investor clienteles and industry factor-price exposure - … · 2010-09-26 · investor clienteles...
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Investor clienteles and industry factor-price exposure
Phil Davies Tippie College of Business
The University of Iowa W334 Pappajohn Business Building
Iowa City, IA 52242-1994 (319) 248-9458
Bernadette A. Minton Fisher College of Business The Ohio State University
834 Fisher Hall 2100 Neil Avenue
Columbus, OH 43210 (614) 688-3125
Catherine Schrand The Wharton School
University of Pennsylvania 1316 SH-DH
Philadelphia, PA 19104 (215) 898-6798
January 2010 The authors thank The Global Association of Risk Professionals (GARP) for funding. We are especially grateful to Brian Bushee for providing his data on institutional ownership classifications. We thank Paul Zarowin and seminar participants at New York University, the London Business School, INSEAD, the University of Rochester, and Southern Methodist University for helpful suggestions on an earlier version of the paper, and David Barker, Matt Billett, Brian Bushee, Eric Lie, Anand Vijh, and seminar participants at the University of Iowa for comments on this version. Minton acknowledges financial support from the Dice Center for Research in Financial Economics.
Investor clienteles and industry factor-price exposure
Abstract
We find robust evidence of investor clienteles for industry factor-price exposure: Investor interest, measured using share turnover and the number of institutions that hold a firm’s stock, is positively associated with stocks’ industry exposure, and institutional investors systematically overweight (underweight) high (low) industry exposure stocks in their portfolios. Clientele effects are most pronounced in industries in which return correlation with the aggregate market is low, where the benefits from learning about industry risk and from substituting investment in high-exposure stocks for investment in the industry assets are greatest. Clientele effects are strongest among small, transient, institutional investors. Contrary to traditional views of risk management, our findings suggest that market frictions may create incentives for some firms to not hedge to attract liquidity.
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In this paper, we document that investors display preferences for stocks with industry
factor-price exposure (hereafter “industry exposure”), measured as the sensitivity of a firm’s
stock return to industry returns after controlling for market returns. During the period from 1984
to 2006, a stock with high industry exposure experiences 50% higher share turnover, 13% more
institutional investors, and 14% more mutual fund owners than a stock with low industry
exposure after controlling for other established determinants of these proxies for investor
interest. Moreover, across all industries, institutions systematically overweight high industry
exposure stocks and underweight low industry exposure stocks in their portfolios.
The preference for industry exposure is inconsistent with asset allocation models based
on perfect and complete capital markets. In such markets, investors will not display preferences
for individual stocks with particular characteristics. However, when the assumptions of perfect
and complete capital markets are relaxed, investors may rationally exhibit preferences for stocks
with particular characteristics. For example, investors display preferences for domestic stocks
(French and Poterba, 1991), particular industries (Kacperczyk, Sialm, and Zheng, 2005), and
dividend payout policies (Graham and Kumar, 2006). This paper is the first to propose and
document investor clienteles associated with industry factor-price exposure.
Understanding investors’ attraction to industry exposure is important because it helps to
explain the determinants of investors’ portfolio allocation decisions. Specifically, we shed light
on the importance of two market frictions, information acquisition costs and incomplete markets.
In addition, understanding investor preferences for industry exposure has implications for how
we think about firms’ risk management decisions. Our analysis suggests that investor
preferences may create incentives for some firms to not hedge and instead to remain exposed to
the underlying assets of the industry in order to obtain the liquidity benefits associated with
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greater investor interest. This prediction is noteworthy because it is opposite to the predictions
from existing optimal hedging theories which suggest that market imperfections increase
incentives for firms to manage risk.1 Our consideration of the effect of investor preferences on
corporate hedging decisions is new, but the general notion that firms might cater to investor
preferences when making corporate decisions is well established. Examples include catering
dividend policy decisions to dividend clienteles and catering cross-listing decisions to investors
in other countries because of home bias. By characterizing the extent and nature of investor
preferences for industry exposure, this study is the necessary first step in understanding firms’
decisions to cater hedging decisions to such preferences.2
One class of existing models that provides an explanation for investor clienteles for
industry exposure assumes that information acquisition is costly, specifically in the sense that
learning is constrained. Investors optimally acquire information (i.e., learn) about a single
underlying risk factor, such as industry risk, and apply that knowledge to invest in stocks that are
exposed to the risk factor (Van Nieuwerburgh and Veldkamp, 2010). Investors trade off the
benefits of returns to private information against the cost of under-diversification in their
portfolio. This investor learning hypothesis has been used to predict investor clienteles in asset
1See, for example, Myers (1977), Smith and Stulz (1985), Froot, Scharfstein, and Stein (1993), and DeMarzo and Duffie (1995). A notable exception is Adam, Dasgupta, and Titman (2007) who develop a theoretical model in which the propensity to hedge is a function of the competitiveness of an industry. Ceteris paribus, there will be more heterogeneity in hedging policies in competitive industries. 2 The dividend clientele/dividend catering literature is similarly segregated. Some studies model investor preferences for dividends, analogous to our examination of investor preferences for exposure, based on the percentage of institutional ownership of stocks (Del Guercio, 1996; Brav and Heaton, 1998) or based on institutions’ portfolio allocations (Strickland, 1996; Hotchkiss and Lawrence, 2007). Other studies model dividend payout policy as a function of dividend clienteles (Li and Lie, 2006; Baker and Wurgler, 2004a, 2004b) which would be analogous to our suggested second step of examining firms’ hedging decisions, relying on the evidence about investors’ preferences to specify the model. Grinstein and Michaely (2005) examine institutional ownership of stocks and dividend policy simultaneously.
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characteristics, including home bias (Van Nieuwerburgh and Veldkamp, 2009), but it also
predicts investor clienteles for industry exposure.
Another explanation for investor clienteles for industry exposure is that high-exposure
stocks are a substitute for investment in an industry’s underlying assets. Most investors do not
hold the “assets” assumed in classic portfolio theory. Investment in certain assets, commodities
for example, is constrained by physical storage costs, asset indivisibility, or regulation. In the
absence of such market frictions, investors would include these assets in their optimal portfolio.
The market, however, is effectively incomplete with respect to these assets, which leads to a
second-best weighting in an investor’s portfolio. A stock that maintains exposure to the
underlying asset of an industry can act as a substitute for investment in the asset and effectively
complete the market. This asset substitution hypothesis is a previously unexplored explanation
for investor clienteles.
The main prediction of both the asset substitution hypothesis and the investor learning
hypothesis is that investor preferences for industry exposure will be greatest for stocks in
industries for which the returns have a low correlation with the returns on the aggregate stock
market. Under the asset substitution hypothesis, if industry returns are highly correlated with
market returns, an investor can simply invest in the market portfolio to obtain exposure to the
underlying asset. In addition, the portfolio diversification benefits of asset substitution are
minimal if the asset is highly correlated with the aggregate market. If, however, industry returns
are not highly correlated with returns on the market portfolio, the incentives to use stocks as a
substitute for the underlying asset are greater. Similarly, under the investor learning hypothesis
investors obtain few benefits from learning about industries with returns that are highly
correlated with the market. A signal about one industry that is highly correlated with the market
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is likely to contain information about other industries that are also highly correlated with the
market, thereby reducing the value of learning. In contrast, for industries with a low correlation
with the market, the returns to learning are greater. Thus, in equilibrium more investors will
learn about and invest in high industry exposure stocks in industries that are not highly correlated
with the market.
Using the average incremental adjusted R2 from adding industry factor returns to a
market model estimated within each of the 30 Fama-French industries, we identify five industries
whose returns have a significantly lower correlation with the aggregate stock market than the
others. We refer to the five industries (mining, coal, utilities, tobacco, and oil and gas) as “high
specificity” industries. The industries we identify as “low specificity” industries, which have
returns that are highly correlated with the stock market, include, for example, consumer goods,
wholesale, and services. Consistent with the asset substitution hypothesis and the investor
learning hypothesis, investor interest in stocks with high industry exposure is greatest in high
specificity industries. For example, a stock with high industry exposure has 30% more
institutional investors than a stock with low industry exposure stock within high specificity
industries, ceteris paribus. In contrast, this difference is just 5% in low specificity industries.
Across all industries, small institutional investors display stronger preferences for
industry exposure than large institutional investors, where institution size is based on the total
market value of an institution’s holdings. Within high specificity industries, institutions of all
sizes display significant preferences for industry exposure, although investor preferences for
industry exposure remain more pronounced for small institutions. These results are consistent
with the asset substitution hypothesis if smaller institutional investors are more wealth
constrained and hence more sensitive to asset indivisibility and storage costs. However, stronger
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preferences by smaller institutions are also consistent with the investor learning hypothesis if
institution size is positively correlated with information endowments or negatively correlated
with information acquisition costs. Under either scenario, smaller institutional investors have
greater incentives to reduce information costs by learning about an industry factor and investing
in stocks with high industry exposure.
Investment style also influences institutional investor preferences for industry exposure.
We use the Bushee (2001) investment style classifications. Across all industries we find that
transient investors, who hold small stakes in many firms and trade frequently on publicly
available information but who do not generally acquire private information, have the strongest
preferences for industry exposure. Quasi-indexers, who tend not to rely heavily on private
information and adopt a passive monitoring style, display the next greatest preferences. Finally,
dedicated owners, who tend to gather private information, and have large, long-term holdings
concentrated in a small number of firms, show no preference for high industry exposure stocks.
The differences in investor preferences across investment style categories – transient
investors, quasi-indexers, and dedicated owners – are most pronounced in high specificity
industries. For example, transient investors overweight high industry exposure stocks by 8% and
underweight low industry exposure stocks by 12%, while dedicated owners overweight high
industry exposure stocks by 3% and underweight low industry exposure stocks by 3%. In low
specificity industries, no investment style partitions exhibit preferences for industry exposure.
The results across institutions classified by investment style provide direct evidence that supports
the investor learning explanation of preferences for industry exposure. The asset substitution
explanation does not make any predictions about preferences being associated with investment
style.
