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Institutional Trading and Asset Pricing

Bart Frijns, Thanh D. Huynh, Alireza Tourani-Rad, P. Joakim Westerholm

This draft: 28 Aug 2015

Preliminary and Incomplete

Abstract

This paper examines whether the activeness of financial institutions in the market

can affect the relation between beta and average returns. We find that this relation

is strong and positive on days with high institutional trading activity (High-IT). In

contrast, on Low-IT days, the relation is negative and statistically significant. This

novel IT effect is strong and not driven by the known effect of macroeconomic news

or other alternative explanations. The evidence suggests that the trading activity of

investors could cause the Security Market Line to change slope.

IBart Frijns (E-mail: [email protected]), Thanh Huynh (Corresponding author, E-mail: [email protected]), and Alireza Tourani-Rad (E-mail: [email protected]) arefrom Auckland University of Technology, New Zealand. Joakim Westerholm (E-mail:[email protected]) is from the University of Sydney, Australia. We thank work-shop participants at McGill Global Asset Management Conference, Conference of the MultinationalFinance Society, World Finance Conference, Auckland Finance Meeting, AUT, Massey University,and Queensland University of Technology for helpful comments. We especially thank JonathanBrogaard, Andrew Karolyi, and Marios Panayides for helpful suggestions. Thanh Huynh gratefullyacknowledges the financial support from AUT Research Grant 2014 and SIRCA/AFAANZ Develop-ing Researcher Grant 2014. All errors are our own.

1. Introduction

Recent research has revisited the issue of the insignificant relation between beta

and average return. Frazzini and Pedersen (2014) suggest that the flat relation could

be attributed to leverage constraints and show that a portfolio that is long low-beta

stocks and short high-beta stocks is profitable. Savor and Wilson (2014) find that the

relation is conditional on news announcement and document a positive relation on

days with macroeconomic announcements. Motivated by DeLong, Shleifer, Summers

and Waldmann (1990) who suggest that financial institutions are less likely to be

noise traders, we hypothesize that when institutions trade intensively, noise in the

market is lower and the relation between beta and returns would be positive. In

contrast, on days when individual investors are more active, the relation would be

negative.

According to the noise trader hypothesis, when noise traders are optimistic about

high-beta stocks, they are willing to pay more for those stocks, causing them to be

overvalued. Thus, on days noise traders dominate the trading volume in the market,

we expect lower returns for high-beta stocks and, in turn, a negative relation between

beta and returns. In contrast, on days institutions are more active, the relation is

expected to be positive. Using a unique Finnish dataset that records daily trading

activities of all financial institutions, we find strong evidence for this hypothesis: on

days with high institutional trading (High-IT), beta is positively related to average

return whereas on other days, this relation is significantly negative. This finding

cannot be attributed to size, book-to-market, momentum, short-sales constraints,

liquidity effects or news announcements.

We obtain data on all stocks listed on the Nasdaq OMX Helsinki Exchange over

the period 1995-2011 from Euroclear Finland Ltd. (Euroclear). This dataset contains

full records of all trades conducted by institutions aggregated at a daily level. We

construct our measure of institutional trading (IT) activity as the fraction of total

trading volume by all institutions over total market volume, and label day t as a high

institutional trading day (High-IT) when IT on that day exceeds its average over the

past quarter. We find that, on High-IT days, an increase in beta by one is associated

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with a significant increase in average return of about 7 basis points (bps). In contrast,

on Low-IT days, we observe a significant reduction in average daily excess return of

approximately 8 bps. These findings hold for beta-sorted portfolios, for size and

book-to-market ratio (BM) portfolios, for industry portfolios as well as for individual

stocks. The results are not driven by well-known institutional trading behaviors such

as the January and turn-of-month effects (Sikes, 2014) or by Nokia’s stock, which is

by far the dominant and most liquid stock in the Finnish market.

Finally, we also replicate our main findings using Thomson Reuters’ quarterly 13F

holdings data for the U.S. markets. Using ten beta-sorted portfolios as testing assets,

we find that the implied market risk premium is higher on High-IT quarters than

on Low-IT quarters, though the difference is weakly significant. The weak results

are primarily attributable to the fact that quarterly holdings miss out important

information in the high-frequency (daily) trade of institutions. We view this result as

strong support for our use of better Finnish daily institutional trading data as well

as a robustness test that our findings could be generalized to large markets such as

those in the U.S..

An alternative explanation for our findings is the announcement effect documented

by Savor and Wilson (2014). They show that the slope of the SML is significantly

different on days with interest rate announcements by the U.S. Federal Open Markets

Committee (FOMC) versus non-announcement days. We confirm these findings in

Finland using the European Central Bank (ECB) interest rate announcements. We

find that, after controlling for announcement days, the positive relation between beta

and average return on High-IT days remains strong and positive. Thus, our results

are not a manifestation of the announcement effect. We further examine which effect

is stronger and find that the announcement effect becomes weaker on Low-IT days,

especially when institutions buying volume is low. These findings indicate that the

IT effect is stronger.

The second alternative explanation is the leverage-constraints hypothesis. Black

(1972) and recently Frazzini and Pedersen (2014) show that, when investors are faced

with leverage constraints, they overweigh high-beta stocks, and therefore require lower

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average returns on those stocks. Because institutions are on average less leverage-

constrained than individual investors, High-IT days may represent times when the

leverage constraint is low. A central prediction of this hypothesis is that institutions

can take advantage of the flat beta by buying low-beta stocks and sell high-beta

stocks. Our evidence is not consistent with this prediction. Regardless of the type

of days, stocks that institutions buy have a higher average beta than those that

they sell. Thus, leverage-constraints hypothesis does not seem to be the explanation.

More importantly, the pattern on Low-IT days is inconsistent with the leverage-

constraints hypothesis because, even when facing leverage constraints, investors would

not demand high-beta portfolios if they offer a risk premium lower than that of the

market.1

We next consider whether the investor disagreement hypothesis can explain the IT

effect. Hong and Sraer (2015) show that because high-beta stocks are more sensitive to

disagreement among investors about the future of the economy and because of limits

to arbitrage, high-beta stocks will be overvalued. Consequently, the relation between

beta and average return is negative on days with strong investor disagreement. On

days with weak disagreement, the SML will be upward sloping. Thus, High-IT days

could be times when investor disagreement is weak while Low-IT days could represent

strong disagreement periods.

Motivated by Dzielinski and Hasseltoft (2014), we proxy for investor disagreement

by news dispersion (defined as the daily standard deviation of firm-specific news tone

scores provided by Thomson Reuters). In line with Hong and Sraer (2015), we find

that the relation between beta and average return is significantly negative on days

with strong investor disagreement. In contrast, on weak disagreement days, the SML

is upward sloping. These results suggest that our proxy of market disagreement is

reasonable. Conditioning Low-IT days, we find that the relation between beta and

return becomes negative even when the day has weak disagreement. These findings

1This point is made clear in the model of Black (1972) (Equation 8) in which there is an upperbound on the Lagrange multiplier on the leverage constraint. This constraint is due to the fact thatno investor would demand the high-beta portfolio if it offered a risk premium lower than that of themarket, and so the Lagrange multiplier drops to zero.

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suggest that disagreement in the market is not reflected in the trade of institutions

whose trades can affect the CAPM relation.

Finally, we examine whether the lottery preference can explain the downward

sloping SML on Low-IT days. According to this hypothesis, individual investors,

whose presence is more prevalent on Low-IT days, have a strong preference for stocks

with high skewness because they can offer more chances of large returns. Since these

stocks typically have higher betas, the strong demand for those stocks on Low-IT

days drive the SML downward. Mitton and Vorkink (2007) and Kumar (2009) show

that individual investors have a strong preference for stocks with lottery features.

Recently, Bali et al. (2015) show that this lottery preference can explain the return

on the betting-against-beta (BAB) portfolio of Frazzini and Pedersen (2014).

As suggested by Kumar (2009), we employ coskewness as a proxy for lottery stocks

and document two main findings. First, on days of low market coskewness, the SML is

upward sloping whereas on high coskewness days, the SML is downward sloping. This

result is consistent with the notion that lottery stocks are also those with high betas

and that lottery preference in the market can affect the CAPM relation. Second,

conditioned on high-coskewness days, we find that the High-IT effect is still strong

and positive, suggesting that lottery preference cannot explain our findings.

Taken together, our results are most consistent with the notion that the impact

of noise traders can cause high-beta stocks to be overvalued and therefore the SML

to be downward sloping on Low-IT days. When noise traders are not active (or

High-IT), we indeed see an upward sloping SML. Our study joins a large literature

including Barber et al. (2009), Grinblatt and Titman (1989, 1993), Daniel et al.

(1997), Grinblatt et al. (1995), and others to shed a positive light on the effect of

institutional trading. These studies generally suggest that institutions are informed

and their trades can improve intraday price discovery. Our study offers an asset

pricing perspective to this literature by linking the IT effect to the relation between

beta and average return. Our findings should not be interpreted to imply that the

performance of institutions can be explained by the CAPM as it is not the objective

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of this paper to take side on this inconclusive debate.2 Our goal is to show that the

prediction of the CAPM relation is supported when (for whatever reason) there is

systematically High-IT activity in the market.

Though related, this study differs from Frazzini and Pedersen (2014) in three

important aspects. First, Frazzini and Pedersen (2014) also use the 13F data to test

whether institutions exploit the BAB strategy while we employ daily trading data for

all institutions. Using daily institutional trades gives us a more timely and accurate

estimate of beta. As our data covers all institutions in the Finnish market, we can

examine the effect of aggregate institutional trading on the relation between beta

and return. Furthermore, we are also able to separately study the buying and selling

sides of institutional trading, which help distinguish various competing hypotheses.

Second, the scope of our study is different. Our paper documents a novel finding

on the SML between High- and Low-IT days whereas Frazzini and Pedersen’s (2014)

focus is to explore the implication of leverage constraints through the BAB portfolio.3

Finally, we document a downward sloping SML on Low-IT days that is not consistent

with the leverage-constraints hypothesis. Our findings offer a new perspective to the

flat beta puzzle that when noise traders are less active, the CAPM is supported in

the data.

2. Data and empirical methodology

In this section, we discuss our unique data for the Finnish market that offers daily

trading records of all institutions. This gives us a comparative advantage over other

U.S.-based asset pricing studies that have to rely on quarterly institutional holdings

data. Nevertheless, we are able confirm that our conclusions do not change when

using U.S. data.

2Lewellen (2011) finds that institutions do not possess stock-selection skills and their performancecan be explained by book-to-market and momentum factors.

3Recently, Bali et al. (2015) show that the BAB phenomenon could be explained via the lotterypreference. It is not the objective of Bali et al. (2015) to explain the larger puzzle of the flat beta.

