Institutional Investor Participation and Stock Market Anomalies TAO SHU * May 2012 * Terry College of Business, University of Georgia. Email: [email protected]. Parts of this paper were drawn from the working paper “Trader Composition, Price Efficiency, and the Cross-Section of Stock Returns.” I acknowledge the helpful comments from John Griffin, Norman Strong (Associate Editor), Sheridan Titman, Julie Wu, and an anonymous referee. I appreciate the financial support from the Terry Sanford Award at the University of Georgia.
Transcript
Institutional Investor Participation and Stock Market Anomalies
TAO SHU*
May 2012
* Terry College of Business, University of Georgia. Email: [email protected]. Parts of this paper were drawn from the working paper “Trader Composition, Price Efficiency, and the Cross-Section of Stock Returns.” I acknowledge the helpful comments from John Griffin, Norman Strong (Associate Editor), Sheridan Titman, Julie Wu, and an anonymous referee. I appreciate the financial support from the Terry Sanford Award at the University of Georgia.
Institutional Investor Participation and Stock Market Anomalies
Abstract This paper investigates the impact of institutional trading volume on the cross-section of stock returns. I construct a measure that evaluates the percentage of total trading volume of a stock accounted for by institutional trades. Using a large sample of firms from 1980-2005, I find strong evidence that the strength of stock market anomalies such as price momentum, post-earnings announcement drift, value premium, and investment anomaly is decreasing in institutional trading volume. Additionally, the effects of institutional trading volume are stronger than those of institutional ownership, the major measure of institutional investor participation in the finance literature. These findings suggest that institutional trading significantly improves stock price efficiency.
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1. INTRODUCTION
The rapid growth of institutional investors has motivated numerous studies on the effects of
institutional investor participation on financial markets. Most of these studies measure institutional
investor participation using a stock’s institutional ownership – i.e., percentage of shares outstanding
of the stock held by institutional investors. In this paper, I examine a different aspect of institutional
investor participation that has been largely ignored by the current finance literature. Specifically, I
study the percentage of trading volume of a stock accounted for by institutional investors and the
impact of institutional trading volume on stock market anomalies.
Institutional trading volume can be vastly different from institutional ownership. Specifically,
institutional ownership evaluates institutional holding of a stock relative to individuals but
institutional trading volume measures how actively institutions trade a stock relative to individuals
Institutional trading volume can deviate substantially from institutional ownership because
shareholders are not necessarily traders. If, for example, passive pension funds hold 90% of a stock’s
shares but rarely trade, then little trading volume of the stock is accounted for by institutions despite
high institutional ownership. Similarly, if a stock has a low institutional ownership but by a group of
hedge funds or active mutual funds, then the stock can have a high institutional trading volume
despite low institutional ownership. Empirically, the correlation between fraction of institutional
trading volume (the FITV measure, described in section 2) and institutional ownership is only 0.41
during 1980-2005, far from a perfect positive correlation.
Institutional trading volume can have significant impact on stock price efficiency and
therefore stock market anomalies. Institutions are generally considered more sophisticated traders
than individuals. For example, Nofsinger and Sias (1999) find a strong positive relation between
change in institutional ownership and future stock returns, suggesting that institutional investors
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have the ability to predict stock returns.1
Institutional ownership and institutional trading volume are both important aspects of
institutional investor participation. Although institutional trading volume has received little attention
from financial researchers, its impact on stock price efficiency and stock market anomalies can be
greater than that of institutional ownership. This is because institutional trading directly moves stock
prices but institutional holding does not. Let’s take post-earnings announcement drift as an example.
If market under-react to an earnings announcement, then such under-reaction will lead to a post-
announcement drift in the same direction as earnings announcement return when prices gradually
adjust to the fundamental value. When institutions observe the under-reaction and trade on earnings
announcement return, their trading moves prices towards fundamentals, reducing or eliminating the
post-earnings announcement drift. In contrast, if institutions observe the under-reaction but simply
act as passive shareholders without trading the stock, then under-reaction or the post-earnings
announcement drift will remain intact.
Individual investors, in contrast, have been documented to
lose significantly from their trading and suffer a number of behavioral biases when they trade (e.g.,
Odean, 1998; Barber and Odean, 2000; Grinblatt and Keloharju, 2001). If institutions are more
sophisticated than individuals, then institutional investor participation can speed up information
diffusion into stock prices and improve stock price efficiency. As a result, institutional investor
participation will reduce the magnitude of observed stock market anomalies if these anomalies are
associated with price inefficiency.
In this paper, I empirically investigate the effects of institutional trading volume on major
stock market anomalies. I construct the FITV measure (fraction of institutional trading volume) that
evaluates for a firm-quarter the percentage of total trading volume accounted for by institutional
1 While many subsequent studies confirm the findings of Nofsinger and Sias (1999), some papers show that institutional trading could also move stock prices away from fundamental values. For example, Brunnermeier and Nagel (2004) and Griffin, Harris, Shu, and Topaloglu (2011) find that institutional trading contributed to the high tech bubble.
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investors. Specifically, for each stock-quarter I use quarterly institutional holdings from Thomson
Reuters’ 13f database and calculate an institution’s trading volume as the absolute value of its change
in holdings over the quarter. The absolute value aims to capture trading volume of the institution
whether it buys or sells the stock over the quarter. I then sum up trading volumes of all institutions
and divide by total trading volume of the stock-quarter (from CRSP) to obtain the FITV measure.
Section 2 describes the construction of the FITV measure.
It is worth noticing that the FITV measure excludes intraquarter round-trip institutional
trades. For example, if an institution purchases and then sells 1% of a stock’s shares within a quarter,
then the FITV measure will not include these two trades because they are not reflected in quarter-
end holdings. Elton, Gruber, Blake, Krasny, and Ozelge (2010) and Puckett and Yan (2011) examine
two institution samples comprised of mutual funds and pension funds, and find that intraquarter
round-trip trades account for approximately 20 percent of all institutional trades.2 Therefore, the
FITV measure likely captures majority of trading volume by mutual funds, pension funds, banks,
and investment advisors but exclude trading volume by day-traders or hedge funds that adopt high-
frequency strategies.3
I calculated the FITV measures for a large panel of 177,613 firm-quarters from 1980-2005,
with average 1,741 firms in each cross-section. The sample is restricted to NYSE/AMEX firms
because trading volumes of NASDAQ stocks are inflated relative to those of NYSE/AMEX stocks
by different trading mechanisms. Stocks priced below $5 or with market capitalizations below NYSE
Since the FITV measure is used for only cross-sectional comparison in this
paper, the missing round-trip trades will not introduce substantial noise to the results unless the
amounts of intraquarter round-trip trades systematically vary across different stocks.