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An alternative explanation of investor preferences for industry exposure is that investors
are attracted to transparency of information about a firm’s fundamental value, and financial
statement transparency is positively correlated with industry exposure. We find, however, that
firms with fewer and less complicated intra-firm relations, which would likely have a more
transparent presentation of risk but also more industry exposure, have similar levels of investor
interest as more complicated and presumably less transparent firms. Thus, greater financial
statement transparency does not appear to explain the results.
We also explore the possibility that investor preferences vary across institution type as a
function of fiduciary standards. If banks, which are subject to the “prudent man” rule, expect
that the courts will view high industry exposure stocks as imprudent (Del Guercio, 1996), then
banks will forgo the benefits of asset substitution or investor learning because of expected
litigation costs. When we examine the number of institutions that hold a firm’s stock by
fiduciary type, we find only weak evidence that banks’ preferences for industry exposure are
different from those of insurance companies, investment advisors, and pension funds and
endowments. Analysis of their portfolio holdings, however, suggests that fiduciary standards do
mitigate banks’ preferences for industry exposure. In low specificity industries, in which the
benefits of investing in high exposure stocks are minimal, banks significantly underweight high
exposure stocks relative to other institutions, consistent with the prudent man constraint. In
high-specificity industries, however, banks do not underweight high exposure stocks, suggesting
that the benefits from investing in these high exposure stocks at least partially offset the expected
litigation costs within these industries.
Our results suggest that market frictions may create incentives for some firms to not
hedge and to remain exposed to the underlying assets of the industry in order to retain (or attract)
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investors. This finding may help to explain why the evidence that hedging increases firm value
is weak, despite theoretical predictions that hedging is a value-maximizing activity in the
presence of market imperfections. Jin and Jorion (2006), for example, find no evidence that
hedging increases firm value for oil and gas exploration companies. However, returns in the oil
and gas industry have a low correlation with the market, and our results show that the benefits of
not hedging are likely to be greatest in such industries. Reductions in industry exposures
associated with hedging may lead to lower investor interest, reduced liquidity, and potentially a
higher cost of capital, which may offset the benefits more traditionally associated with risk
management. We leave this question to future research.
The paper is organized as follows. Section 2 discusses our hypotheses for why investors
may be attracted to industry exposure. Section 3 defines our measures of investor interest,
industry exposure, and control variables, and documents a positive relation between investor
interest and industry exposure. Section 4 examines cross-sectional variation in clientele effects
derived from our proposed explanations for investor preferences for industry exposure. Section
5 examines whether institutional investors systematically overweight high industry exposures
stocks and underweight low industry exposure stocks, and Section 6 concludes.
2. Explanations for investor preferences for industry exposure
In this section, we outline three explanations for the prediction that investors will display
preferences for stocks within an industry that have higher levels of industry exposure: the asset
substitution hypothesis (Section 2.1), the investor learning hypothesis (Section 2.2), and the
transparency hypothesis (Section 2.3).
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2.1 Asset substitution
We propose that there will be investor clientele effects in stocks that have high industry
exposure because investors use the stocks as substitutes for investment in fundamental assets.
Most investors do not hold the “assets” assumed in classic portfolio theory. Physical storage
costs, regulation, and asset indivisibility combined with wealth constraints make markets in
certain assets effectively incomplete for many investors.3 Stocks can act as substitutes for assets
that are otherwise unavailable or costly to attain, but only when the stock retains exposure to the
assets underlying a firm’s operations. Thus, investors will be attracted to stocks with high levels
of industry exposure when markets are incomplete.
Market frictions, such as storage costs, create inaccessibility to an underlying asset, not
an industry, but we predict an attraction to industry exposure because we expect fundamental
assets to be relatively homogeneous within industries. For example, consider the oil industry.
The underlying asset, common to all firms in the oil industry is oil. Although other industries
such as transportation are likely to have some exposure to oil, oil is not the primary driver of
cash flow volatility in those industries. If an investor wants to acquire exposure to oil, then the
most effective way to gain exposure is to purchase shares in an oil firm that maintains its
exposure to oil prices.
In addition to predicting that investor interest in a stock is positively related to industry
exposure, the asset substitution hypothesis also generates two cross-sectional predictions. First,
investor preferences for stocks with industry exposure will be more pronounced when the
diversification benefits from exposure to the fundamental assets of an industry are large. The
3 Entrepreneurs can and do create new assets that overcome these frictions. Securitized loans and real estate investment trusts (REITs) are classic examples of investment vehicles created to make access to an underlying asset, mortgage loans or real estate, feasible investments for a greater number of investors. See Stiglitz (1972), however, for a discussion of market frictions that prevent the creation of new assets that would fully complete markets.
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diversification benefits are negatively related to the correlation between industry returns and
aggregate market returns. If a particular industry has returns that are highly correlated with
market returns, then diversification benefits from investing in firms with high industry exposure
within that industry are small. Purchasing the market portfolio would be just as effective. In
contrast, the diversification benefits are larger if the industry is not highly correlated with the
market. In Section 4.1 we create a measure of industry specificity to capture the diversification
benefits of asset substitution.
Second, the asset substitution hypothesis predicts that investor preferences for industry
exposure will be higher for more wealth constrained investors, given asset indivisibility. In our
cross-sectional analysis, we examine institutional investor preferences based on institution size,
as a proxy for wealth constraints.
2.2 Investor learning
A second hypothesis that predicts investor clientele effects for stocks with high industry
exposure is based on theories that assume private information and costly information acquisition.
As one example of this class of theories, Van Nieuwerburgh and Veldkamp (2010) develop a
model in which agents make both an information processing choice and a portfolio choice. An
important constraint is that agents are assumed to have limited mental processing ability such
that learning about one asset (i.e., acquiring information) reduces the agent’s ability to learn
about other assets.4 Assuming that asset payoffs are correlated with underlying risk factors, Van
Nieuwerburgh and Veldkamp (2010) show that it is optimal for an investor to learn about a
4 Sims (2003) and Peng (2004) also model limited mental processing ability, but only Van Nieuwerburgh and Veldkamp (2010) focus on the interaction between asset portfolio and information choices. Van Nieuwerburgh and Veldkamp (2009) use a similar modeling approach to explain the home bias.
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single underlying risk factor that is costly to learn about and that has increasing returns to
information processing, and to forgo first-best portfolio diversification.5
Based on their model, Van Nieuwerburgh and Veldkamp (2010), propose business cycle
risk, idiosyncratic risk, and industry risk as potential candidates for investor learning and
investment specialization. We specifically predict an attraction to industry exposure because
empirical evidence suggests that analysts perceive significant benefits associated with learning
about industry risk. O’Brien (1990), for example, documents that over 97% of analysts in her
sample specialize in one industry. She speculates that focusing on one industry allows analysts
to better understand the production and cost functions underlying the industry, which leads to
better forecasting for all firms in the industry. Dunn and Nathan (2005) confirm this speculation
by showing that the more business segments an analyst follows, and the greater the
diversification within an industry, the less accurate the earnings forecast. In general, this class of
models will predict investor preferences for industry exposure if the net benefits of costly
information acquisition are positively correlated with industry risk.
The investor learning hypothesis also generates cross-sectional predictions. The first two
predictions are similar to those for the asset substitution hypothesis. First, the investor learning
hypothesis predicts that investors will display stronger preferences for industry exposure in high-
specificity industries. The potential benefits from learning about high specificity industries are
greater, so more investors will choose to learn about high specificity industries. Second, under
the assumption that information acquisition costs per dollar invested are higher for small
institutions than large institutions, the investor learning hypothesis predicts that small institutions
5 There is considerable empirical evidence supporting the notions developed in Van Nieuwerburgh and Veldkamp (2010) that investors will hold a specialized (undiversified) portfolio and a well diversified portfolio. See, for example, Polkovnichenko (2005), Goetzmann and Kumar (2008), and Massa and Simonov (2003), Kacperczyk, Sialm, and Zheng (2005), and Van Nieuwerburgh and Veldkamp (2009).
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are likely to have greater benefits from learning about an industry factor and investing in stocks
that remain exposed to that factor.
A prediction unique to the investor learning hypothesis is that investor interest in
industry exposure will be a function of investment style. Investors who, ex post, are observed to
have different investment styles that are associated with different levels of information
acquisition will exhibit variation in preferences for exposure. In our cross-sectional analysis, we
examine the preferences of transient investors, quasi-indexers, and dedicated investors for
industry exposure (see Section 4.2).
2.3 Transparency
A third hypothesis that we consider, but which ultimately is not supported by the data, is
that investors are attracted to industry exposure because 1) information asymmetry leads
investors to be attracted to financial statement transparency, and 2) financial statement
transparency is positively correlated with industry exposure. The first condition is supported by
models of trade with heterogeneously informed investors that suggest that information
asymmetries introduce adverse selection into securities transactions. Greater financial statement
transparency that reduces information asymmetry will reduce the adverse selection costs (e.g.,
Glosten and Milgrom, 1985; Kyle, 1985; Amihud and Mendelson, 1986; Admati and Pfleiderer,
1988; Diamond and Verrecchia, 1991). Empirical evidence, using a variety of proxies for
transparency, supports this prediction.6
6 For example, greater liquidity is associated with greater transparency measured by more informative financial reporting (e.g., Bartov and Bodnar, 1996; Leuz and Verrecchia, 2000; Boone and Raman, 2001; and Eleswarapu, Thompson, and Venkataraman, 2004); tracking stocks (e.g., Billett and Mauer, 2000); direct marketing to capital markets via investor relations (Bushee and Miller, 2007), or even advertising (Brennan and Tamarowski, 2000). Institutions, in particular, are attracted to transparency (Healey, Hutton, and Palepu, 1999; Bushee and Noe, 2000;
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The second condition – a positive correlation between financial statement transparency
and industry exposure – is more debatable. If firms with greater industry exposure have fewer
operating segments and less complex intra-firm relationships, then the positive correlation with
financial statement transparency is consistent with empirical research that defines transparency
as information precision about firm value (Baldwin, 1984; Bushman, Chen, Engel, and Smith,
2004). The assumption that firms with fewer operating segments and less complex intra-firm
relationships have greater industry exposure seems plausible given evidence in Lamont (1997)
that more concentrated firms have greater cash flow risk associated with the underlying assets.