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2.1. Data

This study employs the daily trading record of financial institutions for Finnish

stocks from Euroclear (also known as Finland’s Central Securties Depository). The

daily frequency gives this study an important advantage over prior research that

uses lower frequency data (e.g., mutual funds holdings or 13F quarterly institutional

holdings data). Moreover, the database contains trades by all institutions (identified

by a unique number of each institution aggregated at the daily level) while most

U.S. data only cover large institutions (small financial firms do not file 13Fs). These

features of our database allow us to compute a timely measure of the systematic

impact of institutional trading. The dataset has separate fields for institutional buy

and sell volumes. To trade on the Helsinki stock exchange, investors must register

with Euroclear and are given a unique account number. Our data consist of 187

stocks listed on the Nasdaq OMX Helsinki exchange between 1996 and 2011.4

We collect daily stock prices, dividends, capitalization adjustment, and the num-

ber of shares outstanding from Compustat Global.5 Book values of equity are obtained

from WorldScope. For tests that use the central bank’s interest rate announcement,

we collect scheduled days of monetary policy announcements from the European Cen-

tral Bank (ECB) website from 1999 when the ECB was officially established.6 Al-

though tests that employ ECB announcement days are limited to the sample period

between 1999 and 2011, the daily frequency of our data gives us well over 3,100 trad-

ing days, with about 370,000 of stock-days observations over this shorter period. All

returns are in euros, and excess returns are above the five-year government bond yield

obtained from Datastream. Betas are computed with respect to the value-weighted

4Grinblatt and Keloharju (2001) provide a detailed description of the Euroclear database and itsclassification between institutions and retail investors. Their data cover two years of 1995 and 1996for the 16 largest Finnish stocks. Exchange-traded funds (ETF) or index funds play a limited rolein the Finnish market because ETFs are not as popular as in the U.S.. For example, there were onlytwo ETFs listed on the Finnish market in 2006.

5Following Ince and Porter (2006) and Griffin et al. (2010), filters are applied on individual stockreturns in order to eliminate data errors. Specifically, returns that are greater than 100% in one dayis treated as missing. If daily return that is greater than 20% and then reversed immediately in thefollowing day, then returns on both days are treated as missing.

6https://www.ecb.europa.eu/press/govcdec/mopo/previous/html/index.en.html,

accessed 14 October 2014.

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market return.7

< INSERT TABLE 1 HERE >

We form nine size- and BM- sorted portfolios in the spirit of Fama and French

(1993). Stocks are first sorted into three groups on the basis of their size (market

capitalization) at the end of June of each year. Big stocks are those in the top

30% of the market cap for the Finnish market and small stocks are those in the

bottom 30%. Independently, stocks are also sorted into three groups on the basis of

their book-to-market (BM) ratios. We use book values for the fiscal year ending in

calendar year t − 1, while market cap is for the end of December in calendar year

t−1. The nine size-BM portfolios are thus the intersections of three size and three BM

portfolios.8 We also form four beta-sorted portfolios and five industry portfolios using

Fama and French’s SIC classifications.9 Except for the beta-sorted portfolios that are

rebalanced monthly, all other portfolios are rebalanced on a yearly basis. Panel A

of Table 1 reports summary statistics for the Finnish market. The number of firms

listed on the Helsinki exchange increases from an average of 79 firms per year with

an average market capitalization (price times number of shares outstanding) of 389.9

million euros at the beginning of the sample to 140 firms per year with an average

market capitalization of 1095.5 million euros in the final years. The average fraction

of institutional trading volume (sell volume over the total market volume) increases

from 19.6% in the first five years to 37.9% in the last four years of the sample. There

is also a sharp increase in sell volume by institutions after 2008. Between 2008 and

2011, institutions’ sell fraction (over the total market volume) is 25.1%, which is

almost double the sell fraction earlier years. The number of trading accounts held by

institutions increases from 563 in the first subperiod to 722 in the third subperiod,

7We compute the market return as the value-weighted average of stock returns using the stock’slagged market capitalization. Our main results do not qualitative change when we use the MSCIindex.

8Due to the small size of the Finnish market, sorting stocks into three bins helps to maintain acertain level of diversification in each portfolio as well as the power of our tests.

9The classification is downloaded from Ken French’s website http ://www.mba.tuck.dartmouth.edu/pages/faculty/ken.french/datalibrary.html.

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but then decreases to 656 for the last subperiod (2008-2011). Due to the substantial

rise in the trading volume in the last subsample, we ensure in one of the robustness

tests that our results are not specific to this subperiod.

Panel B reports average returns on nine value-weighted size-BM sorted portfolios.

We observe a strong size effect for growth stocks. For example, the small-growth

portfolio earns a significant average return of 10 basis points (bps) per day while the

average return on large-growth portfolio is only 6bps. The size effect is, however,

weak for value stocks. The value effect is present but in the opposite direction to the

U.S. evidence in which value stocks earn a lower average return than growth stocks.

This value effect is, nevertheless, strong for small stocks only. For large stocks, value

and growth portfolios earn similar returns. Panel C presents average returns on four

beta-sorted portfolios. The beta puzzle is apparent in the Finnish market where the

high-beta portfolio earns an average return of 3bps per day, which is lower than 5bps

average return of the low-beta portfolio. Panel D reports average returns on five

industry portfolios that are formed using industry classifications downloaded from

Ken French’s website. Only portfolios formed in Industry 2 (manufacturing, energy,

and utilities) and Industry 5 (business services and finance) earn significant average

returns over the sample period that we examine. Industry 3 (business equipment,

telephone, and television transmission), which includes Nokia, earns an average return

of 7bps, which is insignificant at the conventional level.

2.2. Empirical methodology

The main object of our analysis is the aggregate institutional trading (IT) volume

defined as IT = (buy+sell)/total market volume, where buy and sell are buy volume

and sell volume of financial institutions, respectively. We determine a day to be having

high institutional trading (High-IT day) when that day’s IT is higher than its average

over the past quarter.10 One can think of our measure as a time-series dummy variable

that takes a value of one on High-IT days and zero otherwise. There are 1598 High-

10We ensure that the results are robust to using various windows. For example, Appendix Apresents the main results for one-year windows.

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IT days out of the total of 3,567 trading days.11 In some of the tests, we separately

examine institutions’ buying fraction (ITbuy = buy/total market volume) and selling

fraction (ITsell = sell/total market volume).

Our main empirical tests employ the two-pass Fama and MacBeth (1973) proce-

dure for the CAPM and then examine the coefficient estimate on High- and Low-IT

days.12 First, we estimate stock betas using one-year rolling regressions, adjusting

for the potential effect of non-synchronous trading by using Dimson (1979) sum beta.

Specifically, we run the regression Rt = α+β0RM,t+β1RM,t−1+β2∑4

k=2RM,t−k/3+εt,

where Rt and RM,t are excess returns on an asset and the market index, respectively.

The Dimson sum beta is then β0 + β1 + β2.13 The testing assets are four beta-sorted

portfolios, nine Fama and French size- and BM-sorted portfolios, five industry portfo-

lios, and individual stocks. Using portfolios as testing assets provides more accurate

estimates of market betas than those from individual stocks (Fama and French, 1992).

In the second-stage regression, we run the following cross-sectional regressions:

RHi,t+1 = γH0,t + γH1,tβ̂i,t (1)

RLi,t+1 = γL0,t + γL1,tβ̂i,t (2)

where β̂i,t is asset i’s stock market beta for period t estimated in the first stage;

and RHi,t+1 is the excess return on the testing asset on high institutional trading days

(High-IT or High).

As we are interested in studying the marginal effect of High-IT days on the relation

between beta and average return, we focus on the difference in the coefficient estimate

of γH1,t − γL1,t. Standard errors are computed using Newey and West’s (1987) method.

Similar to Savor and Wilson (2014), we also estimate the following pooled regres-

sion to test the difference in implied market risk premia between High-IT days and

11When we form portfolios, we lose one year of data to rank and sort stocks. Thus, the sampleperiod in tests starts in 1997.

12This methodology is similar to that of Fama and French (1992) and Savor and Wilson (2014)and is standard in the literature.

13Our results are robust to using no Dimson adjustment.

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Low-IT days:

Ri,t+1 = γ0 + γ1β̂i,t + γ2Hight+1 + γ3β̂i,tHight+1 (3)

where Hight is a dummy variable that equals one for High-IT days and zero otherwise.

We compute clustered standard errors by date, which adjust for the cross-sectional

correlation of the residuals. The coefficient of interest is γ3, which shows the difference

in the implied risk premium on High- versus Low-IT days.

3. Results

Our empirical analysis focuses on the difference in market risk premium between

High- and Low-IT days (γH1,t − γL1,t in regressions (1) and (2)). We summarize our

findings in Figure 1, which uses portfolio returns as testing assets. We confirm that

our results are robust to individual stocks, subsample periods, the January effect, the

turn-of-month effect, and the use of U.S. 13 institutional holdings data.

3.1. Beta, book-to-market, size, and industry portfolios

Figure 1 summarizes our findings. The figure plots average excess returns on

nine portfolios (four beta-sorted and five industry portfolios) against their average

estimated betas. The upper graph shows the relation on High-IT days while the

lower graph plots the relation on Low-IT days. There is a striking difference between

the two graphs. The graph for High-IT days shows a strong, positive relation between

beta and average return. An increase in beta by 0.1 is associated with an increase in

expected return of about 1.7% per day (not reported in the graph). In stark contrast,

on Low-IT days the relation is significantly negative. An increase in beta by 0.1 is

associated with a statistically significant reduction in average daily excess return of

approximately 1.5% basis points (bps) per day, with an associated t-statistic greater

than three.14 This graph represents our novel findings that beta risk is important

14It is also interesting that the two extreme portfolios on the right-hand side of the graph exhibitcontrasting performances on High- and Low-IT days. Savor and Wilson (2014) document similarextreme performance on macroeconomic announcement days in the U.S. Although there is no reason

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when institutions trade intensively.

Table 2 formally reports results for regressions (1) and (2) using the Fama-

MacBeth procedure. Specifically, panel A shows the results for four beta-sorted

portfolios. On High-IT days, the slope γH1,t is positive 7.3bps per day with an as-

sociated t-statistic of 2.6, significant at the 1% level. This positive slope is consistent

with the prediction of the CAPM and that beta risk is priced on High-IT days. In

contrast, the slope γL1,t on Low-IT days is −7.4bps (t-statistic = −2). The difference in

the slope between High- and Low-IT days (the bottom row) is 14.7bps per day, which

is statistically significant at the 1% level. Thus, there is a significant and negative

relation between beta and average return on Low-IT days.

The right-hand side of panel A presents the results from pooled regression (3) that

tests the difference in the implied market risk premium between High- and Low-IT

days. Consistent with the Fama-MacBeth regression results, the coefficient on the

interaction between High-IT days and beta (High × β) is positive and statistically

significant (2.8bps, with a t-statistic of 4.7 adjusted for clustering by trading day).