2 Elton et al. (2010) examine 214 actively managed mutual funds during 1994-2005. Puckett and Yan (2011) examine institutional trades provided by ANcerno that account for approximately 10 percent of institutional trading volume during 1999 to 2005. My sample includes all institutional investors during 1980-2005. My sample also excludes small stocks, penny stocks, and NASDAQ stocks. 3 Section 2 presents details about how FITV captures institutional trades with various investment horizons. For example, it can be shown that FITV captures 100% of institutional trades with horizon of over three months, 67% of institutional trades with horizon of two months, but only 8.3% of institutional trades with horizon of one week.
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10 percent breakpoint were dropped to control for microstructure effects. The mean of the FITV
measure during 1980-2005 is 0.493, suggesting that institutions account for at least 49.3 percent of
the trading volume of an average sample firm during this period. It is difficult to assess the exact
amount of missing intraquarter round-trip trades for the sample. If intraquarter round-trip trades are
assumed to account for 20 percent of all institutional trades as suggested by previous studies (Elton
et al., 2010; Puckett and Yan, 2011), then after including round-trip trades institutions account for
approximately 61.6 percent (49.3/(1-0.2)) of total trading volume for an average sample firm during
1980-2005. More importantly, the cross-sectional distribution of the FITV measure is quite
dispersed, suggesting that different stocks indeed receive very different amounts of institutional
trading volumes.
Next, I examine the effects of the FITV measure on four major stock market anomalies. I
first examine price momentum (past winners outperform past losers) and post-earnings
announcement drift (PEAD) because they are among the most investigated and robust anomalies in
stock markets (Fama, 1998). I also study value premium (value stocks outperform growth stocks)
because there is a big literature exploring the risk- and behavioral-based explanations of value
premium. The analyses of value premium across levels of institutional trading volume can shed some
light on this debate. Finally, I choose investment anomaly (firms with low corporate investments
outperform firms with high corporate investments) because it has attracted a lot of attention and
that researcher use it to explain other anomalies such as post-SEO underperformance and the
accrual anomaly. 4
The empirical results provide strong evidence that the strength of all four stock market
anomalies is decreasing in the percentage of institutional trading volume. For example, in two-
4 A number of papers provide evidence of investment anomaly and study its relations with other stock market anomalies. An incomplete list of these papers includes Titman, Wei, and Xie (2004), Polk and Sapienza (2007), Xing (2007), Liu, Whited, and Zhang (2007), Wu, Zhang, and Zhang (2007), and Lyandres, Sun, and Zhang (2007).
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dimensional sorting analysis, momentum is 1.52 percent per month (t-stat 6.86) in low FITV stocks
but only 1.06 percent (t-stat 4.87) in high FITV stocks. Similarly, post-earnings announcement drift
is 0.85 percent per month (t-stat 7.01) in low FITV stocks but only 0.40 percent (t-stat 4.00) in high
FITV stocks. The spreads between high and low FITV stocks in momentum (0.46 percent, t-stat
2.94) and post-earnings announcement drift (0.45 percent, t-stat 2.78) are both statistically and
economically significant. These findings are robust with the multivariate regressions that control for
firm characteristics and other variables documented to affect momentum and post-earnings
announcement drift.
More strikingly, I find that value premium and investment anomaly only exist in stocks with
low institutional trading volume. Specifically, value premium is 1.26 percent (t-stat 4.58) per month
in low FITV stocks but an insignificant 0.29 percent (t-stat 1.46) in high FITV stocks. The
difference in value premium between low and high FITV stocks is 0.97 percent (t-stat 3.77).
Additionally, investment anomaly is 0.83 percent per month (t-stat 2.90) in low FITV stocks but
only 0.23 percent (t-stat 1.01) in high FITV stocks. The spread in investment anomaly between low
and high FITV stocks is also statistically and economically significant (0.60 percent, t-stat 2.80).
These findings are also robust with the multivariate regression analyses that control for firm
characteristics and other variables documented to affect value premium and investment anomaly.
Overall, the empirical results consistently suggest that the strength of stock market anomalies is
strongly decreasing in institutional trading volume.
A number of papers find that stocks with higher institutional ownership exhibit weaker
anomalous stock returns (e.g., Alangar, Bathala, and Rao, 1999; Bartov, Radhakrishnan, and Krinsky,
2000; Collins, Gong and Haribar, 2003; Ke and Ramalingegowda, 2005; Nagel, 2005). I use two
approaches to carefully control for the effects of institutional ownership. First, the tests are repeated
using a residual FITV measure constructed as the residual from cross-sectional regression of the
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FITV measure on institutional ownership. The residual measure captures the component of FITV
that is orthogonal to institutional ownership. Second, I estimate multivariate regressions that control
for the effects of institutional ownership on the examined anomalies. The results of both approaches
show that the results on the FITV measure are robust after controlling for institutional ownership,
indicating that the effects of institutional trading volume is not driven by institutional ownership.
Finally, I perform an interesting horse race between the FITV measure and institutional ownership
in the multivariate regression framework. Consistent with previous studies, I find negative relations
between institutional ownership and stock market anomalies. However, these relations largely
disappear after I control for the FITV measure, suggesting that the effects of institutional ownership
may be due to its correlation with institutional trading volume.
This paper makes important contribution to the literature of institutional investors. It is the
first study to show that fraction of institutional trading volume, which has been largely ignored by
the current literature, has significant impact on stock market anomalies. While this paper focuses on
stock market anomalies, future studies can explore the effects of institutional trading volume on
other stock market phenomena.
This paper also makes two contributions to the literature of stock market efficiency. First,
the empirical results provide supporting evidence of institutional investor participation improving
stock price efficiency (e.g., Alangar et al., 1999; Bartov et al., 2000; Gibson, Safieddine, and Sonti,
2004; Nagel, 2005; Boehmer and Kelley, 2009). Second, the decreased strength of all anomalies
examined in stocks with low institutional trading volumes is also consistent with these anomalies
being associated with price inefficiencies. The findings in this paper therefore also shed light on the
large literature that explores the risk- and behavioral-based explanations of stock market anomalies.
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The rest of the paper is organized as follows. Section 2 introduces the construction of the
FITV measure. Section 3 describes data and sample construction. Section 4 examines the effects of
institutional trading volume on stock market anomalies, and Section 5 concludes.
2. MEASURING FRACTION OF INSTITUTIONAL TRADING VOLUME
I construct the FITV measure (fraction of institutional trading volume), which evaluates the
percentage of trading volume of a firm-quarter accounted for by institutional trading, using the
equation below.
ijq
ijqijq
n
jiq Vol
IOIOFITV
|| 11
−=
−=∑
(1)
where FITViq is the FITV measure of stock i in quarter q. IOijq is institution j’s share ownership of
stock i at the end of quarter q, and IOijq-1 is institution j’s share ownership of stock i at the end of
quarter q-1. Voliq is total share volume of stock i in quarter q, calculated as the sum of monthly share
volumes during quarter q. Specifically, to calculate FITV for a firm-quarter, I first sum up the
absolute values of quarterly changes in ownership of the stock across all institutions, and then divide
by total share volume of the stock during the quarter.5
A caveat of the FITV measure is that it double counts the trades between two institutions.