However, it remains an empirical issue since highly concentrated firms may engage in financial
hedging to help reduce their underlying exposure.
3. Investor interest and industry exposure: empirical findings
Throughout the paper, we estimate variants of the following reduced form model of
investor interest:
iy
kkiykiyiy CONTROLINDEXPINTERESTINVESTOR
(1)
where we use three proxies for INVESTOR INTERESTiy for firm i in year y; INDEXPiy is firm-
year industry exposure; and CONTROLkiy is a matrix of firm-year control variables. We estimate
equation (1) annually and report the average of the coefficient estimates. Standard errors are
clustered by industry in the annual regressions. All models throughout the paper include the
control variables, but results for the control variables are tabulated only in the first set of reported
results.
Aggarwal, Klapper, and Wysocki, 2005; Ferreira and Matos, 2008). Finally, Graham, Harvey and Rajgopal (2005) also provide survey evidence that managers believe that transparency is associated with improved liquidity.
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3.1 Investor interest
The asset substitution and investor learning explanations for investor interest both imply
that individual investors are most likely to be attracted to stocks with high industry exposure.
Individual investors are likely to be wealth constrained and unable to invest in underlying
physical assets such as gold and oil. In addition, individual investors have less access to
information and lower information processing capabilities than institutional investors.
Measuring individual investor interest, however, is challenging. We use share turnover as an
indirect proxy for individual investor interest in equation (1). TURNOVER is the natural
logarithm of average monthly turnover (volume divided by shares outstanding), computed for
each firm i in each year y. While evidence suggests that share turnover captures individual
investor interest in part (Barber and Odean, 2008; Hou, Peng, and Xiong, 2006; and Loh, 2008),
it also captures disagreement among investors (Garfinkel, 2009), and it could be driven by a
handful of large institutional investors trading in large quantities, or many investors trading in
smaller quantities.
We also use institutional ownership and mutual fund ownership as measures of investor
interest. LNUMGR is the natural log of 1 + the number of institutions that hold stock i at the end
of year y. Data on annual institutional ownership are from the Thomson Financial 13-F database.
The Thomson database is based on the universe of 13-F filings without any selection or removal
of firms. The only potential selection issues are that holdings under $20,000 are not required to
be reported on a 13-F filing, and institutions that exercise investment discretion over less than
$100 million in equity are not required to file a form 13-F. Since all of our firms are publicly
traded, we assume that the firm has zero institutional investors if it is not included in the reported
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holdings of any institutions on the Thomson Financial database. LNUMFUNDS is the natural
log of 1 + the number of mutual funds that hold stock i at the end of year y. Data on annual fund
ownership are from the Thomson Financial Mutual Fund database.7 We eliminate mutual funds
that have an investment objective code (IOC) equal to 1, 5 or 6, which represent International
funds, Municipal Bond funds, and Bond and Preferred Stock funds, respectively. We also
eliminate funds that have less than three annual observations in which the market value of assets
at the beginning of the year is less than $1 million.
The institutional ownership and mutual fund ownership proxies represent more direct
measures of investor interest than share turnover. However, the disadvantage of these measures
is that preferences for industry exposure may be less pronounced for institutional investors than
for individual investors. However, Bushee and Goodman (2007) find that, with the exception of
institutions that hold large blocks and take big portfolio bets, there is little evidence of private
information trading by institutions.
3.2 Industry exposure
Following Jorion (1990) and Tufano (1998), for each firm (i), we calculate industry
exposure at the end of each year (y) by estimating an extended market model using the past 60
months (t) of return data:
mkt
, i , , , indi t i mkt t i ind t i tr r r
(2)
7 Mutual funds (i.e., registered investment companies) are a class of investors in the Thomson Financial database of 13-F filers, but data on mutual fund holdings in the mutual fund database are different from the data for the “Investment Companies” in the 13-F database. The 13-F database category of investment companies includes institutions that are not registered investment companies (i.e., not mutual funds) but that derive a significant portion of their business from the mutual fund business (determined by Thomson). In addition, holdings data on the Thomson Mutual Fund database is compiled primarily from the funds’ required semi-annual reports to shareholders (N-30D filings) rather than 13-F filings.
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where rmkt denotes the monthly return on the CRSP equally weighted market index, and rind
denotes the monthly return on the appropriate equally weighted industry portfolio.8 We use the
industry definitions provided on Kenneth French’s website to construct 30 industry portfolios.9
For a firm-year observation to be included in the sample we require at least 24 monthly return
observations to estimate the extended market model.
The estimated coefficient indi (INDEXP) measures the sensitivity of stock i’s return to a
one percent return on the underlying industry, after controlling for movements in the aggregate
stock market that affect the returns on stock i independent of industry returns. Indicator
variables identify high and low exposure firms (BETAHIGH and BETALOW, respectively).
BETAHIGH = 1 (BETALOW = 1) if INDEXPiy is above (below) the 70th (30th) percentile
exposure for its industry group for year y. The ranking is done before requiring that the sample
firms have non-missing Compustat data.
Table 1 reports descriptive statistics for the exposure measure by industry. The
magnitudes of the industry exposure estimates vary considerably across the 29 industries. The
average industry exposure (INDEXP) ranges from 0.51 for the Coal industry to in excess of 1.00
for the Retail and Business Equipment industries. Column 3 reports estimates of the average
stock market s across each industry. MKTBETA2 is the estimate of mkti from the extended
(two-factor) market model specified in equation (2).
(Insert Table 1 here.)
8 We use equally weighted returns to ensure that the returns from a small number of large companies do not drive our measure of industry returns. 9 The 30th industry includes firms that do not fall into industries 1 to 29; we discard the small number of firms assigned to the 30th industry.
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Two pieces of evidence indicate that INDEXP measured based on historical returns data
is a reasonable proxy for expected future industry exposure, which is the relevant conceptual
construct in the analysis. First, the industry exposure proxies based on historical data are fairly
stable from year to year (untabulated). Of firms that are classified as high beta (medium beta)
{low beta} in year y - 1, approximately 76.4% (68.5%) {74.9%} are in the same category in year
y. Of firms that are classified as high beta (medium beta) {low beta} in year y - 2, approximately
65.1% (58.6%) {63.6%} are in the same category in year y.
Second, an out of sample experiment suggests that INDEXP predicts future exposure.
For each industry, at the start of year y + 1, we form an equally weighted portfolio that buys
stocks classified as high exposure (BETAHIGH = 1) as of year-end y and shorts low exposure
stocks (BETALOW = 1). The portfolio is rebalanced annually. We regress the returns from the
portfolio strategy on the excess returns from the market portfolio and the relevant industry
portfolio:
exp exp .high low m mkt f ind ind fr r r r r r
(3)
If the BETAHIGH and BETALOW classifications capture meaningful differences in industry
exposure, we expect to observe 0ind . The final column of Table 1 reports that, for all
industries other than Books and Food, there is robust evidence that 0ind .
3.3 Control variables
We draw control variables (CONTROL) from four papers that examine the determinants
of institutional ownership: Del Guercio, 1996, Falkenstein, 1996, Gompers and Metrick, 2001,
and Hong and Kacperczyk, 2009. Broadly speaking, these papers include various specifications
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of proxies for the following constructs: Firm size, leverage, growth, share price, systematic risk,
dividend yield, past returns, return volatility, and firm age. The control variables are particularly
important when considering investor preferences for a particular stock characteristic. Investors
often categorize stocks into broad classes such as large-capitalization, value, growth, and
momentum, before deciding how to allocate funds across the classes (Barberis and Shleifer,
2003). By including proxies for size, value, and momentum as control variables we minimize
the likelihood that our findings with respect to investor preferences for industry exposure are
driven by a positive correlation between industry exposure and a common characteristic used by
investors to assign stocks into a particular asset class. The Appendix provides a detailed
description of our proxies for these constructs.
Table 2 reports means and medians for the control variables separately for the high
exposure (BETAHIGH), low exposure (BETALOW), and the remaining medium exposure
(BETAMED) firms. There is a robust monotonic relation between industry exposure and
turnover. High exposure firms also tend to be younger, have lower dividend yields and higher
debt-to-equity ratios than low exposure firms. Market-to-book ratios, which prior literature has
used as a proxy for growth opportunities, do not vary significantly across the exposure
categories. Overall, Table 2 shows that there are substantial differences in firm characteristics
across low, medium, and high industry exposure firms.
(Insert Table 2 here.)
3.4 Investor interest and industry exposure
Table 3 reports the results of regressions that measure the association between industry
exposure and our three proxies for investor interest: share turnover (TURNOVER), the number of
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institutional owners (LNUMGR), and the number of mutual fund owners (LNUMFUNDS). The
results for the control variables are generally consistent with prior research. We find that
investor interest is positively associated with firm size, market-to-book ratios, return volatility,
stock market betas, past returns, inclusion in the S&P 500 index, and listing on NASDAQ; and
negatively associated with the inverse of price, dividend yields, debt-to-equity ratios, and firm
age.
(Insert Table 3 here.)
Overall, the results in Table 3 provide strong evidence of a positive relation between our
proxies for investor interest and our proxy for industry exposure. As reported in Columns 1, 3,
and 5, the coefficients on the continuous industry exposure proxy (INDEXP) are positive and
significant, which is consistent with the prediction that higher industry exposure is associated
with higher investor interest.
In Columns 2, 4, and 6, we use the indicator variables defined in the previous section,
BETAHIGH and BETALOW, to measure industry exposure. The results indicate that the positive
associations between INDEXP and our proxies for investor interest come from both an attraction
to high exposure stocks and an aversion to low exposure stocks. For example, ceteris paribus,
turnover in firms with high industry exposure is 33% higher than that of firms with medium
levels of exposure, while turnover of low exposure firms is 19% lower (Column 2). Similarly, a
change in a firm’s industry exposure from the 30th percentile to the 70th percentile is associated
with a 13% increase in the number of institutions holding the stock (Column 4).
When the proxy for investor interest is the number of mutual funds that hold a firm’s
stock (LNUMFUNDS), the results using indictor variables for industry exposure (Column 6)
indicate only a significant aversion to low exposure stocks. In general, the relation between
19
industry exposure and mutual fund investors is weaker than the relation for institutional investors
as a whole.10
(Insert Table 4 here.)