Again, these results show that beta is a much more important systematic risk on

High-IT days. It is also interesting to note the coefficient on High-IT dummy is

insignificant, suggesting that the difference in returns between High- and Low-IT

days is attributable to their portfolio betas, which carry a higher risk premium on

High-IT days.

As suggested by Lewellen, Nagel and Shanken (2010), it is useful to examine how

the model performs when more portfolios are included in the tests. Consequently,

panel B adds five industry portfolios to the test and brings the total of test portfolios

to nine. Consistently, we see that High-IT days exhibit a strong, positive relation

between beta and average return. The coefficient on beta is positive 7.6bps, which is

statistically significant at the 1% level. On the other hand, this coefficient is negative

to exclude those portfolios as we have already applied filters to our returns data, in unreported tests,we attempted to do so in the FM regression analysis and our main results do not qualitatively change.The slope of the security market line is still apparent when we exclude those portfolios. Finally,the results are robust to the use of individual stocks, which should span the SML more than doportfolios.

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8bps on Low-IT days. The difference between the two days is 15.6bps per day with

an associated t-statistic of 3.2. Further, the pooled regression shows that a positive

coefficient of 18.9bps (t-statistic = 3.72) on the interaction term (High× β). These

results suggest that adding five industry portfolios to the set of testing assets does

not change our conclusions that beta risk is much more important on High-IT days.

Panel C further raises the hurdle by including nine size-BM portfolios as additional

test portfolios to panel B. The slope coefficient on beta is positive 7bps on High-IT

days (t-statistic = 2.8). On Low-IT days, the slope is again significant and negative

8.2bps. The difference in the slope on beta between High- and Low-IT samples

is 15bps with an associated t-statistic of 3.5. Consequently, we conclude that the

CAPM is supported on High-IT days, but fails on Low-IT days.15 16


3.2. Excess returns on individual stocks

In this subsection, we employ individual stocks as testing assets and examine

whether the market risk premium is higher on High-IT days than on Low-IT days.

We report the results in Table 3.


Panel A shows that, on High-IT days, the relation between beta and return is still

positive 3.9bps with an associated t-statistic of 1.8.17 In contrast, on Low-IT days,

15As pointed out by Savor and Wilson (2014), the difference in risk premium between High- andLow-IT days could be attributed to the difference in beta rather than in market risk premium.This concern arises because this methodology uses all data over the past year to estimate betas. Inunreported robustness tests, we follow Savor and Wilson (2014) and estimate beta in two separatesamples: High-IT days only sample and Low-IT days only sample. We then compute the difference inbetas between the two samples. Similar to the conclusion of Savor and Wilson (2014), the differenceis not economically and statistically significant, suggesting that using all data over the past yeardoes not badly affect our results. Furthermore, the results suggest that the difference in the impliedrisk premium, not betas, between High- and Low-IT days drives our findings.

16In another robustness test, we employ lagged High-IT and control for the market return inRegression (3) and find that the coefficient on β̂i,tHight+1 remains statistically significant. This isperhaps due to the persistence of the High-IT effect.

17The lower statistical significance reflects the estimation uncertainty when the testing asset isindividual stock return.

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the slope coefficient on beta is negative 7.8bps (t-statistic = −3.2), significant at the

1% level. The difference in the slope on High- and Low-IT days (last row) is positive

11.7bps, which is statistically significant at the 1% level. In panel B, the coefficient

on the interaction term (High×β) is also significantly positive. Thus, our conclusion

that the market risk premium is higher on High-IT days than Low-IT days holds for

individual stocks.

In panels C and D, we include log(size), log(BM), and past one-year returns as

additional controls to the baseline model. On High-IT days, the coefficient on beta

is still positive while on Low-IT days, the coefficient is significantly negative. Again,

the difference in the slope on beta between two samples is positive and statistically

significant. Panel D shows that including size, BM, and past returns does not affect

the positive coefficient on the interaction term (High× β).

Another confounding effect that may affect the trade of financial institutions is

liquidity. As institutions tend to hold liquid stocks that have more efficient prices, the

significant difference in the coefficient on beta between high- and Low-IT days may be

confounded by the liquidity effect: Low-IT days may represent days with low liquidity

in the market and therefore stock prices are less efficient. We argue that liquidity

does not seem to significantly affect our results for two reasons. First, Boehmer and

Kelly (2009) show in an intraday analysis that institutions’ trade enhances market

efficiency that is beyond the effect of liquidity provision itself. Second, the effect of

liquidity, if any, would be larger for selling than for buying activity of institutions. As

we show in the next section, our results are driven by the buying, rather than selling,

side of institutional trades.

Nevertheless, we attempt to control for liquidity (proxied by turnover) in panels

E and F of Table 3. Turnover is defined as the average daily trading volume over

the past year divided by the number of shares outstanding. On each day t, we then

rank stocks based on their turnover and use the rank (Turn) as an additional control

in both Fama-MacBeth regression (panel E) and pooled regression (panel F).18 The

18As a robustness test, we also proxy for the level of liquidity of a stock by the number of oc-currences of zero returns over the past year. This liquidity proxy is in the spirit of Lesmond et al.

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coefficient on Turn is positive but insignificant. On High-IT days, the coefficient

on beta is positive 4bps whereas on Low-IT days, this coefficient is negative 7.7bps

(t-statistic = −2.82). This leads to the average difference in the slope between High-

and Low-IT days of 12bps (t-statistic = 3.1). The pooled regression in panel F shows

consistent results.

Panels G and H repeat the regressions in panels E and F, but exclude Nokia from

the sample. Over our sample period, Nokia is the most liquid and largest firm by

market capitalization. Our findings are not driven by the dominant stock of Nokia

and that beta risk is significant on High-IT days.

3.3. Turn-of-month and January effects

Because institutions are known to pursue window-dressing behavior (Sikes, 2014),

we examine whether our results are due to their trading regularity throughout the

calendar year by excluding days that are turn-of-month from the analysis. Figure 2

contrasts the SML between high- and Low-IT days conditioned on non-turn-of-month

days. High-IT days still exhibit the positive relation between beta and average return

while Low-IT days show the negative relation. On High-IT days, an increase in beta

of the portfolio by one is associated with an rise in average return of 35bps per day

(t-statistic = 7.02). On Low-IT days, an increase in beta by one is associated with a

decrease in average return of 7bps per day (t-statistic = -3.8). These results suggest

that the turn-of-month behavior is not the explanation.

< INSERT FIGURES 2 and 3 HERE >

To complete the analysis, Figure 3 controls for the well-known January effect

(Sias and Starks, 1997) by excluding the month of January from the sample. Out-

side January, High-IT days still show strong support for the CAPM while Low-IT

days indicate the CAPM failure in explaining average returns. On High-IT days, an

(1999). Intuitively, stocks that were more illiquid over the past year should have more zero returnsthan those that were traded more frequently. Griffin et al. (2010) show that, in smaller and emerg-ing markets, this type of measure captures the illiquidity and transaction costs better than othermeasures. Our conclusions do not change.

14

increase in beta of the portfolio by one is associated with an rise in average return

of 27bps per day (t-statistic = 5.14). On Low-IT days, an increase in beta by one is

associated with a decrease in average return of 5.5bps per day (t-statistic = −2.9).

We conclude that regularities in stock returns throughout the calendar year cannot

explain our findings.

3.4. Subsample analysis: pre- and post-2008

The summary statistics in Table 1 show that there is an increase in trading volume

of institutions over the period between 2008 and 2011 that covers the period of the

Global Financial Crisis. Consequently, this subsection examines whether our results

are attributable to this later period by looking at two subsamples: pre- and post-2008.


Table 4 reports results from Fama-MacBeth regressions and pooled regressions

that are estimated using samples before (panel A) and after 2008 (panel B). As

before, estimates are reported separately for High- and Low-IT days and testing

assets are four beta-sorted, nine size-BM, and five industry-sorted portfolios. The

general conclusion is that the difference in implied market risk premium between

High- and Low-IT days remains positive and statistically significant in all subsamples.

Before 2008, the slope on beta is positive 14bps on High-IT days with an associated

t-statistic of 2.1. On Low-IT days, the slope is negative 6bps, though statistically

insignificant. The difference in the coefficient on beta between two samples is 21bps

with a significant associated t-statistic of 2.6. The pooled regression also shows that

the coefficient on the interaction term is positive 26bps (t-statistic = 6.1).

We see a similar picture in panel B for the period between 2008 and 2011. The

relation between beta and average return is still strong and positive on High-IT days

while this relation is significantly negative on Low-IT days. The pooled regression

also shows that the implied market risk premium is much higher on High-IT days than

on Low-IT days. Consequently, the IT effect is not specific to a subsample period.

15

3.5. The use of U.S. 13F holdings data

In this robustness test, we employ Thomson Reuters 13F quarterly holdings data

for the U.S. markets and show that the High-IT effect does not seem to be specific

to the Finnish market. In each quarter, we compute aggregate change in holdings

of all 13F institutions scaled by the total trading volume between 1980 and 2014.

Similar to the main analysis, we define a quarter as High-IT when the fraction of

institutional volume over the market volume is higher than its average over the past

year. The High-IT dummy is then a monthly time-series dummy variable that takes

the value of one in months of High-IT quarters. We collect monthly market data

for all U.S. common equities (share codes of 10 and 11) from Center for Research

in Security Prices (CRSP). We estimate pre-ranking betas by regressing 60 months

of excess returns on excess market returns (adjusting for potential non-synchronous

trading using Dimson’s sum beta). We then form 10 value-weighted portfolios on the

basis of individual stocks’ monthly betas. We repeat the Fama-MacBeth regression

and pooled regression and report the results in Table 5. The primary limitation of 13F

holdings data (that motivates us to use a much comprehensive and high-frequency

Finnish dataset) is its low frequency. Since the quarterly frequency of 13F data

cannot not capture the timely trade of institutions, the power of our tests would be

significantly reduced, and inferences would have to rely on the sign and magnitude

of the coefficient rather than its statistical significance.


Panel A of Table 5 reports the estimate from Fama-MacBeth regressions. On High-

IT quarters, the coefficient on beta is positive 27bps whereas, on Low-IT quarters,

this coefficient is negative 35bps. The difference in the coefficient on beta between

two types of day is 63bps (t-statistic = 1.8). The pooled regression shows a similar

picture that the coefficient on the interaction term (High× β) is positive 58bps with

an associated t-statistic of 2.5. Compared with the Finnish results in Table 2, the

magnitude of these coefficients is higher, and hence economically significant. These

16

findings show that the relation between beta and average returns is pronounced on

High-IT quarters in the U.S. markets.

These out-of-sample tests confirm our main conclusions and show that the High-

IT effect is not specific to the Finnish market. The results mitigate any potential

concerns of small markets and suggest that the institutional settings of the Finnish

market do not seem to be driving our findings.

4. Alternative explanations

In this section, we test whether other potential explanations of the flat beta puzzle

can explain our findings. Specifically, we investigate the role of macroeconomic an-

nouncements, short-sale constraints, leverage constraints, investor disagreement, and

lottery preference.

