.81 0

Ireland 1.81 1.71 1.55 1.61 1.59 1.64 1.58 1.87 1.79 1.88 -0

Italy 1.60 1.51 1.40 1.39 1.15 1.05 0.94 0.99 1.00 0.91 0

Japan 2.42 2.27 1.98 1.79 1.53 1.48 1.54 1.40 1.37 1.17

Luxemboug 0.87 1.40 1.26 1.19 1.44 1.88 1.81 1.34 1.05 0.90 -0

Netherlands 2.63 2.46 2.01 1.795 1.74 1.53 1.77 1.73 1.28 1.12


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TABLE 2. ..Continued.

Age

1 2 3 4 5 6 7 8 9 10

New Zealand 1.36 1.22 1.07 1.02 1.02 1.03 1.04 1.31 1.35 1.30 0Norway 1.50 1.30 1.30 1.26 1.32 1.31 1.28 1.29 1.38 1.25 0Portugal 1.84 1.76 1.63 1.51 1.35 1.30 1.28 1.00 1.09 1.20 0Singapore 2.02 2.01 1.72 1.80 1.71 1.36 1.52 1.42 1.44 1.30 0Spain 1.83 1.66 1.55 1.30 1.37 1.30 1.43 1.41 1.48 1.66 0Sweden 2.07 1.99 1.90 1.70 1.45 1.55 1.59 1.55 1.49 1.61 0

Switzerland 1.50 1.31 1.17 1.19 1.11 1.07 1.16 1.11 1.11 1.04 0

UK 2.61 2.22 1.97 1.83 1.79 1.75 1.70 1.68 1.66 1.76 0

USA 2.25 1.80 1.57 1.49 1.39 1.38 1.35 1.33 1.27 1.25 1

Panel B: Emerging markets

Argentina 1.22 1.00 1.04 1.34 1.19 0.84 0.78 0.58 0.4 0.65 0.

Brazil 0.83 1.28 0.82 0.75 0.92 0.97 0.95 0.93 0.66 0.585 0.

Chile 1.18 1.12 1.63 1.43 1.28 1.44 1.33 1.08 0.97 0.74 0.

China 3.33 2.70 2.50 2.75 2.76 2.56 2.58 3.10 3.31 3.01 0.

Colombia 1.09 0.72 0.74 0.65 0.43 0.53 0.56 0.51 0.48 0.545 0.

Czech Rep. 0.77 1.20 1.01 0.66 0.74 0.5 0.45 0.54 0.82 0.42 0.

Egypt 2.34 2.87 1.63 1.6 1.27 0.93 0.82 1.04 0.5 0.34 2.

Ethiopia 2.5 1.47 2.1 2.78 2.85 2.5 1.47 2.1 2.14 2.99 -0.Greece 2.53 2.23 1.89 1.91 1.42 1.53 1.35 1.25 1.22 1.98 0.

Hungary 1.49 1.06 1.20 1.10 0.96 0.9 0.94 0.94 0.78 0.74 0.

India 1.84 1.85 2.01 2.16 2.12 2.23 1.54 1.34 1.04 0.89 0.


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TABLE 2. ..Continued.

Age

1 2 3 4 5 6 7 8 9 10

C

iv

Indonesia 1.69 1.28 1.18 1.25 0.93 0.88 0.83 0.79 0.75 1.15 0.

Israel 1.94 1.84 2.31 1.86 1.51 1.16 1.54 1.92 1.61 1.83 0

Korea 1.45 1.26 1.06 0.87 0.83 0.91 0.78 0.69 0.61 0.66 0.

Malaysia 1.47 1.44 1.42 1.50 1.28 1.17 1.12 1.29 1.41 1.40 0

Mexico 1.23 1.49 1.25 1.19 1.13 1.19 1.11 0.97 1.06 1.00 0.

Morocco 2.98 2.07 2.40 2.01 2.03 2.31 2.10 1.74 1.62 1.48 1.

Pakistan 1.81 2.61 1.96 1.18 0.93 1.00 0.78 0.83 1.01 0.92 0.

Peru 1.31 1.08 1.18 1.10 1.06 0.95 0.84 0.81 0.75 0.77 0.

Philippines 1.86 1.75 1.39 1.21 1.29 1.24 1.14 1.02 0.81 0.8 1.

Poland 1.13 1.10 1.12 1.20 1.12 1.33 1.28 1.31 1.04 1.00 0.

Russia 0.39 0.30 0.62 0.31 0.43 0.37 0.36 0.30 0.65 0.19 0.

South Africa 2.31 2.22 1.61 1.19 1.25 1.32 1.25 1.44 1.03 1.16 1.

