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How Do Firm’s Increases in R&D Affect
Long-Run Performance of Intra-Industry
Competitors?
Weifeng Hung
Feng Chia Universty
Sheng-Syan Chen
National Taiwan University
Yanzhi Wang
Yuan Ze University
R&D investment is a favorable strategy
• When a firm increases its R&D outlay, the
firm earns positive abnormal return both in the
short run (Chan, Martin and Kenisnger, 1990
JFE; Szewczyk, Tsetsekos and Zantout, 1996
FM) and in the long run (Chan, Lakonishok,
and Sougiannis, 2001 JF; Eberhart, Maxwell
and Siddique, 2004 JF; 2008 JAR).
R&D spillover effect
• According to Bernstein and Nadiri (1989),
• “A feature of R&D investment that
distinguishes it from other forms of investment
is that firms which do the investing are often
not able to exclude others from freely
obtaining the benefits from the R&D
projects…”.
Short-run results are not consistent with the
R&D spillover hypothesis
• Zantout and Tsetsekos (1994) document that
the rivals of firms that make announcements of
increases in R&D expenditures suffer a
statistically significant negative abnormal
return.
• Sundaram, John and John (1996) find that the
market reaction to competitors varies
depending on their competitive strategy
measure.
Motivations
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The long-term impact of R&D increase on intra-industry
competitors remains unknown. Particularly, no study explores
the long-run market reactions to spillover effect.
Lev and Sougiannis (1996) and Chan, Lakonishok and
Sougiannis (2001) suggest that because the R&D valuation is
hardly realized and not easily evaluated in a short horizon,
long-term study is more adequate to capture the intangible
information of the R&D investment.
Managers seldom announce R&D increases formally. There
might be a large time elapses between firm’s investment and
market’s perception. As a result, the market might take time to
fully reflect managers’ investment decision.
Motivations
• If the market underreacts to the direct future benefits
of the R&D increases, it might also underreact to any
indirect future benefits, if any, which a firm’s rivals
might gain from that firm’s R&D increase.
• Fama (1998) argues that the abnormal returns might
reflect normal random variations that occur in
efficient markets, the long-term results can be viewed
as an important challenge to the efficient market
hypothesis.
The benefits of R&D spillover effect
• Bernstein and Nadiri (1988) have indicated that the
R&D investment by a firm reduces its own
production cost and, as a result of spillovers, costs of
other firms are also reduced.
• If spillovers do lower rivals’ production cost, then we
would expect this effect to show up in the operating
performance of rivals.
• We use changes in operating performance and analyst
forecast revisions to proxy for improvements in
profitability.
Spillover Hypothesis
• Firms undertaking R&D investment are often not able to
appropriate the R&D benefits; that is, the benefits from R&D
investment may extend to other firms in the same industry
and/or economy.
• It is possible for rival firms to abstain from R&D investment,
and yet to take advantage of the knowledge generated by a
firm that does invest in R&D. (Jeffe, 1986; Bernstein, 1989;
Bernstein and Nadiri, 1988; Goto and Suzuki, 1989; Nadiri
and Kim, 1996; Srinivasan, 1995).
• Intra-industry rivals earn positive long-term abnormal return
and experience improvements in operating performance and
analyst’s forecast revisions
Strategic Reaction Hypothesis
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A firm that increases its R&D spending might gain
unfriendly attention from its rivals.
R&D increasing behaviors might be taken as that firm is
moving ahead in the race to be the first to innovate to exploit
the future benefits.
Sundaram, John, and John (1996) suggest that firms adopt
R&D announcement as for a means of strategic interaction.
Massa, Rehman, and Vermaelen (2007) suggest that a
repurchase firm conveys a valuable signal about firm
undervaluation, which threatens competitors. To undo this
effect, the rival may mimic and repurchase shares.
Massa, Rehman, and Vermaelen (2007) suggest that most
strategic reacting behavior occurs in concentrated markets.
Do managers use a non-announcement channel, such as
R&D investment racing, to strategically react to their rivals?
Sample selection
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Source:
•
The sample includes listed stocks in NYSE/AMEX/NASDAQ
during the period 1974 to 2006. Data on stock price and number
of shares outstanding to compute market value of equity are
obtained from the CRSP database.
•
Sample selection criterion:
•
(1) R&D intensity (measured by the ratio of R&D-to-assets (RDA,
data46/data6) and R&D-to-sales (RDS, data46/data12) of at least
5%,
(2) increases in dollar R&D by at least 5% (R&D growth rate, or
RDI),
(3) increases in ratio of R&D-to-assets (RDAI) by at least 5%.