4.1. Macroeconomic announcements

Savor and Wilson (2014) find that the relation between beta and average return

is strong and positive on days when there is an interest rate announcement. This

relation on non-announcement days, in contrast, is downward sloping. Thus, one

possible explanation for our findings is that the IT effect could be a manifestation of

the announcement effect.

Figure 4 plots the SML on ECB scheduled monetary policy announcement days

and non-announcement days between 1999 and 2011. Similar to Savor and Wilson

(2014), we find a positive relation between beta and average returns on announce-

ment days. Non-announcement days, on the other hand, exhibit the well-documented

negative relation. Thus, the announcement effect is present in the Finnish market.

Consequently, this section examines whether the High-IT effect is driven by the an-

nouncement days. The analysis has two parts. First, we show that the announcement

effect cannot explain our findings. Second, we examine which effect is stronger by

running regressions on various combinations of High-IT and announcement days.

< INSERT FIGURE 4 HERE >

17

If the effect of institutional trading is a manifestation of the announcement effect

then we should not see the upward sloping SML on High-IT days when announcement

days are excluded from the sample. Figure 5 clearly shows that the positive relation

between beta and average returns is still present on High-IT days. Consequently, our

results cannot be explained by macroeconomic announcements.19


Table 6 reports regression results for various test portfolios that are estimated

on High- and Low-IT days, conditioned on those days being announcement or non-

announcement days. Panel A1 contrasts the slope coefficient on beta on days that are

both High-IT and having announcements versus all other days. (It is a time-series

dummy variable that takes the value of one if the day is High-IT and an announcement

day and zero otherwise.) On High-IT and announcement days, the coefficient on beta

is positive and statistically significant. In contrast, on other days, the relation between

beta and average return is flat. The difference in betas between the two samples is

positive and significant as shown in the last row of panel A1 as well as the interaction

term in the pooled regression.

Panel A2 considers days that are both Low-IT and having announcement ver-

sus other days. Thus, this panel is meant to study the effect of macroeconomic

announcements when the day is also Low-IT. If the announcement effect is robust

and strong, we should see a significant and positive coefficient on Low-IT and an-

nouncement days. In the Fama-MacBeth regression, although the coefficient on beta

is positive on announcement days and negative on non-announcement days, they

are both statistically insignificant and the difference between the two types of days

is also statistically insignificant (t-statistic = 1.2). However, the pooled regression

shows that the coefficient on the interaction term between announcement day dummy

and beta is positive 0.37% (t=3.4), indicating that the implied risk premium is higher

19Lucca and Moench (2014) document a drift on the day before FOMC announcement. In unre-ported robustness tests, we exclude both the ECB day and pre-ECB day. Our conclusions do notchange: the upward sloping SML is still strong on High-IT days even after excluding both days fromthe analysis. Thus, the results could not be explained by the effect of interest rate announcements.

18

on announcement days. These results suggest that the announcement effect becomes

weaker in the present of Low-IT days, though the beta risk is still important on

announcement days.

Panel A3 examines days that are High-IT and having no announcement versus

other days. This panel tests whether the effect of institutional trading is still robust

even when the day does not have macroeconomic announcement. High-IT days indeed

exhibit a positive relation between beta and average return while, on Low-IT days

this relation is significantly negative. The difference between two days is positive

25bps, which is statistically significant at the 1% level. In pooled regression results,

the coefficient on the interaction term (High × β) is also positive and statistically

significant. Consequently, these results show that the IT effect is still strong on

non-announcement days.

In short, panel A shows that the IT effect is distinct from the announcement effect

and if anything, it is even stronger.20


Due to the conflicting results between Fama-MacBeth regression and pooled re-

gression on Low-IT-announcement days, we examine whether splitting the analysis

into IT buying and IT selling sides could offer a clearer picture. This test also shows

whether the stronger IT effect comes from buying or selling side. Panel B therefore

repeats the analysis for High-IT buying. The general conclusion from panel B is that,

when we examine the buying side of IT, the effect is stronger than the evidence of

total IT volume in panel A. The CAPM is strongly supported on High-IT buying

days, regardless of whether the day has announcement. In contrast, on Low-IT days,

the relation between beta and average return is flat even though those days also have

announcements.

20There are 85 days with High-IT and announcement while there are 92 Low-IT-announcementdays. These statistics suggest that there are enough number of observations in the two tests. Infact, the test of High-IT could be more conservative because the number of High-IT-announcementdays is fewer.

19

Panel C examines the effect of High-IT selling. We can see that when institutions

sell intensively, the relation between beta and average returns becomes insignificant

regardless of whether the day has macroeconomic announcement. On Low-IT selling

days, however, the relation is significant because days with low selling volume could

be associated with high buying volume, and therefore the low selling effect partly

picks up the effect of High-IT buying.

In short, this subsection shows that the announcement effect cannot explained

the IT effect (particularly buying). It is interesting that the effect of macroeconomic

announcements becomes insignificant when the days are Low-IT buying days. In

Appendix B, we further confirm that the IT effect cannot be explained by the U.S.

Federal Open Market Committee (FOMC) announcements.21

4.2. Short-sale constraints hypothesis

We examine whether short-sale constraints can explain our findings. This hy-

pothesis posits that High-IT days could be times when short-sale constraints are

less binding, which makes it less costly for institutions to short sell and, in turn, help

cause the stock price to be more efficiently. In fact, Nagel (2005) employs institutional

holdings as a proxy for short-sale constraints and finds that prices of stocks with low

institutional ownership do not quickly reflect cash-flow news.22 Although our data

do not allow us to identify short sales, under this hypothesis, we should observe that

the IT effect is stronger on the selling side of institutional trading. Consequently, we

separately examine the effects of High-IT buying and High-IT selling.

21Although we think that interest rate announcements are the most important macroeconomicnews, we also confirm the results in this subsection with other types of macroeconomic news aswell. Specifically, we employ the database of Thomson Reuters News Analytics (TRNA) to iden-tify macroeconomic news in Finland. Following Hendershott et al. (2015), we identify days withnews of the following topic codes as provided in the TRNA database: ECI (Economic Indicator),GVD (Government/Sovereign Debt), MCE (Macroeconomics). To be conservative, we also considerother U.S.-related macroeconomic news with the topic codes of FED (Federal Reserve) and WASH(Washington/U.S. Government news). We then repeat the exercise of this subsection. We confirmthat our conclusions do not change and the effect of High-IT is still strong even on days withoutany macroeconomic news, suggesting that macroeconomic information cannot explain our findings.These unreported results are available upon request.

22In an intraday analysis, Boehmer et al. (2008) and Boehmer and Wu (2013) find that shortselling activities tend to be more informed and enhance price discovery.

20


Panel A of Table 7 reports regression results for High- versus Low-IT buying

days. On High-IT days, the coefficient on beta is positive 8bps with an associated

t-statistic of 3. This coefficient is even more significant than that reported in Table 2

for the total IT volume. The picture is reversed on Low-IT buying days on which the

coefficient is negative and statistically significant. The difference between two days is

17bps with an associated t-statistic above 3. The pooled regression shows a consistent

result that the coefficient on the interaction (High × β) is positive and statistically

significant. These results indicate that the IT effect is stronger on the buying side.

Panel B reports regression results for High- versus Low-IT selling days. In contrast

to the buying side, the selling side of IT exhibits a much weaker effect on the CAPM.

On High-IT selling days, the slope on beta in the Fama-MacBeth regresion is small

and statistically insignificant. The pooled regression shows a positive and significant

coefficient on the interaction term (High × β), but the magnitude is much smaller

than that for IT buying volume.

In short, Table 7 shows that the effect of IT is strong on High-IT buying days and

weak on Low-IT selling days – in consistent with the short-sale constraints hypothesis.

These findings are in fact intuitive. When institutions buy, they tend rely more on

the analysis of risk and return and could be using the standard capital asset pricing

model. On the other hand, they are less likely to sell a stock because of its risk level.23

4.3. Leverage-constraints hypothesis

We next consider the leverage-constraints hypothesis as a possible explanation of

the IT effect. Frazzini and Pedersen (2014) argue that the relation between beta

and average return is flat because investors are constrained in the leverage that they

23A related hypothesis that may explain our findings is liquidity: High-IT days may representtimes when liquidity in the market is high, and therefore stocks are priced more rationally. Thishypothesis is unlikely the explanation because we show in the previous section that our results arerobust to the control of liquidity. Moreover, if the liquidity effect drives our results, we shouldalso see stronger results on High-IT selling days because selling relies more on the liquidity in themarket. Because the effect of institutional trading is driven by the buying side, liquidity is unlikelythe explanation.

21

can take. They show that institutions that are less leverage-constrained can take

advantage of this flat beta by pursuing a profitable betting-against-beta strategy,

which buys low-beta stocks and sells high-beta stocks.

If High-IT days represent times when the overall leverage constraint is less binding

so that institutions can employ leverage to bet against beta, stocks that they buy

should have low betas while stocks they sell should have high betas. Table 8 shows

the average beta of stocks that institutions buy and sell on High- and Low-IT days.

On High-IT days, we can see that stocks that institutions buy have a higher average

beta than those that they sell and the difference is statistically significant. We also see

a similar picture on Low-IT days. Thus, the evidence does not suggest that Finnish

institutions take advantage of the betting-against-beta strategy.


Finally, the downward sloping SML on Low-IT days is another evidence that

is inconsistent with the leverage-constraints hypothesis. Theoretically, Hong and

Sraer (2015) pointed out a limitation of the leverage-constraints hypothesis: it cannot

explain the downward sloping SML that we document on Low-IT days. This is

because investors would not buy stocks with high betas if they offer a risk premium

lower than that of the market.

4.4. Investor-disagreement hypothesis

Hong and Sraer (2015) contend that high-beta stocks are more sensitive to dis-

agreement among investors about the future of the economy. Consequently, in times

of high disagreement and together with the presence of limits to arbitrage, the op-

timists outweigh the pessimists in their trading impact on high-beta stocks. When

disagreement is sufficiently large, the expected return on high beta stock exhibits an

inverted U-shape curve. During times of weak investor disagreement, the SML will

be upward sloping. The investor disagreement hypothesis does not make a distinction

with the noise trader hypothesis and therefore our Low-IT days could also be times

when disagreement is strong.

22

Empirically, testing this hypothesis requires one to make a choice of proxy for

investor disagreement. Hong and Sraer (2015) employ the dispersion (standard devi-

ation) of analyst forecasts of the earnings-per-share and find that this measure can

yield the upward sloping SML when disagreement is low and downward sloping SML

when disagreement is high. In the context of our study that employs daily IT, this

measure is not preferable because of its low frequency and little variation in analyst

forecast over time. Consequently, we employ a new measure of aggregate news tone

dispersion that is similar in spirit to Dzielinski and Hasseltoft (2014) as a proxy of

investor disagreement.