For example, if institution A sells 100 shares to institution B, then this trade will be counted twice as
it is incorporated into the changes in holdings of both A and B. While it is difficult to evaluate the
magnitude of double counting, this issue does not seem to have a big impact on my inferences.
Conceptually, double counting assigns greater weights to the trades with intensive participation of
institutional traders, which is consistent with the goal of the measure. Empirically, since my tests
5 I carefully adjust for stock split during the quarter when constructing the FITV measure. Specifically, I calculate the numerator using change in shares holdings adjusted for stock split, expressed as a percentage of shares outstanding at the end of the quarter. I then calculate quarterly share volume in the denominator as sum of monthly share volumes, expressed as percentages of shares outstanding of the months. To control for outliers, both numerator and denominator are winsorized at 99% cutoff points.
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focus on cross-sectional comparison of the FITV measure, the inferences will be largely unaffected
unless there is evidence that double counting varies significantly across individual stocks.
It is worth noticing that the FITV measure excludes round-trip institutional trades during a
quarter. Specifically, FITV includes a trade with investment horizon longer than three months and
excludes a trade with investment horizon shorter than one day (day-trading). For a trade with
investment horizon between one day to three months, its probability of being captured by FITV is
an increasing function of investment horizon. For example, assuming that trades occur randomly in
a quarter, then a trade with investment horizon of two months will be included in FITV as long as it
occurs in the second or the third month of the quarter (included with 2/3 probability). In contrast, a
trade with investment horizon of one week will be included in FITV only when it occurs in the last
week of the quarter (included with a probability of one-twelfth, or 8.3 percent). The chart below lists
the corresponding probabilities for trades with different investment horizons.
Investment Horizon Percentage of Trades Captured by FITV ≥ 3 months 100.0% 2 months 66.6% 1 month 33.3% 2 weeks 16.7% 1 week 8.3% 2 days 3.0% <=1 day 0.0%
Elton et al. (2010) and Puckett and Yan (2011) both find that intraquarter round-trip trades
account for approximately 20 percent of all institutional trades. Their findings indicate that the
FITV measure captures the vast majority of institutional trades. An alternative approach to examine
missing round-trip trades is through portfolio turnovers of institutional investors. Carhart (1997), for
example, documents that mutual funds have an average turnover of 77.3% per year. Reca, Sias, and
Turtle (2012) find that annualized turnover rate is 109% for hedge funds and 41% for non-hedge
fund institutions. Since the turnover ratios in both studies are based on quarterly data, after
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considering the 20 percent missing round-trip trades the observed turnovers can be converted to
approximately 51% (41%/0.8) for non-hedge fund institutions, 96% (77%/0.8) for mutual funds,
and 136% (109%/0.8) for hedge funds. These turnover ratios correspond to hold periods of 23.5
months, 11.0 months, and 8.8 months, respectively, all well above the one-quarter interval of trading
volume calculation. Therefore the evidence of investment horizon also indicates that the FITV
measure captures the vast majority of institutional trades.
To distinguish the FITV measure from institutional ownership, I also construct a residual
FITV measure (ResFITV) that adjusts for institutional ownership. Specifically, in each quarter I
estimate a cross-sectional regression of the FITV measure on institutional ownership and take the
residuals. The ResFITV measure is therefore the component of the FITV measure that is orthogonal
to institutional ownership. I then repeat all the tests using the residual FITV measure to control for
institutional ownership.
3. DATA AND SAMPLE CONSTRUCTION
I obtain quarterly institutional holdings from Thomson Reuters’ 13f database, which contains the
filings by institutions under Section 13f of the Security and Exchange Act of 1934. Section 13f
stipulates that all investment managers with discretion over 13f securities worth $100 million or
more report their holdings to the SEC each quarter. Common stock, preferred stock, and
convertible debt are included in 13f securities. The types of institutions covered by Section 13f are
and foundations, insurance companies, broker-dealers, and investment banks. For each firm-quarter,
I obtain the reported net changes in share holdings from the prior report date for each institution to
calculate the FITV measure in equation (1).
Stock data including return, share volume, price, and shares outstanding are obtained from
the monthly CRSP database. The sample is restricted to ordinary common shares (share code 10 or
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11). If a firm-quarter exists in CRSP but not 13f, I do not drop the firm-quarter but follow the
literature (e.g., Gompers and Metrick, 2001) and assign zero to institutional holding. Annual
accounting data and quarterly earnings announcement data are obtained from annual and quarterly
Compustat databases. The data on analyst coverage are collected from I/B/E/S. Finally, daily stock
data including return, stock price, and share volume are obtained from the daily CRSP database.
NASDAQ stocks are excluded from the sample because their share volumes are inflated
relative to those of NYSE/AMEX stocks due to different trading systems. To control for the
microstructure effects, I drop the stocks priced below $5 or market capitalizations below the NYSE
10 percent breakpoint. Firms with negative book-to-market ratios are also excluded from the sample
as they could be associated with either data errors or extreme operating conditions. My final sample
contains 177,613 firm-quarters from the third quarter of 1980 to the last quarter of 2005, with an
average of 1,741 firms in each cross-section.
4. THE EFFECTS OF INSTITUTIONAL TRADING VOLUME ON STOCK MARKET ANOMALIES
(i) Summary Statistics
Table 1 presents summary statistics of sample firms. The average of the FITV measure is
49.3 percent for sample firms. Additionally, the FITV measures vary significantly across individual
stocks. For example, the 90th percentile of FITV is 87.8 percent, about ten times the 10th percentile
of 8.7 percent. These results suggest that individual stocks experience vastly different amounts of
institutional trading volume. The equal-weighted average of institutional ownership is 41.1 percent
for our sample firms (value-weighted average is 50.2 percent), which is consistent with previous
studies.
