A Direct Test of Private Information on Analysts’ Recommendations: Examination of Profits among Institutions and Individuals on Brokerages Recommendations Yu-Jane, Liu Department of Finance National Chengchi University Taipei, Taiwan Tel: (886) 2-2939-3091 Ext. 81123 Email: [email protected]K.C. John Wei Department of Finance Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Tel: (852) 2358-7676 Email: [email protected]Vivian W. Tai Department Banking and Finance National Chi Nan University Puli, Taiwan Tel: (886) 49-291-0960 Ext. 4984 Email: [email protected]Nov. 1, 2006 (PRELIMINARY) PLEASE DO NOT QUOTE WITHOUT PERMISSION
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A Direct Test of Private Information on Analysts’ Recommendations: Examination of Profits among Institutions and Individuals
Abstract Traditionally, the information value of analysts’ recommendations has been well recognized by cumulative abnormal returns. Recent studies however show that individuals are underperformed, and therefore, it is a critical issue if analysts’ recommendations are helpful to individuals’ welfares. The first contribution of this paper to literature is to examine whether individual investors are capable of capturing the information value. To classify the information value of recommendations for individuals and institutions, respectively, we thus use a direct measure to calculate the actual trading profits of types of traders. To our best knowledge, this is the first paper that demonstrates the information value for types of investors. Our results indicate that, all investors get positive and significant profits in brokerages’ buy recommendations, no matter what types of investors are measured. As to sell recommendations, only foreign investors and mutual funds have positive returns. We also find that professional institutions earn more profits than retail investors during the recommendation event periods. Further, the second objective of this paper is to test whether the information value is caused by private information from brokerage houses; we separate the profits of types of investors into customer and non-customer based. The findings are that only domestic institutional customers of recommending brokerages are more beneficial than those of non-recommending brokerages in buy recommendations, which implies that brokerage houses may reveal private information to their own institutional customers before buy recommendations are made public. This does not hold for sell recommendations. Keywords: Profits, Information Value, Analysts Recommendations, Private Information
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I. Introductions
A large body of literature has studied the information value of analysts’ researches,
including earnings forecasts (e.g., Stickel, 1992; Abarbanell, Lanen, and Verrechia,
1995; Dechow, Hutton, and Sloan, 2000; Cooper, Day, and Lewis, 2001) and analysts’
al., 1979; Womack, 1996; Stickel, 1995; Kim, Lin, and Slovin, 1997; Lin and
McNichols, 1998; Michaely and Womack, 1999; D’Mello and Ferris, 2000; Barber,
Lehavy, McNichols, and Trueman, 2001; Brav and Lehavi, 2002). The information
value of analysts’ researches, in these existing studies, is mostly recognized by
cumulative abnormal returns1, assuming that all investors in securities markets are
homogenous and have likely abilities and resources to capture the information value of
analysts (e.g., Barber, Lehavy, McNichols, and Trueman, 2003; Lin and McNichols,
1998; Barron, Byard, Kile, and Riedl, 2002; Mikhail, Walther, and Willis, 2001; Hong,
Kubik, and Solomon, 2000; De Bondt and Thaler, 1990; Mendenhall, 1991; Abarbanell
and Bernard, 1992; Klein, 1990; Easterwood and Nutt, 1999). From the prevalence
of behavioral finance, a large growing body of researches2 began to emphasize the
differences among investors and how these differences affect market efficiency. Yet, 1 Bidwell (1977) and Elton, Gruber ,and Grossman (1986) use beta-adjusted benchmark to measure abnormal return on recommendations; Fama -French factors and industry-adjusted returns were used by Womack (1996); Barber, Lehavy, McNichols, and Trueman (2001) use Fama -French and momentum factors model to measure abnormal returns. 2 Some researches studies the differential investment behaviors of institutions and/or individuals, and the relationship between types of investors and stock returns based on theories about expertise, investment experience, resources, information accessibility, and even behavioral biases controllability, which makes institutional investors are mostly informed and rational traders than individual investors, and provide some evidence that individuals are underperformed (e.g., Lakonishok et al. , 1992; Nofsinger and Sias, 1999; Grinblatt, Titman and Wermers, 1995; Wermers, 1999 and 2000; Chen, Jegadeesh and Wermers, 2000; Grinblatt and Keloharju, 2000 and 2001; Griffin et al., 2003). And others show that institutional buying is correlated with past and contemporaneous stock returns. In the case of individuals, Odean (1998) and Griffin et al. (2003) provide evidence that individual stock buying is negatively correlated with past returns and positively correlated with contemporaneous returns. The evidence that institutional (individual) buying is positively (negatively) correlated with short-term expected returns or that institutions (individuals) produce superior (inferior) investment performance is mixed (Jensen, 1968; Lakonishok et al., 1992; Nofsinger and Sias, 1999; Odean, 1999; Barber and Odean, 2000; Grinblatt and Keloharju, 2000; Wermers, 2000; Gompers and Metrick, 2001; Griffin et al., 2003; Barber, Lee, Liu and Odean, 2005).
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it is an unsolved question whether both of institutions and/or individuals do benefit
from analysts’ reports.
Barber, Lee, Liu and Odean (2005) find that institutional investors perform much
better than individual investors. If individuals are poorly performed, and analysts’
recommendations really provide some information value, it may be one of the important
information sources to them. Unfortunately, the existing literature provides little
evidence on whether analysts’ recommendations can enhance individuals’ welfares. To
fill this gap, we adopt actual trading profits as a direct measure to shed further light on
the information value of brokerages’ recommendations for various types of investors,
especially individual investors.
Recently, some studies begin to focus on trading behaviors of institutions and/or
individual investors on analysts’ information. Chen and Cheng (2002) investigate how
types of institutions allocate their assets in response to analysts’ recommendations based
on the information acquisition theory of Grossman and Stiglitz (1980). They find
institutions actually follow buy/sell recommendations and significantly capture the
opportunity of profits using quarterly holding change as a proxy. By separating
recommendations into underwriters and non-underwriters based, Iskoz (2003) find
institutional investors generate traders only in response to unaffiliated recommendations.
More than investigating institutional investors’ reactions in the study of Iskoz (2003),
Malmendier and Shanthikumar (2003) further classify investors into big and small
traders as proxies of institutional and individual investors. They conclude that big
investors (institutional investors) tend to account for the distortions of affiliated analysts,
while small investors (individual investors) follow recommendations naively. Lee, Liu,
and Tai (2004) analyze investors’ actual demands by tracing trading timing of
professional and public investors on brokerages’ recommendations, and the results
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indicate that institutions do follow favorable/unfavorable recommendations while
individuals react slowly to recommendations when reports were made public. Unlike
the previous researches that focus on investors’ reactions to analysts’ reports, this paper
further investigates the information value of analyst’s recommendations for institutions
and individuals, respectively. This will help to justify the demands on analysts’ reports
for types of investors.
Why do institutions and individuals react to analysts’ recommendation differently?
First, to maximize the revenues, brokerage houses have incentives to maintain
long-term relationship with institutions. Institutions are main customers in brokerage
houses, because their trades may create important commission revenue for the
brokerages, and their managements provide a channel for brokerage to access the
information of recommended firms. 3 Besides, institutions may also buy research
reports for their investment strategies, and have some other business opportunities (for
example underwriting4) with brokerage houses. Thus, analysts from brokerage houses
may provide more detailed research reports to institutional investors than individual
investors. Additionally, a bulk of studies claim brokers may reveal private information
to their institutional clients before releasing to public for entering into long-term
cooperative relationships (e.g., Kim, Lin, and Slovin, 19975; Goldstein, Irvine, and
Kandel, 20046; Lee, Liu, and Tai, 2004). We assert that the information value of
3 Lim (2001) 4 Lin and McNichols (1998), Michaely and Womack (1999), Iskoz (2002), Malmendier and Shanthikumar (2003) 5 Kim, Lin and Slovin (1997) find a strong positive valuation effect at the open and then, suppose information about analysts ’ recommendations typically is given first to important clients of the brokerage firm, mostly institutional investors, and subsequently released to the general public. 6 Goldstein, Irvine, and Kandel (2004) analyze orders of institutional clients, as those who traded on event days through the recommending brokers, in the recommended stock on the day analysts ’ change their recommendations. They use the buy and sell indicator variables and the price of the order relative to the close price to calculate order profitability. The profitability results are consistent with brokers’ services as valuable for important clients, but are inconsistent with the idea of informed traders mentioned in Kim, Lin, and Slovin (1997). Additionally, using the notional profits of event day to proxy the actual trading profit doesn’t make sense for institutional trading behaviors who are not short-term strategy traders.
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institutions v.s. individuals may be differential because institutions have deep business
relationship with the brokerage houses. In particular, we classify traders as customers
and non-customers based, and therefore can directly test whether institutional customers
do really enjoy the value from information provided by analysts’ recommendations, if
any.
Second, borrowing from behavioral finance, institutions and individuals may
behave in a different way due to variations in expertise, investment experience,
resources, information accessibility, and even behavioral bias controllability.7 We
may expect to see that institutions account for the information distortion or behavioral
bias of analysts’ reports, while retail investors tend to trade naively.
Malmendier and Shanthikumar (2005) provide the conflict of interest on affiliated
relationship of analysts as the rationale of why institutions and individuals trade
differently on analysts’ recommendations. They find analysts may choose to distort
recommendations but to prove their excellent analyzing quality in their forecasts, which
are directed towards more sophisticated, institutional investors. And their additional
results on trading reactions indicate that small traders react indeed more strongly to
recommendations, while large traders discount recommendations and react more
strongly to analysts’ earnings forecasts. Malmendier and Shanthikumar conclude these
different trading behaviors of large and small traders on recommendations due to
institutions’ superior ability8. Their findings provide one possibility of that brokerages
7 Lakonishok et al., 1992; Nofsinger and Sias, 1999; Grinblatt, Titman and Wermers, 1995; Wermers, 1999 and 2000; Chen, Jegadeesh and Wermers, 2000; Grinblatt and Keloharju, 2000 and 2001; Griffin et al., 2003; Odean, 1998; Jensen, 1968; Lakonishok et al., 1992; Odean, 1999; Barber and Odean, 2000; Gompers and Metrick, 2001; Griffin et al., 2003; Barber, Lee, Liu and Odean, 2005. 8 The definition of superior ability here is as mention in Ke and Petroni (2003); “institutional investors are better informed and/or have a superior ability to process information relative to other investors”, which highlight institutions’ superior ability on realizing and using information. A growing body of literature showing informational advantages of institutional investors in various area. For example, studies have shown that institutional investors have an edge over retail investors in picking stocks (e.g., Grinblatt and Titman,1989 and 1992; Nofsinger ans Sias, 1999; and Wermers, 2000), in predicting CEO
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generate trading for commission revenue through analysts coverage, not through biased
earnings forecasts, which are found in Irvine (2001, 2002).