We perform several additional analyses to test the robustness of the results reported in
Table 3. The results are presented in Table 4. In Panel A, we show that the results are robust
across firm size quartiles (the smallest quartile, middle two quartiles and the largest quartile).
The primary model in Table 3 includes a continuous measure of firm size as a control variable,
but the separate quartile regressions allow for non-linearities in the relation between firm size
and investor attraction to industry exposure. Further, an analysis within size quartiles helps to
address the possibility that our estimates of industry exposure for infrequently traded (i.e., low
turnover) stocks are biased downward, which would induce a positive relation between industry
exposures and turnover. If the results in Table 3 are driven by nonsynchronous trading, we
would expect to observe a positive association between industry exposure and investor interest
among the quartile of small firms where nonsynchronous trading is likely to be greatest, but no
relation among large firms.
As shown in Table 4 Panel A, the investor interest measures with the exception of
LNUMFUNDS are significantly positively associated with industry exposure across all size
groups. For the smallest quartile of firms, LNUMFUNDS is not statistically related to measures
of industry exposure. The coefficient estimates on BETAHIGH also are not statistically
10 Subsequent analysis uses the Thomson 13-F database classifications of investment advisors and registered investment companies as an alternative measure of fund ownership.
20
significant in any firm size group when investor interest is measured by LNUMGR and
LNUMFUNDS.11
In Panel B we find that the results in Table 3 are robust to alternative measures of
industry exposure. The first alternative is a demeaned continuous measure of industry exposure.
The summary statistics in Table 1 indicate substantial differences in the average industry
exposure coefficient across industries. The demeaned measure controls for the possibility that
investors are attracted to particular industries rather than to within-industry exposure. The
results using the demeaned measure are similar to the results for the continuous specification of
INDEXP in Table 3. The second alternative measure controls directly for the Fama-French
factors, SMB and HML. When estimating industry exposure (equation (2)), we include not just
market returns, but also returns on the Fama-French factors, SMB and HML as control variables.
Having obtained the estimates of industry exposure, we estimate a model identical to the
specification reported in Table 3 but with the new estimates of industry exposure. The results
are qualitatively similar to the results in Table 3. Finally, we also include the factor loadings on
SMB and HML when examining the determinants of investor interest. The inclusion of the
factor loadings on SMB and HML does not affect investor preferences for industry exposure.
In addition, in untabulated regressions, we estimate the turnover regression separately for
NASDAQ and non-NASDAQ firms, recognizing that turnover is measured differently on the
11 To mitigate the concern that nonsynchronous trading causes a downward bias in the exposure estimates and hence a mechanical relation between exposure and turnover, we also estimate the turnover model for a sample that excludes observations with a stock price at the start of the year below $5. The results using either the continuous specification for industry exposure (INDEXP) or the indicator variables are similar to those reported in Table 3, both in terms of statistical and economic significance. In addition, if measurement error affects our results, we would expect to observe a negative relation between low industry exposure firms and turnover, but we would not necessarily expect to observe a positive relation between high industry exposure firms and turnover.
21
NASDAQ exchange. The results are similar. The results are also similar if stocks of financial
firms, which represent approximately 20% of the sample observations, are excluded.
Throughout the remainder of our analysis we run all regressions with and without financial
firms. The results are not sensitive to the exclusion of financial firms.
3.5 Transparency and industry exposure
One explanation for investor attraction to industry exposure is that investors are attracted
to financial statement transparency and firms with more industry exposure are more transparent.
This transparency explanation is different from the asset substitution explanation and the
investor learning explanation in that investors are not attracted to industry exposure per se.
Rather, they are attracted to firms that have more transparent information about their asset
exposures.
To rule out this explanation, we sort firms into sub-samples based on measures of line-of-
business diversification. We expect line-of-business diversification to be negatively correlated
with financial statement transparency and industry exposure. If investors are attracted to
industry exposure because it is negatively correlated with line-of-business diversification, and
therefore positively correlated with financial statement transparency, we expect to observe no
evidence that investors are attracted to industry exposure within a sample of firms with similar
levels of diversification.
(Insert Table 5 here.)
We use two proxies for line-of-business diversification. The first proxy (MULTISEG) is
an indicator variable equal to one if the firm is a multi-segment firm (regardless of the number of
segments) and equal to zero if the firm is a single-segment firm. The second proxy is one minus
22
the firm’s revenue-based concentration ratio (DIVERSE), which is computed following
Comment and Jarrell (1995). The minimum value of DIVERSE is zero for a single segment firm
and it approaches one as diversity increases (concentration decreases).
Table 5 Panel A presents the results from estimating equation (1) separately within
portfolios of single segment firms (MULTISEG = 0) and multi-segment firms (MULTISEG = 1).
Panel B sorts the firms into three portfolios based on the rank of the continuous measure
DIVERSE: the bottom quartile, the middle two quartiles, and the upper quartile. Firms are
ranked within industry by year. We only report the results for the model specification that
includes the indicator variables BETAHIGH and BETALOW to measure industry exposure.12
Results using the continuous variable (INDEXP) yield similar inferences.
For all three proxies of investor interest (TURNOVER, LNUMGR, and LNUMFUNDS),
the coefficients on BETAHIGH and BETALOW within all the diversification-sorted sub-samples
are similar in magnitude and significance levels to those presented in Table 3. Across all proxies
for investor interest investors display a strong aversion to stocks with low industry exposure.
Furthermore, using TURNOVER as a proxy for investor interest, there is robust evidence that
investors display a preference for stocks with high industry exposure across all the sub-samples.
Thus the evidence in Table 5 provides little support for the hypothesis that investors exhibit
preferences for industry exposure because industry exposure is correlated with financial
statement transparency.
12Segment reporting requirements changed significantly during the sample period. Between 1984 and 1997, a firm’s operating segments roughly correspond to its distinct product market industries. For the remainder of the sample period, firms defined their segments based on the internal management structure of the firm, which may or may not be by industry-level product line. For example, a firm could report segment data for its wholesale and retail operations (i.e., by customer type). Our diversification proxies are more likely to capture the negative correlation between diversification and industry exposure in the earlier period when segments were primarily defined at the industry level. In results not reported, we re-estimate the regression models in Table 5 for the period 1984 to 1997 and the period 1998 to 2006. The results in both periods are similar to those reported.
23
4. Understanding investor preferences for industry exposure
4.1 Industry specificity
The asset substitution explanation for investor attraction to industry exposure predicts
that investor demand to substitute investment in the stock of a firm that maintains industry
exposure for investment in the underlying asset will be greatest in industries with returns that are
not highly correlated with the aggregate market. The diversification benefits are greatest in these
industries. The investor learning explanation also predicts stronger preferences for industry
exposure among such industries. For industries with returns that are highly correlated with
aggregate market returns, a signal about one industry is likely to contain information about other
industries that are also highly correlated with the aggregate market, thereby reducing the value of
learning.
We refer to the degree to which industry returns are correlated with aggregate market
returns as industry specificity. Industry specificity for industry j in year y is measured using
parameter estimates from estimation of a standard market model and an extended market model
for each firm within industry j. The extended market model includes the equally-weighted
market returns and the appropriate equally-weighted industry returns. The models are estimated
using monthly return data over the period January of year y – 4 to December of year y. For each
firm, i, in year y, we compute the difference between the adjusted R2 values of the two models.
We use the average difference in adjusted R2 values within an industry as our measure of
24
industry specificity.13 The result is 23 annual observations for the 29 Fama-French industry
groups, which is 667 industry-year observations of industry specificity (SPECIFICITY).14
(Insert Figure 1 here.)
Figure 1 presents boxplots of the average industry specificity metric over our sample
period from 1984 to 2006 for each of the 29 Fama-French industries. The greater is the average
difference in adjusted R2s, the greater is the industry specificity. Five industries stand out as
having substantially higher specificity measures: Utilities, Mining, Tobacco, Crude Oil and
Natural Gas, and Coal.15 These five industries are designated “high-specificity” industries in the
empirical analysis. For these high-specificity industries, the adjusted R2 in the two factor market
model is 17% higher in absolute terms, on average, than the adjusted R2 in the standard market
model. The industries with the lowest average specificity are: Wholesale, Electrical Equipment,
Services, Games, and Consumer Goods. These five industries are considered low-specificity
industries in the empirical analysis. For the low-specificity industries, the adjusted R2 increases
by just 1.3% in absolute terms following the addition of industry returns to the market model.16
Our classification of industries as high, medium, or low specificity is static over the
sample period, but the boxplots in Figure 1 illustrate variation in specificity during the sample
period for many industries. Much of the variation in SPECIFITY within each industry, however,
13An alternative metric to the difference in adjusted R2 is the F-statistic associated with the test of whether industry returns improve model specification. The correlation between these two variables is 0.98. 14 Our measure of industry specificity is similar to the country-level synchronicity measures developed by Morck, Li, Yang, and Yeung (2004). 15 It is worth noting that these industries are not necessarily the industries with the highest industry factor price exposure (see Table 1). 16 Estimates of specificity based on the Fama-French 3-factor model are highly correlated (97%) and the estimates identify the same industries as high or low specificity industries.
25
is due to an increasing trend in SPECIFICITY from 1984 through 2006 across all industries. The
relative ranking of the industries generally remains stable throughout the sample period.17
Our measure of industry specificity has two important features. First, it is estimated
using market data, which is available monthly. While the covariances of industry cash flows
with aggregate market cash flows might be a more conceptually appropriate construct for
industry specificity, we would have only 23 annual observations of financial statement data to
estimate the covariances. Second, our measure applies equally well across industries. Measures
based on an ex ante identifiable market frictions, such as asset indivisibility or storage costs,
would, by necessity, be industry-specific. It is worth noting, however, that the industries that our
market-based measure identifies as “high” specificity are those in which ex ante identifiable
market frictions, that make direct investment costly, are likely to be high.
(Insert Table 6 here.)