Sri Lanka 1.65 2.16 0.98 1.16 1.49 0.94 1.89 1.72 1.07 1.03 0.

Taiwan 2.46 2.31 1.96 1.67 1.61 1.67 1.59 1.46 1.43 1.30 1.

Thailand 2.64 2.33 2.08 1.61 1.57 1.52 1.28 1.13 1.01 1.06 1.

Turkey 1.35 1.91 1.34 1.58 1.25 1.43 1.37 1.29 1.40 1.85 -0.

Venezuela 0.21 0.49 0.41 0.37 0.85 0.42 0.53 0.58 0.22 0.27 -0.

Zimbabwe 1.27 1.24 0.83 1.01 0.62 0.485 0.51 0.285 0.31 0.38 0.

Number (and percentage) of countries with year 1 M/B higher than year 10 M/B Number (and percentage) of countries with statistically significant M/B changes


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Table 3. Clustered standard errors regression analysis on M/B ratio and the firms age for globa

The following panel regression is estimated on pooled dataset while using clustered standard errors as

log(M/B)i,t = a + b.AGEi,t + c.DDi,t + d.LEVi,t + e.SIZEi,t + f.ROEi,t + g.ROE(1)i,t + h.ROE(2)i,t + i.ROE(3)i,t

+ j.RET(1)i,t + k.RET(2)i,t + l.RET(3)i,t + i,t

where (i = 1- N, N is the # of firms). M/Bi,t is the market to book ratio for firm i in period t. AGE is de

Age), which captures the convex relationship between M/B and firm age in the Pastor and Veronesi (2

is the dividend dummy with value 1 for dividend paying firm and 0 otherwise. LEV is the debt ratio. Sthe firms totol asset. ROE is the return on equity and regressed up to three years following year t. RE

return up to three years from current period. i,t is the error term, which we cluster in SAS using proc sfirms in each country are measured in its own currency. These regressions are based on 10,656 interna

historic data for all variables in the model are available in Datastream. All t-statistics are reported in p

Intercept AGE DD LEV SIZE ROE ROE(1) ROE(2) ROE(3) RET(1) RET

Panel A: All firms

Coefficient 0.203 -0.813 -0.052 0.026 0.009 0.241 0.569 0.413 0.114 -0.373 -0.3

T-statistics (5.02) (-17.22) (-3.07) (0.53) (3.70) (9.74) (19.73) (18.92) (6.86) (-45.04) (-38

Panel B: Developed markets firms

Coefficient 0.177 -0.686 -0.145 0.072 0.018 0.258 0.538 0.400 0.113 -0.364 -0.2

T-statistics (4.02) (-13.00) (-7.09) (1.34) (7.21) (8.66) (15.05) (15.07) (5.42) (-36.23) (-28

Panel C: Emerging markets firms

Coefficient 0.569 -1.673 0.136 -0.129 -0.039 0.268 0.670 0.448 0.112 -0.413 -0.3

T-statistics (5.58) (-16.61) (5.08) (-1.23) (-6.37) (7.07) (15.92) (12.34) (4.24) (-30.32) (-29


31/35

Table 4. The effect of dividend payment on the learning process and price multiples

The following panel data regression is estimated on pooled dataset while using clustered standard erroPetersen (2009):

log(M/B)i,t = a + b.AGEi,t + c.DDi,t + e.DD.AGEi,t + f.LEVi,t + g.SIZEi,t + h.ROEi,t + i.RO+ k.ROE(3)i,t + l.RET(1)i,t + m.RET(2)i,t + n.RET(3)i,t + i,t

The term DD.AGE captures the incremental effect of dividend payment on the learning process. Rest

definitions from the previous table. The dataset is identical to one used in the previous table.Intercept AGE DD AGE.DD LEV SIZE ROE ROE(1) ROE(2) ROE(3) RET(1)

Panel A: All Sample

Coeff. 0.134 -1.218 0.04 0.564 0.027 0.009 0.239 0.569 0.413 0.113 -0.373

T-stat (3.06) (-12.97) (1.50) (19.78) (0.55) (3.69) (9.73) (19.78) (18.97) (6.83) (-45.07)

Panel B: Developed market firms

Coeff. 0.103 -1.129 -0.05 0.60 0.072 0.018 0.257 0.539 0.401 0.113 -0.364

T-stat (2.13) (-10.10) (-1.59) (5.07) (1.33) (7.23) (8.69) (15.12) (15.14) (5.42) (-36.30)

Panel C: Emerging market firms

Coeff. 0.561 -1.722 0.15 0.079 -0.129 -0.039 0.267 0.67 0.448 0.112 -0.413

T-stat (5.23) (-11.13) (3.19) (0.43) (-1.23) (-6.37) (7.06) (15.93) (12.34) (4.24) (-30.24)