The summarized studies mentioned above present two possibilities to answer why
institutions and individuals trade differently on analysts’ recommendations. The first
is institutional investors have superior selectivity while most individual investors are
naive. The second is institutional investors have great business relationships with
brokers, thus they get more precise research reports, consistent with Grossman and
Stiglitz (1990)’s information acquisition theory, while individual investors have less
bargaining power with brokerages. To clarify the interaction between these
possibilities and different trading behaviors on analysts’ researches will help us to
realize the incentives of brokerages and provide implications for investors’ investment
strategies. Besides, if this inconsistency of brokerages is really a strategic intention,
there is still no direct evidence to prove that the main customers of brokerages, mostly
institutions, get benefit from the private information or strategic intention of brokerages,
and individual investors do get hurt. Thus, it remains an open question whether the
differential trading reactions between institutional investors and individua l investors
make institutions wealthier and individuals worse off.
To sum up, Chen and Cheng (2002) and Iskoz (2003) adopt quarterly holding data
to measure institutional demands, Malmendier and Shanthikumar (2003, 2005) classify
investors into big and small traders based on the net buys daily information. Our
paper compliments existing studies related analysts in several ways. First, instead of
investigating trading behaviors of institutional (big) and/or individual (small) traders
of covered stocks, this paper provides a direct measure, actual trading profits, to
turnovers, in part because they are better information on the prospects of the firms (Parrino, Sias, and Starks, 2003), in SEOs (Gibson, Safieddine, and Sonti, 2004; Chemmanur, He, and Hu, 2005); in post acquisition markets (Chen, Harford, and Li, 2004), and in post IPO markets (Boehmer, Boehmer, and Fishe, 2005; Field and Lowry, 2005; Field, 1997).
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measure who do really benefit from the information value of brokerage
recommendations by group traders into institutions v.s. individuals. Further, we
classify institutions v.s. individuals into customers and non-customers to directly test
what’s reason induce institutional customers and individual customers do behave
differently. The evidence helps to address if there is any private information for
institutions and/or individuals between customers and non-customers, and to clarify
whether institutions’ different behaviors from individuals results from their superior
abilities or their deep relationships with brokerages.
Our unique database includes all orders submitted and executed on the Taiwan
Stock Exchange. In our database, each order can be mapped to a re-coding code of
trader’s identity and a brokerage code that enables us to calculate the actual trading
profits of each investor on analysts’ recommendations, and can directly identify
investors into institutions v.s. individuals, and customers v.s. non-customers. By
means of the actual trading profits of investors, we fill in the void with the information
value to various types of investors. Through analyzing the relationships between
clients’ profits and their relationship with recommending brokerages, we clarify the
reasons of differential trading behaviors of institutions and individuals on analysts’
recommendations. In this regard, this paper provides direct evidence to answer
following questions:
l Who does really benefit from analysts’ information?
l Do customers of recommending brokerages benefit from the private information
from brokerages?
l Is the information value of institutions caused by clients’ relationships with
brokerage houses or their superior selectivity?
This empirical study focuses on (1) examining the information value of
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recommendations to different types of investors, including institutions and individuals
via actual trading profit, (2) proving the existence of private information provided by
recommending brokerages through comparing profits between customers and
non-customers of recommending brokerages, (3) clarifying institutions’ benefits results
from their superior abilities or their deep relationships with brokerages. To our best
knowledge, this is the first paper to use actual trading profits to demonstrate the
information value of brokerage reports.
Generally speaking, individual traders are less assessable to private information.
If there really exist the information value of analysts’ recommendations and analysts’
researches may one of the important intermediates to improve noise traders’ welfares.
Hence, the first contribution of this research is to estimate whether individual investors
are capable of capturing the information value of analysts’ recommendations by using
actual trading profits; it is not sufficient to measure by excess returns.
Second, extending Lee, Liu, and Tai (2004), Malmendier and Shanthikumar (2003,
2005), and Iskoz (2003), we further test whether the different trading behaviors
between institutions and individuals on recommendations makes benefit from
recommendations asymmetrically between individual investors and institutional
investors. Based on expertise of institutional investors, and long-term cooperation
between institutions and brokers, we expect institutional investors earn more profits
than individual investors from brokerages’ recommendations. Our results indicate
that, all investors, including individuals, get positive and significant profits in
brokerages’ buy recommendations. We also find that professional investors earn
more profits than retail investors during the recommendation event periods. This
finding implies analysts’ favorable recommendations improve both individuals’ and
institutions’ wealth, but there still exist unequal benefits between individuals and
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institutions. As to sell recommendations, only foreign investors and mutual funds
demonstratively have positive returns.
Additionally, we classify institutional investors into foreign and domestic
investors, and this classification helps us to understand whether foreign investors make
more profits than domestic institutions on analysts’ recommendations. According to
expertise hypothesis, we expect that foreign institutions generate more profits than
domestic institutions on brokerages’ recommendations. In our findings, foreigner
investors earn the largest profits among types of investors during event periods.
Third, to further examine whether the information value is caused by private
information from brokerage houses, we separated the profits of types of investors into
customers and non-customers. To boost commission revenue contributed by clients,
we expect recommending brokerages will reveal private information to their clients
before releasing to the public. Further, following the argument of Kim, Lin and
Slovin (1997) and Irvine (2001, 2002), brokers will provide better information and
pre-release recommendations to important clients, mostly institutions, who generate
more traders and other business opportunities. We use institutional investors as a
proxy of larger trading generators of recommending brokerages, and individual
investors as a proxy of small trading generators. We expect clients who contribute
more trading volume or business opportunities as important customers to
recommending brokerages, which will get more private information from
recommending brokerages. Hence, profits of mutual funds will be larger than
corporations, and profits of corporations will be larger than individual investors. To
distinguish the superior ability of institutions and better private information from
recommending brokerages, we further compare the difference of actual trading profits
among mutual funds, corporations, and individual investors in recommending
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brokerages and those in non-recommending brokerages. If recommending brokerages
provide better private information to institutional clients to win long-term customer
relationships, then the evidence will show the profit difference between Mutual
Funds/corporations and individual investors in recommending brokerages will be
larger than those in non-recommending brokerages.
As expectation, only domestic institutional customers of recommending
brokerages get more profits than non-customers of recommending brokerages in buy
recommendations, which implies that brokerage houses may reveal private information
to their own institutional customers before favorable recommendations are made public.
But this does not hold in sell recommendations, which supports previous findings;
unfavorable recommendations retain more information value to public investors.
Finally, we run regressions to examine the relation between actual trading profits
of investors and customer relationships. We expect the main explanatory, ‘customers
of recommending brokerages dummy,’ will only be significantly positive in
institutional investors, but not significantly in individual investors, if private
information is revealed only to important clients. If private information provided by
recommending brokerages is displayed by stock selectivity, then the significance of
‘customers of recommending brokerages dummy’ will disappear under other
controlling variables and the significance will be displayed on variables, ‘customer
dummy multiply stock characteristics.’ Otherwise, if the significance of ‘customers
dummy’ still exist under controlling stock characteristics, it shows private information
provided by recommending brokerages is partially represented by pre-releasing
information to clients, and partially contained in stock selectivity.
In profit regression analysis, we find customers of mutual funds and corporations
really generate more profit than non-customers in buy recommendations without
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controlling recommending types, consensus scores, and stock characteristics. After
controlling recommending types, consensus scores, and stock characteristics, there is
no significantly different profit between customers and non-customers and
simultaneously the variables, ‘customer dummy multiply stock characteristics,’ are
significant. These results suggest that private information provided by
The remainder of this dissertation proceeds as follows. Section II summaries
related literature. Section III presents hypotheses. Section IV presents empirical
and measurement designs. Section V describes our data sources and sample
distributions. Section VI is empirical results, while section VII is conclusions.
II. Hypotheses
From 1990, the proportion of individual investors has steadily increased. A cluster of
literature indicates irrational “noise traders” will cause asset prices to move
unpredictably and without reference to information flows (e.g., Black, 1986; De Long,
Shleiher, Summers, and Waldmann, 1990; and Campbell and Kyle, 1993). Previous
studies in classical financial theories document that irrational trading volatility of noise
traders results from a lack of ability to use information and factors that exactly affect
asset prices. Recent studies in behavioral finance show that individuals are
underperformed because of behavior bias, overconfidence. If there really exists
information value of analysts’ recommendations and analysts’ information is one of the
important intermediates to enhance market efficiency, it is interesting to know whether
concise buy/sell recommendations improve noise traders’ wealth. Otherwise, there is
not any opportunity for noise traders to earn when information of analysts are released
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publicly under efficient market. Hence, the first objective of this research is to
estimate whether noise traders get profit from analysts’ recommendations using actual
profits of individual investors, it is not sufficient to measure by cumulative abnormal
returns.
Hypothesis 1: Noise traders, proxies by individual investors, could earn positive profits from recommendations.
Second, extending Lee, Liu, and Tai (2004), Malmendier and Shanthikumar (2003,
2005), and Iskoz (2003), we, further, would like to understand whether the difference
in trading behaviors between institutions and individuals on recommendations makes
both individual and institutional investors wealthier, and whether exist benefit
asymmetry between individuals and institutions. We use actual trading profits to
investigate which type of investors could capture the information value of
recommendations. Based on the expertise of institutional investors, and long-term
cooperation between institutions and brokers, we expect institutiona l investors,
foreigners, mutual funds and corporations, earn more profit than individual investors
from brokerages’ recommendations.
Hypothesis 2: Institutional investors earn more profits than individual investors from recommendations.
Third, to further examine whether the information value is caused by private
information from brokerage houses, we separated the profits of types of investors into
customers and non-customers. To boost commission revenue contributed by clients,
as mentioned in Irvine (2001, 2002), we expect recommending brokerages will reveal
private information to their clients before releasing to the public.
Hypothesis 3: Customers in recommending brokerages earn more profits than non-customers in recommending brokerages.
Following the argument of Kim, Lin, and Slovin (1997) and Goldstein, Irvine, and
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Kandel (2004), brokers will provide better research reports and pre-release
recommendations to important clients, mostly institutions, who generate more traders
and other business opportunitie s. I expect institutional clients, who contribute more
trading volume and business opportunities to recommending brokerages, will get more
valuable private information from recommending brokerages. Hence, profit
differences between customers and non-customers in mutual funds will be larger than
those in corporations, and those in individual investors.
Hypothesis 4: Institutional customers earn more benefits than individual customers.
To distinguish the superior ability of institutions and better private information
from recommending brokerages, we further compare the difference of actual profits
among mutual funds, corporations, and individual investors in customers and
non-customers. If recommending brokerages provide better private information to
institutional clients to win long-term customer relationships, then the evidence will
show the profit differences among mutual funds, corporations, and individual investors
in customers will be larger than those in non-customers.
Hypothesis 5: Profit differences among customers in mutual funds, corporations and individual investors are larger in customers than those in non-customers.