Table 6 reports results of regressions that measure the association between industry
exposure and investor interest as a function of specificity. Panel A reports results of estimation
of equation (1) with three additional regressors: SPECIFICITY and the interaction of
SPECIFICTY with the indicator variables, BETALOW and BETAHIGH. If industry specificity
increases investor attraction to industry exposure, we expect a positive coefficient on the
interaction between BETAHIGH and SPECIFICITY and a negative coefficient on the interaction
between BETALOW and SPECIFICITY.
The results in Panel A show that the coefficient on the interaction term between
BETALOW and SPECIFICITY is negative and significantly different from zero for all three
17 The only exception is the specificity measure for the Coal industry. We classify the Coal industry as a high-specificity industry in all years, but it has the most significant time-series variation, which is not surprising given the small number of firms in the industry (Table 1). All of the results are robust to exclusion of the Coal industry observations from the regressions.
26
measures of investor interest, indicating that investors, ceteris paribus, seek to avoid low
industry exposure stocks particularly in higher specificity industries. Moreover, the coefficient
on the interaction term between BETAHIGH and SPECIFICITY is positive and significantly
different from zero for two measures of investor interest (LNUMGR and LNUMFUNDS),
indicating that higher specificity is associated with stronger preferences for industry exposure.
Panel B reports results for high-, medium-, and low-specificity industries, respectively.
We expect to observe stronger investor preferences for industry exposure within the high
specificity sub-sample. In Column 1, share turnover is used as a proxy for investor interest.
Within high-specificity industries, there is robust evidence that investors are attracted to high
industry exposure stocks and display an aversion to low industry exposure stocks. In medium-
and low-specificity industries, investors also display an attraction to high industry exposure and
an aversion to low industry exposure, but the coefficients are attenuated relative to the high-
specificity industries. The results using the number of institutional owners and mutual funds as
proxies for investor interest follow a similar pattern. The differences between the coefficients on
BETAHIGH and BETALOW increase with industry specificity.
4.2 Institution characteristics and investor clienteles in industry exposure
In this section, we investigate which types of institutional investors display preferences
for industry exposure. First, we classify institutions annually into three groups based on the total
equity value of their holdings: small, medium, and large. Small (large) institutions are defined as
those in the lower (upper) quartile. Second, we use the classification system in the Thomson
Financial database and numerous studies of institutional ownership: (1) bank trusts, (2) insurance
companies, (3) investment companies, (4) investment advisors, and (5) other. The “other”
27
category includes pension and endowment funds.18 We aggregate the investment companies and
investment advisors into one institution type.19 Third, we use the Bushee (2001) classifications
of institutional investors. Bushee (2001) identifies three types of institutional investors:
Dedicated owners, quasi-indexers, and transient investors.20 Dedicated owners have large, long-
term holdings concentrated in a small number of firms, and are more likely to gather private
information about a firm and directly monitor its managers. Quasi-indexers tend not to rely
heavily on private information and adopt a passive monitoring style. Transient investors hold
small stakes in many firms and trade frequently on publicly available information, but they do
not generally acquire private information.
For each partitioning of the institutional investors, we examine preferences for exposure
across all industries (Table 7) and separately within high and low specificity industries (Table 8).
We report the results only for the model specification that includes the indicator variables
BETAHIGH and BETALOW to measure industry exposure. Results using the continuous
variable (INDEXP) yield similar inferences.
(Insert Table 7 here.)
4.2.1 Attraction to industry exposure by institution size
18 We thank Brian Bushee for providing us with his institution classifications during the sample period. There is a coding error in the Thomson Financial 13-F database. Thomson reports that partway through 1998, and in subsequent years, many banks (Type 1) and independent investment advisors (Type 4) are misclassified as other institutions (Type 5). Bushee’s database provides a consistent classification of the institutions on the Thomson Financial database. 19 The Investment Company category (Type 3) in the Thomson 13-F database includes investment advisors that Thomson determines derive a “significant” portion of their advisory services from the mutual fund business. 20 The Bushee (2001) annual institution classifications are based on k-means clustering of standardized factor scores, which are created on an institution-year basis using the weighted average of firm-specific characteristics of an institution’s portfolio holdings. Approximately 4% of institution-year observations are dedicated owners, 60% are quasi-indexers, and 36% are transient investors. Brian Bushee generously provided us with his classifications of investors.
28
Table 7 Panel A shows that small institutions display preferences for high industry
exposure stocks and an aversion to low industry exposure stocks. Medium sized institutions also
display an aversion to low industry exposure stocks, but the aversion is less pronounced in terms
of economic magnitude. Medium sized institutions show no attraction to high exposure stocks.
Among large institutions there is no evidence that industry exposure, either high or low, matters.
Within high specificity industries (Table 8 Panel A), all institutions, regardless of size,
display robust preferences for high exposure stocks. Ceteris paribus, a high industry exposure
stock will experience between 6% and 8% more institutional investors of all sizes than stocks
with average levels of industry exposure. Small and medium sized institutions display
considerable aversion to low industry exposure stocks, but large institutions do not.
(Insert Table 8 here.)
Among low specificity industries there is little evidence that funds of any size display
robust preferences for industry exposure. Small institutions avoid stocks with low industry
exposure but are not attracted to stocks with high industry exposure, while medium and large
institutions display a small but statistically significant aversion to high exposure stocks.
The stronger attraction to industry exposure among the smaller institutions is consistent
with both asset substitution and investor learning. Large institutions are likely to have fewer
wealth constraints and be better able to invest directly in underlying assets to which they seek
exposure. This option is not likely to be available to smaller wealth constrained institutions. At
the same time, large institutions tend to take larger dollar positions than small institutions when
investing in stocks, resulting in lower information acquisition costs per dollar invested relative to
small institutions. As such, small institutions are likely to have greater benefits from learning
about an industry factor and investing in stocks that remain exposed to that factor.
29
4.2.2 Attraction to industry exposure by institutional fiduciary standards
Table 7 Panel B shows that, except for banks, all types of institutions display a significant
attraction to high industry exposure stocks and a significant aversion to low exposure stocks.
Table 8 shows that the attraction to high exposure stocks and the aversion to low exposure stocks
for the non-bank institutions is greatest in the high specificity industries, consistent with the
results throughout the paper.
The results for banks, however, are different. Looking across all industries, banks exhibit
only a significant aversion to low-exposure stocks (Table 7). For low specificity industries,
banks show an aversion to high exposure stocks. It is only for the high-specificity industries that
banks exhibit a strong positive attraction to high exposure stocks.
The bank results are consistent with the different fiduciary standards of banks relative to
other institutions. Del Guercio (1996) finds that banks, which are subject to the prudent man
rule, tend to avoid stocks that they expect the courts to view as imprudent.21 The expected
litigation costs appear to be a constraint on banks’ attraction to industry exposure in low
specificity industries where the benefits of investing in high industry exposure stocks are
smallest. However, in high specificity industries banks appear to trade off the benefits of asset
substitution/investor learning against the expected litigation costs associated with an imprudent
investment in a stock with high industry exposure. While the results support this interpretation
based on direction, we caution that there are no statistically significant differences in preferences
across the institutional types.
21 When the courts consider whether an investment is prudent or not, they tend to focus on the characteristics of assets in isolation, rather than considering the role of the asset in the bank’s overall portfolio.
30
4.2.3 Attraction to industry exposure by institution investment style
Table 7 Panel C reports cross-sectional analysis of the association between industry
exposure and investor interest as a function of the institutional investor’s style, which is related
to the institution’s information acquisition practices (Bushee, 2001). Dedicated owners show an
aversion to low industry exposure stocks, but they do not exhibit a preference for high industry
exposure stocks. In contrast, quasi-indexers and transient investors show an aversion to low
exposure stocks and a preference for high exposure stocks. Ceteris paribus, the number of
transient investors investing in a stock is 15% higher for stocks with high industry exposure
relative to stocks with low industry exposure, while the difference for dedicated owners is less
than 4%. The differences in preferences between transient investors and dedicated owners are
statistically significant in 17 of the 23 sample years.
Table 8 Panel C shows that the patterns across investment style categories are most
pronounced in high specificity industries. All institutions, regardless of their investment style
display significant preferences for industry exposure, but the difference between preferences for
high and low industry exposure stocks is greatest for transient investors followed by quasi-
indexers, and dedicated owners. A change in a firm’s industry exposure from the 30th percentile
to the 70th percentile is associated with a 37% (15.70%) increase in the number of transient
investors (dedicated owners) ceteris paribus. The difference between the preferences of
transient investors and dedicated owners is statistically significant in 12 out of 23 years.
These results are consistent with the investor learning explanation for investor attraction
to industry exposure. Transient investors invest in a large number of stocks and seek to
minimize the per-stock information acquisition costs. One way to minimize information
acquisition costs is to obtain a signal about an underlying risk factor and apply this information
31
to all stocks that are exposed to the risk factor. In contrast, dedicated investors, who invest in a
handful of firms, will choose to bear the costs of acquiring unique firm-specific information.
The asset substitution explanation, in contrast, does not make any predictions about a relation
between investment styles and preferences for industry exposure.
The presentation of the averages of the annual coefficient estimates in Tables 7 and 8
obscures a distinct time trend in the significance of the BETAHIGH coefficient estimate for
transient and quasi-indexers investors (not tabulated). The coefficient estimates for the transient
and quasi-indexers investors are not statistically different from zero in the 1980’s and early
1990’s, but they are consistently significantly positive in the last ten years of the sample period.
While the time trend does not affect our statistical analysis, it does suggest that preferences for
industry exposure have increased for transient and quasi-index investors in the last decade.
This increased preference for industry exposure by transient investors coincides with the
introduction and rapid growth of Exchange Traded Funds (ETFs). In 1993 the first ETF was
traded on the American Stock Exchange (AMEX). The number of funds grew from one in 1993
to 359 by the end of 2006, and the assets invested in ETFs grew from approximately $1 billion to
$422 billion.22 ETFs are designed to track returns in particular sectors or markets, providing
investors with access to sector or market exposure at a lower cost than more traditional mutual
funds. A cost effective way for ETFs to track returns in a particular sector, such as oil and gas, is
to invest in the stocks of firms that have high industry exposure. ETFs typically hold a large
number of stocks, so they are likely to be classified as either transient or quasi-index investors.