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Table 5. The learning process and declining analyst forecast errors with advancement in firms age

The following OLS regression is estimated to capture the relationship between analyst forecast errors and

FEi,t = a + b.AGEi,t + c.SIZEi,t + d.LEVi,t + e.RETVOLi,t + f.NUMESTi,t + g.DDi,t + h.ROj.ROE(2)i,t + k.ROE(3)i,t + l.RET(1)i,t + m.RET(2)i,t + n.RET(3)i,t + i,t

where (i = 1- N, N is the # of firms). White-adjusted standard errors are used to calculate t-statistics reFE is analyst forecast error which is calculated as the (absolute) difference between median forecasted

reported EPS scaled by the year-end stock price. AGE is defined as the natural log of age (first year o

Datastream dataset). LEV is the debt ratio. SIZE is the natural log of the firms total asset. RETVOL iof each stock as defined by the standard deviation of monthly returns during the observation year. NU

analysts involved in predicting EPS for the relevant period (i.e., analyst coverage). There are 14,594 f

our Datastream-IBES merged sample.

Intercept AGE SIZE LEV RET

VOL

NUMEST DD ROE ROE(1) ROE(2) ROE(3) RE

Panel A: All Firms

Coeff

T-Stat

0.093

(19.70)-0.018

(-8.57)

Coeff

T-Stat

0.07

(6.23)-0.014

(-6.18)

-0.001

(-1.32)

0.007

(0.68)

0.402

(13.5)

-0.002

(-6.92)

Coeff

T-Stat

0.092

(7.99)-0.013

(-5.86)

-0.013

(-5.86)

0.002

(0.17)

0.32

(10.1)

-0.002

(-6.35)

-0.005

(-1.14)

-0.071

(-9.30)

-0.053

(-6.17)

-0.012

(-1.47)

-0.0004

(-0.05)

0.0

(3.5

Panel B: Developed markets

CoeffT-Stat 0.087(18.69) -0.016(-7.67)

Coeff

T-Stat

0.063

(5.66)-0.012

(-5.43)

-0.001

(-0.90)

0.012

(1.23)

0.377

(12.5)

-0.002

(-7.43)

Coeff

T-Stat

0.09

(7.73)-0.011

(-5.04)

-0.001

(-0.93)

0.005

(0.54)

0.284

(8.78)

-0.002

(-6.80)

-0.008

(-1.92)

-0.071

(-9.44)

-0.052

(-6.02)

-0.011

(-1.40)

-0.001

(-0.14)

0.0

(2.7


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Table 5. Continued

Intercept AGE SIZE LEV RET

VOL

NUMEST DD ROE ROE(1) ROE(2) ROE(3) RE

Panel C: Emerging markets

Coeff

T-Stat

0.395

(5.96)-0.141

(-4.12)

Coeff

T-Stat

0.729

(5.18)-0.136

(-4.00)

-0.0229

(-3.18)

-0.186

(-1.25)

0.587

(2.58)

0.002

(0.85)

CoeffT-Stat

0.711(4.61)

-0.132

(-3.88)

-0.027(-2.89)

-0.142(-0.95)

0.493(2.12)

0.002(0.80)

-0.001(-0.02)

-0.13(-1.73)

-0.05(-0.77)

-0.048(-0.64)

0.046(0.49)

0.1(3.2

Panel D: US market

Coeff

T-Stat

0.066

(19.70)-0.015

(-9.69)

CoeffT-Stat 0.035(4.03) -0.014(-8.91) 0.0003(-0.45) 0.043(5.93) 0.269(11.2) -0.001(-4.49)

Coeff

T-Stat

0.048

(5.31)-0.013

(-8.31)

0.0001

(0.12)

0.38

(5.25)

0.241

(9.47)

-0.001

(-3.89)

-0.005

(-1.49)

-0.056

(-9.80)

-0.019

(-2.91)

-0.009

(-1.40)

0.002

(0.43)

0.0

(2.5


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Table 6. Learning environment and analyst forecast errors

The following simple ordinary least squares regression model is estimated to capture therelationship between analyst forecast errors and firm age:

FEi,t = a + b.Sizei,t + c.Leveragei,t + d.Return Volatilityi,t + e.Number of Analystsi,t

+ f.ENVIRONMENT.AGEi,t + g.ANTI.ENVIRONMENT.AGE i,t + i,t

where FEi,t is analyst forecast error for firm i in yeart, which is calculated as the (absolute)

difference between median forecasted EPS and the actually reported EPS scaled by theactual EPS. Size is the natural log of the firms total asset. Leverage is the debt ratio.