Table 1 also reports other characteristics of sample firms. Size for a firm-quarter is the
natural log of a firm’s market capitalization at the beginning of the quarter. B/M is the book-to-
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market ratio calculated as the sum of a firm’s book equity and deferred tax divided by the firm’s
market equity. 6
The Amihud illiquidity measure for a stock is constructed following Amihud (2002) using
the following equation
I apply the book-to-market ratio at fiscal year-end in calendar year t to the one-year
period starting from the July of year t+1. Ret [-6,-1] for a firm-quarter is the six-month buy-and-hold
returns up to the end of previous quarter. Beta is estimated annually with market model using daily
stock returns in the previous calendar year. Adjusted analyst coverage for a firm-quarter is analyst
coverage at the end of the previous quarter minus the average coverage of the firm’s NYSE size
quartile (Griffin and Lemmon, 2002). Quarterly stock turnover is calculated by summing up monthly
turnovers during the quarter, where monthly turnover is monthly share volume divided by shares
outstanding.
nDvol
r
Illiq
n
t t
t∑== 1
||
(2)
where rt is stock return on day t and Dvolt is dollar volume on day t. The illiquidity measure is
calculated annually using all daily returns and dollar volumes in the previous year.7
(ii) The Effect of Institutional Trading Volume on Price Momentum
The square-root
adjustment in Equation (2) is proposed by Hasbrouck (2006) to address skewness of the original
Amihud measure.
I examine the effect of institutional trading volume on price momentum using the rolling
momentum strategy proposed by Jegadeesh and Titman (1993). At the beginning of each month of
the sample period, an independent sort is used to rank stocks into terciles of the one-quarter lag
FITV measures and deciles of past six-month returns. These two-dimensional portfolios are then
6 To eliminate outliers, I winsorize the book-to-market ratios at the 99th percentile as did in the literature. 7 I follow Amihud (2002) and drop the firms with less than 200 valid daily observations in the estimation window.
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held for six months. To control for microstructure effects, I skip a month between portfolio
formation and return measurement.
I calculate monthly returns of momentum portfolios using the decomposed buy-and-hold
method proposed by Liu and Strong (2008). Specifically, portfolio return in an individual month τ of
the holding period (τ=2, 3,…, 6) is calculated as weighted-average of the month-τ stock returns with
the weight being a stock’s buy-and-hold return from month 1 to τ-1 in the holding period. This
approach assumes that a portfolio, once formed, is not rebalanced in the holding period. In contrast,
a commonly used approach that calculates portfolio return in each month as simple average of
monthly stock returns (“rebalances method”) actually assumes rebalancing the portfolio every
month during the holding period.8
Panel A of Table 2 reports for each portfolio the average monthly return and t-statistic
calculated using Newey-West robust standard error with five lags.
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8 Liu and Strong (2008) demonstrate that the “rebalance method” can create substantial biases especially in small or low-price stocks because of the negative return autocorrelations in these stocks. They show that the rebalance method exaggerates size premium but underestimates price momentum. For robustness, I also repeat the tests in this paper with the rebalance method and obtain similar results.
The monthly momentum profit
(Winner-Loser) is 1.52 percent (t-stat 6.86) for the lowest FITV tercile but only 1.06 percent (t-stat
4.87) for the highest FITV tercile. The difference in momentum profit between the lowest and
highest FITV terciles is 0.46 percent (t-stat 2.94), both statistically and economically significant. To
separate the effect of the FITV measure from that of institutional ownership, I repeat the sorting
analysis using the residual FITV measure which is orthogonal to institutional ownership (described
in Section 2). The results in Panel B of Table 2 show that momentum profit of the bottom ResFITV
group exceeds the top group by 0.41 percent (t-stat 2.70). This result indicates that the effect of
institutional trading volume persists after controlling for institutional ownership.
9 For robustness I also calculate Newey-West errors with different numbers of lags and the results are similar.
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I further estimate multivariate Fama-Macbeth regressions to control for other variables that
also affect momentum, such as firm size (Jegadeesh and Titman, 2001), book-to-market ratio
(Daniel and Titman, 1999), analyst coverage (Hong, Lim, and Stein, 2000), and stock turnover (Lee
and Swaminthan, 2000). I estimate cross-sectional regressions of quarterly stock returns and report
time-series means of the coefficients and associated t-statistics calculated using Newey-West robust
standard errors with five lags. To control for non-linearity of the variables and to ease comparison
of economic significances, I follow Chan, Jegadeesh, and Lakonishok (1996) and transform all the
independent variables into ranks uniformly distributed between 0 and 1. One month is skipped
before return measurement to control for the microstructure effects.
Table 3 presents the results on the regressions. The variable of interest is the interaction
between FITV and past six-month returns. I expect the coefficient on the interaction to be
significantly negative based on the negative association between FITV and momentum found with
the sorting analysis (Table 2). The results of Model 1 show that, indeed, the coefficient of the
interaction between FITV and past return is a significantly negative -4.00 (t-stat -4.46). This result
persists when I control for firm characteristics including beta, firm size and book-to-market ratio in
Model 2.
I also repeat the regression but with institutional ownership instead of the FITV measure in
Model 3. The interaction between institutional ownership and past return is also significantly
negative but much smaller than the FITV interactions in Models 1 and 2. These results suggest that
institutional ownership also negatively impacts momentum but the effect is much weaker than that
of FITV.
For a horse race between the FITV measure and institutional ownership, I include both the
FITV interaction and the institutional ownership interaction in Model 4. I further include
interactions of past returns with size, book-to-market ratio, turnover, and analyst coverage in Model
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5 to control for the effects of these characteristics on price momentum. Interestingly, I observe that
while the coefficient on the FITV interaction remains significantly negative, the coefficient on the
ownership interaction becomes insignificantly positive after controlling for FITV. These results
indicate that the effect of institutional ownership on momentum is largely due to its correlation with
institutional trading volume.
The coefficients on the other interaction terms are consistent with the previous studies that
momentum is stronger in smaller firms, growth firms, higher turnover firms, and firms with less
analyst coverage. The economic significances of coefficients on the FITV interactions are also
consistent with the sorting analyses in Table 2. For example, in Model 5, the coefficient on the
FITV interaction is -2.66 (t-stat -2.91), which suggests that quarterly momentum profit is about 1.58
percent stronger in the bottom FITV tercile than in the top FITV tercile.10
(iii) The Effect of Institutional Trading Volume on Post-Earnings Announcement Drift
To summarize, the
multivariate regression analyses in Table 3 confirm that the strength of price momentum is
decreasing in institutional trading volume.
I examine the effect of institutional trading volume on post earnings-announcement drift (PEAD)
using a rolling PEAD trading strategy proposed by Chan, Jegadeesh, and Lakonishok (1996).
Specifically, I construct an earnings shock measure for each firm-month as the four-day cumulative
abnormal return (CAR) during the [-2, 1] window centered on the firm’s most recent earnings
announcement. Daily abnormal return is calculated using the market model, where the market beta
is estimated in the one-year window up to two months before the announcement.11
10 The 1.58 percent is calculated as follows. Since the independent variables are transformed into ranks uniformly distributed between 0 and 1, the gap of 1.58 percent is calculated as 0.9 (difference between the top and bottom past-return deciles) times 0.66 (difference between the top and bottom FITV terciles) times the coefficient of -2.66.