•
•
Sample selection criterion:
• Further, we exclude the non-common stock ADRs, SBIs, unit
trusts, closed-end funds, REITs, and financial firms, as the
work done by Fama and French (1992, 1993).
• Sample stocks are also excluded if they have the following
conditions:
• (1) non-positive book equity, (2) without sales, operating
income before depreciation (data13), earnings before interest
and taxes (data178), total assets, or market value (3) without
industry concentration measures (4) a firm have not appeared
in COMPUSTAT for more than two years (Banz and Breen,
1986).
• The final sample consists of 10,452 firm-year observations, in
which the sample includes 3,646 R&D increasing firms.
Definition of industry rivals
• Throughout the paper, we use CRSP four-digit SIC
classification to define industry membership.
• We measure the industry concentration using
Herfindahl-Hirschman Index (HHI).
• HHI is a commonly accepted as the measure of the
product market concentration. HHI is the sum of
squared market share of each firm in the industry.
• Each year, we classy three groups based on HHI,
where low concentration portfolio corresponds to the
30% of industries with the lowest concentration, while
high concentration portfolio corresponds to the 30% of
industries with the highest concentration.
Definition of industry rivals cont.
• For each sample firm, we construct its corresponding
industry portfolio as all stocks, except the sample firm
itself, in the same four-digit SIC industry as the sample
stock.
• The returns on industry portfolio are equally and value
weighted.
• That is, if we have 10,452 firm-year observations, then
10,452 industry portfolios will be obtained.
Methodologies
• Calendar time abnormal returns
• For each calendar month t in our sample period, we form a
portfolio of all sample firms that have significantly increased
their R&D investment in the previous five years (60 months).
• We then run the Fama and French three-factor model and
Carhart four-factor model for long-term abnormal stock
returns shown in the following equation:
• Both equally- and value-weighted portfolio returns are
calculated.
• Rolling-over method:
• A firm’s risk may change in response to its R&D change (Berk,
Green, and Naik, 1999; Chan, Lakonishok, and Sougiannis,
2001)
• We use the first 60 monthly returns (e.g., from April 1975 to
March 1980) of the portfolio to estimate its factor loadings,
and calculate the expected portfolio return in month 61 (e.g.,
April 1980) based on these factor loadings estimated over the
previous 60 months multiplied by their corresponding factor
returns in month 61.
• The abnormal return in month 61 is the difference between the
actual portfolio return and expected portfolio return.
• Delisted-adjusted returns:
• To mitigate survival-ship bias in returns for firms delisted from
CRSP for performance reasons, we follow the procedure of
Shumway (1997) and Shumway and Warther (1999).
• Specifically, for firms delisted for performance reasons, we
substitute -30% as the last return for NYSE and Amex stocks
and -55% for Nsadaq firms.
• Cumulative benchmark adjusted returns:
• Our procedure for calculating benchmark-adjusted returns
follows the methodology outlined in the Daniel, Grinblatt,
Titman, and Wermers (1997, JF) study that developed
benchmarks to evaluate mutual fund performance.
• Specifically, we form 25 benchmark portfolios that capture three
stock characteristics namely book-to-market equity and size
which are significantly related to the cross-sectional variation in
returns.
• Each stock, in each year, is assigned to a benchmark portfolio
according to its rank based on SZ and BM. Excess monthly
returns of a particular stock are then calculated by subtracting
the stock’s corresponding benchmark portfolio’s returns from
the stock’s returns. Specifically, the characteristics-adjusted
return is defined as:
• where Rit and RtCH are the return on security i and the return on a
SZ-BM-matched portfolio in month t, respectively.
• Each month, we use characteristics-adjusted return to calculate
portfolio’s abnormal returns, then the abnormal monthly
returns after formation period are cumulated as cumulative
abnormal returns.
i
• RATS approach :
• Sock excess returns are regressed on the Carhart (1997) fourfactor for each month in event time, and the estimated intercept
represents the monthly average abnormal return for each event
month.
• The long-run abnormal returns between 1 month and 60 months
(j) after a large increase in R&D at a sample firm are adopted.
• The following regression is run each event month j:
• ri,t are the equally- and value-weighted portfolio returns on
industry portfolios in calendar month t that corresponds to the
event month j, with j = 0 being the month of the beginning of the
fourth month following fiscal year-end in which there is a large
increase in R&D at a sample firm.