III. Empirical and Measurement Designs
In order to distinguish the above hypotheses empirically, we need to employ measures
of actual trading profit of investors during event periods and define who are customers
and non-customers of recommending brokerages.
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3.1. Types of Investors and Definition of Customers
To further examine whether the information values are caused by private information
from brokerage houses, we separate the profits of types of investors into customers and
non-customers based. Investors who have been submitted orders from recommending
brokerages are defined as customers. If one investor A has been submitted orders
from brokerage X and Y, and now, one covered stock is recommended by brokerage X,
then investors A is the customer in this event. Those who do not belong to customers
and trade during this event period are defined as non-customers.
If one same covered stock S, which is recommended by brokerage A at t=0, is
recommended by another brokerage B during this event period, t=[-15,+15] or
t=[-5,+5], for example t=-3, then customers of brokerage A are defined as customers,
but customers of brokerage B are still not defined as customers. In this situation,
customers of brokerage B might obtain the information of covered stock S, but the
private information is not obtained directly from brokerage A and their trading does not
directly contribute to brokerage A. Hence, the power of statistics in difference test
between customers and non-customers might be affected, if the customers of brokerage
B are still not deducted from non-customers.
3.2. Actual Trading Profits Measurement
To directly measure information value of recommendations, we adopt a direct
measurement, actual trading profits of investors. Actual trading profit is revenue,
measured by executed order quantity and price in each sell moment, deducted by
mapped costs, measured by the First- in First-out (FIFO) method and the Averaged
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method, and then deducted transaction costs9. Actual trading profit (in percentage) of
each investor during an event period is summarized total selling revenue, deducted by
total mapped costs and transaction costs, then divide by total mapped costs, and, finally,
multiply 100%. The mean actual trading profit of customers, non-customers, and
types of investors, are respectively equal weighted and value weighted actual trading
profits of customers, non-customers, and types of investors categories.
From the trading timing of different types of investors based on recommendations
in Lee, Liu, and Tai (2004), they find that major institutional investors buy stocks,
beginning at least 15-days before analysts’ reports are made public in Short/Strong
recommendations and sell stocks after the 5th day after analysts make
recommendations. For Buy and Hold recommendations, foreigners and dealers begin
to buy stocks ten days before recommendations are announced, and mutual funds trade
on the buy side much earlier than recommendations are announced. For Sell/Neutral
recommendations, foreigners and mutual funds seem to capture the information two to
four days before announcement and begin to sell stocks five days before
recommendations announce. Following the findings in Lee, Liu, and Tai (2004), we
define the event period as t=[-15,+15] and t=[-5,+5] for completely including the
private information which might pre- lease to mainly institutional clients from
brokerages.
To exactly measure the information value of recommendations, we adopt another
direct measurement, adjusted actual trading profits, which consider the two following
conditions. First, the adjusted actual trading profits are deducted those results from
original holding early before event period. Thus, we adjust the holding costs of
9 Barber et al (2001) document the profitability of investment strategies on consensus recommendations, and show that high trading levels are required to capture the excess returns generated by the strategies analyzed, entailing substantial transactions costs and leading to abnormal net returns for these strategies that are not reliable greater than zero.
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investors who trade during event periods by the close price of the day before event
period, t=-16 or t=-6. Hence, adjusted actual trading profits of these holding
positions are summarized sell revenue of these holding positions deducted adjusted
holding costs and transaction costs.
Besides, to completely include the benefit from recommendations, we also
calculate and summary the paper gain/loss of positions, which be hold by investors
who trade during event periods, into adjusted actual trading profits. The paper
gain/loss is calculated by sell revenue using close price at the final event day, t=+15 or
t=+5, deducted by mapped costs of these non-sell positions.
Following the steps of calculating actual trading profits mentioned above, it is
assumed investors buy and hold position before sell actions, which is consistent with
the concept of buy recommendations. For sell recommendations, it is interested to
understand the information value from sell actions before buy actions. However,
Individuals and corporations are free to short sell, though dealers, mutual funds, and
foreigners are prohibited from doing so on the TSE. Hence, it would not be able to
compare the information value of sell recommendations between institutions and
individuals by calculating actual trading profits based on strategy of sell before buy
actions. On the other hand, there are some limitations for individuals’ short sale.
First, individual investors should meet some requirements to open a margin account10
10 The regulation about opening margin account in Article 2 of “Terms for Establishment of Margin Accounts With Securities Firms for Margin and Stock Loans” are as follows, A customer applying to establish a margin account and entering into a margin agreement shall meet the following thresholds, except in case of renewing an existing agreement at its expiration: 1. The customer is an ROC national of at least 20 years of age and with disposing capacity, or a juristic person organized and registered under the laws of the ROC. 2. Three months have elapsed since the customer established a brokerage account. 3. At least ten trade orders have been executed for the customer's account during the most recent year, with an aggregate amount of at least 50% of the applied maximum loan value; the same also applies where a period of less than one year has elapsed since the customer established a brokerage account. 4. The customer's income and assets for the most recent year aggregate at least 30% of the applied maximum loan value, save that the applied maximum loan value does not exceed NT$500,000. In the case of a customer applying to establish margin accounts in an aggregate number of five or more,
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and make margin transactions, and each short sale trading must follow the optimal rule.
Second, the first option (put/call) instruments begin to trade on TAIFEX (Taiwan
Futures Exchange) from 2002. Hence, there is no option as hedge instruments for
short sales strategies in the sample period. In the meantime, the first warrant, another
hedge instruments, allow to issue (trade) in Taiwan from August (September) in 1997,
and, hence, there are still not many warrants to be chose as hedge instruments when
investors short sale recommended stocks and warrant market is not much active and
integrity during the sample period. Besides, previous evidence supports that
individual investors seldom short sale. Odean (2003) finds only 0.29 percent of the
more than 66,000 traders in the room take short positions. In our sample, averagely,
the using percentage of total individuals is no more than 3.31% (4.12%) to adopt short
sale strategy during event periods in buy (sell) recommendations 11. Additionally,
existing evidences support that the reaction periods of sell recommendations last
longer than buy recommendations for short sale restrictions (e.g.; Womack, 1996;
Barber et. al., 2001; Busse and Green, 2002; Lee, Liu, and Tai, 2004). As a result,
sell recommendations are mainly provided to those who had hold covered stocks.
Therefore, we use the same spirit to calculate actual trading profits of buy and sell the calculation of the applied maximum loan value under subparagraph 3 or 4 of the preceding paragraph shall include the maximum loan values approved for previously established margin accounts, and, where the property certificate is a certificate of deposit issued by a financial institution under Article 3, paragraph 1, subparagraph 2, the calculation shall be made based upon the average balance for the most recent month. A customer renewing a margin agreement during its term or at its expiration shall meet the requirements set out in paragraph 1, subparagraph 4. A put warrants issuer and an enterprise exclusively or concurrently engaged in futures proprietary trading that is also an equity options market maker may apply to establish a margin account without being subject to the requirements set out in paragraph 1 and paragraph 2. For a privately placed securities investment trust fund managed by a securities investment trust enterprise, the fund custodian institution may apply to establish a margin account without being subject to the requirements set out in paragraph 1, subparagraphs 1, 2, and 3, for which the amount limit for margin purchase and short sale may not exceed 50 percent of the fund size. The combined total of the amount limit for short sale under the preceding paragraph and actual sales with borrowed securities may not exceed 50 percent of the fund size. A securities firm may raise the percentage under paragraph 1, subparagraph 3 or 4 as it deems necessary after assessment. 11 In our sample, averagely, the share of short sale to total trading shares is no more than 3.81% (4.63%) during event periods in buy (sell) recommendations.
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recommendations.
IV. Data and Sample Distributions
4.1. Data
Recommendations data were collected from all reports of brokerages from the Central
News Agency, including daily morning reports, monthly reports, etc. Reports of
brokerages appear to be collected and summarized by the Central News Agency from
the middle of 1995. And Central News Agency re-arranged these reports released by
brokerages to table lists that contain all recommending brokerages and recommended
stocks. These table lists are available on their web news of each day and also may be
read via the stock web site of Yahoo and several web sites of related investment and
consulting firms. At the same time, these released reports could be read in the
recommending brokerage, or gotten from account executives of recommending
brokerages. Each report includes an exact time stamp and explicit name of the
recommending brokerage. Following Chang (2003), we use the keywords defined in
Appendix Table 112 to give each recommendation of brokerages a rating.
To meet the intra-day trading data of each investor from the Taiwan Stock
Exchange from 1995 to 1999, we recorded 57,437 recommendations from June of
1995 to December of 1999. Excluding stocks experienc ing IPO, de- list, moving from
the OTC to the TSE market, and the sample which is covered in recommendation lists
of brokerages less than 5 times in the sample period, we get 56,655 recommendations
and an event number of recommended day-stock is 33,919. At the same time, we
12 We calculate the cumulative abnormal return of each recommendation with key words as shown in Appendix Table 1, and order the keywords by the size of cumulative abnormal returns with balancing the distribution of recommendations in each rating to set the translation table in Appendix Table 1.
18
delete the recommending brokerages which reports recommendations infrequently
(less than 10 times in the sample period). Finally, the number of recommending
brokerages is 46.
4.2. Recommendations Characteristics
From the earlier research of analysts’ recommendations, the information value of
recommendations is measured both by “level of rating” and “change of rating”. Elton,
Gruber, and Grossman (1986) focused on changes each month to a new rating from a
lower (“upgrades”) or a higher (“downgrades”) one. Upgrades, especially to the most
favorable category, resulted in significant (beta-adjusted) excess returns of 3.43% in
the month of the announcement, while Downgrades resulted in negative excess returns
of -2.26%. Womack (1996) reports that the average return in the three-day period
surrounding changes to “buy”, “strong buy” or “add to the recommended list” was over
3%. A stock that was added to the “sell” recommendation list experience, on average,
a price drop of 4.5%. Barber, Lehavy, McNichols, and Trueman (2003) examine the
relation between the distribution of stock ratings at investment banks and brokerage
firms and the profitability of analysts’ recommendations. They point out distribution
of broker’s stock ratings can predict the profitability of its recommendations. Besides,
upgrades to buy issued by brokers with the smallest percentage of buy
recommendations significantly outperformed those of brokers with the greatest
percentage of buys. Conversely, downgrades to hold or sell coming from brokers
issuing the most buy recommendations significantly outperformed those of brokers
issuing the fewest.
Different from other existing researches, Jegadeesh et al (2004) find that the level
19
of the consensus analyst recommendation does not contain incremental information for
the general population of stocks when it is used in conjunction with other predictive
signals (by using monthly cumulative abnormal returns). The explanatory power of
the change in the consensus analyst recommendation is more robust than that of the
level of the recommendation. Changes in recommendations over the prior quarter
predict future returns when used separately and when used in conjunction with other
predictive signals. These findings suggest that the return-relevant information
contained in analyst recommendation changes is, to a large extent, orthogonal to the
information contained in the other variables. One interpretation of their finding is
that recommendation changes capture qualitative aspects of a firm’s operations (e.g.,
managerial abilities, strategic alliances, intangible assets, competitive position in
industry, or other growth opportunities) that do not appear in the quantitative signals
they examine. Their evidence is at least consistent with the analysts’ claim that they
bring some new information to market.
