Reactions of the China Stock Market to Analysts’
Stock Recommendations
Master Thesis in Finance
Wenting Shen
ANR: 490405
Date: July 20, 2014
Supervisor: Dr. D.A. Hollanders
Table of contents
Abstract
1
1. Introduction
2
2. Literature review and questions development
4
3. Data
7
4. Methodology
8
4.1 The determination of the event time and the event window
8
4.2 The calculation of normal returns and abnormal returns
9
4.3 Test statistic
9
5. Empirical results
10
6. Discussion
20
7. Conclusion
23
References
24
Abstract
This study investigates the China A-share market’s reactions to the publishing of stock
recommendations made by financial analysts in the weekly column “The Best Potential Stocks
Recommended by Analysts This Week” of the Shanghai Securities News by using the methodology
of event study. The sample period starts on the 24th of June 2013 till the 23th of December 2013. The
stock recommendations in the column provide abundant new information, so the readers who follow
these recommendations can expect to obtain certain abnormal returns. The recommendations have
significant average abnormal returns of 5.72% during a five days period around the publication date.
The results support the information hypothesis and to a lesser extent with price pressure hypothesis.
The higher level of analysts’ concentration on recommended stocks shows higher degree of impacts
on stock prices and returns. In general, analysts’ recommendations are valuable for most of
individual investors in China; they can profit from the recommendations if they trade in very short
term.
1
1
Introduction
There are almost 47 million active investors in China; however, a majority of them do not have any
investment expertise, which are so called retail investors. Their investment decisions are strongly
affected by all kinds of stock recommendations provided by public media, including newspaper,
magazine and TV columns. With the high audience ratings and circulation of these stock
recommendations, this study investigates whether the prices and returns of these recommended
stocks will be influenced around the announcement date, and whether the different levels of analysts’
concentration on recommended stocks show different degree of impacts on stock prices. Considering
the situation in China, the major columns provide buy recommendations by analysts, while sell
recommendations usually being asked by individual audiences through the hot lines. In addition,
because of the large effects of external factors and over-speculation in China stock market, it is hard
to tell whether the changes are caused by recommendations in more than one month; besides,
Chinese individual investors prefer trading stocks in short term. Hence, this study focuses on the
impact of buy stock recommendations in short term.
The characteristics in China stock market make academia are skeptical about the value of
recommendations made by analysts. The basis of accurate firm evaluation made by analysts is that
the financial statements should reflect actual financial conditions; however, the earnings
manipulation in accounting statements of listed companies often happens. Meanwhile, the stock
pricing mechanism in China may be affected by speculation to some extent, rather than only
depending on the reasonable firm evaluation. In the domestic study field, there are fewer researches
about the impact of stock recommendations to the market.
This analysis based on a column called “The Best Potential Stocks Recommended by Analysts This
Week” of Shanghai Securities News is posted every Monday (exclude national holidays). It collects
stocks which have been recommended last Saturday by at least three analysts from China Securities
Journal, Securities Times, Shanghai Securities News, Securities Market Study, Information Morning
Post, Jiangnan Times, Outpost, Finance and Investment, Business Times, Jiangnan Securities,
International Finance News, Daily Business News, Shanghai Business News, East China Daily
2
Information, New Securities, Guangdong-Hong Kong Information Daily, Securities References and
others in total more than 17 domestic media, and ranking the stocks based on how many times it has
been recommended. This column always recommends dozens of stocks. On one hand, this column
chooses mainstream securities media to ensure a larger sample range and its effectiveness. On the
other hand, the stock recommendations have been published last Saturday, it ensures the sufficiency
of information circulation through two days. However, the column does not provide first-hand
analysts’ recommendations— the brokerages have already given the information to their clients
before it is reported. The column writer’s job is collecting and reporting the stock recommendations
without his or her own opinions. Therefore, while brokerages’ clients have first-hand information, the
readers of the column obtain second-hand information of recommendations.