To avoid using
11 I use CRSP value-weighted index as the market portfolio. Firms with less than six months of stock returns in the estimation window are excluded to avoid estimation errors.
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stale earnings data, I drop a firm-month if the firm’s most recent earnings announcement is more
than three months away.
The PEAD strategy is similar to the rolling momentum strategy proposed by Jegadeesh and
Titman (1993) except that the portfolios are formed on earnings shocks rather than past returns. At
the beginning of each month, an independent sort is used to rank stocks into terciles of the one-
quarter lag FITV measure, and deciles of earnings shocks. The two-dimensional portfolios are held
for six months, and PEAD is the difference in returns between the top and the bottom portfolios of
earnings shock. Monthly portfolio returns are calculated using the decomposed buy-and-hold
method proposed by Liu and Strong (2008).12
In Panel A of Table 4, I observe that PEAD is 0.85 percent per month (t-stat 7.01) in the
bottom FITV tercile but only 0.40 percent (t-stat 4.00) in the top FITV tercile. The gap in PEAD is
0.45 percent per month (t-stat 2.78) between the top and bottom FITV terciles, both statistically and
economically significant. To control for the effect of institutional ownership, I repeat the sorting
analysis with the residual FITV measure that is orthogonal to institutional ownership. The results in
Panel B show that, similar to Panel A, PEAD is 0.27 percent (t-stat 2.01) stronger in the bottom
ResFITV tercile than in the top ResFITV tercile.
I skip one month before return measurement to
control for microstructure effects and calculate t-statistics using Newey-West robust standard errors
with five lags.
I further perform multivariate regressions to verify the findings from sorting analysis.
Specifically, I estimate Fama-Macbeth regressions of quarterly stock returns on the interactions
between the FITV measure and earnings shock. Since the sorting analysis in Table 4 shows that
PEAD is decreasing in the FITV measure, I expect the coefficient on the interaction term to be
significantly negative. The independent variables also include interactions of earnings shock with 12 Section 4(ii) discusses the details of the decomposed buy-and-hold method. I also conduct robustness tests using the rebalance method and obtain similar results.
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institutional ownership and firm size because previous studies find that PEAD is stronger in stocks
with lower institutional ownerships and smaller stocks (e.g., Bartov et al., 2000).
Table 5 reports the results of return regressions. Models 1 and 2 show that the coefficient on
the interaction between FITV and earnings shock is significantly negative, and this result holds
when I control for the commonly examined firm characteristics including beta, size, book-to-market
ratio, and momentum. These results are consistent with the negative relation between PEAD and
institutional trading volume documented in the sorting analysis (Table 4).
For a comparison, I also examine the effect of institutional ownership on PEAD in Model 3.
Consistent with Bartov et al. (2000), I observe a significantly negative coefficient on the interaction
between institutional ownership and earnings shock. However, the coefficient (-2.37) is smaller than
that on the FITV interaction (-2.70). Models 4 and 5 include both the FITV and ownership
interactions. I further control for the effect of size on PEAD using an interaction term between firm
size and earnings shock. Interestingly, the results show that after including the size interaction, the
coefficient on the FITV interaction remains significantly negative but that on the ownership
The economic significance of the regression coefficients is also consistent with sorting
analyses in Table 4. For example, in Model 5, the coefficient on the FITV interaction is -1.47 (t-stat
-1.87), which suggests that post-earnings announcement drift is about 0.87 percent per quarter
stronger in the bottom FITV tercile than in the top FITV tercile.13
To summarize, both the sorting
analyses (Table 4) and regression analyses (Tables 5) provide strong evidence that institutional
trading volume has significantly negative impact on post-earnings announcement drift.
13 The 0.87 percent is calculated as follows. Since the independent variables are transformed into ranks uniformly distributed between 0 and 1, the gap of 0.87 percent is calculated as 0.9 (difference between the top and bottom earnings shock deciles) times 0.66 (difference between the top and bottom FITV terciles) times the coefficient of -1.47.
17
(iv) The Effect of Institutional Trading Volume on Value Premium
I first perform sorting analysis to examine the effect of institutional trading volume on value
premium. At the beginning of each quarter, stocks are independently sorted into terciles of the one-
quarter lag FITV measures and deciles of book-to-market ratios. Then the time-series means of the
monthly portfolio returns and the associated t-statistics calculated using Newey-West robust
standard errors are reported.14
Panel A of Table 6 reports the results of the sorting analysis. These results are striking
because I observe that value premium exists only in stocks with low institutional trading volume and
disappears in stocks with high institutional trading volume. Value premium (return difference
between the top and the bottom book-to-market deciles) monotonically decreases in the FITV
measure, ranging from 1.26 percent per month (t-stat 4.58) in the lowest FITV tercile to 0.29
percent (t-stat 1.46) in the highest FITV tercile. The spread in value premium between the extreme
FITV terciles is 0.97 percent per month (t-stat 3.77), both statistically and economically significant. I
also repeat the sorting analysis using the residual FITV measure that controls for institutional
ownership and find similar results in the Panel B of Table 6.
I further perform multivariate Fama-Macbeth regressions to examine the effect of
institutional trading volume on value premium. The dependent variables are monthly stock returns
and the independent variable of interest is the interaction between FITV and book-to-market ratio. I
predict the coefficient on the FITV interaction to be significantly negative because the sorting
analysis in Table 6 suggests a negative relation between value premium and institutional trading
volume. A cross-sectional regression of stock returns is estimated for each month and time-series
14 Since book-to-market ratio is annual measure and FITV is quarterly measure, the two-dimensional portfolios are formed quarterly and held for three months. I follow Liu and Strong (2008) and calculate monthly portfolio returns using the decomposed buy-and-hold method (discussed in Section 4(ii)). I also use the decomposed buy-and-hold method for the sub-portfolio analysis on investment anomaly in the next subsection. For robustness, I repeat the tests using the rebalance method and observe similar results.
18
means of the coefficients are reported. I also report associated t-statistics calculated using Newey-
West robust errors with five lags.
Table 7 presents the results of the return regressions. In Models 1 and 2, I observe
significantly negative coefficients on the FITV interactions and they are robust to the controls of
firm characteristics. For a comparison, Model 3 examines the effect of institutional ownership on
value premium. Consistent with Nagel (2005), I find significantly negative coefficient on the
ownership interaction, which suggests institutional ownership also has a significantly negative effect
on value premium. Interestingly, when I include both the FITV interaction and the ownership
interaction in Model 4, the coefficient on FITV interaction remains significantly negative (-1.03, t-
stat -3.43) but that on ownership interaction becomes insignificant (-0.30, t-stat -0.94). The results
are similar after I control for the effect of firm size on value premium in Model 5.15
The coefficients of the FITV interactions are also economically significant and in line with
the sorting analyses in Table 6. For example, in Model 5, the coefficient on the FITV interaction is -
0.98, translating into a gap in monthly value premium of 0.58 percent between the lowest and the
highest FITV terciles.