Summary statistics
• The statistics reported in Table 1 are very
similar to the those reported in EMS.
• The average (median) HHI is 0.245 (0.176),
suggesting that the most of sample firms are
within less concentrated industries.
• The average (median) number of rival firms in
each industry portfolio is around 91 (58).
Table 1 Summary statistics
Spillover effect
• Consistent with EMS, Panel A of Table 2 shows that both
equally and value-weighted long-run abnormal returns on
sample stocks are significantly positive. The abnormal returns
are 0.86% and 0.34% for equally- and value-weighted method.
The results are quantitatively similar to EMS.
• There are significantly positive abnormal returns
for the rival portfolio.
Table 2 Long-Term Abnormal Return for Large
R&D-Increase Firms and Rival Portfolios
The influence of strategic reaction
• Table 3 shows that the coefficient of the Concentration
x R&D increase wave term is 0.261 (Model 3), which is
significant at 1% confident level.
• This indicates that the higher the concentration of the
industry and the higher total number of firms that
largely increase R&D expense over past five years in
the industry, the more likely that the firms located in
that industry will increase their R&D expense.
Table 3. Probit Regression of Indicator for Large
Increases in R&D
The influence of strategic reaction
• Table 4 shows significant positive abnormal
returns for the rival portfolio in less
concentrated industries.
• Instead, in the concentrated industries, the
abnormal returns for the rival portfolios are not
significant, and some rival portfolios even earn
negative abnormal returns.
Table 4 Long-Term Abnormal Return for Rival
Portfolio Sorted by Industry Concentrations
The influence of strategic reaction
• Fig. 1 shows that the long-term return of the rival portfolio in
low concentration industry experiences high return. In
particular, the rival portfolio in high concentration industry
earns negative BHARs.
• Table 5 demonstrates that over 12 (24, 36, 48, 60) months, for
the full sample, the cumulative equally-weighted average
abnormal returns of 10.05% (22.28%, 34.93%, 46.54%,
58.50%), are all significant at the 1% level. The results of the
subsample indicate that for the low industry concentration
group, the CARs are all significant at the 1% level.
• Therefore, these results further provide supports for the
strategic reaction hypothesis
Figure 1 Cumulative Abnormal Return for Rival
Portfolios
Table 5. Long-Term Cumulative Abnormal for
Rival Portfolios
Cross-sectional regression analysis
• The dependent variable is 60-month buy-andhold abnormal returns (BHAR) of each
industry portfolio, in which the buy-and-hold
abnormal return is controlled for the size, B/M
matching portfolio return.
Further spillover evidence
• In Model 1 and 2, the results show that the BHAR of
sample firm term is positive and highly significantly
across all the models indicating that the higher the
buy-and-hold abnormal returns to sample firms occur
following the R&D increases, the greater the buyand-hold abnormal returns to rival portfolios will earn.
• The long-run abnormal returns of industry portfolio
are also positively associated with the level of R&D
increases by largely R&D-increase firm.
• This clearly suggests that the R&D increases has
spillover effect on rival firms, and is consistent with
the spillover hypothesis.
Further strategic reaction evidence
• The coefficient estimate of Concentration is
significantly negative. Thus, the higher the
concentration of the industry, the lower the
long-run abnormal returns to industry
portfolios will be.
• The coefficients of the interacting terms are
negative.
Table 6 Cross-Sectional Analysis of Long-Run
Abnormal Returns to Rival Portfolios
Changes in operating performance
• First, the operating performance of the rival portfolios
deteriorates prior to the event year and increased
subsequent to the event year.
• Second, the improvements in post-event operating
performance are the higher for rivals with low
concentration and lower for rivals with high
concentration.
Figure 2 Changes in Return on Assets (ROA) of
Rival Portfolios
Figure 3 Changes in Profit Margins (PM) of
Rival Portfolios
Cross-sectional regression analysis
• The dependent variable is five-year average post-event
changes in operating performance (ROA and PM) of each
industry portfolio.
• First, for all models, the intercept indicates that industry
portfolio experiences positive changes in ROA (PM) post to
the R&D increasing year.
• The long-run post-event changes in ROA of industry portfolio
are positively associated with the level of R&D increases by
sample firm.
• Second, the coefficient estimate of Concentration is
significantly negative.