To find out if investors can earn money by using only the public information provided in the column
“The Best Potential Stocks Recommended by Analysts This Week”, an event study is conducted to
show what pattern the cumulative average abnormal returns are following around the event date. This
study finds evidence that the average recommendation in the column causes the stock prices to react
in an abnormal way; it shows market reaction is significant around the publication date and generate
positive cumulative average abnormal returns. It indicates that the stock recommendations in the
column “The Best Potential Stocks Recommended by Analysts This Week” give the readers with
new information which has not been reflected by stock prices. Moreover, the recommended stocks
are classified into three groups by different levels of analysts’ concentration; the results show that
higher level of analysts’ concentration on recommended stocks lead to higher degree of impacts on
stock prices and returns. Finally, I separate information effects from price pressure effects in order to
get a clear picture about how they lead the price movements. Overall, it is confirmed that the market
reaction due to both information effect and price pressure effect, but price pressure effect accounts
for a tiny part. In conclusion, the column seems to publish valuable information; the readers who
follow those recommendations could earn abnormal returns.
3
2
Literature review and questions development
Based on the efficient market hypothesis (Fama, 1970), stock prices would instantly reflect all
available information in semi-strong efficient market. Hence, if the sources for analysts only come
from public information, their investment recommendations should not bring abnormal returns to
investors. Moreover, even if analysts use nonpublic information, their investment recommendations
could not result in stock price adjustments as well under the strong efficient market. Some early
studies about American market discovered that the investment value of recommendations made by
analysts is not significant. For example, Cowles (1933) found that most recommendations made by
analysts could not bring abnormal returns. In later studies, the results from Logue and Tuttle (1973)
and Bidwell (1977) also supported the opinion that analysts’ recommendations are useless.
With the development of finance theory, Grossman and Stiglitz (1980) realized that costless
information is the sufficient and necessary condition for prices fully reflecting all available
information, otherwise it would cause paradox. So people who spent money to obtain information
can get compensation— abnormal returns. Lloyd Davies and Canes (1978) found that there are
obvious price fluctuations around the publication date of “Heard on the Street” column reporting the
recommendations. However, the statistically significant price reactions to the publishing of those
recommendations in the column are too small to earn abnormal returns after transaction costs.
Therefore, they concluded that the US stock market is actually efficient. Lin (2000) obtained the
sample data of domestic securities consulting institutions from China Securities Journal in the period
of April 1998 and June 1999. The sustaining positive cumulative abnormal returns of the
recommended stocks are found, along with significant increase of trading volume, thus he considered
consulting companies own private information.
Question 1: Do stock recommendations in the column “The Best Potential Stocks Recommended by
Analysts This Week” give the readers with new information that has not been reflected by stock
prices?
The recommendations published in the column are collected from many different brokerages, it is
4
uncertain whether they contain new information, since they are already been sent out to brokerages’
clients. If there is no new information provided by the column, the existence of this kind of column is
meaningless. If there is certain new information provided by the column, the individual investors
should pay attention to the column and choose whether to follow those recommendations. If there is
much new information provided by the column, the China stock market is inefficient; because in that
case, investors only need to pay several Chinese Yuan to get the information and expect to earn
abnormal returns.
Question 2: Does the publishing of stock recommendations in the column “The Best Potential Stocks
Recommended by Analysts This Week” bring positive cumulative average abnormal returns around
the announcement date?
Question 3: Will different levels of analysts’ concentration on recommended stocks show different
degree of impacts on stock prices and returns?
Since June 1986, the Wall Street Journal and Zacks Investment Research of Chicago have begun a
joint study to investigate the value of stock recommendations by brokerage firm analysts. The study
results showed that from 60 months of monthly data during January 1993 and December 1997, the
recommended stocks slightly, but consistently outperform the S&P 500 and beat the market by 106
basis points per year. Womack (1996) analyzed the value of stock recommendations reports provided
by 14 major U.S. brokerage firms in the 1989-1991 time periods; he found out similar results as the
former one. But he used a different method: analyzing whether added-to-buy and removed-from-buy
stocks would have abnormal returns in short term and long term. It turned out that stock prices have
significant reactions to analysts’ recommendations, not only around the announcement date but also
in subsequent months. Stickel (1995) analyzed the short-term and long-term price reactions of buy
and sell recommendations with a five-point scale. “Strong buy” have larger positive effect than
“buy”; “strong sell” and “sell” have larger negative effect than “hold”. Neumann and Kenny (2007),
Keasler and McNeil (2008) and Engelberg et al. (2010) studied Jim Cramer’s recommendations on
his daily TV-show “Mad Money”. They all found a short run effect and it reverses in the long term. It
suggests that his recommendations do not provide new information. Ding and Sun (2001) used data
5
from ‘Potential Stock Recommendations’ of China Securities Journal during January 2000 and
August 2000, and found average abnormal returns at 1.39% in two days after the announcement,
however, the third day tend to have obviously negative abnormal returns. Wang et al. (2006)
discovered that the portfolios recommended by analysts graded as ‘increase holding’ obtain 23.85%
annual abnormal returns, and the results are significant so that analysts’ recommendations are
valuable; however, the portfolios graded as ‘decrease holding’ have negative annual abnormal returns,
the results are statistically insignificant. Kumar et al. (2009) investigated the India stock market
reactions of buy and sell recommendations made by analysts. The study finds that buy
recommendations can help investors earn abnormal returns on the event day; however, sell
recommendations do not have significant negative abnormal returns. According to Barber and Odean
(2008), investors are driven by attention. If stocks get more attention on average, a greater impact is
expected according to the attention hypothesis.