While these
results confirm the robustness of the FITV effect, they suggest that the effect of institutional
ownership on value premium is likely due the correlation between institutional ownership and
institutional trading volume.
16
To summarize, both sorting analyses in Table 6 and multivariate regression
analyses in Table 7 suggest that institutional trading volume strongly reduces value premium.
15 Fama and French (1992) find that value premium is larger in smaller stocks. In Model 5, the interaction term is insignificant, probably because I exclude small stocks from the sample by dropping firms priced below $5 or with market capitalization below NYSE 10 percent break point. 16 The 0.58 percent is calculated as follows. Since all independent variables are transformed into the ranks uniformly distributed between 0 and 1, the difference of 0.70 percent is calculated as 0.9 (difference between between the lowest and the highest book-to-market deciles) times 0.66 (difference between the lowest and the highest FITV terciles) times the coefficient of -0.98.
19
(v) The Effect of Institutional Trading Volume on Investment Anomaly
I examine the effect of institutional trading volume on investment anomaly using the corporate
investment measure from Titman, Wei, and Xie (2004). Specifically, I define investment of a firm as
its capital expenditure (Compustat annual item 128) divided by sales (annual item 12) and apply the
investment of a fiscal year ending in calendar year t to the one-year period from July of year t + 1.
For robustness tests, I also repeat the tests using the alternative investment measures that scale
capital expenditure by net property, plant, and equipment or asset values (Xing, 2008; Wu, Zhang,
and Zhang, 2010) and observe similar results.
At the beginning of each quarter, firms are independently sorted into terciles of the one-
quarter lag FITV measures and deciles of investments. I then calculate time-series means of the
monthly returns of the two-dimentional portfolios and the associated t-statistics using Newey-West
robust standard errors with five lags. Panel A of Table 8 reports the results. Interestingly, investment
anomaly concentrates in the firms with low institutional trading volume. Specifically, investment
anomaly (return difference between low and high investment firms) is 0.83 percent per month (t-stat
2.90) among the highest FITV tercile, but only 0.23 percent and insignificant (t-stat 1.01) in the
lowest FITV tercile. The spread in investment anomaly between the top and bottom FITV terciles
is 0.60 percent (t-stat 2.80), both statistically and economically significant. In Panel B of Table 8, I
control for the effect of institutional ownership using the residual FITV measure and obtain results
similar to those in Panel A. I also perform multivariate Fama-Macbeth regressions of monthly stock
returns similar to those in the previous subsections. For brevity these results are not tabulated but
they are consistent with the sorting analysis in Table 8 in terms of both statistical and economic
significances.
To summarize, I find strong empirical results that institutional trading volume has significant
effects on the stock market anomalies including momentum, post-earnings announcement drift,
20
value premium, and investment anomaly. These findings provide supporting evidence that
This paper is the first study that investigates the impact of institutional trading volume on stock
market anomalies. I construct a quarterly measure that evaluates the percentage of total trading
volume of a stock accounted for by institutional trading, and examine its effects on four major stock
market anomalies. I find that all four anomalies, including price momentum, post-earnings
announcement drift, value premium, and investment anomaly, are significantly weaker in stocks with
higher fractions of institutional trading volumes. Additionally, value premium and investment
anomaly exist only in stocks with low institutional trading volumes. These results are robust with
both sorting analyses and multivariate regression analyses that control for firm characteristics and
other factors documented to affect these anomalies.
This paper makes important contributions to the literature of institutional investors and the
literature of stock market efficiency. I present evidence that fraction of institutional trading volume,
which has been largely ignored by the current finance literature, has significant impact on stock
market anomalies. Additionally, the effects of institutional trading volume are stronger than those of
institutional ownership, the most commonly used measure of institutional investor participation in
the current finance literature. The findings in this paper also suggest that institutional investor
participation can improve stock price efficiency and therefore significantly weaken stock market
anomalies associated with price inefficiencies. While this paper focuses on stock market anomalies,
future studies can further explore the effects of institutional trading volume on other stock market
phenomena or corporate events.
21
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24
Table 1 Summary Statistics
This table reports summary statistics of sample firms. The sample contains 177,613 firm-quarters during 1980 to 2005. The FITV measure (fraction of institutional trading volume) of a firm-quarter is the percentage of total trading volume accounted for by institutional trading. Institutional Ownership of a firm-quarter is the aggregate institutional ownership of the firm’s shares at the beginning of the quarter, expressed as a percentage of the firm’s total shares outstanding. Turnover of a firm-quarter is the sum of monthly turnovers of the firm during the quarter, where monthly turnover is share volume in a month divided by total shares outstanding. Size of a firm-quarter is the natural log of the firm’s market capitalization at the beginning of the quarter. B/M is the book-to-market ratio calculated as the sum of a firm’s book equity and deferred tax, divided by the firm’s market equity. Ret [-6,-1] of a firm-quarter is the firm’s six-month buy-and-hold return up to the beginning of the quarter. Beta of a firm-quarter is market beta estimated using market model of daily returns of the firm in the previous year. Adjusted analyst coverage of a firm-quarter is the firm’s analyst coverage at the beginning of the quarter. I further calculate size-adjusted analyst coverage of a firm by subtracting average analyst coverage of the firm’s corresponding NYSE size quartile. Stock Price of a firm-quarter is price of the firm’s stock at the beginning of the quarter. Amihud Illiquidity of a firm-quarter is Amihud (2002)’s illiquidity measure (multiplied by 1,000) constructed using daily returns and trading volumes in the previous year. Variables Mean STD P10 P25 P50 P75 P90 FITV 0.493 0.327 0.087 0.272 0.464 0.668 0.878 Institutional Ownership 0.411 0.252 0.071 0.198 0.407 0.605 0.754 Size 19.88 1.91 17.38 18.47 19.88 21.19 22.35 B/M 0.798 0.565 0.251 0.417 0.679 1.037 1.471 Turnover 0.197 0.186 0.039 0.075 0.141 0.251 0.422 Ret [-6,-1] 0.105 0.340 -0.224 -0.083 0.068 0.234 0.448 Beta 0.889 0.552 0.235 0.513 0.847 1.214 1.595 Adjusted Analyst Coverage 0.930 4.991 -4.056 -1.475 0.091 3.068 7.157 Stock Price 44.15 1017.68 7.94 13.00 22.25 34.75 50.56 Amihud Illiquidity 0.314 0.449 0.027 0.055 0.145 0.389 0.813
25
Table 2 Sorting Analysis: Momentum Profits across Groups of FITV Measures
Panel A reports the performance of momentum strategy across groups of the FITV measures. The sample include 530,680 firm-month observations from 1980-2005. The momentum strategy is the same as the six-month/six-month strategy proposed by Jegadeesh and Titman (1993). At the beginning of each month, an independent sort is used to rank stocks into deciles of past six-month stock returns and terciles of the FITV measures in the previous quarter. The FITV measure is the percentage of total trading volume accounted for by institutional trading. The two-dimensionally sorted portfolios are then held for six months. I report the time-series means of monthly portfolio returns and associated t-statistics (in parentheses) for the two-dimensional portfolios as well as the differences between winner and loser portfolios (momentum profits). I also report the difference in momentum profits between the top and bottom FITV terciles. To control for microstructure effects, I skip one month before return measurement as did in Jegadeesh and Titman (1993). Panel B is similar to Panel A except that the portfolios are formed according to the residual FITV measures instead of the original FITV measures. The residual FITV measure is constructed as residual from the cross-sectional regression of the FITV measure on institutional ownership. T-statistics are calculated with Newey-West robust standard errors with five lags.