Hence, following the previous findings, we both adopt “rating” (consensus scores)
and “changes of rating” (recommendation type) in this research, which enable us to
realize whether institutions and individuals react differentially on the level/change of
ratings of recommendations.
The consensus scores of the same stocks in the same day are calculated by
averaging the ratings given by recommending brokerages. According to consensus
scores, we classify recommendations into 5 levels with considering the information
content of keywords categories and balancing the score distribution of
recommendations. “Sell” is a consensus score between [0,2.5], “Neutral” is a
consensus score between (2.5,3.5), “Long-term Buy” is a consensus score between
[3.5,5), “Add and Continuous Holding” is a consensus score equal to 5, and finally,
20
“Short/Strong Buy” is a score above 5. The distribution of recommendation levels
are presented in Panel B of Table 1. Consistent with Malmendier and Shanthikumar
(2003) and Barber et al (2003), 62.9% (21,334) stand at “Short/Strong Buy”, and only
11.56% are at “Sell” and “Neutral” categories. It shows brokerages may intentionally
give biased recommendations towards buying because buy recommendations are more
likely to generate trading business than sell recommendations or to win more
underwriting business. If we combine “Sell”, “Neutral” into sell recommendations
and “Long-term Buy”, “Add and Continuous Holding” and “Short/Strong Buy” into
buy recommendations, the number of buy recommendations is 29,996 and the number
of sell recommendations are 3,923.
We further divide recommendation revisions into four types, including “Initiate,”
“Continuous,” “Upgrade,” and “Downgrade” for the same recommended stocks in
each recommending day. “Initiate” is defined as there are no recommendations 5
trading days 13 before a recommendation was made; “Continuous” means that the
difference of recommendation consensus score at date t and the averaged consensus
score of recommendations across trading days from date t-5 to t-1 is within -0.5 and
0.5; “Upgrade” means that the difference of recommendation consensus score at date t
and the averaged consensus score of recommendations across trading days from date
t-5 to t-1 is greater than 0.5; and “Downgrade” means the difference of
recommendation consensus score at date t and the averaged consensus score of
recommendations across trading days from date t-5 to t-1 is less than -0.5.
The distribution of recommendation types is presented in Panels C and D of Table
13 We have also defined the recommendation type by comparing the consensus score on event day and the mean of consensus score between t=-1 to t=-15. The percentage in “Continuous” is almost the same as result by comparing the consensus score at t=0 and the average consensus score between t=-1 to t=-5. To balance the distribution of four recommendation types, we choose the definition of 5-days-window.
21
1. In buy recommendations, the largest number of recommendation type is 12,488
(42%) at “Continuous”, and it shows analysts hesitate to change their opinions. The
second highest scores are located at “Initiate” and “Upgrade”. But in sell
recommendations, the highest percentage is located at “Downgrade” including 59%,
which reveals sell recommendations covered by brokerages are those stocks that have
been covered in buy recommendations. In other words, the information of sell
recommendations are mainly provided to those who have hold this recommended
stocks.
4.3. Cumulative Abnormal Return Analysis
To understand the information value of recommendations, we calculate cumulative
abnormal returns (hereafter CARs) by Fama-French 3-factor model 14 of each
recommendation event in different period, including t=[-15,+15], t=[-5,+5], t=[-1,+5],
t=[0,+5], and t=[0,+15]. The mean values of CARs in different consensus score
levels and recommendation types are presented in Table 1. The values of CARs
follow consensus scores of recommendations in different periods. As consensus
scores increase, the values of CARs go up. If investors buy “Short/Strong Buy”
stocks from 15 days before recommendations are announced and sell at the 15th-day
after an event day, they could earn significantly positive profits, 3.80%. In the same
period, there were significantly negative profits (-4.14%) in “Sell” recommendations.
The value of CARs starting at the event day, the day recommendations go public,
become negative in buy recommendations, no matter in period t=[0,+5] or t=[0,+15].
It shows investors react to buy recommendations immediately. But the value reverse
14 We also calculate the cumulative abnormal returns of each recommendation event by Market model, Size-factor model and four-factor model (Beta, Size, Book to Market, and Momentum). These results are similar to the Fama-French 3-factor model.
22
of CARs after recommendations go public don’t exist in sell recommendations. It
shows there are lasting effects in brokerages’ sell recommendations, as previous
findings in Womack (1996), Barber et. al. (2001), and Busse and Green (2002), the
information of sell recommendations circulate more slowly than buy recommendations,
which might induce by short sale restrictions.
The value reversal effects of buy recommendations are significant in
recommendation type “Continuous,” “Upgrade,” and “Downgrade.” It shows price
movements of “Continuous,” “Upgrade,” and “Downgrade” recommendations have
contained the information of previous recommendations information, and the
incremental information value are limited. But we can still get significantly positive
CAR from “Initiate” buy recommendations during t=0 and t=+5. Comparing to 3%
for initiate buy recommendations during event period t=[-3,+3] in Womack (1996), the
mean values of CARs of “Initiate” strong buy events during t=[-1,+5] and t=[0,+5] are
2.50% and 0.24% respectively in Panel A of Table 1. This means the information
value of “Initiate” buy recommendations is stronger than “Continuous,” “Upgrade,”
and “Downgrade” in the event day. The mean values of CARs in “Downgrade” sell
recommendations are still significantly positive at t=[-15,+15] and t=[-5,+5], and
change to significantly negative after recommendations go public, t=[0,+5] and
t=[0,+15]. But the mean values of CARs in “Initiate” are all negative in different
periods. This shows the information of “Initiate” sell recommendations are released
to market before announcements, but the information of “Downgrade” sell
recommendations that have been covered by brokerages in positive opinion affect price
movements from the event day, t=0.
The speed of adjustment to analysts’ comments and recommendations depend on
the type of audience and distribution method. If recommendations are disseminated
23
through mass media, such as internet, newspapers or television, their price impact
should be more immediate. Overall, the price response pattern in this research is
similar to the pattern of abnormal performance in work on traditional analyst
recommendations, such as Womack (1996), only the speeds of price adjustment are
faster. Empirical results show that the semi-strong form of market efficiency is
probably not violated by information of brokerages’ recommendations; in essence, our
findings support that implications of the earlier studies that the market significantly
responds to analyst information, but that the value of that information to investors
decays rapidly over 2 to 5 days for buy recommendations and somewhat longer for sell
recommendations. Thus it is not easy to get positive abnormal returns after
information of recommendations went public, and if it is profitable, our results reveal
information value of analysts’ recommendations is only useful in short-term
investment strategies, especially to those who get this information earlier.
[Insert Table 1]
V. Empirical Results
The empirical sets of this study are focused on in sequence (1) examining the
information value of recommendations to different types of investors, including
institutions and individuals via actual trading profit, (2) testing the existence of private
information provided by recommending brokerages through comparing profits between
customers and non-customers of recommending brokerages, and (3) clarifying
institutions’ benefits resulted from their superior abilities or their deep relationships
with brokerages.
24
5.1. Do Recommendations provide Information Value for Types of Investors?
We calculate the profits of each investor during event periods t=[-5,+5] and t=[-15,+15]
by using the FIFO method or the Average method to calculate mapped costs in each
selling moment, and then calculate equal-weighted15 profits in the same investor type
in each event, respectively. The mean profits of types of investors across events are
presented in Table 2. In buy recommendations, foreign investors ge t more profits
than other investors, no matter the event period t=[-5,+5] or t=[-15,+15], and no matter
if cost is calculated by the FIFO or the Average method. Mutual funds get the second
highest profits. They could earn on average 8.02% during event period, t=[-15,+15]
and 8.85% during event period, t=[-5,+5] in the FIFO method. Following mutual
funds, corporations get significantly positive profits, 3.11% during event period
t=[-15,+15] and 3.84% during event period t=[-5,+5] in the Average method. Even
the lowest profit among types of investors, individual investors could still earn
significantly positive in buy recommendations, supporting Hypothesis 1; brokerages’
recommendations benefit individuals. These results indicate that, all investors could
capture the information value of brokerages’ buy recommendations to improve their
wealth.
[Insert Table 2]
In sell recommendations, only foreigners and mutual funds retain significantly
positive profits during event periods. In the Average method, profits of corporations
and individual investors are significantly negative during event period t=[-15,+15] and
t=[-5,+5]. It shows that foreigners and mutual funds have superior abilities to figure
out good stocks and adequate timing to trade even in sell recommendations.
From the ranking of investors’ profitability, our findings in buy and sell
15 Value-weighted profits are also calculated, and the results are similar.
25
recommendations are consistent with hypothesis 2; institutional investors earn more
profits than individual investors from brokerages’ recommendations. One possibility
is institutions’ deep relationships with brokerages, which provide detailed, informative
and valuable researches to institutional investors or pre- leasing recommendations to
institutional investors to earn the long-term agreement with another business. The
other explanatory is institutions, which have richer resources or channel to access the
information of covered stocks16, and superior ability than retail investors to process
related information and discover prices, could select the good recommendations, exact
timing and right price to trade. Thus, institutions will discount the incentives of
recommending brokerages and react to good recommendations while individual
investors natively react to whole recommendations. Or, analysts just follow the
institutions’ trading directions and price movement to make recommendations, hence,
the information value is less to individuals, especially when individuals adopt these
recommendations for their investment strategies.
Further, we classify institutional investors into foreign and domestic investors,
including mutual funds and corporations, and this classification helps us to understand
whether foreign investors make more profits on recommendations than domestic
institutions. In previous studies, both Chang and Seasholes (2003) and Lee, Liu, and
Tai (2004) document net foreign buying precedes positive reports and net foreign
selling precedes unfavorable recommendations and provide indirect evidence of
analyst information leakage. However, Lee, Liu, and Tai (2004) argue investment
expertise and superior ability of foreign institutions is another possibility to make
foreigners move earlier than the information released. According to the expertise
hypothesis, we claim foreign institutions get more profits than domestic institutions
from brokerages’ recommendations. Otherwise, it means local domestic institutions 16 as findings of Bowen, Davis, and Matsumoto (2002) and Frankel, Johnson, and Skinner (1999)
26
have an information advantage by accessibility to local companies. From the first
column in Panels A and B of Table 2, profits of foreign investors are larger than
domestic institutional investors, mutual funds and corporations, no matter in buy or sell
recommendations. This result supports that foreign institutions earn more profits than
domestic institutions from brokerages’ recommendations based on their professional
skill and rich resources, but not based on customer relationships with brokers. Our
findings partially support the findings of Chang (2003), where expatriates outperform
local analysts, in other words, reports of foreign professional institut ions contain more
information value than those of local brokerages.17
To exactly measure the information value of brokerages recommendations, we
adopt adjusted actual trading profits, which consider investors’ holding before event
period and unrealized paper gain/loss at the end of event periods in buy
recommendations. The adjusted actual trading profits are measured by FIFO method
and Average method during event period t=[-15,+15] and the results of adjusted profits
of different types of investors are presented in Panel A of Table 3. Comparing to
unadjusted actual trading profits in Table 2, the value of adjusted profits are smaller
than un-adjusted profits in types of investors, no matter in FIFO method or Average
method for buy and sell recommendations, but value remain positive and significant in
buy recommendations for each type of investors, which still support Hypothesis 1 as
the results in Table 2. One possibility is that the adjusted actual trading profits are
withdrawn the benefits form previous lower holding costs before event periods. The
other possibility may be that adjusted actual trading profits contain paper loss of
investors’ holding at the end of event periods.