We collect news data from Thomson Reuters News Analytics (TRNA) that sys-

tematically quantifies the tone of firm-specific news for Finnish stocks between 2003

and 2011 (2267 trading days).24 For every news item, TRNA provides the probability

that a news item has good, neutral, or negative tone (the three scores sum to one).

For each news article, we compute a unified tone score as the difference between good

and bad score.25 On each day t, we then compute news tone dispersion as the stan-

dard deviation of the tone score of all articles on that day. Finally, we define a day

to have high news dispersion if the day’s tone dispersion is greater than the average

dispersion over the past one month. As TRNA is reasonably comprehensive, approx-

imately 90% of the trading days have news and on average there are 52 news articles

per day. The average news dispersion is 0.48 per day and 1260 days are defined as

having high news dispersion (strong investor disagreement).


Dzielinski and Hasseltoft (2014) find that this news dispersion is strongly associ-

ated with, and can even drive, the analyst disagreement. Using this measure allows

us to construct a timely measure of investor disagreement. Figure 6 plots the SML

24This database has been employed in the literature (e.g., Hendershott et al. (2015)) to examinethe relation between news and stock returns or institutional trading behavior. Interested readersare referred to Hendershott et al. (2015) and Heston and Sinha (2014) for a detailed description ofthe database.

25We use all news items, but our results are robust to examining news with the relevance score ofone, which means that the firm’s ticker code is mentioned in the headline or title of the news article.

23

on weak- and strong-disagreement days. On weak disagreement days, the relation

between beta and average return is positive. An increase in beta by 0.1 is associated

with 5bps increase in average return, with a significant associated t-statistic at the 1%

level. In contrast, on days with strong disagreement, the SML is downward sloping:

an increase in beta by 0.1 is associated with 14bps lower average return per day, with

an associated t-statistic of 5.8. In short, with the new proxy for investor disagreement,

we are able to test and confirm the prediction of Hong and Sraer (2015).


Table 9 tests whether the investor disagreement can explain the IT effect by run-

ning regressions on various combinations of High-IT and weak disagreement days.

In panel A, we can see that the IT effect is significant after we condition the day

on having weak disagreement, which is not surprising because both days exhibit an

upward SML. In panel B, the relation between beta and average return is flat on

Low-IT days even though the day has weak disagreement (which, as shown in Figure

6, should exhibit a significant and positive relation). This result suggests that the

IT effect is stronger than the effect of investor disagreement. Panel C shows that

the relation between beta and return is positive on days of High-IT and strong dis-

agreement. Although the relation on this day is insignificant, the difference in risk

premium between this day and others is statistically significant at the 10% level.

Further, the pooled regression shows a positive coefficient on the interaction term be-

tween beta and High-IT-strong-disagreement day, suggesting that the High-IT effect

is still stronger.

In short, the results are not consistent with the notion that High-IT days are

times of weak disagreement and Low-IT days are times of strong disagreement in the

market. We however cannot rule out the possibility that disagreement and noise are

correlated because, as for prior studies, our measure is a noisy proxy of the true market

disagreement. To the extent that our measure of market disagreement can successfully

exhibit the upward or downward slopes of the SML as shown in Figure 6, the findings

show that noise, rather than market disagreement, is apparent on Low-IT days and

24

absent on High-IT days. At the minimum the findings suggest a novel implication

that follows from the disagreement hypothesis: the degree of disagreement among

institutions, which is reflected through their trades, is an important determinant of

the relation between beta and return.

4.5. Lottery preference by individual investors on Low-IT days

Another potential explanation for the downward sloping SML on Low-IT days is

the lottery preference by individual investors as put forth by Mitton and Vorkink

(2007). According to this hypothesis, individual investors, whose trade’s impact is

more prominent on Low-IT days, are willing to sacrifice some returns to hold stocks

with lottery features such as high beta and high coskewness. In this subsection,

we test whether this hypothesis can explain the IT effect. We estimate the stock’s

coskewness (denoted as the coefficient ci) as in Harvey and Siddique (2000) and Mitton

and Vorkink (2007) by running the following quarterly regression:

Ri,t = a+ biRM,t + ciR2M,t + εi,t (4)

where Ri,t and RM,t are excess returns on the stock and the market, respectively.

The coefficient ci is the coskewness of the stock. The market coskewness is then the

simple average of individual stocks’ coskewness on each day. Day t exhibits high

coskewness if the market coskewness is higher than its average over the past quarter.

Figure 7 shows that on days of low coskewness, the SML is upward sloping while on

high-coskewness days, the relation is downward sloping. These results suggest that

lottery preference in the market can also affect the relation between beta and return.


Table 10 examines the IT effect conditional on days of high or low coskewness.

Panel A shows that the relation between beta and return is positive on days of High-

IT and low coskewness. This is not surprising because both types of days exhibit an

upward SML. On days of Low-IT and low coskewness, however, the relation is flat

(panel B). This result indicates that the effect of low coskewness in the market is not

25

strong enough to drive the SML upward on Low-IT days. We also observe that the

High-IT effect is strong on high coskewness days (panel C). Together, these results

suggest that the lottery preference hypothesis cannot explain our findings.26


4.6. Endogeneity concern

One may be concerned about the endogeneity issue of the IT effect. Specifically,

the CAPM works on High-IT days either because high IT causes the CAPM to work

or because institutional traders predict that the CAPM could work on High-IT days

and therefore try to trade more. First and foremost, regardless of the direction of

the causality, our findings that the CAPM is supported on High-IT days is still novel

and interesting. Since we are interested in testing whether the CAPM works on

High-IT days, disentangling the two-way causality should not affect our conclusions.

Second, if institutions could predict the return to be higher on High-IT days, they

would trade more heavily in the direction of the return on those days. The pooled

regression in Table 2 shows that the coefficient on High-IT day dummy is statistically

insignificant, indicating that the difference in returns between High- and Low-IT days

is not significant. This result suggests that institutions are unlikely to predict the

positive relation between risk and returns on High-IT days.

Finally, if institutional traders expect the CAPM to work on High-IT days, we

should observe that the stocks they buy would have higher beta (to earn higher

returns) than those they sell. In contrast, on Low-IT days the stocks that they buy

would have lower beta than those they sell. Table 8 shows that the stocks institutions

buy have a higher average beta regardless of the type of IT days. Further, beta on

26In unreported robustness tests, we confirm that our conclusions do not change when we use thereturn on MAX portfolios of Bali, Cakici and Whitelaw (2011) as an alternative proxy for individualinvestors’ lottery preference. Specifically, stocks are ranked and split into five groups on the basis oftheir average five maximum daily returns over the past month. Portfolio 1 contains stocks with thelowest maximum daily returns while portfolio 5 consists of stocks with the highest maximum dailyreturns. The value-weighted average raw return difference between portfolio 5 and portfolio 1 is theMAX portfolio. Bali et al. (2011) show that this MAX portfolio earns a negative average returnbecause investors have a strong preference for lottery-type stocks.

26

High-IT days is lower than that on Low-IT days and the difference is statistically

significant. These findings suggest that institutions do not predict the CAPM to work

on High-IT days and then trade accordingly. Rather, the evidence points toward the

first scenario.

5. Conclusion

By employing a comprehensive dataset of daily institutional trades, this study is

one of the first to directly link the effect of institutional trading (IT) to the relation

between beta and average return. We find that this relation is strong and positive

on days when there is high institutional trading (High-IT). On Low-IT days, how-

ever, this relation is negative and statistically significant. Moreover, the difference

in market risk premium between High- and Low-IT days is positive and statistically

significant. These findings hold for various test portfolios and for individual stocks.

The results are not driven by a specific subsample period, the January effect, or the

turn-of-month effect.

Our findings are consistent with the notion that it is the trading activity of two

main trader groups that causes the relation between beta and return to change sign –

depending on who is active in the market. When individual investors are more active

(or institutions are less active), we see a downward sloping SML – consistent with the

notion that noise in the market is high on those days. Our results are unlikely to be

explained by alternative hypotheses such as the short-sale constraints hypothesis, the

liquidity effect, small market’s problems, macroeconomic announcements, leverage-

constraints, market disagreement, or lottery preference. Further, the evidence does

not seem to suggest that institutions expect the CAPM to work and therefore act

accordingly on High-IT days.

27

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31

Figure 1: Capital asset pricing model on high and low institutional tradingAverage excess returns for four beta-sorted and five industry portfolios on high and low institutionaltrading (IT) days. This figure plots average daily excess returns (in percentages) against marketbetas for four beta-sorted (value-weighted) portfolios, nine value-weighted size-BM portfolios, andfive value-weighted industry portfolios. Day t has High-IT volume (scaled by total market volume)when the IT volume at t is greater than the average IT volume over the past quarter. The firstgraph shows the relation between beta and average return on High-IT days while the second graphis on days with Low-IT. The implied ordinary least squares estimates of the securities market linefor each type of day are also plotted. The sample period is between 1997 and 2011. Individualstocks’ betas are corrected for potential asynchronous trading using the Dimson method. Thisfigure shows that the relation between beta and average return is much higher and more positiveon High-IT days (first graph) whereas, on Low-IT days (second graph), the relation is significantlynegative.

32

Figure 2: Capital asset pricing model on non-turn-of-month daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on High- andLow-IT days accounting for the turn-of-month effect. This figure plots average daily excess returns(in percentages) against market betas for four beta-sorted portfolios, nine size-BM portfolios,and five industry portfolios. The first graph shows days with High-IT total volume but not theturn-of-month day. The second graph presents the relationship between excess returns and betason days with Low-IT total volume and not the turn-of-month day. The implied ordinary leastsquares estimates of the securities market line for each type of day are also plotted. The sampleperiod is between 1997 and 2011. Betas to form portfolios are corrected for potential asynchronoustrading using the Dimson method. This figure shows that, after excluding turn-of-month days, thedifference in the risk premium between High- and Low-IT days remains robust.

33

Figure 3: Capital asset pricing model on non-January daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on High-and Low-IT days accounting for the January effect. This figure plots average daily excess returns(in percentages) against market betas for four beta-sorted portfolios, nine size-BM portfolios, andfive industry portfolios. The first graph shows days with High-IT volume but not in January. Thesecond graph presents the relationship between excess returns and betas on days with Low-ITvolume and not January days. The implied ordinary least squares estimates of the securities marketline for each type of day are also plotted. The sample period is between 1997 and 2011. Betas toform portfolios are corrected for potential asynchronous trading using the Dimson method. Thisfigure shows that, after excluding January, the difference in the risk premium between High- andLow-IT days remains robust.