Return Volatility is simple return volatility of each stock as defined by the standard

deviation of monthly returns during the observation year. Number of analysts involved inpredicting EPS for the relevant period is the proxy for analyst coverage. Age is defined as

the plain age (first year of appearance on the Datastream dataset). Separate regressions are

estimated for each learning environment. For insider trading law, Environment equals 1 if

insider trading law is enforced and 0 otherwise. Anti.Environement is the complement ofenvironment. Both environment and anti.environment are interacted with AGE. The

interactive variables are computed analogously for the other two learning environment

features. For shortsell feasibility, environment equals 1 if shortselling transactions areallowed and 0 otherwise. For foreign trading, environment equals 1 if foreign institutional

traders are active in the countrys market and 0 otherwise. All variables of firms in each

country are measured in its own currency. There are 20,416 firm-year observations in ourDatastream-IBES merged sample. We use White-adjusted standard errors to calculate t-

statistics reported in the parentheses.

Analyst Errors

when Insider

trading law is

enforced

Analyst Errors

when Shortselling

is feasible

Analyst Errors

when Foreign

trading is above

median

Intercept 0.115(9.50)

0.114(9.41)

0.116(9.79)

Size -0.016

(-8.28)

-0.015

(-7.46)

-0.016

(-8.47)

Leverage 0.031(3.67)

0.027(3.19)

0.046(5.51)

Return Volatility 0.516

(21.12)

0.493

(19.99)

0.475

(19.87)

Number of Analysts -0.001

(-3.94)

-0.001

(-4.55)

-0.001

(-2.54)

ENVIRONMENT*AGE -0.005

(-2.62)

-0.006

(-3.24)

0.101

(37.85)

ANTI.ENVIRONMENT*AGE

0.077(16.91)

0.062(12.15)

-0.010(-5.24)

Adjusted R-squared

0.05 0.04 0.10


35/35

Table 7. Incremental effect regression: Impact of learning environments on valuation

We defineEquilibrium Valuation Speedas the rate of change of M/B ratio (i.e.,log(M/B)t - log(M/B)t-1)and regress it on various explanatory variables focusing on

firms age and the learning environment per the methodology of He and Ng (1998):

Equilibrium Valuation Speed = c0 + c1Agei + cd0Environment + cd1Environment*Agei+ c2DDi + c3LEVi + c4SIZEi + c5ROEi + c6ROE(1)i + c7ROE(2)i + c8ROE(3)i

+ c9RET(1)i + c10RET(2)i + c11D RET(3)i + cd2E DIVi + cd3E.LEVi + cd4E.SIZEi

+ cd5E ROEi + cd6E ROE(1)i + cd7E ROE(2)i + cd8E ROE(3)i + cd9E RET(1)i+ cd10E RET(2)i + cd11E RET(3)i + i

where Age and other base variables retain their definitions from Table 3 and interactive

variables are obtained by multiplying the value of Environment variable with the basevariable. Environment (E) represents the learning environment indicator variable as

defined in Table 6. For example, E equals 1 if insider trading law is enforced in a given

country in a given year and 0 otherwise and then that value is assigned to all applicablefirm-year observations. All variables of firms in each country are measured in its own

currency. There are 9,640 firms (68,034 firm-year observations) with valid data for thewhole sample, and 6,631 and 3,009 representing developed and emerging marketsrespectively. For brevity, we report only three regression coefficients, AGE, E, and

AGE*E. All t-statistics reported in parentheses are based on clustered standard errors as

suggested by Petersen (2009).

Valuation Speed AGE E = Learning

environment

E*AGE Adj. R2

Panel A: All countriesInsider trading law enforced -0.217

(-7.02)

0.039(1.71)

-0.16(-3.97)

0.27

Shortselling feasible -0.631(-12.63)

0.068(1.81)

0.338(6.22)

0.27

Foreign trading -0.449(-16.65)

0.108(3.68)

0.169(4.20)

0.27

Panel B: Developed markets

Insider trading law enforced -0.078

(-2.88)

0.095

(3.40)-0.237(-6.32)

0.29


0.03(0.06)

-0.253(-3.25)

0.28

Foreign trading -0.389

(-14.25)

0.018

(0.59)0.188(4.68)

0.29

Panel C: Emerging marketsInsider trading law enforced -0.477

(-6.03)

-0.346(-4.03)

-0.398(-3.18)

0.24


-0.112(-0.85)

0.518(3.13)

0.22

Foreign trading -0.707(-9.14)

0.67(5.28)

0.002(0.01)

0.25

speed of learning and price multiples

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