The rapid growth in ETFs may, at least in part, explain the increased preferences for industry
exposure among transient and quasi-index investors over the last decade.
22 Source: Investment Company Institute (ICI) Fact Book, 2008.
32
5. Analysis of portfolio diversification
In addition to predicting that investors will be attracted to high industry exposure stocks,
the asset substitution and investor learning explanations for investor demand for industry
exposure also predict that investor portfolios will be overly concentrated in (i.e., under-
diversified) in high industry exposure stocks. We test this prediction by examining the reported
holdings of institutional investors in the Thomson 13-F database.
The percent of the portfolio that institutional owner (j) holds in stocks of exposure type e
in each of the 29 Fama-French industries (i) in year y (PHELD) is:
1 1
S Fe s fijy ijy ijy
s f
PHELD MV MV
(4)
where MV is the market value of the stocks. S denotes the number of firms within industry i in
year y with industry exposure level e (high, moderate, or low) that institution j invests in, and F
denotes the total number of firms within industry i in year y that institution j invests in. The
weight is computed for each of the 13-F institution types for each year (y) between 1984 and
2006.
An institution’s percent held is compared to a benchmark weight that reflects no
preference for exposure. The benchmark weight (w) is computed as the value weighted
percentage of stocks classified as having high, moderate, or low exposure (e) in our sample for
each industry (i) in each year (y):
1 1
G Ne g niy iy iy
g n
w MV MV
(5)
33
where G denotes the number of firms in industry i in year y with industry exposure level e, and N
denotes the total number of firms in industry i in year y.
The excess (XS) weight in each exposure category equals the percent held minus the
benchmark:
.e e eijy ijy iyXS BETA PHELD w
(6)
The excess weights across the three exposure categories for each institution-industry-year
observation sum to zero. The null hypothesis that institutions do not overweight (underweight)
high (low) industry exposure stocks implies that the excess invested in the high (low) exposure
category is zero: 0.eijyXS BETA
(Insert Table 9 here.)
Table 9 reports the average excess weights. Panel A shows that both small and large
institutions systematically overweight high industry exposure stocks. These findings are most
pronounced within high specificity industries. For example, large institutions overweight high
industry exposure stocks by 5% and underweight low industry exposure stocks by 8%. Among
low specificity industries, there is no robust evidence that either small or large institutions have
preferences for industry exposure.
Panel B of Table 9 reports that banks underweight high industry exposure stocks. The
underweighting is significant in low specificity industries. This result is consistent with the
prudent man constraint on portfolio allocation decisions of banks (Del Guercio, 1996). In high
specificity industries, however, banks do not underweight high industry exposure stocks,
consistent with the benefits of investing in these industries at least partially offsetting the
34
fiduciary costs. In contrast, investment advisors and pensions and endowments significantly
overweight high industry exposure stocks in the high-specificity industries and underweight low
exposure stocks.
Results across investment style are reported in Panel C. Transient investors display the
greatest preferences for high industry exposure stocks and the greatest aversion to low industry
exposure stocks. For example, in high specificity industries, transient investors overweight high
exposure stocks by 8% while underweighting low exposure stocks by 12%. In contrast, there is
no robust evidence that quasi-indexers overweight stocks with high industry exposure. The
dedicated owners do tend to overweight high industry exposure stocks and underweight low
industry exposure stocks, but the magnitudes are much smaller.
6. Conclusion
This paper shows investor clientele effects in industry factor price exposure and
characterizes sources of investor preferences for industry exposure in stocks. Industry exposure
is positively associated with share turnover, the number of institutional investors that hold the
firm’s stock, and the number of mutual funds that hold the firm’s stock. The positive association
between industry exposure and investor interest comes from both an attraction to high exposure
stocks and an aversion to low exposure stocks. Furthermore, institutional investors
systematically overweight high industry exposure stocks in their portfolios while underweighting
low industry exposure stocks.
The attraction to high exposure stocks and the aversion to low exposure stocks is most
pronounced among industries for which the correlation of industry returns with aggregate market
returns is low: Mining, coal, utilities, tobacco, and oil and gas. The diversification benefits of
35
substituting stocks with high exposure to the underlying assets for direct investment in the
underlying assets are greatest in these industries, as are the benefits from learning about an
industry factor. Investor preferences for exposure are greatest for small institutions and
institutions that follow a transient investment style, meaning they hold a large number of stocks
and rely mostly on public information. Preferences for exposure are least prevalent among large
institutions and dedicated institutional investors who invest in only a small number of stocks.
36
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41
Appendix: Summary of control variables Summary of control variables used in the analysis. We draw the control variable constructs for the determinants of institutional ownership from four sources: Del Guercio (1996), Falkenstein (1996), Gompers and Metrick (2001), and Hong and Kacperczyk (2009). All variables except firm age and the indicator variables for inclusion in the S&P 500 index and Nasdaq stocks are winsorized at the 1st and 99th percentiles.
Construct Name Description
Firm size LOGSIZE_MVE Natural log of the market value of equity (in $ thousands) at year end.
Market-to-book ratio LOGMB Natural log of market value of equity divided by common book equity at year end.
Share price INVPRICE Inverse of stock price at year end.
Systematic risk MKTBETA2 Market beta from estimation of the two-factor extended market model in equation (2).
Dividends DIVYLD Annual dividend yield.
Leverage DE RATIO Total long-term debt (including current portion) divided by total common equity at year end.
Turnover TURNOVER Natural log of average monthly turnover during the year.
Returns AVGRET Average monthly return during the year.
Idiosyncratic return volatility RETVOL Standard deviation of daily firm returns during the year.
Firm age FIRMAGE Natural log of the number of months from the CRSP start date to year end.
Included in the S&P 500 index S&P500 Indicator variable = 1 if the firm is in the S&P 500 index as of year end, and = 0 otherwise.
Trades on NASDAQ exchange NASDAQ Indicator variable = 1 if the firm is traded on the NASDAQ exchange as of year end according to CRSP and = 0 if it is traded on the NYSE/AMEX.
42
Figure 1: Industry specificity To measure industry specificity for industry j in year y, we estimate a standard market model and an extended market model for each firm i within industry j using monthly return data over the period January of year y – 4 to December of year y. The extended market model includes the appropriate equally-weighted industry returns and the equally-weighted market returns. For each firm i in year y, we compute the difference between the adjusted R2 values of the two models. We use the average difference in adjusted R2 values within an industry as our measure of industry specificity. The box plot below summarizes the industry specificity across our sample period of 1984 – 2006.
43
Table 1. Summary of exposure measures by industry INDEXP is the mean of the firm-specific estimates of the monthly industry factor betas. MKTBETA2 is the mean of the firm-specific estimates of stock market betas from the two-factor market model. δind is the estimated industry factor exposure in year y+1 for a portfolio that buys high industry exposure firms and shorts low industry exposure firms, where exposure is measured using historical data (see equation 3). δind is hypothesized to be greater than zero if the BETAHIGH and BETALOW classifications capture meaningful differences in industry factor price exposure. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level.
Industry
N
INDEXP
MKTBETA2
δind
18 Coal 105 0.5051 0.3570 0.1478*
21 Telecommunications 1,743 0.5378 0.3679 0.1175**
2 Beer 264 0.5540 0.2606 0.5398***
4 Games 2,049 0.5623 0.4628 0.3936***
26 Wholesale 3,564 0.5648 0.4066 0.1870*
3 Smoke 102 0.6081 0.1596 0.3732***
14 Electrical Equipment 1,304 0.6361 0.3794 0.2120*
6 Household 1,812 0.6917 0.2537 0.1851**
5 Books 1,204 0.7056 0.1650 -0.0804
10 Textiles 648 0.7375 0.2881 0.2124**
1 Food 1,938 0.7677 0.1646 0.1287
25 Transportation 2,189 0.7694 0.1434 0.6189***
22 Services 8,523 0.7907 0.2317 0.7209***
15 Autos 1,247 0.7963 0.1939 0.4708***
28 Meals 1,473 0.7983 0.1665 0.1421**
11 Construction 3,203 0.8001 0.1842 0.4825***
12 Steel 1,396 0.8084 0.1446 0.3523***
16 Carry 626 0.8314 0.1056 0.4064***
9 Chemicals 1,556 0.8672 0.1044 0.3294***
13 FabPr 3,817 0.8807 0.1593 0.5408***
7 Clothes 1,307 0.8810 0.1272 0.3090***
17 Mines 1,153 0.8916 0.0693 0.5389***
29 Financial 18,254 0.9027 0.0416 0.5770***
24 Paper 1,821 0.9507 0.0491 0.6032***
8 Health 7,578 0.9557 -0.0076 0.7174***
19 Oil 3,454 0.9582 0.0231 0.5498***
20 Utilities 3,666 0.9778 -0.0002 0.6856***
27 Retail 4,492 1.0135 -0.0127 0.4196***
23 Business Equipment 11,287 1.0317 -0.0634 0.5985***
44
Table 2. Descriptive characteristics of sample firms Means and medians (in parentheses) of industry factor price exposure (INDEXP), log monthly turnover (TURNOVER), and regression control variables across low exposure (BETALOW), medium exposure (BETAMED), and high exposure (BETAHIGH) firm-year observations. The control variables include the natural logarithm of the market value of equity, inverse price ratio, natural logarithm of the market-to-book ratio, dividend yield, debt-equity ratio, idiosyncratic return volatility, average monthly firm return, stock market beta, firm age, and indicator variables for stocks included in the S&P 500 and for NASDAQ listed stocks. The Appendix provides variable measurement details. A firm is considered high exposure (low exposure) if its industry factor price exposure is greater (less) than the 70th (30th) percentile exposure, respectively. The percentiles are recalculated for each industry for each calendar year. The means and medians are measured for the sample across the years 1984 - 2006. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics.23
BETALOW (n = 27,847)
BETAMED (n = 36,084)
BETAHIGH (n = 27,844)
HIGH vs
MED
HIGH vs.
LOW
MED vs.