From the ranking of adjusted profits, the results remain the same as the results of 17 Considering more information value in recommendation revisions mentioned in previous researches, we re -run these results with “Initiate”, “Continuous” and “Downgrade” events, and the results are similar.
27
un-adjusted profits in Table 2, and are presented that profits of foreigners are highest,
which are followed by mutual funds, corporations, and individuals. This finding is
still consistent with hypothesis 2; institutional investors earn more profits than
individual investors from recommendations. Further, the highest value of adjusted
profits locates at foreigners again support the findings of Chang (2003) and Lee, Liu,
and Tai (2004).
In sell recommendations, adjusted profits are measured by each sell revenue
during event period matched with the close price at the end of event periods, t=+15 or
t=+5, multiplying the same sell share amount as the costs. And, the adjusted profits
of types of investors are cost value-weighted profits. If the sell recommendation
contain information, then the price of recommended stocks follows a downside trend at
the recommendation day. Thus, if investors follow brokerages’ sell recommendations
to do the sell actions as soon as possible, they could at least avoid the loss from the
selling day to the end of the event day. We treat the loss avoidance as a benefit (profit)
from the sell recommendations. The significant positive adjusted profits in Panel B
of Table 3 shows there are really information value in brokerages’ sell
recommendations. The values of adjusted profits are not significantly different
among types of investors, which is not the same rank as the results of un-adjusted
profits, support analysts are prudent to make sell recommendations and less manipulate
for self- or brokerage- interests as they make buy recommendations. This finding
keep support that sell recommendations contain more information value to public in
previous empirical findings.
[Insert Table 3]
Moreover, the mean values of profits in different consensus recommendation
scores and recommendation types are presented in Table 4. Not surprisingly,
28
investors earn more profits as consensus scores grow up. Consistent with the results
of CARs in Table 1, profits in “Continuous”, “Upgrade”, and “Downgrade” are higher
than those in “Initiate” in buy recommendations during event period t=[-15,+15] and
t=[-5,+5]. In sell recommendations, each type of investors get loss from “Initiate”
events, which consistent with previous findings that analysts are prudence to make sell
recommendations.18 19
[Insert Table 4]
Lots of previous recommendation researches discuss stock characteristics of
recommended stocks which analysts prefer to follow. First, there is a large
capitalization bias in the stocks followed and recommended by sell-side analysts.
Using size decile cutoffs on the NYSE/AMEX CRSP files, Womack reports that 57%
of recommendations by the top 14 brokers are on stocks in the top two capitalization
deciles while only 1% of recommendations are on stocks in the bottom two size deciles.
This is to be expected if analysts are producing research that caters to investors’ needs,
since investors by definition own larger holdings in large cap stocks and also because
institutional investors face severe trading costs and constraints in smaller stocks and
thus would be less likely to own them.
Barron, Byard, Kile, and Riedl (2002) find that the consensus in analysts’
forecasts is negatively associated with a firm’s level of intangible assets. On the
contrary, Barth, Kasznik, and McNichols (2001) find that analyst coverage is
significantly greater for firms with larger research and development and advertising
expenses relative to their industry, and for firms in industries with larger research and
18 We also run these results of Table 4 with trading profits is calculated in Average method during event period t=[-15,+15] and in FIFO method during short-term event period, t=[-5,+5] respectively. The results are similar. 19 Adjusted profits in different consensus recommendation scores and recommendation types are also calculated. The similar results are upon request.
29
development expense. They also predict and find that analyst coverage is increasing
in growth, and firm size, consistent with Womack’s findings.
Jegadeesh et al (2004) document that analysts tend to prefer growth stocks with
glamour characteristics, consistent with the finding of Barth et al (2001). Specifically,
they find stocks with high positive price momentum, high volume, greater past sales
growth, and higher expected long-term earnings growth rates are given more positive
recommendations by analysts. Thus, analysts typically favor growth firms that are
over-valued according to traditional valuation metrics.
Summarized above findings, we can find that analysts coverage is related to the
preference of investors. If brokerage would like earn more commission revenue
through analysts coverage, then the coverage need meet the tastes of investors/their
customers, but these coverage are not necessary valuable targets of investment. In the
study of Jegadeesh et al (2002), they evaluate the investment value of
recommendations by adopting cumulative abnormal returns to investigate the
relationship between characteristics of analysts coverage and future predicted returns
of recommendations. They find that the level of the consensus recommendation adds
value only among stocks with positive quantitative characteristics (i.e., high value and
positive momentum stocks). Thus, the evidence that recommendations contain
characteristics of predicted returns is mixed. Hence, it is interested to realize whether
institutions and individuals react differentially on these stock characteristics of
recommendations, and how these characteristics influence the profits of institutions
and individuals on recommendations.
To further realize the relationship between recommendation and stock
characteristics and actual trading profits of types of investors, we run an OLS
regression as equation (1) for each type of investors.
30
kikkkk
kkkkki
turnRePastBMSizeRating
DTypeUTypeITypeCTypeofitPr
,8765
43210, ____
εαααα
ααααα
+⋅+⋅+⋅+⋅+
⋅+⋅+⋅+⋅+=
(1)
where,
i is the type of investors, including foreigners, mutual funds, corporations, or individuals.
k is the kth event for k=1, … , K. Type_C is a dummy variable. Value is 1 for recommendation type
“Continuous” and value is 0 for other recommendation types. Type_I is a dummy variable. Value is 1 for recommendation type
“Initiate” and value is 0 for other recommendation types. Type_U is a dummy variable. Value is 1 for recommendation type
“Upgrade” and value is 0 for other recommendation types. Type_D is a dummy variable. Value is 1 for recommendation type
“Downgrade” and value is 0 for other recommendation types. Rating is consensus score of event. Size is market capitalization of recommendation on event day. BM is book-to-market ratio of recommendation, where book value
is measured from the balance sheet of previous year. PastReturn is past return of event during t=[-30,-16].
The coefficients in regression (1) are presented in Table 5. Both in buy and sell
recommendations, the coefficient of size are significantly positive, which provide a
direct reason why previous studies find analysts coverage exist a large capitalization
bias to cater investors. In other words, analysts prefer recommend stocks with large
size is not a bias. Besides, the coefficients of size are largest in foreigners, which is
consistent with home biases hypothesis; only famous companies will be attracted by
foreign investors. The coefficients of book-to-market are all significantly negative in
various types of investors, which imply value stocks are profitable. This result shows
that previous studies, Jegadeesh et al (2004) and Barth et al (2001), find analysts tend
to prefer growth stocks is a bias. From the rank of coefficients of book-to-market, the
largest value (-8.944 and -9.953) is located in individual buy and sell regressions,
which imply individual investors might prefer growth stocks than institutional
31
investors and therefore analysts tend to recommend growth stocks for ingratiating
individuals’ trading. The coefficients of past-return are all significantly positive in
types of investors, which means recommendations capture the profit source of
momentum. Besides, the largest value of past return are 0.508 in foreigners’ buy
regression and 0.820 in foreigners’ sell regression, which indirectly support that
foreigners are the main movement of price (e.g.; Lee, Liu, and Tai, 2004) and
recommendations of domestic brokerages might just follow the foreigners’ trading or
foreigners’ researches (e.g.; Chang and Seasholes, 2003).20
[Insert Table 5]
5.2. Does Private Information Benefit Clients of Recommending Brokerages?
To further examine whether the information value is caused by private information
from brokerage houses, we separate types of investors into customers and
non-customers based. The mean values of actual trading profits of customers and
non-customers are presented in Table 6. In buy recommendations, only domestic
institutional customers, mutual funds and corporations, earn significant ly more profits
than non-customers, no matter what cost methods and event periods are measured.
Profits of foreign and individual customers are less than those of non-customers.
These results imply that brokerage houses may reveal private information mainly to
their domestic institutional customers, not all customers, before buy recommendations
are made public. Once foreign customers follow local brokerages’ recommendations
may not get more profits than those foreigners only reference to their own research
reports, which supporting previous findings of that information value of foreign
investment banks is more valuable than those of local brokerages (e.g.; Chang and
Seasholes, 2003). This finding may not absolutely support hypothesis 3, but is 20 Adjusted profits regression in each type of investors are also run. The similar results are upon request.
32
consistent with hypothesis 4; private information provided by recommending
brokerages is given to important customers, mostly institutions, whose trading are
more related to commission revenue, or whom are those brokers want to win long-term
business agreements with.
[Insert Table 6]
This phenomenon does not hold for sell recommendations. Except individual
investors, the profit differences between customers and non-customers are not
significant from zero, no matter what types of investors are measured. From the
value of column 4 in Panel C and D of Table 6, profits of individual customers and
individual non-customers are both significantly negative in sell recommendations, but
the absolute value of profits in individual customers are significantly less than
individual non-customers, which imply the private information of brokerages’ sell
recommendations could reduce individual customers’ loss. On the other hand, this
shows that if brokerages want to increase their commission revenues through analysts
coverage, as mentioned in Irvine (2001, 2002), it is possible; this intention only
happens in buy recommendations. Additionally, this result points out that analysts
prefer to make favorable recommendations and are hesitant and cautious in making sell
recommendations. Once a sell recommendation is made, this coverage must contain
much valuable information to public investors, as previous findings in Womack (1996)
and Lohue and Tuttle (1973) demonstrate.21
To distinguish the superior ability of institutions and better private information
from recommending brokerages, we further compare the difference of actual profits
among mutual funds, corporations, and individual investors in customers and
non-customers based. In buy recommendations, the profit difference between mutual 21 Considering more information value in recommendation revisions mentioned in previous researches, we re -run these results with “Initiate”, “Continuous” and “Downgrade” events, and the results are similar.