Table 7 Cross-Sectional Analysis of Changes in
Long-run Operating Performance of Rivals
Analysts forecast revisions
• The dependent variable is the post-event 60-month average of
abnormal analysts’ EPS forecast revisions of industry
portfolios.
• The evidence indicates that the long-run averages of abnormal
analysts’ forecast revisions of industry portfolio are positively
associated with the level of R&D increases by largely R&Dincrease firm.
• On the other hand, the coefficient estimate of Concentration is
significantly negative.
Table 8 Cross-Sectional Analysis of Changes in
Analysts’ EPS Forecast Revisions of Rivals
Institutional Trading Surrounding Share
Repurchase Announcements (SRA)
Weifeng Hung (洪偉峰)
Department of Finance, Feng Chia University, Taiwan
Agenda
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Motivations
Contributions
Data and methodologies
Empirical Results
Conclusions
Motivation: SRA attracts institutions?
• Allen, Bernardo, and Welch (2000) argue that undervalued
firms who want to signal their worth would like to attract
institutions because institutions are better at assessing the
firm’s true worth.
• Several studies indicate that SRA attracts institutions
(Grinstein and Michaely, 2005; Shleifer and Vishny, 1986;
Allen, Bernardo, and Welch, 2000).
• On the other hand, unlike individual investors, institutions are
expected to be less prone to attention-driven trading behavior
(Barber and Odean, 2008).
Motivation: Institutional response
• Institutional investors are expected to have ability to move
prices directly through their own trading, as well as indirectly,
by influencing the trading decisions of other market
participants who may follow their actions.
• Institutional trading affects stock returns(Bannet et al., 2003;
Gompers and Metrick, 2001).
• Understanding of whether SRA attracts institutions is of great
importance for firms announcing share repurchases.
Motivation: Superior information?
• Institutions would be expected as sophisticated investors in
processing information to arbitrage the repurchases anomaly
to earn superior returns.
• Prior studies indicate that institutional investors are able to
correctly identify corporate events, such as IPO and SEO.
• Why SRA?
• The buyback anomaly has persisted for 25 years in U.S. stock
market (Peyer and Vermalen, 2008).
Motivation: Superior information cont.
• SRA in Taiwan:
• On average, firms announcing share buyback earn significantly
positive abnormal returns.
• However, about 45% of events in Taiwan experience negative
cumulative abnormal returns in the 30 days following SRA.
• If institutional investors do have informational advantages in
processing corporate activities, it is intuitively credible that
individual investors can profit from the buy-sell information by
imitating institutional trades surrounding the corporate
announcements.
Motivation: Unique datasets
• Since daily institutional trading data is not easily assessed,
most empirical studies of institutional trading have focused on
quarterly or annual data, such as 13(F) database.
• Few studies have explored the relationship between
institutional daily trading behavior and SRA.
• Puckett and Yan (2010) suggest that trading performance
shown by prior studies using quarterly data are biased
downwards because of inability of publicly accessing interim
trades.
Motivation: Unique datasets, cont.
• We argue that the quarterly holdings data cannot capture the
intra-quarter institutional trading, such as the exact timing of
institutional trading surrounding the share repurchases
announcements.
• Particularly, we show that institutional trading occurs very
near to the SRA date, about 10 days before SRA and a month
after.
• Daily institutional trading data in Taiwan allows us, for the first
time, to contribute to the literature by examining the daily
institutional trading behavior in response to SRA.
Contributions
• 1. SRA significantly attracts institutions, switching their trading
behavior from net selling to net buying. This finding is consistent
with the argument that SRA attracts institutions (Grinstein and
Michaely, 2005; Shleifer and Vishny, 1986; Allen, Bernardo, and
Welch, 2000).
• 2. There is an institutional price impact before and after SRA.
• 3. Institutional trading seems to have predictive ability for the
post-SRA stock performance.
• 4. However, this trading skill disappears after controlling for
their post-SRA price impact.
Data
• We obtain daily data from Taiwan Economic Journal (TEJ),
including stock repurchases announcement events (for the
interests of shareholders), market index returns (including
dividends), and institutional trading volumes.
• Annual accounting data, such as book equity, are also
retrieved from TEJ. This paper includes 610 repurchasing
samples from October 13, 2000 through December 31, 2006.
• We exclude events without institutional trading, stock returns,
market value, and accounting variables at announcement date.
The stocks with less than 130 trading days prior to the share
repurchase announcement are also dropped.