LOW Industry exposure INDEXP
-0.2931 (-0.1029)
0.7959 (0.8116)
2.1168 (1.8283)
*** *** ***
Log monthly turnover TURNOVER
-3.1560 (-3.0972)
-2.9844 (-2.9584)
-2.6325 (-2.6146)
*** *** ***
Firm size (Log MV of equity) LOGSIZE_MVE
4.8457 (4.6323)
5.4391 (5.3593)
5.1677 (5.1532)
*** *** ***
Inverse share price INVPRICE
0.2363 (0.0759)
0.1744 (0.0580)
0.2652 (0.0792)
*** *** ***
Log market-to-book ratio LOGMB
0.5828 (0.5275)
0.5848 (0.5329)
0.5912 (0.5187)
Dividend yield DIVYLD
0.0172 (0.0000)
0.0173 (0.0055)
0.0116 (0.0000)
*** ***
Leverage (Debt/equity ratio) DE RATIO
1.2623 (0.6333)
1.1713 (0.6311)
1.4832 (0.6874)
*** *** ***
Daily return volatility RETVOL
0.0322 (0.0267)
0.0297 (0.0247)
0.0370 (0.0319)
*** *** ***
Systematic risk MKTBETA2
1.2048 (0.9577)
0.1029 (0.0147)
-0.9896 (-0.7295)
*** *** ***
Average monthly return AVGRET
0.0128 (0.0118)
0.0130 (0.0124)
0.0177 (0.0146)
** *
Log firm age in months FIRMAGE
4.8338 (4.8675)
4.9548 (4.9767)
4.7490 (4.7362)
*** *** ***
S&P 500 index inclusion dummy S&P500
0.0773 (0.0000)
0.1258 (0.0000)
0.0943 (0.0000)
*** ***
NASDAQ firm indicator NASDAQ
0.5378 (1.0000)
0.5043 (1.0000)
0.5931 (1.0000)
*** * **
23 tNtZ 1 where tj is the t-statistic for year j, N is the number of years, and t and )(t are the mean
and standard deviation, respectively, of the N realizations of tj. Z has a t distribution with N−1 degrees of freedom.
45
Table 3. Determinants of investor interest Models of industry factor price exposure as a determinant of investor interest in a firm’s stock. Proxies for investor interest include the natural logarithm of average monthly turnover (TURNOVER), institutional investor interest (LNUMGR), and mutual fund interest (LNUMFUNDS). Factor price exposure is measured by the continuous variable INDEXP and by indicator variables that equal 1 if a firm’s industry factor price exposure is greater (less) than the 70th (30th) percentile exposure (BETAHIGH and BETALOW). The percentiles are recalculated for each industry for each calendar year. Control variables measured at or for the year ended t-1 include: the natural logarithm of the market value of equity (LOGSIZE_MVE), the inverse price ratio (INVPRICE), the natural logarithm of the market-to-book ratio (LOGMB), dividend yield (DIVYLD), debt equity ratio (DE RATIO), idiosyncratic return volatility (RETVOL), average monthly firm returns (AVGRET), and turnover (TURNOVER, except in TURNOVER regressions). Control variables measured at or for the year ended t include: stock market betas (MKTBETA2), firm age (FIRMAGE), and indicator variables for S&P 500 stocks (S&P500) and NASDAQ (NASDAQ) listed stocks. The models are estimated annually from 1984 through 2006. The coefficient estimates, adjusted R2s, and number of observations (N) are the averages of the annual estimates. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). Parenthetical amounts represent the number of annual test statistics that are significant at the 10% level in the 23 annual regressions.
TURNOVER LNUMGR LNUMFUNDS Intercept -4.6089*** -4.2382*** 1.2015*** 1.3267*** 0.5761*** 0.6912*** LOGSIZE_MVE 0.1879*** 0.1987*** 0.4693*** 0.4689*** 0.4810*** 0.4806*** INVPRICE -0.2937*** -0.2872*** 0.1100*** 0.1125*** 0.0296 0.0323 LOGMB 0.0791*** 0.0736*** -0.0300** -0.0313** -0.0623** -0.0635** DIVYLD -3.8037*** -3.9146*** -1.5618*** -1.4895*** -5.8242*** -5.7147*** DE RATIO -0.0210*** -0.0151*** -0.0078*** -0.0066*** -0.0091*** -0.0081*** TURNOVER 0.2387*** 0.2442*** 0.2470*** 0.2513*** RETVOL 13.4460*** 15.6287*** -6.9094*** -6.5498*** -10.0423*** -9.6792*** MKTBETA2 0.3729*** 0.0882*** 0.0472*** 0.0013 0.0368** -0.0024 AVGRET 1.2600*** 1.5147*** 0.0259 0.0457 0.9553*** 0.9478*** FIRMAGE -0.0643*** -0.0787*** 0.1549*** 0.1526*** 0.1372*** 0.1357*** S&P500 0.1544*** 0.1151*** 0.3500*** 0.3424*** 0.6624*** 0.6559*** NASDAQ 0.2280*** 0.2135*** -0.1452*** -0.1486*** -0.2218*** -0.2246*** INDEXP 0.4966*** 0.0962*** 0.0917*** BETALOW -0.1869*** -0.0994*** -0.1073*** BETAHIGH 0.3273*** 0.0346*** 0.0328 Difference 0.5142 0.1340 0.1401 # of annual diffs sig (23/23) (19/23) (14/23) Average annual N 2,441 2,441 2,441 2,441 2,441 2,441 Average annual Adj R2 29.62% 26.69% 83.16% 83.14% 75.35% 75.33%
46
Table 4. Robustness tests for Table 3 This table reports additional analyses to validate the robustness of the main results reported in Table 3. Panel A reports the results across size quartiles of the sample firms. The quartiles are recalculated for each calendar year. Panel B reports the results using alternative measures of industry exposure. The first alternative is a standardized (demeaned) measure of the continuous variable INDEXP within industry/year. The second alternative measure is the estimate of industry exposure using an extended 3-factor Fama-French model (INDEXP3). We report results of estimation of identical models to those specified in Table 3, but using the INDEXP3 estimates and indicator variables based on the extended 3-factor model estimates (BETAHIGH3 and BETALOW3). We also report results using these alternative measures and augmenting the models in Table 3 to include the coefficient estimates on SMB and HML. All models include the control variables defined in Table 3; coefficient estimates for the control variables are not tabulated. The models are estimated annually from 1984 through 2006. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2).
TURNOVER LNUMGR LNUMFUNDS Panel A: By sample firm size quartiles
Smallest quartile INDEXP 0.3921*** 0.0515*** 0.0269 BETALOW -0.1575*** -0.0598*** -0.0436 BETAHI 0.3202*** 0.0183 0.0034 Middle two quartiles INDEXP 0.4965*** 0.0722*** 0.0744*** BETALOW -0.1760*** -0.0637*** -0.0816*** BETAHI 0.3269*** 0.0156 0.0261 Largest quartile INDEXP 0.3254*** 0.0963*** 0.1421*** BETALOW -0.0802*** -0.0634*** -0.0943*** BETAHIGH
0.1508*** 0.0128 0.0072
Panel B: Alternate measures of industry exposureDemeaned continuous measures INDEXP
0.3089*** 0.0787*** 0.0594***
Estimates from extended 3-factor model
INDEXP3 0.3502*** 0.0564*** 0.0526*** BETALOW3 -0.1523*** -0.0845*** -0.0895*** BETAHIGH3
0.2643*** 0.0252** 0.0119
Estimates from extended 3-factor model with factor betas included in the regressions
INDEXP3 0.4612*** 0.0886*** 0.0807*** BETALOW3 -0.1619*** -0.0920*** -0.0958*** BETAHIGH3 0.2676*** 0.0320** 0.0175
47
Table 5. Determinants of investor interest for diversification-sorted portfolios Average annual coefficient estimates on the proxies for industry factor price exposure from models of the determinants of investor interest estimated as a function of diversification. Proxies for investor interest include the natural logarithm of average monthly turnover (TURNOVER), institutional investor interest (LNUMGR), and mutual fund investor interest (LNUMFUNDS). Industry factor price exposure is measured by indicator variables that equal 1 if a firm’s industry factor price exposure is greater (less) than the 70th (30th) percentile exposure (BETAHIGH and BETALOW). The percentiles are recalculated for each industry for each calendar year. Panel A reports results for single segment firms vs. multi-segment firms. Panel B reports results for firms in the lower quartile, middle two quartiles, and upper quartile of the variable DIVERSE, which is 1 - a revenue-based concentration ratio such that lower values represent greater concentration. Firms are ranked within industry by year. All models include the control variables defined in Table 3; the coefficient estimates for the control variables are not tabulated. The models are estimated annually from 1984 through 2006. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). Parenthetical amounts represent the number of annual test statistics that are significant at the 10% level in the 23 annual regressions.