33
funds and individual investors is 10.75% (12.68%-1.93%) in customers during event
period t=[-15,+15] in the FIFO method, and the profit difference is 5.57%
(8.03%-2.26%) in non-customers, less than those in customers. In the meantime, the
profit difference between corporations and individual investors is 2.28%
(4.31%-1.49%) in customers during event period t=[-15,+15] in the Average method,
and the profit difference is 1.53 (3.15%-1.62%) in non-customers, less than those in
customers. In sell recommendations, the profit difference between institutional
investors and individual investors is 9.17% (8.54%-(-0.63%)) in customers during
event period t=[-15,+15] in FIFO method, and the profit difference is 4.59%
(3.99%-(-0.60%)) in non-customers, less than those in customers. Deducting the
influence of superior picking ability in institutions, the evidence shows private
information provided by recommending brokerages absolutely benefit more to
institutional customers than individual clients, consistent with hypothesis 4 and 5;
recommending brokerages provide better private information to institutional clients to
win long-term customer relationships.
To exactly capture the private information value of brokerages’ recommendations,
we also compare adjusted trading profits, which is adjusted the cost of previous
holding before event period and included unrealized paper gain/loss at the end of event
periods in buy recommendations, between customers and non-customers in each type
of investors and the results of value-weighted adjusted profits of customers and
non-customers in different types of investors are respectively presented in Table 7. In
buy recommendations, the value of adjusted profits of foreign customers are still less
than those of non-customers, but the value are not significant as the results of
un-adjusted profits. The significance and size in the value of adjusted profits between
customers and non-customers in corporations and individual investors remain the same
as the results of un-adjusted profits. On the other hand, from the results of adjusted
34
actual trading profits in the second column in Panel A of Table 7, it is interested to find
the value of adjusted profits in customers of mutual funds are not significantly positive
than those of non-customers as the results of un-adjusted actual trading profits in Table
6. From the second column in the right part in Panel A of Table 6, we find actual
trading profits of customers in mutual funds are significantly negative than those of
non-customers. From the same column in the left part in Panel A of Table 6, we find
actual trading profits of customers in mutual funds are significantly positive than those
of non-customers. These different results between adjusted profits and un-adjusted
profits and between short-term (t=[-5,+5]) and long-term (t=[-15,+15]) event periods
reveal that event periods t=[-15,+15] may not be capture the whole investment horizon
of mutual funds’ trading strategies while it is suitable to use t=[-15,+15] or t=[-5,+5] as
event periods to capture the information value of recommendations for corporations
and individuals. Thus, we find that the results between adjusted and un-adjusted
profits in corporations and individuals remain the same, but the results in mutual funds
are changed. There are two possibilities to explain the significance of different test
between customers and non-customers disappear in adjusted profit of mutual funds.
One is mutual funds get private information from brokerages far early from event
periods, the other possibility is brokerages’ recommendations just follow the price that
move by mutual funds, and recommendation announcement provide mutual funds
good timing to liquidate/realize their holdings of recommended stocks. The
phenomenon of mutual funds is worthy to further research to clear the exact reason as
the findings in buy recommendations.
[Insert Table 7]
The adjusted profits of sell recommendations are presented in Table 8, there are
not significantly different between customers and non-customers in each type of
35
investors, which is almost the same results as the results of un-adjusted actual trading
profits. It shows brokerages don’t reserve private information to their customers in
sell recommendations.
[Insert Table 8]
To further examine the relation between actual trading profits of investors and
customer relationships, we run profit regressions on ‘customer of recommending
brokerages dummy variable’, and recommendation characteristics, including types and
consensus scores of events, and stock characteristics, including size, book to market
and past return, for different types of investors as follows,
(2)
bi,k,εαααααααα
ααα
ααα
αααα
+⋅+⋅+⋅+⋅+⋅+⋅+⋅+⋅+
⋅⋅+⋅⋅+⋅⋅+
⋅⋅+⋅⋅+⋅⋅+
⋅⋅+⋅⋅+⋅+=
kkk
k
kbikbi
kbikbikbi
kbikbibibki
turnRePastBMSizeRatingDTypeUTypeITypeCType
trunRePastCustomersBMCustomersSizeCustomers
RatingCustomersDTypeCustomersUTypeCustomers
ITypeCustomersCTypeCustomersCustomersofitPr
171615
1413121110
9,8,7
,6,5,4
,3,2,10,,
____
__
__
where,
i is the type of investors, including foreigners, mutual funds, corporations, or individuals.
b is brokerages, including recommending brokerages, and non-brokerages.
k is the kth event for k=1, … , K. Customers is a dummy variable. Value is 1 for customers of types of
investors in recommending brokerages and value is 0 for non-customers of types of investors in recommending brokerages.
Type_C is a dummy variable. Value is 1 for recommendation type “Continuous” and value is 0 for other recommendation types.
Type_I is a dummy variable. Value is 1 for recommendation type “Initiate” and value is 0 for other recommendation types.
Type_U is a dummy variable. Value is 1 for recommendation type “Upgrade” and value is 0 for other recommendation types.
Type_D is a dummy variable. Value is 1 for recommendation type “Downgrade” and value is 0 for other recommendation types.
Rating is consensus score of event.
36
Size is market capitalization of recommendation on event day. BM is book-to-market ratio of recommendation, where book value
is measured from the balance sheet of previous year. PastReturn is past return of event during t=[-30,-16].
The dependent variable is equal-weighted profits of foreigners, mutual funds,
corporations, and individual investors in each recommendation. Profit (in percentage)
in Table 9 and 10 is measured by the FIFO method during event period t=[-15,+15].
If private information provided by recommending brokerages benefit only important
clients, mostly institutions, then explanatory ‘customer of recommending brokerages
dummy’ will only be significantly positive in institutional investors, not significant in
individual investors. If private information provided by recommending brokerages
displays stock selectivity, then the significance of ‘customer dummy’ will disappear
and will be significant on variable, ‘customer dummy multiply stock characteristics.’
Otherwise, if the significance of ‘customer dummy’ still exists under controlling stock
characteristics, it shows private information provided by recommending brokerages
may be partially represents by pre-releasing information to clients.
In Table 9, for buy recommendations, ‘customers dummy variables’ are
significantly positive in regression (1) for mutual funds and corporations, and
significantly negative in foreigners and individual investors. This finding is similar to
the findings of Table 6 and consistent with hypothesis 4; brokerages provide
information to their main clients, mostly domestic institutions. In regression (2),
coefficients of recommendation types are only significant and negative in “Initiate”, no
matter regressions run in various type of investors, which imply profits of “Initiate”
events are smaller than profits of “Continuous” events. The coefficients of three
stock characteristics, size, book-to-market, and past return are all significant, which
shows three factors provide good explanations on actual trading profits, no matter what
37
type of investors. The coefficients of size are all significantly positive and the
coefficient is largest, 0.03, in foreign investors regression, which is consistent with
previous studies that foreigners prefer to invest in large and well-known companies.
The coefficients of book-to-market are all significantly negative and the absolute
values of coefficients in institutions’ regressions are all larger than those in individuals’
regressions, which explain why institutional investors prefer value stocks and
individuals prefer growth stocks. The coefficients of past return are all significantly
positive and the values of coefficients in institutions’ regression are all larger than
those in individuals’ regression, which shows momentum is one important
characteristic in institutions’ investment strategies and institutions are good at
capturing the price movements and trading timings.
It is interesting to find coefficients of ‘customers dummy variable’ are not
significant for mutual funds and corporations after controlling recommendation type,
consensus scores of events, and stock characteristics from results of regressions (3) and
(4). At the same time, the coefficients of ‘customers dummy multiply stock
characteristics variables’ are significant in regressions (4) and (5). This implies that
private information provided by recommending brokerages contains distinguished
stock picking ability. In institutional regression (5), the coefficients of ‘customers
dummy multiply book-to-market’ are all significantly positive, which is different from
the coefficient is significantly negative in individual regression. This evidence shows
that institutional customers’ higher profit results in their portfolios do not contain too
many value stocks, but if individual investors would like to improve their profitability,
they should add more value stocks into their portfolios.
[Insert Table 9]
In sell recommendations, the coefficients of ‘customers dummy variable’ are not
38
significant, no matter controlling recommendation types, consensus ration, or stock
characteristics in regressions (1), (3), (4) of type of investors, and coefficients of
‘customers dummy multiple these control variables’ are not significant in regression (5)
of foreigners, mutual funds, and corporations. The coefficients of ‘customers dummy
multiple stock characteristics’ in regression (5) are only significant for individuals,
which shows analysts’ superior picking ability still help individuals to lose less in sell
recommendations.22, 23, 24
[Insert Table 9 and Table 10]
VI. Conclusions
Unlike previous researches on the information value of analysts’ reports by cumulative
abnormal returns, we document the information values of recommendations by looking
at a direct measurement, actual trading profits, for types of investors. This allows us
to clarify recommendations benefit to what types of investors, especially individual
investors.
Using the FIFO and the Average method to calculate actual trading profits of
types of traders during event periods, we document that all investors get positive
benefits and significantly profit from brokerages’ buy recommendations, as even the
wealth of individual investors improved. These results support the information value
of analysts. As to sell recommendations, only foreign investors and mutual funds
22 We re-run these results of profit regressions with trading profits which is calculated in Average method during event period t=[-15,+15] and in FIFO method during short-term event period, t=[-5,+5] respectively, and we find the results are similar. 23 Considering more information value in recommendation revisions mentioned in previous researches, we re -run these results with “Initiate”, “Continuous” and “Downgrade” events, and the results are similar. 24 We also run adjusted profit regressions, and ,except the results of mutual funds, the similar results are upon request.
39
have positive returns, which shows brokerages’ unfavorable recommendations don’t
make individuals better off. We also find that professional institutions earn more
profits than retail investors during the recommendation event periods, no matter in buy
or sell recommendations. This evidence is consistent with previous findings that
professional investors have superior selectivity than retail investors or deep business
relationship with brokerages.
Further, to test whether the information values are caused by private information
from brokerage houses; we separate the profits of types of investors into customers and
non-customers based. Our results point out that private information provided by
brokerages made their domestic institutional clients wealthier during buy
recommendations, but, during sell recommendations, except individual customers lose
less than individual non-customers, there are no significantly difference between
institutional customers and institutional non-customers. Controlling superior abilities
of institutions, institutional customers earn more than institutional non-customers,
which support domestic institutions get benefit from their deep relationship with
brokerages while individuals have no chance to enjoy this advantage. Our finding
both supports institutions’ superior ability hypothesis and customer relationship
hypothesis.
To our knowledge, this is the first study to analyze information value of
recommendations by adopting actual trading profits. Our results generally indicate
that private information of brokerages recommendation benefit domestic institutional
clients. Since the effect of private information for mutual funds is affected by the
horizon of event periods and different measurement of actual trading profit, separating
adjusted actual trading profits into profits contributed by previous holdings, profits
contributed just during event period, and profits contributed by holding at the end of
40
event periods could shed light on real profit sources and investment strategies of
different types of investors, who trade on recommendations, especially customers of
mutual funds. Further, linking private information with affiliation between
brokerages and mutual funds will provide a good opportunity to test whether private
information is due to conflict of interest. In addition, even short sale traders are small
portion in market, but they are mostly informative especially under a lot of short sale
limitations. Thus, it is interested to realize their trading behaviors and profits on
recommendations. These and other possible topics are left for future research.