Stock Portfolios Sorted on Past Six-month Returns
Loser 2 3 4 5 6 7 8 9 Winner W-L t-stat Panel A: Monthly Returns of Portfolios Sorted on Momentum and the FITV Measure
Panel B: Monthly Returns of Portfolios Sorted on Momentum and the Residual FITV Measure Low Res FITV 0.14 0.73 0.92 1.06 1.22 1.18 1.25 1.27 1.40 1.58 1.44 (6.38) Med. Res FITV 0.47 1.01 1.14 1.23 1.27 1.31 1.30 1.34 1.38 1.52 1.04 (5.00) High Res FITV 0.65 1.12 1.24 1.30 1.36 1.37 1.37 1.43 1.47 1.68 1.03 (4.90) Low – High 0.41 (2.70)
26
Table 3 Fama-Macbeth Regressions of Quarterly Stock Returns: The Effect of FITV on Momentum
This table reports the results of Fama-Macbeth regressions of quarterly stock returns. The sample includes 143,903 firm-quarters from 1980-2005. The dependent variables are quarterly buy-and-hold stock returns (%).The independent variables include the FITV measure, institutional ownership, and their interactions with past stock returns. FITV is the percentage of total trading volume accounted for by institutional trading, measured in the previous quarter. Inst. Ownership is institutional ownership at the beginning of the quarter. Ret[-6,-1] is the buy-and-hold return of a firm during the six-month period up to the end of previous quarter. Beta is market beta of a firm estimated in the previous year. Size is the natural log of market capitalization at the beginning of the quarter. B/M is the book-to-market ratio. Turnover is total turnover of the previous quarter. Analyst Coverage is size-adjusted analyst coverage at the beginning of the quarter. To control for microstructure effects, I skip one month before the return measurement. To ease the comparison of economic significances, I follow Chan, Jegadeesh and Lakonishok (1996) and transform the independent variables into ranks uniformly distributed between 0 and 1. A cross-sectional regression is estimated each quarter and then the time-series means of the coefficients and associated t-statistics (in parentheses) are reported. T-statistics are calculated using Newey-West robust standard errors with five lags. The regressions are estimated with constants, which are not reported for brevity. ***, **, and * represent statistical significances at the 1%, 5%, and 10% levels, respectively. Models Model 1 Model 2 Model 3 Model 4 Model 5 FITV*Ret[-6,-1] -4.00*** -3.86*** -3.38*** -2.66*** (-4.46) (-4.45) (-3.57) (-2.91) Inst. Ownership*Ret[-6,-1] -2.46*** -1.00 0.66
Table 4 Sorting Analysis: Post-Earnings Announcement Drift across Groups of the FITV Measures
Panel A reports the performance of the post-earnings announcement drift (PEAD) strategy across groups of the FITV measures. The sample includes 481,442 firm-months from 1980-2005. The rolling PEAD strategy is the same as the one proposed by Chan, Jegadeesh, and Lakonishok (1996). At the beginning of each month, an independent sort is used to rank stocks into deciles of their earnings shocks and terciles of the FITV measures of the previous quarter. Earnings shock is four-day cumulative abnormal returns in the [-2, 1] window centered on the most recent earnings announcement. The FITV measure is the percentage of total trading volume accounted for by institutional trading. The two-dimensionally sorted portfolios are then held for six months. I report the time-series means of monthly portfolio returns and associated t-statistics (in parentheses) for the two-dimensional portfolios, as well as the differences between the top and bottom deciles of earnings shocks (PEAD). I also report the difference in PEAD between the top and the bottom FITV terciles. To control for microstructure effects, I skip one month before return measurement. Panel B is similar to Panel A except that the portfolios are formed according to the residual FITV measures instead of the original FITV measures. The residual FITV measure is constructed as residual from the cross-sectional regression of the FITV measure on institutional ownership. T-statistics are calculated with Newey-West robust standard errors with five lags.