34

Figure 4: Capital asset pricing model on announcement and non-announcement daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on ECBannouncement versus non-announcement days. This figure plots average daily excess returns (inpercentages) against market betas for four beta-sorted portfolios, nine size-BM portfolios, and fiveindustry portfolios. The first graph shows the relation between beta and average return on dayswith ECB monetary policy decision. The second graph presents similar line on non-announcementdays. The implied ordinary least squares estimates of the security market line for each type of dayare also plotted. The sample period is between 1999 and 2011 (the ECB was formally established in1999). Betas to form portfolios are corrected for potential asynchronous trading using the Dimsonmethod. This figure shows that, consistent with Savor and Wilson (2014), the relation betweenbeta and average return is much more positive on monetary announcement days than on normal days.

35

Figure 5: Capital asset pricing model – Non-announcement daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on High-and Low-IT days accounting for the ECB announcement effect. This figure plots average dailyexcess returns (in percentages) against market betas for four beta-sorted portfolios, nine size-BMportfolios, and five industry portfolios. The first graph shows days with High-IT volume but noECB monetary policy decision. The second graph presents the relationship between excess returnsand betas on days with Low-IT volume and no announcement. The implied ordinary least squaresestimates of the securities market line for each type of day are also plotted. The sample period isbetween 1999 and 2011 (the ECB was formally established in 1999). Betas to form portfolios arecorrected for potential asynchronous trading using the Dimson method. The purpose of this figureis to show that, even when the day has no macroeconomic announcement, the difference in the riskpremium between High- and Low-IT days remains robust. Consequently, the effect of institutionaltrading is not a manifestation of announcement effects.

36

Figure 6: Capital asset pricing model on days with high or low degree of investor disagreementAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on high andlow investor-disagreement days accounting for the ECB announcement effect. This figure plotsaverage daily excess returns (in percentages) against market betas for four beta-sorted portfolios,nine size-BM portfolios, and five industry portfolios. A day t has strong investor disagreement ifthe cross-sectional standard deviation of news tone on day t is higher than its average over thepast month. Dzielinski and Hasseltoft (2014) show that high news dispersion is associated withstrong investor disagreement. The first graph shows days with weak investor disagreement. Thesecond graph presents the relation between excess returns and betas on days with strong investordisagreement. The implied ordinary least squares estimates of the securities market line for eachtype of day are also plotted. The sample period is daily frequency between 2003 and 2011 (the newsdataset starts in 2003). Betas to form portfolios are corrected for potential asynchronous tradingusing the Dimson method. This figure provides evidence for Hong and Sraer’s (2014) theory thatthe relation between beta and average return is negative (positive) when the aggregate investordisagreement is strong (weak).

37

Figure 7: Capital asset pricing model on days with high or low degree of coskewnessAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on high andlow investor-disagreement days accounting for the coskewness effect. This figure plots average dailyexcess returns (in percentages) against market betas for four beta-sorted portfolios, nine size-BMportfolios, and five industry portfolios. A stock coskewness is estimated using Regression (4) andthen the overall coskewness on day t is the average of individual stocks’ coskewness. Day t exhibitshigh coskewness if the overall coskewness is higher than its average over the past quarter. The firstgraph shows days with low coskewness. The second graph presents the relation between excessreturns and betas on days with high coskewness. The implied ordinary least squares estimatesof the securities market line for each type of day are also plotted. Betas to form portfolios arecorrected for potential asynchronous trading using the Dimson method. This figure provides evi-dence for the hypothesis that when coskewness is low (high) the SML is upward (downward) sloping.

38

Table 1: Summary statistics for Finnish marketThis table reports summary statistics for the Helsinki stock exchange between 2 January 1996 and 30December 2011. “Mean Firms” are the average number of firms. “Mean ME” is the average marketcapitalization in millions of euros (price times number of shares outstanding). “Mean Fractionof Total IT Volume” is the average fraction of trading volume by financial institutions over thetotal market volume. “Mean Fraction of Sell Volume” is the average fraction of sell volume byfinancial institutions over the total market volume. “Number of Accounts” is the count of uniqueaccounts held by financial institutions. Panel B reports average returns on nine value-weightedsize-BM sorted portfolios. Panel C shows the average return on four value-weighted beta-sortedportfolios. Panel D presents the average return on five industry portfolios using Fama-French’sclassifications (downloaded from Ken French’s website), where Industry 1 is consumer-related suchas consumer durables, non-durables, wholesale and retail; Industry 2 is manufacturing; Industry3 is high-tech firms; Industry 4 is health-related services; and Industry 5 is others (e.g., mines,construction, transportation, entertainment, and finance). t-statistics computed using Newey-Weststandard errors with five lags are reported in parentheses.

Panel A: Summary statistics for the Finnish marketYear Mean Firms Mean ME Mean Fraction of Mean Fraction of Number of

(’000,000) Total IT Volume Sell Volume Accounts

1995-1999 79 389.868 0.196 0.123 5632000-2003 138 1767.843 0.169 0.104 6432004-2007 141 1335.035 0.232 0.128 7222008-2011 140 1095.511 0.379 0.251 656

Panel B: Summary statistics for nine VW size-BM sorted portfoliosLow M High tLow tM tHigh

Small 0.0010 0.0012 0.0006 (4.13) (4.43) (1.85)M 0.0006 0.0005 0.0003 (2.63) (2.73) (1.15)Big 0.0006 0.0006 0.0005 (2.15) (2.55) (1.23)

Panel C: Summary statistics for four VW beta-sorted portfoliosBeta Low 2 3 High

0.0005 0.0006 0.0005 0.0003(1.88) (3.74) (2.43) (0.90)

Panel D: Summary statistics for five VW industry portfoliosIndustry 1 2 3 4 5

0.0004 0.0006 0.0007 0.0006 0.0008(1.79) (2.43) (1.49) (1.35) (3.35)

39

Table 2: Daily excess returns on days of high and low institutional tradingThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios. Estimates arecomputed for days with high institutional trading (High-IT days or High) and other days (Low-IT days or Low). Day t has High-IT volume(scaled by total market volume) when the IT volume at t is greater than the average IT volume over the past quarter. The difference inthe coefficient between high- and Low-IT days is reported in the last row of each panel. There are 1598 days with High-IT volume. Theright-hand side panel reports estimates from pooled regression of excess returns on betas, High-IT day dummy, and interaction between betaand High-IT (High*Beta). Panel A shows results for four beta-sorted (value-weighted) portfolios. Individual stocks’ betas are adjusted forpotential asynchronous trading effects using Dimson’s method. Panel B presents results for four beta-sorted and five value-weighted industryportfolios. Panel C reports results for nine value-weighted size-BM portfolios, four beta-sorted portfolios, and five industry portfolios. Thesample period is between 1997 and 2011 (we lost one year to form portfolios). t-statistics, which are computed using Newey-West standarderrors with five lags, are reported in parentheses. For pooled regressions, standard errors are corrected for clustering by trading day. Thistable shows that the difference in the coefficient on beta between High- and Low-IT days is always positive and statistically significant.

Fama-MacBeth regression Pooled regression

Type Intercept Beta Avg. R2 Intercept Beta High High*Beta Avg. R2

of day

Panel A: four beta-sorted portfoliosHigh 0.00013 0.00073 0.24 0.00068 −0.00186 0.000258 0.00283 0.53

(0.77) (2.61) (1.62) (−4.54) (0.42) (4.69)Low 0.00011 −0.00074 0.31

(0.54) (−2.00)High− Low 0.00002 0.00147

(0.08) (3.18)

Panel B: four beta-sorted and five industry portfoliosHigh 0.00011 0.00076 0.16 0.00095 −0.00120 −0.00070 0.00189 0.39

(0.65) (2.83) (3.19) −3.67 −1.51 (3.72)Low 0.00016 −0.00080 0.20

(0.50) (−1.80)High− Low −0.00005 0.00156

(−0.14) 3.20

Panel C: four beta-sorted, nine size & BM portfolios, and five industry portfoliosHigh 0.00019 0.00070 0.11 0.00060 −0.00173 0.00022 0.00296 0.37

(1.41) (2.81) (1.88) (−7.19) (0.46) (8.40)Low 0.00017 −0.00082 0.14

(0.72) (−2.26)High− Low 0.00003 0.00152

(0.12) 3.50

40

Table 3: Daily excess returns for individual stocks on days of high and low institutional tradingThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios. Estimates arecomputed for days with high domestic institutional trading (High-IT days or High) and other days (Low-IT days or Low). Day t has High-ITvolume (scaled by total market volume) when the IT volume at t is greater than the average IT volume over the past quarter. The differencein the coefficient between High- and Low-IT days is reported in the last row of each panel. There are 1598 days with High-IT volume. Theright-hand side panel reports estimates from pooled regression of excess returns on betas, High-IT day dummy, and interaction between betaand High-IT days (High*Beta). Panels A and B show results for beta as the right-hand side variable in Fama-MacBeth regressions andpooled regressions, respectively. Panels C and D are similar to the first two panels, but include log(size), BM, and past one-year returns asadditional controls. Panels E and F include liquidity rank (LIQ) as an additional control. This liquidity measure is constructed based on theoccurrence of zero returns over the past year. The sample period is between 1997 and 2011 (we lost one year to form portfolios). Panels Gand H repeat the regressions in Panels E and F, but excluding Nokia, which is the largest firm on the Finnish market. t-statistics, which arecomputed using Newey-West standard errors with five lags, are reported in parentheses. For pooled regressions, standard errors are correctedfor clustering by trading day. This table shows that, for individual stocks, the coefficient on beta on High-IT days is higher than on Low-ITdays.

Panel A: beta only (Fama-MacBeth regressions)Type of day Beta Avg. R2

High 0.00039 0.02(1.80)

Low −0.00078 0.02(−3.20)

High− Low 0.00117(3.52)

Panel B: beta and interaction with High-IT (Pooled regressions)Beta High High*Beta Avg. R2

−0.00123 0.00058 0.00242 0.09(−6.59) (1.52) (8.68)

Panel C: size, BM, and past returns as controls (Fama-MacBeth regressions)Type of day Beta Size BM Past one-year Avg. R2

High 0.00044 −0.00008 −0.00009 −0.02032 0.04(1.81) (−2.59) (−3.13) (−1.18)

Low −0.00077 −0.00007 −0.00007 0.00026 0.05(−2.85) (−2.29) (−2.19) (1.43)

High− Low 0.001205 −0.00001 −0.00002 −0.020583.214 (−0.02) (−0.40) (−1.76)

41

Table 3 continued.