41
References Anarbanell, Jeffery S., and Victor L. Bernard, 1992, “Tests of Analysts’ Overreaction /
Underreaction to Earnings Information as an Explanation for Anomalous Stock Price Behavior,” Journal of Finance, Vol 47, Issue 3, pages 1181-1207.
Abanbanell, Jeffery S., William N., Lanen, and Rebert E., Verrecchia, 1995, “Analysts’
Forecasts as Proxies of Investor Beliefs in Empirical Research,” Journal of Accounting Economics 20, pages 31-60.
Holds, and Sells: The Distribution of Investment Banks’ Stock Ratings and The Implications for The Profitability of Analysts’ Recommendations,” Journal of Accounting Research, forthcoming.
Barber, Brad, and Terrance Odean, 2000, “Trading is Hazardous to Your Wealth: The
Common Stock Investment Performance of Individual Investors,” Journal of Finance 55(2), pages 773-806.
Barron, Orie E., Donal Byard, Charles Kile, and Edward J. Riedl, 2001,
“High-Technology Intangibles and Analysts’ Forecasts,” Journal of Accounting Research, Vol. 40, No. 2, pages 289-312.
Barth, Mary E., Ron Kasznik, and Maureen F., McNichols, 2001, “Analyst Coverage
and Intangible Assets,” Journal of Accounting Research, Vol. 39, No. 1, pages 1-34.
Bidwell, Clinton M. III., 1977, “How Good is Institutional Brokerage Research?”
Journal of Portfolio Management, pages 26-31.
42
Black, Fisher, 1986, “Noise,” Journal of Finance 41, pages 529-543. Boehmer, B., E. Boehmer, and R. Fishe, 2005, “Do Institutions Receive Favorable
Allocations in IPOs with Better Long Run Returns?” Journal of Financial and Quantitative Analysis, forthcoming.
Bowen, R., Davis, A., and Matsumoto, D., 2002, “Do Conference Calls Affect
Analysts’ Forecasts?” The Accounting Review 77:2, pages 285-316. Brav, Alon and Reuven Lehavy, 2002, “An Empirical Analysis of Analysts’ Target
Price: Short-Term Informativeness and Long-Term Dynamics,” Journal of Finance VLIII (5), pages 1933-1968.
Busse, Jeffrey A., T. Clifton Green, 2002, “Market Efficiency in Real Time,” Journal
of Financial Economics 65(3), pages 415-437. Campbell, John, and Albert Kyle, 1993, “Smart Money, Noise Trading, and Stock Price
Behavior,” Review of Economic Studies 60, pages 1-34. Chang, Charles, 2003, “Information Footholds: Expatriate Analysts in an Emerging
Market,” Dissertation, University of California Berkeley. Chemmanur, T., S. He, and G. Hu, 2005, “The Role of Institutional Investors in
Seasoned Equity Offerings,” Working Paper, Boston College. Chen, H., N. Jegadeesh, N. Wermers, 2000, “The Value of Active Mutual Fund
Management: An Examination of the Stockholdings and Trades of Fund Managers,” Journal of Financial and Quantitative Analysis 35, pages 343-368.
Chen, Xia, and Qiang Cheng, 2002, “Institutional Holdings and Analysts’ Stock
Recommendations,” Working Paper, University of Wisconsin-Madison. Chen, X., J. Harford, and K. Li, 2004, “Institutional Investors and Corporate
Acquisitions Decisions,” Working Paper, University of British Columbia. Colker, S.S., 1963, “An Analysis of Security Recommendations by Brokerage
Houses,” The Quarterly Review of Economics and Business, pages 19-28.
43
Cooper, Rick A., Theodore E., Day, Craig M., Lewis, 2001, “Following the Leader: A Study of Individual Analysts’ Earnings Forecasts,” Journal of Financial Economics 61, pages 383-416.
De Bondt, Werner F.M., Richard H. Thaler, 1990, “Do Security Analysts Overreact?,”
American Economic Review, Vol. 80, Issue 2, pages 52-57. Dechow, Patricia M., Amy P. Hutton, Richard G. Sloan, 2000, “The Relation between
Analysts’ Forecasts of Long-Term Earnings Growth and Stock Price Performance Following Equity Offerings,” Contemporary Accounting Research, Vol. 17, No. 1 (Spring 2000), pages 1-32.
De Long, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J.
Waldmann, 1990, “Noise Trader Risk in Financial Markets,” Journal of Political Economy 98, pages 703-738.
D’Mello, Ranjan, and Stephen P. Ferris, 2000, “The Information Effects of Analyst
Activity at the Announcement of New Equity Issues,” Financial Management, Spring 2000, pages 78-95.
Easterwood, J., and S. Nutt, 1999, “Inefficiency in Analysts Earnings Forecasts:
Systematic Misreaction or Systematic Optimism?,” Journal of Finance, Vol. 54, October, pages 1777-1797.
Elton, Edwin, Martin J. Gruber, and Seth Grossman, 1986, “Discrete Expectational
Data and Portfolio Performance,” Journal of Finance XLL (3), pages 699-714. Field, L., 1997, “Is Institutional Investment in Initial Public Offerings Related to the
Long-run Performance of These Firms,” Working Paper, Penn State University. Frankel, R., Johnson, M., and Skinner, D., 1999 “An Empirical Examination of
Conference Calls As a Voluntary Disclosure Medium,” Journal of Accounting Research 37(1), 133-150.
Gibson, S., A. Safiennine, and R. Sonti, 2004, “Smart Investments by Smart Money:
Evidence from Seasoned Equity Offerings,” Journal of Financial Economics 72, pages 581-604.
44
Goldstein, Michael, Paul Irvine, Eugene Kandel, and Zvi Wiener, 2004, “Brokerage Commissions and Institutional Trading Patterns,” Working Paper, University of Georgia.
Gompers, Paul A.m and Andrew Metrick, 2001, “Institutional Investors and Equity
Prices,” Quarterly Journal of Economics 116, pages 229-259. Griffin, Josn, Jeffrey Harris, and Selim Topaloglu, 2003, “The Dynamics of
Institutional and Individual Trading,” Journal of Finance 58(6), pages 2285-2320. Grinblatt, M., and M., Keloharju, 2000, “The Investment Behavior and Performance of
Various Investor-types: A Study of Finland’s Unique Data Set,” Journal of Financial Economics 55, pages 43-67.
Grinblatt, M., and M., Keloharju, 2001, “What Makes Investors Trade?,” Journal of
Finance 56, pages 589-616. Grinblatt, M., and S. Titman, 1989, “Mutual Fund Performance: An Analysis of
Quarterly Portfolio Holdings,” Journal of Business 62, pages 394-415. Grinblatt, M., and S. Titman, 1992, “The Persistence of Mutual Fund Performance,”
Journal of Finance 47, pages 1977-1984. Grinblatt, M., S Titman, and R. Wermers, 1995, “Momentum Investment Strategies,
Portfolio Performance, and Herding: A Study of Mutual Fund Behavior,” American Economic Review 85, pages 1088-1105.
Grossman, S.J., and J.E. Stiglitz, 1980, ‘On the Possibility of Informationally Efficient
Markets’, American Economic Review, Vol. 70, pages 393-408. Groth, John C., Wilbur G. Lewwelen, Gary G. Schlarbaum, and Ronald C. Lease, 1979,
“An Analysis of Brokerage House Securities Recommendations,” Financial Analysts Journal, January-February, pages 32-40.
Hong, Harrison, Jeggrey D. Kubik, and Amit Solomon, 2000, “Security Analysts’
Career Concerns and Herding of earnings forecasts,” RAND Journal of Economics, Vol. 31, No. 1, Spring 2000, pages 1221-144.
45
Irvine, Paul.J., 2001, “Do Analysts Generate Trade for Their Firms? Evidence from the Toronto stock exchange,” Journal of Accounting and Economics 30, pages 209-226.
Irvine, Paul.J., 2002, “Analysts’ Forecasts and Brokerage-Firm Volume,” Working
Paper, Emort University. Iskoz, Sergey, 2003, “Bias in Underwriter Analysts Recommendations: Does it
Matter?,” Working Paper, Massachusetts Institute of Technology. Jegadeesh, Narasimhan, Joonghyuk Kim, Susan D. Krische, and Charles M.C. Lee,
2004, “Analyzing the Analysts: When do recommendations Add Value?” Journal of Finance 59, pages 1083-1124.
Jensen, M., 1968, “The Performance of Mutual Funds in the Period 1945-1964,”
Journal of Finance 23, pages 389-416. Ke, Bin and Kathy Petroni, 2003 “How Informed Are Institutional Investors? Evidence
from Their TradingBbehavior before a Break in a String of Consecutive Earnings Increases,” Working Paper, Michigan State University.
Kim, Sok Tae, Ji-Chai Lin, and Myron B. Slovin, 1997, “Market Structure, Informed
Trading, and Analysts’ Recommendations,” Journal of Financial and Quantitative Analysis, Vol. 32, No. 4., pages 507-524.
Klein, A., 1990, “A Direct Test of Cognitive Bias Theory, ” Journal Account and
Economics 13, pages 155-166. Lakonishok, Josef, Andrei Shleifer, and Robert Vishny, 1992, “The Impact of
Institutional Trading on Stock Prices,” Journal of Financial Economics 32, pages 23-43.
Lee, Yi-Tsung, Yu-Jane, Liu, and Vivian W. Tai, 2004, “Are Analysts’
Recommendations Valuable? : An Analysis of Investors’ Behaviors on Brokerage House Recommendations,” Working Paper, National Chengchi University.
Lim, T., 2001, “Rationality and Analysts’ Forecast Bias,” Journal of Finance 56, pages
369-385.
46
Lin, Hsiou-wei, and Maureen F. McNichols, 1998, “Underwriting Relationships,
Analysts’ Earnings Forecasts and Investment Recommendations,” Journal of Accounting and Economics 25, pages 101-127.
Logue, Dennis E., and Donald L. Tuttle, 1973, “Brokerage House Investment Advice,”
Financial Review, pages 38-54. Malmendier, Ulrike, and Devin Shanthikumar, 2003, “Are Small Investors Naïve?,”
NBER Working Paper 10812 (Standard University). Malmendier, Ulrike, and Devin Shanthikumar, 2005, “Do Security Analysts Speak in
Two Tongues?,” Working Paper (Standford University). Mendenhall, R., 1991, “Evidence of Possible under Weighting of Earnings-Related
Information,” Journal of Accounting Research 29(2), pages 397-417. Michaely, Roni, and Kent L. Womack, 1999, “Conflict of Interest and the Credibility
of Underwriter Analyst Recommendations,” Review of Financial Studies Special 1999, Vol. 12, No. 4, pages 653-686.