Panel A: Single Segment firms vs. multi-segment firms
Single segment firms TURNOVER LNUMGR LNUMFUNDS
Intercept -4.1859*** 1.2069*** 0.4683*** BETALOW -0.1984*** -0.0868*** -0.0920*** BETAHIGH 0.3289*** 0.0041 -0.0046 Difference 0.5273 0.0909 0.0874 # of annual differences that are significant (23/23) (9/23) (8/23)
Multi-segment firms
Intercept -4.1259*** 1.5883*** 0.9248*** BETALOW -0.2199*** -0.1011*** -0.1106*** BETAHIGH 0.3161*** 0.0198 0.0041
Difference 0.5360 0.1209 0.1147 # of annual differences that are significant (23/23) (14/23) (9/23)
Panel B: By quartiles of DIVERSE
Bottom quartile DIVERSE
Intercept -4.1824*** 1.1910*** 0.4536*** BETALOW -0.1976*** -0.0867*** -0.0926*** BETAHIGH 0.3284*** 0.0048 -0.0038 Difference 0.5260 0.0915 0.0888 # of annual differences that are significant (23/23) (10/23) (8/23)
Middle quartiles DIVERSE
Intercept -4.3384*** 1.8390*** 1.0264*** BETALOW -0.2469*** -0.0833*** -0.0977*** BETAHIGH 0.3312*** 0.0117 0.0059 Difference 0.5781 0.0950 0.1036 # of annual differences that are significant (23/23) (4/23) (4/23)
Upper quartile DIVERSE
Intercept -3.9604*** 1.4729*** 0.8957*** BETALOW -0.1871*** -0.1155*** -0.1182*** BETAHIGH 0.3253*** 0.0264 0.0093 Difference 0.5124 0.1419 0.1275 # of annual differences that are significant (23/23) (12/23) (8/23)
48
Table 6. Industry specificity and investor attraction to exposure Average annual coefficient estimates on proxies for industry factor price exposure from models of the determinants of investor interest. Proxies for investor interest include the natural logarithm of average monthly turnover (TURNOVER), institutional investor interest (LNUMGR), and mutual fund interest (LNUMFUNDS). Industry factor price exposure is measured by indicator variables that equal 1 if a firm’s industry factor price exposure is greater (less) than the 70th (30th) percentile exposure (BETAHIGH and BETALOW). Panel A reports results for models that include a continuous measure of industry specificity (SPECIFICITY) and interaction terms of SPECIFICITY with BETAHIGH and BETALOW. Panel B reports coefficient estimates on BETAHIGH and BETALOW for firms that operate in high-specificity, medium-specificity, and low-specificity industries. All models include the control variables defined in Table 3; the coefficient estimates for the control variables are not tabulated. The models are estimated annually from 1984 through 2006. The coefficient estimates presented are the averages of the annual estimates. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). Standard errors are clustered by industry within each annual regression.
Panel A: Interaction variables for SPECIFICITY TURNOVER LNUMGR LNUMFUNDS Intercept -4.2687*** 1.3383*** 0.9702***
SPECIFICITY -0.3808** 0.2461*** -0.1476 BETALOW -0.1539*** -0.0529*** -0.0209* BETALOW*SPECIFICITY -0.5082** -0.6123*** -0.5715* BETAHIGH 0.3402*** 0.0025 -0.0018 BETAHIGH*SPECIFICITY -0.2461 0.3593*** 0.3445*** Average annual N 2,441 2,441 2,158 Average annual adjusted R2 26.85% 83.20% 74.91%
Panel B: By quartiles of SPECIFICITY High Specificity Industries Intercept -4.2322*** 1.6390*** 0.9083*** BETALOW -0.3466*** -0.1914*** -0.2409*** BETAHIGH 0.2869*** 0.0812 0.0498 Difference 0.6335 0.2726 0.2907 Average annual N 253 253 253 Average annual adjusted R2 27.07% 83.15% 75.20% Medium Specificity Industries Intercept -4.2226*** 1.2467*** 0.5363*** BETALOW -0.1679*** -0.0878*** -0.0832*** BETAHIGH 0.3089*** 0.0307 0.0342 Difference 0.4768 0.1185 0.1174 Average annual N 1,613 1,613 1,613 Average annual adjusted R2 28.09% 83.72% 76.12% Low Specificity Industries Intercept -4.3897*** 1.4185*** 0.9383*** BETALOW -0.1647*** -0.0499*** -0.0791*** BETAHIGH 0.3946*** -0.0237 -0.0218 Difference 0.5593 0.0262 0.0573 Average annual N 575 575 575 Average annual adjusted R2 26.26% 81.73% 74.09%
49
Table 7. Institutional investor attraction to exposure in the full sample of industries by investor type Average annual coefficient estimates on the industry factor price exposure proxies from multivariate models of the determinants of the log of 1 + the number of institutions of a given type that hold a firm’s stock. Industry factor price exposure is measured by indicator variables that equal 1 if a firm’s industry factor price exposure is greater (less) than the 70th (30th) percentile exposure (BETAHIGH and BETALOW). The percentiles are recalculated for each industry for each calendar year. All models include the control variables defined in Table 3; the coefficient estimates for the control variables are not tabulated. The models are estimated annually from 1984 through 2006. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). Parenthetical amounts represent the number of annual test statistics that are significant at the 10% level in the 23 annual regressions.
Panel A: Institutional investors by size (market cap)
Small Medium Large
BETALOW -0.1066*** -0.0555*** 0.0028
BETAHIGH 0.0395*** 0.0157 -0.0013
Difference 0.1461 0.0712 0.0041
# of annual differences that are significant (20/23) (14 23) (4/23)
Test vs. Small (11/23) (21/23)
Test vs. Medium (14/23)
Panel B: Institutional investors by fiduciary standards
Banks
Insurance Companies
Investment
Advisors
Pensions/
Endowments
BETALOW -0.0731*** -0.0722*** -0.0880*** -0.0745***
BETAHIGH 0.0158 0.0462*** 0.0331** 0.0404***
Difference 0.0889 0.1184 0.1211 0.1149
# of annual differences that are significant (14/23) (19/23) (18/23) (18/23)
Test vs. Banks (4/23) (10/23) (8/23)
Panel C: Institutional investors by investment style
Dedicated
Owners Quasi-
indexers
Transient
Investors
BETALOW -0.0425*** -0.0922*** -0.0907***
BETAHIGH -0.0058 0.0271** 0.0614***
Difference 0.0367 0.1193 0.1521
# of annual differences that are significant (11/23) (19/23) (19/23)
Test vs. Dedicated Owners (14/23) (17/23)
Test vs. Quasi-indexers (7/23)
50
Table 8. Institutional investor attraction to exposure in high and low specificity industries by investor type Average annual coefficient estimates for industry factor price exposure proxies from multivariate models of the determinants of the log of 1 + the number of institutions of a given type that hold a firm’s stock estimated separately for high and low specificity industries. Factor price exposure is measured by indicator variables that equal 1 if a firm’s industry factor price exposure is greater (less) than the 70th (30th) percentile exposure (BETAHIGH and BETALOW). The percentiles are recalculated for each industry for each calendar year. All models include the control variables defined in Table 3; the coefficient estimates for the control variables are not tabulated. The models are estimated annually from 1984 through 2006. The coefficient estimates presented are the averages of the annual estimates. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). Parenthetical amounts represent the number of annual test statistics that are significant at the 10% level in the 23 annual regressions.
HIGH SPECIFICTY INDUSTRIES LOW SPECIFICTY INDUSTRIES Panel A: Institutional investors by fund size (market cap)
Small Medium Large Small Medium Large BETALOW -0.2062*** -0.1609*** -0.0648 -0.0515*** -0.0046 0.0245**
BETAHIGH 0.0784*** 0.0833*** 0.0634*** -0.0250 -0.0486** -0.0411*** Difference 0.2846 0.2442 0.1282 0.0265 -0.0440 -0.0656 # of annual sig differences (18/23) (17/23) (9/23) (5/23) (4/23) (7/23) Test vs. Small (4/23) (11/23) (8/23) (8/23) Test vs. Medium (7/23) (1/23)
Panel B: Institutional investors by fiduciary standards
Banks Insurance
Companies Investment Advisors
Pensions/ Endowments
Banks
Insurance Companies
Investment Advisors
Pensions/ Endowments
BETALOW -0.1228*** -0.2032*** -0.1779*** -0.1899*** -0.0295* -0.0327*** -0.0351** -0.0255* BETAHIGH 0.0771*** 0.0928*** 0.0699*** 0.0942*** -0.0408*** -0.0046 -0.0254 -0.0328* Difference 0.1999 0.2960 0.2478 0.2841 -0.0113 0.0281 0.0097 -0.0073 # of annual sig differences (16/23) (18/23) (14/23) (17/23) (5/23) (5/23) (3/23) (3/23) Test vs. Banks (6/23) (7/23) (8/23) (3/23) (4/23) (1/23)
Panel C: Institutional investors by investment style Dedicated
Owners Quasi-
indexers Transient Investors
Dedicated Owners
Quasi-indexers
Transient Investors
BETALOW -0.1059*** -0.1429*** -0.2608*** -0.0099 -0.0466*** -0.0350** BETAHIGH 0.0511** 0.0696*** 0.1138*** -0.0361*** -0.0283 -0.0143 Difference 0.1570 0.2125 0.3746 -0.0262 0.0183 0.0207 # of annual sig differences (14/23) (17/23) (21/23) (3/23) (4/23) (4/23) Test vs. Dedicated Owners (3/23) (12/23) (2/23) (4/23) Test vs. Quasi-indexers (13/23) (4/23)
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Table 9. Institutional investor portfolio composition and industry exposure Average annual excess weight placed on high and low exposure stocks in institutional investor portfolios over the period from 1984 to 2006. Each year we calculate the average weight invested in high and low industry exposure stocks. XS-BETAHIGH and XS-BETALOW represent the excess weight calculated relative to the value-weighted percentage of stocks with high and low industry exposure, respectively, within each industry each year. The null hypotheses of no investor preference for industry exposure is that XS-BETAHIGH = 0. (*){**}[***] indicate statistical significance at the (10%) {5%} [1%] level. Significance levels are based on a Z-statistic associated with the annual t-statistics (see Table 2). All industries High specificity industries Low specificity industries XS-BETAHIGH XS-BETALOW XS-BETAHIGH XS-BETALOW XS-BETAHIGH XS-BETALOW Panel A: Fund Size Small institutions 0.0107*** -0.0165*** 0.0373** -0.0534** 0.0015 -0.0174*** Large institutions 0.0127*** -0.0056 0.0485** -0.0835*** -0.0008 -0.0076 Panel B: Fiduciary Standard Banks -0.0273*** 0.0017 0.0089 0.0108 -0.0365*** -0.0036 Insurance Companies 0.0060* -0.0145*** 0.0191 -0.0526*** -0.0076** -0.0099* Investment Advisors 0.0174*** -0.0124*** 0.0478** -0.0759*** 0.0098*** -0.0151*** Pensions/Endowments 0.0048 -0.0128*** 0.0498** -0.0790*** -0.0093** -0.0082** Panel C: Investment Style Dedicated Owners 0.0178*** -0.0027 0.0277** -0.0266** 0.0241*** -0.0205* Quasi-indexers -0.0092*** -0.0017 0.0231 -0.0316 -0.0175*** -0.0061* Transient Investors 0.0425*** -0.0301*** 0.0754*** -0.1216*** 0.0230*** -0.0239***