Institutional trade imbalance
 A positive (negative) institutional trade imbalance for a stock
stems from institutional net buying (net selling) activities and
increases (decreases) in institutional ownership for the stock.
 We use the mean institutional trade imbalance of period from
day -130 through day -31 (relative to the initial announcement
day 0) to estimate the expected institutional trade imbalance.
 The daily abnormal institutional trade imbalance is calculated as
the difference between the actual trade imbalance and expected
trade imbalance across stocks for each day.
Operating performance
• We define the unexpected change in performance as the change
in performance of the repurchasing firm minus the change in
performance of a matching firm.
• Empirical results
Favorable information
• The mean (median) CAR for the announcement period (-2,+2) of
1.20% (1.18%) is positive and significant at the 1% level.
• The positive mean (median) CAR of 2.96% (2.23%) for the postannouncement (+3,+30) period indicates a significant reversal
for firms announcing stock repurchases.
Table 1 Summary Statistics
S.R.A. attracts institutions
• SRA attracts institutions:
• The SRA significantly affects institutional trading behavior,
i.e., from net selling behavior to net buying behavior.
• Price impact:
• There is a positive concurrent relationship between
institutional trading and stock returns around SRA window.
Table 1 Summary Statistics
Table 2 Short-Run Price Reactions and
Institutional Reactions of Each Year
Table 3 Summary Statistics of Each Industry
Predictive ability
• Institutional trading has predictive ability for the stock
performance following SRA.
• Specifically, it appears that institutional investors are able
to identify stocks with good (underpriced) or bad
(overpriced) SRA.
Table 5 Abnormal Institutional Trade Imbalances and
Price Behavior Surrounding S.R.A.
Institutional trading following S.R.A.
• Institutional investors are not feedback traders following SRA,
i.e., their post-SRA trading behavior is not driven by prior
returns.
• The decisions of institutional trading following share repurchase
announcements seem to be consistent with the institutional
herding hypothesis, i.e., they trade by following themselves or
others’ trades.
Price impact
• Post-event institutional trading indeed impacts stock price.
• For the net sell and net buy groups, the CARs(+3,+30) are
significantly negative and positive at the 1% level, respectively.
• However, the impact accounts for partial market reactions to
SRA.
• The CAR(+3,+30) of the neutral group, 4.15%, is significant at the
1% level.
Table 10 Portfolios based on CATI(+3,+30)
Short-run predictive ability V.S. price impact
 It thus seems natural to ask whether the short-run trading
skill of institutional trading surrounding SRA is due to the
price impact caused by their trading persistence.
 If institutional trading skill mainly results from the price
impact caused by their persistent trading, we should see an
insignificant cumulative abnormal return following SRA.
 For groups without persistent trading, their market
reactions are not significantly different from zero, implying
that the predictive ability of institutional trading
surrounding SRA mainly results from their post-eve nt price
impact.
Table 11 Independent Double Classifications based on
CATI(-2,+2) and CATI(+3,+30)
Conclusions
• We use daily data to study institutions in response to buyback
announcements.
• Buyback announcement attracts institutions. Institutions are
net sellers before buyback announcement and net buyers
after.
• Institution’s trading skill is driven by their post-event price
impact.
• The evidence does not support institutional informed trading.
Can Institutional and Individual
Trading Drive Value and Size
Premiums in Japan?
Weifeng Hung
Associate Professor, Department of Finance,
Feng Chia University, Taichung, Taiwan
Agenda
•
•
•
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Motivations
Data descriptions and variable definitions
Empirical results
Conclusions
The possible explanations of value and size
premiums
• 1. Rational: compensation for risks (Fama and
French, 1993; 1996)
• 2. Behavioral bias: overreaction (Lakonishok,
Shleifer, and Vishny, 1994)
• 3. Data snooping (Lo and MacKinlay, 1990)
Price impact by institutional trading
• Institutional trading has a dynamic relation with
stock returns .
• Two possible effects:
• 1. Destabilization: If trading results from fads, reputational concerns,
or preference for certain firm characteristics, such trading may drive asset
prices away from fundamental values and create return reversals in the
subsequent period. (DeLong et al., 1990; Choe et al., 1999; Wermers, 1999;
Sias, 2004)
• 2. Stabilization: Institutional buying (selling) may stabilize the stock
market when prices are undervalued (overvalued).
• Which one does institutional trading have?