Mikhail, Michael B., Beverly R. Walther, Richard H. Willis, 2001, “The Effect of
Experience on Security Analyst Underreaction and Post-Earnings-Announcement Drift,” Working Paper (Duke University).
Nofsinger, J., Sias, R., 1999, “Herding and Feedback Trading by Institutional and
Individual Investors,” The Journal of Finance 54, pages 2263-2295. Odean, Terrance, 1998, “Are Investors Reluctant to Realize Their Losses?,” Journal of
Finance 53, pages 1775-1798. Odean, Terrance, 1999, “Do Investors Trade Too Much?,” American Economic Review
89, pages 1279-1298. Parrion, R., Sias, R., and Starks, L., 2003, “Voting with Their Feet: Institutional
Ownership Changes Around Forced CEO Turnover,” Journal of Financial Economics 68, pages 3-46.
47
Stickel, Scott E., 1995, “The Anatomy of the Performance of Buy and Sell Recommendations,” Financial Analysts Journal, Sept-Oct. 1995, pages 25-39.
Stickel, Scott E., 1992, “Reputation and Performance among Security Analysts,”
Journal of Finance, vol. 47, issue 5, pages 1811-36. Tkac, P., 1999, “A Trading Volume Benchmark: Theory and Evidence,” Journal of
Financial and Quantitative Analysis 34(1), pages 89-114. Wermers, R. 1999, “Mutual Fund Herding and the Impact on Stock Prices,” Journal of
Finance 54, pages 581-622. Wermers, R., 2000 “Mutual Fund Performance: An Empirical Decomposition into
Stock-Picking Talent, Style, Transaction Costs, and Expenses,” Journal of Finance 55, pages 1655-1695.
Womack, Kent L, 1996, “Do Brokerage Analysts’ Recommendations Have Information
Value?,” Journal of Finance 51(1), pages 137-167
48
Table 1 Cumulative Abnormal Return This table presents mean of cumulative abnormal returns of different recommendation types or recommendation consensus score categories during event period, t=[-15,+15], t=[-5,+5], t=[-1,+5], t=[0,+5], t=[0,+15], respectively. The consensus scores of the same stock in the same day are calculated by average the scores given by recommending brokerages. According consensus scores, we classify recommendations into 5 levels, “Sell”(1) is a consensus score between [0,2.5], “Neutral”(2) is a consensus score between (2.5,3.5), “Long-term Buy”(3) is a consensus score between [3.5,5), “Add and Continuous Holding”(4) is a consensus score equal to 5, and finally, “Short/Strong Buy”(5) is a score above 5. We divide recommendation types into “Initiate,” “Continuous,” “Upgrade,” and “Downgrade” for the same recommended stock in each recommended day. “Initiate” is defined as there are no recommendations in 5 trading days before a recommendation was made; “Continuous” means that the difference of recommendation consensus score at date t and the averaged consensus score of recommendations across trading days from date t-5 to t-1 is within -0.5 and 0.5; “Upgrade” means that the difference of recommendation consensus score at date t and the averaged consensus score of recommendations across trading days from date t-5 to t-1 is greater than 0.5; and “Downgrade” means the difference of recommendation consensus score at date t and the averaged consensus score of recommendations across trading days from date t-5 to t-1 is less than -0.5. The t-test is conducted to test whether CARs in each recommendation type or consensus score level is different from zero. The F-test is conducted to test cumulative abnormal return difference among recommendation scores or recommendation types in each measurement period. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level. CAR [-15,+15] CAR [-5,+5] CAR [-1,+5] CAR [0,+5] CAR [0,+15]
Table 2 Profits of different type of investors This table presents mean value of actual trading profits of types of investors during recommendation event period. Profits of each investor are calculated by first-in and first-out (FIFO) and average methods in event periods, t=[-5,+5] and t=[-15,+15], respectively. The profits of each type of investors in same recommended stocks are averaged profits of each investor’s profits (in percentage) in the same type, foreigners, mutual funds, corporations, or individual investors. The number of buy recommendations is 29,996 and the number of sell recommendations is 3,923. The T-test is conducted to test whether actual trading profit of each type of investors is different from zero in different cost calculations and different event periods. The F-test is conducted to test profit difference among types of investors. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level.
Panel A. Buy Recommendations FIFO method Average method Event Period t=[-15,+15] Event Period t=[-5,+5] Event Period t=[-15,+15] Event Period t=[-5,+5]
Panel B. Sell Recommendations FIFO method Average method Event Period t=[-15,+15] Event Period t=[-5,+5] Event Period t=[-15,+15] Event Period t=[-5,+5]
Table 3 Adjusted profits of different type of investors The table presents mean value of adjusted actual trading profits of types of investors during recommendation event period. Comparing to Table 2, adjusted profits here are calculated by each sell price (revenue) during t=[-15,+15] matched the close price at t=+15 with the same sell volume as the cost.. The profits of each type of investors in same recommended stock are value-weighted profits of each investor’s profits (in percentage) in the same type, foreigners, mutual funds, corporations, or individual investors. N is mean value of number of investors who trade in each recommendation event. The number of buy recommendations is 29,996 and the number of sell recommendations is 3,923. The F-test is conducted to test profit difference among types of investors. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level. Panel A. Buy Recommendations
Table 5 Profit regression analysis on recommendation and stock characteristics
We use OLS regression models to test the relationships between recommendation and stock characteristics
and actual trading profits of different types of investors as follows:
kikkkk
kkkkki
turnRePastBMSizeRating
DTypeUTypeITypeCTypeofitPr
,8765
43210, ____
εαααα
ααααα
+⋅+⋅+⋅+⋅+
⋅+⋅+⋅+⋅+=
The subscript i is the type of investors, including foreigners, mutual funds, corporations, or individuals, and k is the kth event for k=1, … , K. The dependent variable is mean profit of
foreigners, mutual funds, corporations, and individual investors in recommended recommendations. Profit (in percentage) is measured by FIFO method during event period t=[-15,+15]. It is regressed on
recommendations type dummies, consensus rating of event, event stock characteristics. Event stock
characteristics include size (1000 million TWD), book to market ratio, past return at t=[-30,-16]. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. ***
Table 6 Profits of customers and non-customers This table presents mean value of actual trading profits of types of investors which are separated into customers and non-customers of recommending brokerages during recommendation event period. Profits of each investor are calculated by first-in and first-out (FIFO) and average methods in event periods, t=[-5,+5] and t=[-15,+15], respectively. The profits of each type of investors in same recommended stock are equally-weighted profits of each investor’s profits (in percentage) in the same type, including customers of foreigners, non-customers of foreigners, customers of mutual funds, non-customers of mutual funds, customers of corporations, non-customers of corporations, customers of individual investors and non-customers of individual investors. The number of buy recommendations is 29,996 and the number of sell recommendations is 3,923. The difference T test is conducted to test whether profits of customers are larger than those of non-customers in each type of investors, including foreigners, mutual funds, corporations, and individual investors across buy/sell recommendations. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level. Panel A. Buy Recommendations and profit calculated by FIFO method Event Period t=[-15,+15] Event Period t=[-5,+5] Customers Non-customers Diff. Test Customers Non-customers Diff. Test Mean
Table 7 Adjusted profits of customers and non-customers in Buy Recommendations
This table presents mean value of adjusted actual trading profits of types of investors which are separated into
customers and non-customers of recommending brokerages during recommendation event period. Comparing to
Table 6, total adjusted FIFO profits here are further included profits of past holding by cost adjustment and the paper gain/loss that investors have not sell till the end of event period. Event period in this table is defined as
t=[-15,+15]. The profits of each type of investors in same recommended stock are cost value-weighted profits of
each investor’s profits in the same type, including customers of foreigners, non-customers of foreigners, customers of mutual funds, non-customers of mutual funds, customers of corporations, non-customers of
corporations, customers of individual investors and non-customers of individual investors. The number of buy
recommendations is 29,996. The difference T test is conducted to test whether profits of customers are larger than those of non-customers in each type of investors, including foreigners, mutual funds, corporations, and
individual investors across buy/sell recommendations. * significantly different from zero at 0.1 level. **
significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level.
Table 8 Adjusted profits of customers and non-customers in Sell Recommendations
This table presents mean value of adjusted actual trading profits of types of investors which are separated into
customers and non-customers of recommending brokerages during recommendation event period. Comparing to
Table 6, adjusted profits here are calculated by each sell price (revenue) during t=[-15,+15] matched the close price at t=+15 with the same sell volume as the cost. The profits of each type of investors in same recommended
stock are cost value-weighted profits of each investor’s profits in the same type, including customers of foreigners,
non-customers of foreigners, customers of mutual funds, non-customers of mutual funds, customers of corporations, non-customers of corporations, customers of individual investors and non-customers of individual
investors. The number of sell recommendations is 3,923. The difference T test is conducted to test whether
profits of customers are larger than those of non-customers in each type of investors, including foreigners, mutual funds, corporations, and individual investors across buy/sell recommendations. * significantly different from
zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01
Table 9 Profits on Customers of Recommending Brokerages, Recommendations Characteristics and Stocks Characteristics in Buy Recommendations
We use OLS regression models to test whether being customers of recommending brokerages affects its actual trading profits during buy recommendation events in different types of investors as follows:
bi,k,εαααααααα
ααα
ααα
αααα
+⋅+⋅+⋅+⋅+⋅+⋅+⋅+⋅+
⋅⋅+⋅⋅+⋅⋅+
⋅⋅+⋅⋅+⋅⋅+
⋅⋅+⋅⋅+⋅+=
kkk
k
kbikbi
kbikbikbi
kbikbibibki
turnRePastBMSizeRatingDTypeUTypeITypeCType
trunRePastCustomersBMCustomersSizeCustomers
RatingCustomersDTypeCustomersUTypeCustomers
ITypeCustomersCTypeCustomersCustomersofitPr
171615
1413121110
9,8,7
,6,5,4
,3,2,10,,
____
__
__
The subscript i is the type of investors, including foreigners, mutual funds, corporations, or individuals, b is brokerages, including recommending brokerages, and non-brokerages, and k is the kth event for k=1, … , K. The dependent variable is mean profit of foreigners, mutual funds, corporations, and individual investors in buy recommendations. Profit (in percentage) is measured by FIFO method during event period t=[-15,+15]. It is regressed on customers dummy variable, recommendation type dummies, consensus rating of event, event stock characteristics, customers dummy variable multiple recommendation type dummies, customers dummy variable multiply consensus rating, and customers dummy variable multiply event stock characteristics. Event stock characteristics include size (1000 million TWD), book to market ratio, past return at t=[-30,-16]. * significantly different from zero at 0.1 level. ** significantly different from zero at 0.05 level. *** significantly different from zero at 0.01 level.