Panel D: size, BM, and past returns as controls (Pooled regressions)Beta Size BM Past one-year High High*Beta Avg. R2

−0.00165 −0.00016 0.00002 0.00015 0.00020 0.00280 0.09(−8.01) (−4.74) (1.26) (0.70) (0.50) (9.51)

Panel E: size, BM, past returns, and liquidity as controls (Fama-MacBeth regressions)Type of day Beta Size BM Past one-year Turn Avg. R2

High 0.00041 −0.00008 −0.00009 −0.01801 0.00413 0.04(1.88) (−2.85) (−3.15) (−1.05) (0.76)

Low −0.00077 −0.00007 −0.00007 0.00028 −0.00001 0.06(−2.82) (−2.21) (−2.19) (1.51) −0.20

High− Low 0.00118 −0.00001 −0.00002 −0.01829 0.00415(3.14) (−0.28) (−0.38) (−1.73) (0.58)

Panel F: size, BM, past returns, and liquidity as controls (Pooled regressions)Beta Size BM Past one-year Turn High High*Beta Avg. R2

−0.00168 −0.00017 0.00002 0.00014 0.00007 0.00019 0.00281 0.09(−8.03) (−4.68) (1.24) (0.68) (0.90) (0.49) (9.52)

Panel G: size, BM, past returns, and liquidity as controls (Fama-MacBeth regressions) – excluding NokiaType of day Beta Size BM Past one-year Turn Avg. R2

High 0.00039 −0.00009 −0.00010 −0.01746 0.00419 0.04(1.78) (−2.96) (−3.26) (−1.02) (0.77)

Low −0.00078 −0.00007 −0.00008 0.00028 −0.00001 0.06(−2.79) (−2.24) (−2.32) (1.53) (−0.08)

High− Low 0.001171 −0.00002 −0.00002 −0.01773 0.00419(3.10) (−0.32) (−0.31) (−1.69) (0.50)

Panel H: size, BM, past returns, and liquidity as controls (Pooled regressions) – excluding NokiaBeta Size BM Past one-year Turn High High*Beta Avg. R2

−0.00168 −0.00018 0.00001 0.00014 0.00008 0.00020 0.00280 0.09(−7.84) (−4.68) (0.33) (0.69) (0.94) (0.50) (9.26)

42

Table 4: Daily excess returns on High- and Low-IT volume: subsample analysisThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios in two subsamples:before and after 2008. Estimates are computed for days with high domestic institutional trading (High-IT days or High) and other days(Low-IT days or Low). Day t has High-IT volume (scaled by total market volume) when the IT volume at t is greater than the average ITvolume over the past quarter. The difference in the coefficient between high- and Low-IT days is reported in the last row of each panel. Theright-hand side panel reports estimates from pooled regression of excess returns on betas, High-IT day dummy, and interaction between betaand High-IT (High*Beta). Test assets are four beta-sorted portfolios, nine value-weighted size-BM portfolios, and five industry portfolios.The sample period is between 1997 and 2011. Panel A uses sample before 2008 while the data in Panel B are after 2008. t-statistics, which arecomputed using Newey-West standard errors with five lags, are reported in parentheses. For pooled regressions, standard errors are correctedfor clustering by trading day. Betas are estimated using all returns data over the past one year. This table shows that the difference in thecoefficient on beta between High- and Low-IT days is always positive and statistically significant.



of day

Panel A: four beta-sorted, nine size-BM portfolios, and five industry portfolios, pre-2008High 0.00053 0.00143 0.26 0.00089 −0.00142 −0.0001 0.00261 0.29

(1.41) (2.09) (2.50) (−4.85) (−0.01) (6.13)Low 0.00030 −0.00064 0.141

(0.99) (−1.36)High− Low 0.00024 0.00207

(0.55) (2.67)

Panel B: four beta-sorted, nine size-BM portfolios, and five industry portfolios, post-2008High 0.00018 0.00186 0.25 0.00016 −0.00249 0.00011 0.00424 0.29

(0.317) (2.06) (0.26) (−6.31) (0.12) (7.05)Low −0.00013 −0.00124 0.15

(−0.38) (−2.48)High− Low 0.00031 0.00310

(0.51) (2.80)

43

Table 5: U.S. markets’ results: Excess returns on quarters of high and low institutional holdingsThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas forten beta-sorted portfolios using U.S. CRSP data. Estimates are computed for quarters with highinstitutional trading (High-IT or High) and other quarters (Low-IT or Low). Using data fromThomson Reuters SEC 13F holdings data, we compute aggregate change in institutional holdingsscaled by CRSP’s total market trading volume per quarter. Quarter t has Low-IT volume (scaledby total market volume) when the aggregate change in quarterly holdings at t is greater thanthe average IT volume over the past year (four quarters). Betas are estimated by running 60-month rolling window of excess returns on excess market returns (with Dimson’s adjustment). Thedifference in the coefficient between high- and Low-IT quarters is reported in the last row of eachpanel. Panel B reports estimates from pooled regression of excess returns on betas, High-IT quarterdummy, and interaction between beta and High-IT (High*Beta). The sample period is betweenQ1-1984 and Q1-2014. t-statistics, which are computed using Newey-West standard errors with fivelags, are reported in parentheses. For pooled regressions, standard errors are corrected for clusteringby month. This table shows that the difference in the coefficient on beta between High- and Low-ITquarters is positive.

Panel A: Fama-MacBeth regressionType Intercept Beta Avg. R2

of day

High 0.00040 0.002791 0.25(0.20) 1.040

Low 0.006491 −0.003520 0.27(3.56) −1.19

High− Low −0.00609 0.00631(−2.55) (1.80)

Panel B: Pooled regressionIntercept Beta High High*Beta Avg. R2

0.00979 −0.00368 −0.00491 0.00583 0.74(2.31) (−2.23) (−0.81) (2.45)

44

Table 6: Daily excess returns on various day typesThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios on High- and Low-ITdays accounting for the ECB announcement effect. Day t has High-IT volume when the IT volume (scaled by market volume) on day t isgreater than its average over the past quarter. “Announcement days” are days when the ECB announced monetary policy decisions. “Yes”represents days that have the characteristics displayed in the heading whereas “No” represents days that do not have any or both of thecharacteristics displayed in the heading. For example, in panel A1, a day is a “Yes” day when it has both High-IT and announcement;otherwise, it is a “No” day. The difference in the coefficient on beta between Yes and No days is reported in the last row of each panel.The right-hand side panel reports estimates from pooled regression of excess returns on betas, Yes day dummy, and interaction between betaand Yes (Yes*Beta). Test assets are four beta-sorted portfolios, nine size-BM portfolios, and five industry portfolios. Panel A reports resultsfor IT total volume. Panels B and C break the analysis into buying and selling volume, respectively. t-statistics, which are computed usingNewey-West standard errors with five lags, are reported in parentheses. For pooled regressions, standard errors are corrected for clusteringby trading day. There are 85 days with High-IT and announcements and 92 days with Low-IT and announcement, 1,331 days with High-ITand no announcements, and 1,673 days with Low-IT and no announcements.This table shows that the IT effect is still strong on non-announcement days, suggesting that the two effects are distinct from each other.


Type Intercept Beta Avg. R2 Intercept Beta Yes Yes*Beta Avg. R2

of day

Panel A: IT total volumePanel A1: High-IT and announcement daysY es −0.00123 0.00555 0.31 0.00073 −0.00064 −0.00139 0.00558 0.36

(−0.73) (2.45) (3.05) (−3.56) (−0.95) (5.10)No 0.00055 −0.00055 0.26

(1.90) (−1.15)Y es−No −0.00178 0.00607

(−1.20) (2.19)Panel A2: Low-IT and announcement daysY es −0.00150 0.00287 0.31 0.00077 −0.00059 −0.00277 0.00365 0.37

(−0.94) (0.73) (3.21) (−3.29) (−1.96) (3.39)No 0.00056 −0.00046 0.26

(1.93) (−1.03)Y es−No −0.00206 0.00333

(−1.44) (1.25)Panel A3: High-IT and non-announcement daysY es 0.00063 0.00106 0.26 0.00058 −0.00151 0.00031 0.00236 0.36

(2.04) (1.84) (1.88) (−6.35) (0.65) (6.55)No 0.00040 −0.00139 0.27

(0.93) (−2.10)Y es−No 0.00023 0.00245

(0.47) (2.71)

45

Table 6 continued.



of day

Panel B: IT buying volume

Panel B1: High-IT buying and announcement daysY es −0.00159 0.008189 0.33 0.00076 −0.00072 −0.00265 0.00874 0.37

(−1.20) (2.25) (3.18) (−3.97) (−1.78) (7.78)No 0.00056 −0.000589 0.26

(1.93) (−1.32)Y es−No −0.00215 0.00878

(−1.43) (3.12)

Panel B2: Low-IT buying and announcement daysY es −0.00117 0.00067 0.30 0.00074 −0.00052 −0.00165 0.00096 0.36

(−0.75) (0.20) (3.08) (−2.87) (−1.19) (0.92)No 0.00055 −0.00040 0.26

(1.90) (−0.87)Y es−No −0.00172 0.00107

(−1.23) (0.407)

Panel B3: High-IT buying and non-announcement daysY es 0.00028 0.001250 0.26 0.00090 −0.00170 −0.00041 0.00276 0.37

(0.84) (2.11) (2.85) (−7.12) (−0.87) (7.66)No 0.00066 −0.00157 0.26

(1.64) (−2.48)Y es−No −0.00038 0.00282

(−0.78) (3.13)

46

Table 6 continued.



of day

Panel C: IT selling volume

Panel C1: High-IT selling and announcement daysY es −0.00113 0.00234 0.31 0.00072 −0.00055 −0.00110 0.00235 0.36

(−0.62) (0.90) (3.00) (−3.05) (−0.73) (2.08)No 0.000541 −0.00043 0.26

(1.90) (−0.95)Y es−No −0.00167 0.00278

(−1.09) (0.98)

Panel C2: Low-IT selling and announcement daysY es −0.00157 0.00565 0.32 0.00078 −0.00069 −0.00299 0.00651 0.36

(−1.13) (1.33) (3.26) (−3.80) (−2.17) (6.23)No 0.00056 −0.00055 0.26

(1.93) (−1.24)Y es−No −0.00213 0.00620

(−1.54) (2.39)

Panel C3: High-IT selling and non-announcement daysY es 0.00090 0.000309 0.261 0.00043 −0.00106 0.00062 0.00132 0.36

(2.53) (0.53) (1.40) (−4.47) (1.30) (3.67)No 0.00021 −0.000854 0.262

(0.56) (−1.44)Y es−No 0.000697 0.00116

(1.45) (1.29)

47

Table 7: Daily excess returns on High-IT buying and High-IT selling daysThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios on high- and low-ITbuying and selling days (rather than total IT volume as in previous tables). Estimates are computed for days with high institutional trading(High-IT days or High) and other days (Low-IT days or Low). Day t is a High-IT day when IT buy (sell) volume (scaled by total marketvolume) at t is greater than its average over the past quarter. The difference in the coefficient between High- and Low-IT days is reported inthe last row of each panel. The right-hand side panel reports estimates from pooled regression of excess returns on betas, High-IT day dummy,and interaction between beta and High-IT (High*Beta). Panel A shows the estimate for buying volume (scaled by total market volume)while Panel B shows the results for selling volume (scaled by total market volume). Test assets are four beta-sorted portfolios, nine size-BMportfolios, and five industry portfolios. t-statistics, which are computed using Newey-West standard errors with five lags, are reported inparentheses. For pooled regressions, standard errors are corrected for clustering trading day. Betas are estimated using all returns data overthe past one year. This table shows that the difference in the coefficient on beta between High- and Low-IT days is driven by the buying sideof institutional trading.