Trading preference by institutional investor
• Frazzini and Lamont (2008) and Sharma, Hur,
and Lee (2006) indicate that institutions tend
to buy growth stocks and sell value stocks.
• However, seldom studies explicitly show
evidence that trading preference by
institutional investor drives or mitigates the
value and size premiums.
Trading preference by individual investor
• Kaniel, Saar, and Titman (2008) examine NYSE trading data
and find that individual investor tend to be contrarian
traders in the short-run, i.e., they buy stocks after prices
decrease and sell stocks after prices increases.
• However, there is less agreement about the long-run
trading preference by individual investors.
• Particularly, the long-run relation between individual
trading and future stock returns has received little
attention.
Purposes
• 1. Do institutional (individual) investors buy (sell) growth
stocks and sell (buy) value stock in Japan?
• 2. What is the dynamic relation between institutional
(individual) trading and stock returns?
• 3. Does institutional trading and/or individual trading drive
value and size premiums?
• 4. Can the strategy based on trading preferences by
institutional investors or individual investors enhance value
and/or size strategy?
Data
• From Pacific Basin Capital Market Research
Center (PACAP)
• 2. The sample period from 1975 to 2005
• 3. The risk-free interest rate: 30-day Gensaki
rate
• 4. 36,233 firm-year observations
Variables
• We compute BE/ME as the ratio of book value
of equity (as Fama and French, 1992) at the
end of March (the end of the fiscal year)
divided by the market value of equity at the
end of March from 1975 to 2005.
• 2. We compute market capitalization (ME)
using market equity at the end of June in the
calendar year t.
Variables
• 3.We calculate institutional trading (DITH) as changes in
institutional ownership between fiscal year end t-2 and
fiscal year end t-1.
• To control for systematic component, we compute
industry adjusted change in institutional ownership
(AdjDITH) as DITH subtracts median value of industry
DITH, where industry DITH is measured by two-digits SIC
industry.
• Adjusted change in individual ownership (AdjDIND) is
defined similarly.
Relations among BE/ME, size, AdjDITH, and
AdjDIND
• BE/ME is negatively associated with
institutional trading and positively associated
with individual trading.
• Size is negatively associated with individual
trading, however, unrelated with institutional
trading.
Table 3. Characteristics for quintile portfolios formed on
book-to-market equity ratio or size
Table 4. Average parameter values from cross-sectional
regressions of annual book-to-market ratio and size on
changes in institutional and individual ownership
AdjDITH, AdjDIND, and the cross-section of
average stock returns
• The institutional trading is significantly and
negatively related to future stock returns.
• Individual trading has no significant influence
on current price. Particularly, their trading is
also unrelated to future stock returns.
Table 5. Average monthly percent returns and
characteristics for decile portfolios formed on AdjDITH
Table 6. Average monthly percent returns and
characteristics for decile portfolios formed on the
AdjDIND
Table 7. Average parameter values from cross-sectional
regressions of monthly returns on size, book-to-market
ratio, and AdjDITH and AdjDIND
Institutional and individual trading behavior and
BE/ME and size premiums
• The relation between AdjDITH (or AdjDIND)
and BE/ME (or size) seems to be weak.
• After purging the premiums associated with
AdjDITH and AdjDIND, the BE/ME and size
premiums are still significantly positive.
Table 8. Portfolio returns based on two-way
independent sorts
Table 9. Portfolio returns based on dependent double
sorts
Table 10. Average returns of BE/ME decile portfolios
Table 11. Average returns of size decile portfolios
Size strategies with institutional and
individual trading preference
• Neither the institutional trading nor individual
trading can significantly improve the size
strategy!
Table 12. Investing strategies based on independent
double sorts
Value strategies with institutional and
individual trading preference
• The strategy (3) is the highest profits among strategies (1)
to (4).
• This suggests that by including the information about
institutional trading preference, i.e., buy growth stock and
sell value stock, one can improve the profitability of the
value strategy.
• Information about individual trading preference has limited
ability in improving value strategy.
Table 12. Investing strategies based on independent
double sorts
Conclusions
• A significantly and economically negative relation between
institutional trading and future stocks returns exists.
• There is a negative association between institutional trading
and book-to-market ratio (BE/ME). However, insignificant
relation between institutional trading and size has been found.
• Although institutional and individual trading seem to be
associated with BE/ME and size, their impacts appear to be
limited on BE/ME and size premiums.
• Incorporating information about the institutional trading
preference can significantly enhance the value strategy.
The End
Thanks