Stock Portfolios Sorted on Earnings Shocks Low 2 3 4 5 6 7 8 9 High H-L t-stat
Panel A: Monthly Returns of Portfolios Sorted on Earnings Shocks and the FITV Measure Low FITV 0.50 0.87 1.01 1.07 1.11 1.12 1.25 1.22 1.15 1.37 0.85 (7.01) Med FITV 1.05 1.12 1.18 1.25 1.24 1.32 1.33 1.29 1.32 1.23 0.20 (2.16) High FITV 1.00 1.25 1.25 1.25 1.28 1.35 1.31 1.36 1.46 1.40 0.40 (4.00) Low – High 0.45 (2.78)
Panel B: Monthly Returns of Portfolios Sorted on Earnings Shocks and the Residual FITV Measure Low ResFITV 0.57 0.92 0.96 1.06 1.07 1.10 1.22 1.16 1.11 1.34 0.77 (6.80) Med ResFITV 0.95 1.13 1.19 1.21 1.22 1.31 1.28 1.33 1.34 1.20 0.26 (2.51) High ResFITV 1.01 1.21 1.27 1.31 1.31 1.38 1.38 1.39 1.49 1.50 0.49 (4.65) Low – High 0.27 (2.01)
28
Table 5 Fama-Macbeth Regressions of Quarterly Stock Returns: The Effect of FITV on
Post-Earnings Announcement Drift This table reports the results of Fama-Macbeth regressions of quarterly stock returns. The sample includes 137,960 firm-quarters from 1980-2005. The dependent variables are quarterly buy-and-hold stock returns (%). The independent variables include the FITV measure, institutional ownership, and their interactions with earnings shocks. FITV is the percentage of total trading volume accounted for by institutional trading, measured in the previous quarter. Inst. Ownership is institutional ownership at the beginning of the quarter. Earnings Shock is four-day abnormal return in the [-2, 1] window centered on the most recent earnings announcement. Beta is market beta of a firm estimated in the previous year. Size is the natural log of market capitalization at the beginning of the quarter. B/M is the book-to-market ratio. Ret[-6,-1] is buy-and-hold return of a firm during the six-month period up to the end of previous quarter. To control for microstructure effects, I skip one month before the return measurement. To ease the comparison of economic significances, I follow Chan, Jegadeesh and Lakonishok (1996) and transform the independent variables into ranks uniformly distributed between 0 and 1. A cross-sectional regression is estimated each quarter and then the time-series means of the coefficients and associated t-statistics (in parentheses) are reported. T-statistics are calculated using Newey-West robust standard errors with five lags. The regressions are estimated with constants, which are not reported for brevity. ***, **, and * represent statistical significances at the 1%, 5%, and 10% levels, respectively. Models Model 1 Model 2 Model 3 Model 4 Model 5 FITV*Earnings Shock -2.69*** -2.70*** -1.73** -1.47* (-3.61) (-3.90) (-2.39) (-1.87) Inst. Own*Earnings Shock -2.37*** -1.63** -0.51 (-3.00) (-2.01) (-0.69) Earnings Shock 2.40*** 2.01*** 1.98*** 2.40*** 2.72***
Table 6 Sorting Analysis: Value Premium across Groups of the FITV Measures
Panel A presents value premium across groups of the FITV measures. The sample includes 444,513 firm-months from 1980-2005. At the beginning of each month, an independent sort is used to rank stocks into deciles of their book-to-market ratios and terciles of their FITV measures of the previous quarter. FITV is the percentage of total trading volume accounted for by institutional trading. I then report time-series means of monthly portfolio returns and associated t-statistics (in parentheses) for the two-dimensional portfolios as well as the differences between the top and bottom deciles of the book-to-market ratios (value premium). I also report the difference in value premium between the top and the bottom FITV terciles. Panel B is similar to Panel A except that portfolios are formed according to the residual FITV measures instead of the original FITV measures. The residual FITV measure is constructed as residual from the cross-sectional regression of the FITV measure on institutional ownership. T-statistics are calculated using Newey-West robust standard errors with five lags.
Stock Portfolios Sorted on Book-to-Market Ratios Low 2 3 4 5 6 7 8 9 High H–L t-stat
Panel A: Monthly Returns of Portfolios Sorted on Book-to-Market Ratio and the FITV Measure Low FITV 0.17 0.63 0.83 1.12 1.18 1.16 1.28 1.27 1.51 1.43 1.26 (4.58) Med FITV 1.01 1.09 1.19 1.27 1.23 1.23 1.30 1.42 1.52 1.52 0.51 (2.16) High FITV 1.12 1.30 1.29 1.35 1.41 1.49 1.47 1.28 1.44 1.41 0.29 (1.46) Low – High 0.97 (3.77)
Panel B: Monthly Returns of Portfolios Sorted on Book-to-Market Ratio and the Residual FITV Measure Low ResFITV 0.39 0.69 0.99 1.12 1.08 1.14 1.17 1.22 1.43 1.37 0.97 (3.71) Med ResFITV 0.77 1.15 1.18 1.29 1.31 1.27 1.39 1.38 1.58 1.64 0.86 (3.66) High ResFITV 1.16 1.27 1.22 1.32 1.42 1.46 1.46 1.36 1.46 1.41 0.24 (1.23) Low – High 0.73 (3.25)
30
Table 7 Fama-Macbeth Regressions of Monthly Stock Returns: The Effect of FITV on Value Premium
This table reports the results of Fama-Macbeth regressions of monthly stock returns. The sample includes 437,466 firm-months from 1980-2005. The dependent variables are monthly stock returns (%). The independent variables include the FITV measure, institutional ownership, and their interactions with the book-to-market ratio. FITV is the percentage of total trading volume accounted for by institutional trading, measured in the previous quarter. Inst. Ownership is institutional ownership at the beginning of the quarter. B/M is the book-to-market ratio. Beta is market beta of a firm estimated in the previous year. Size is the natural log of market capitalization at the beginning of the quarter. Ret[-6,-1] is the six-month buy-and-hold return up to the end of previous quarter. To ease the comparison of economic significances, I follow Chan, Jegadeesh and Lakonishok (1996) and transform the independent variables into ranks uniformly distributed between 0 and 1. A cross-sectional regression is estimated each quarter and then the time-series means of the coefficients and associated t-statistics (in parentheses) are reported. T-statistics are calculated using Newey-West robust standard errors with five lags. The regressions are estimated with constants, which are not reported for brevity. ***, **, and * represent statistical significances at the 1%, 5%, and 10% levels, respectively. Models Model 1 Model 2 Model 3 Model 4 Model 5 FITV*B/M -0.98*** -1.17*** -1.03*** -0.98***
Ret [-6,-1] 0.65*** 0.67*** 0.67*** 0.67*** (3.31) (3.44) (3.45) (3.47)
Size*B/M 0.014 (0.04)
Adj. R2 0.014 0.046 0.046 0.049 0.050
31
Table 8 Sorting Analysis: Investment Anomaly across Groups of the FITV Measures
Panel A presents investment anomaly across groups of the FITV measures. The sample includes 468,379 firm-months from 1980-2005. At the beginning of each month, an independent sort is used to rank stocks into deciles of their investment expenditures and terciles of their FITV measure of the previous quarter. Investment expenditure is a firm’s investment expenditure of the previous fiscal year, scaled by total sales. FITV is percentage of total trading volume accounted for by institutional trading. I then report time-series means of monthly portfolio returns and associated t-statistics (in parentheses) for the two-dimensional portfolios as well as the differences between the bottom and top deciles of investment expenditure (investment anomaly). I also report the difference in investment anomaly between the top and the bottom FITV terciles. Panel B is similar to Panel A except that I form portfolios according to the residual FITV measures instead of the original FITV measures. The residual FITV measure is constructed as residual from the cross-sectional regression of the FITV measure on institutional ownership. T-statistics are calculated using Newey-West robust standard errors with five lags.
Stock Portfolios Sorted on Corporate Investments Low 2 3 4 5 6 7 8 9 High L - H t-stat
Panel A: Monthly Returns of Portfolios Sorted on Corporate Investments and the FITV Measure Low FITV 1.34 1.23 1.36 1.33 1.11 1.03 1.01 0.94 0.92 0.50 0.83 (2.90) Med FITV 1.39 1.38 1.32 1.36 1.35 1.22 1.25 1.22 1.15 1.15 0.24 (0.94) High FITV 1.49 1.41 1.56 1.41 1.42 1.28 1.31 1.16 1.23 1.25 0.23 (1.01) Low – High 0.60 (2.80) Panel B: Monthly Returns of Portfolios Sorted on Corporate Investments and the Residual FITV Measure
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