of day

Panel A: High- versus Low-IT buying daysHigh 0.000077 0.00080 0.12 0.00095 −0.00200 −0.00056 0.00353 0.37

(0.54) (3.03) (2.99) (−8.32) (−1.19) (10.02)Low 0.00029 −0.000925 0.14

(1.32) (−2.72)High− Low −0.00021 0.00173

(−0.90) (3.99)

Panel B: High- versus Low-IT selling daysHigh 0.00036 0.000129 0.12 0.00046 −0.00084 0.00046 0.00104 0.37

(2.22) 0.500 (1.44) (−3.50) (0.97) (2.94)Low 0.00001 −0.00025 0.14

(0.05) (−0.74)High− Low 0.000349 0.00038

(1.48) (0.89)

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Table 8: Beta on High- and Low-IT daysThis table reports the average of, IT imbalance and beta on High- and Low-IT days. Stocks are splitinto two groups on the basis of their institutional trade imbalance where the first group (Buy) haspositive average imbalance and the second group (Sell) has negative average imbalance. “IT imb”is the average imbalance (imb=(buy volume − sell volume)/total market volume) of institutions.“Beta” is the average beta across stocks in each buy or sell portfolios. High − Low in the bottompanel reports the difference in beta and news dispersion between High- and Low-IT days. The lastcolumn reports the difference in the characteristic between Buy and Sell. # denote the differencethat is statistically significant at either the 5% or 1% level. Standard errors are computed using theNewey-West method with five lags.

Buy − SellIT day Buy/Sell IT Imb Beta Beta

Low Sell −0.0504 0.6783 0.0136#

Buy 0.0408 0.6919

High Sell −0.0605 0.6453 0.0114#

Buy 0.0514 0.6567

High−Low Sell −0.0101# −0.0330#

Buy 0.0106# −0.0352#

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Table 9: Institutional trading and investor disagreementThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios on High- and Low-ITdays accounting for the market disagreement. Day t has High-IT volume when the IT volume (scaled by market volume) on day t is greaterthan its average over the past quarter. “Strong disagreement days” are days when news dispersion is higher than its average over the pastquarter. “Yes” represents days that have the characteristics displayed in the heading whereas “No” represents days that do not have any orboth of the characteristics displayed in the heading. For example, in panel A1, a day is a “Yes” day when it has both High-IT and weakdisagreement; otherwise, it is a “No” day. The difference in the coefficient on beta between Yes and No days is reported in the last row ofeach panel. The right-hand side panel reports estimates from pooled regression of excess returns on betas, Yes day dummy, and interactionbetween beta and Yes (Yes*Beta). Test assets are four beta-sorted portfolios, nine size-BM portfolios, and five industry portfolios. t-statistics,which are computed using Newey-West standard errors with five lags, are reported in parentheses. For pooled regressions, standard errorsare corrected for clustering by trading day. There are 444 days with High-IT and weak disagreement, 534 days with Low-IT and weakdisagreement, and 555 days with High-IT and strong disagreement.



of day

Panel A: High-IT and weak disagreement daysY es 0.000890 0.001889 0.24 0.00075 −0.00135 0.00017 0.00317 0.45

(1.98) (2.59) (2.61) (−6.99) (0.27) (7.41)No 0.000549 −0.001113 0.24

(1.41) (−2.07)Y es−No 0.000340 0.00300

(0.50) (2.74)Panel B: Low-IT and weak disagreement daysY es 0.000646 −0.001305 0.24 0.00088 −0.00058 −0.00046 -0.00052 0.45

0.779 −1.13 (3.00) (−2.99) (−0.76) (-1.26)No 0.000608 −0.000270 0.24

2.044 −0.61Y es−No 0.000037 -0.001034

(0.60) (-1.00)Panel C: High-IT and strong disagreement daysY es 0.000680 0.000716 0.24 0.00071 −0.00107 0.00030 0.00138 0.45

(1.39) (1.03) (2.38) (−5.31) (0.51) (3.53)No 0.000596 −0.000924 0.24

(1.47) (−1.68)Y es−No 0.000083 0.001640

(0.13) (1.67)

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Table 10: Institutional trading and coskewnessThis table reports estimates from Fama-MacBeth regressions of daily excess returns on betas for various test portfolios on High- and Low-ITdays accounting for the coskewness effect. Day t has High-IT volume when the IT volume (scaled by market volume) on day t is greater thanits average over the past quarter. A stock coskewness is estimated using Regression (4) and then the overall coskewness on day t is the averageof individual stocks’ coskewness. Day t exhibits high coskewness if the overall coskewness is higher than its average over the past quarter.“Yes” represents days that have the characteristics displayed in the heading whereas “No” represents days that do not have any or both ofthe characteristics displayed in the heading. For example, in panel A1, a day is a “Yes” day when it has both High-IT and low coskewness;otherwise, it is a “No” day. The difference in the coefficient on beta between Yes and No days is reported in the last row of each panel.The right-hand side panel reports estimates from pooled regression of excess returns on betas, Yes day dummy, and interaction between betaand Yes (Yes*Beta). Test assets are four beta-sorted portfolios, nine size-BM portfolios, and five industry portfolios. t-statistics, which arecomputed using Newey-West standard errors with five lags, are reported in parentheses. For pooled regressions, standard errors are correctedfor clustering by trading day. There are 682 days with High-IT and low coskewness, 811 days with Low-IT and low coskewness, and 852 dayswith High-IT and high coskewness.



of day

Panel A: High-IT and low coskewness daysY es 0.000321 0.001544 0.25 0.00063 −0.00071 0.00015 0.00182 0.37

(0.75) (2.06) (2.42) (−3.63) (0.26) (4.26)No 0.000549 −0.001113 0.26

(1.30) (−1.22)Y es−No −0.000109 0.002150

(−0.18) (2.00)Panel B: Low-IT and low coskewness daysY es −0.000173 0.000165 0.27 0.00083 −0.00042 −0.00080 0.00039 0.37

(−0.25) (0.16) (3.11) (−2.12) (−1.47) (0.95)No 0.000592 −0.000281 0.25

(2.05) (−0.62)Y es−No −0.000765 0.000447

(−1.38) (0.44)Panel C: High-IT and high coskewness daysY es 0.000545 0.001531 0.25 0.00059 −0.00092 0.00015 0.00236 0.37

(1.232) (1.97) (2.22) −4.56) 0.29) (5.83)No 0.000364 −0.000744 0.26

(1.059) (−1.41)Y es−No 0.000181 0.002275

(0.333) (2.28)

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Appendices

A. Capital asset pricing model between High- and Low-IT days (one-yearwindow)

In the main text, we report results from High- and How-IT days that are definedon the basis of the past quarter. In this appendix, we use the window of one year asthe basis to determine a High- or Low-IT day. Specifically, a day has High-IT whenthe fraction of institutional trading volume over the market volume is greater thanits average over the past year.

Figure B.1 plots the security market line for four beta-sorted, nine size-BM, andfive industry portfolios. The relation between beta and average return is still muchhigher and positive on High-IT days. An increase in beta by 0.5 is associated with astatistically significant increase in daily excess return of 9.7bps per day. In contrast,this relation is significantly negative on Low-IT days (second graph). An increasein beta by 0.5 leads to a reduction in average excess return of 3.1bps per day (t-statistic = -3.1). We thus conclude that our results are not sensitive to the choiceof window length to define High- or Low-IT days. In untabulated results, we alsorepeat the estimation for the level institutional volume (without scaling by marketvolume). The difference in implied market risk premium between High- and Low-ITdays remains significant.

B. High-IT vs. FOMC announcements

This section examines whether the IT effect in Finland could be driven by days ofscheduled FOMC announcements in the U.S.. First, Figure B.2 establishes the effectof FOMC announcements on the relation between beta and returns in the Finnishmarket. On days when the FOMC announces interest rate, the SML is upwardsloping whereas on non-announcement days, the SML is downward sloping. We thentest whether the IT effect in Finland could be driven by FOMC announcement days.Replicating the methodology in the main text for Figure 5, we examine the SML ondays of High-IT and non-FOMC announcement. We find that the High-IT effect inFinland is still strong even though the day does not have an announcement. Theseresults confirm that our IT effects are distinct from the announcement effect.

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Figure B.1: Capital asset pricing model on High- and Low-IT volume daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on Highand Low-IT volume. This figure plots average daily excess returns (in percentages) against marketbetas for three beta-sorted portfolios, nine size-BM portfolios, and five industry portfolios. Thefirst graph shows days with high-IT volume (days with the fraction of IT volume greater than theaverage past one-year volume). The second graph presents the relationship between returns andbetas on low-IT volume days. The implied ordinary least squares estimates of the securities marketline for each type of day are also plotted. The sample period is between 1996 and 2011 (we lost oneyear to form portfolios). Betas to form portfolios are corrected for potential asynchronous tradingusing the Dimson method. This figure shows that the relation between beta and average return ismuch higher and more positive on High-IT days (first graph) than that on Low-IT days (secondgraph).

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Figure B.2: Capital asset pricing model on FOMC announcement and non-announcement daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on FOMCscheduled announcement versus non-announcement days. This figure plots average daily excessreturns (in percentages) against market betas for four beta-sorted portfolios, nine size-BMportfolios, and five industry portfolios. The first graph shows the relation between beta andaverage return on days with FOMC monetary policy decision. The second graph presents similarline on non-announcement days. The implied ordinary least squares estimates of the securitymarket line for each type of day are also plotted. The sample period is between 1997 and 2011.Betas to form portfolios are corrected for potential asynchronous trading using the Dimsonmethod. This figure shows that, consistent with Savor and Wilson (2014), the relation betweenbeta and average return is much more positive on monetary announcement days than on normal days.

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Figure B.3: Capital asset pricing model – Non-FOMC-announcement daysAverage excess returns for four beta-sorted, nine size-BM, and five industry portfolios on High-and Low-IT days accounting for the FOMC announcement effect. This figure plots average dailyexcess returns (in percentages) against market betas for four beta-sorted portfolios, nine size-BMportfolios, and five industry portfolios. The first graph shows days with High-IT volume butno FOMC monetary policy decision. The second graph presents the relationship between excessreturns and betas on days with Low-IT volume and no announcement. The implied ordinary leastsquares estimates of the securities market line for each type of day are also plotted. The sampleperiod is between 1997 and 2011. Betas to form portfolios are corrected for potential asynchronoustrading using the Dimson method. The purpose of this figure is to show that, even when theday has no macroeconomic announcement, the difference in the risk premium between High- andLow-IT days remains robust. Consequently, the effect of institutional trading is not a manifestationof announcement effects.

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institutional trading and asset pricing · institutional trading and asset pricing bart frijns,...

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