Lin and Kuo (2007) studied on the Taiwan stock market. Analysts’ recommendations can bring
significant positive abnormal returns; whereas, when the transaction costs are taken into account, the
abnormal returns can be ignored. They also found that recommended stock perform worse in the long
run after publication date. The similar results were found by Boni and Womack (2003), they
investigated that the abnormal returns in U.S. market are significant and positive; but the abnormal
returns almost disappear after correcting for transaction costs. In the contrary, Chan and Fong (1996)
discovered the stock recommendations in newspapers based on Hong Kong stock market can
generate abnormal returns, the results show that the abnormal returns of the two days after the
publication date are sufficient to cover transaction costs. Xu and Liu (2008) found the
recommendation of analysts is significantly influenced by the entire development of the market while
it only has a short term effect on price; however, the impact of the recommendation on price is also
tiny. Moreover, when one stock gets more attention from analysts, it can generate more abnormal
returns. The results from most of studies indicated that the recommendations made by analysts
generate positive abnormal returns around the announcement date.
Question 4: Does the results support the information and price pressure hypotheses?
6
Barber and Loeffler (1993) used their data on second-hand information price effects to test
information and price pressure hypotheses. The price pressure hypothesis states that temporary
buying or selling pressure by naïve investors in the recommended stocks results in positive or
negative abnormal returns following the buy or sell recommendations released as second-hand
information. Price pressure effects will be short-lived, as the abnormal returns will reverse. The
information hypothesis posits that the release of second-hand information provides new information
to the market, resulting in a permanent revaluation of the recommended stock. The hypothesis
predicts that new information contained in the buy or sell recommendations results in positive or
negative abnormal returns since the market adjusts to the value of the new information. And the
effects will be permanent. They found evidence provided by the “Dartboard” column is consistent
with both the information and price pressure hypotheses. Kerl and Walter (2007) analyzed buy stock
recommendations published in German Personal Finance Magazine during 1995-2003. Both the
information hypothesis and the price pressure hypothesis are confirmed by their data. Specifically,
glamour stocks and small stocks have larger price pressure effect. Using data on stock
recommendations in the column “The Best Potential Stocks Recommended by Analysts This Week”,
I discuss whether observed average abnormal returns on the publication day have a temporary
(supporting price pressure hypothesis) or permanent effect (supporting information hypothesis).
3
Data
The sample period of this study starts on the 24th of June 2013 till the 23th of December 2013. It is
the latest data available in database for study. In 2013, especially in the second half of the year, China
stock market experienced a lot more ups and downs. Ordinary investors seemed to be more relying
on the experts’ advises. Meanwhile, this study focus on A-share market, so I exclude government
bonds, convertible bonds and B-shares (trading use foreign currency, Hong Kong dollars in Shenzhen
and US dollars in Shanghai), as well as ST (Special Treatment) shares and PT (Particular Transfer)
shares which have discontinuous transactions caused by consecutive days of gains and losses or
delisting. Moreover, the recommended stocks are from both Shanghai Stock Exchange and Shenzhen
Stock Exchange; there is no essential difference between these two markets. Therefore, in order to
7
simplify the calculation and keep the accuracy of analysis, I only focus on stocks from Shanghai
Stock Exchange. The column provides recommendations made by 10 analysts, 9 analysts, 8 analysts,
7 analysts, 6 analysts, 5 analysts, 4 analysts and 3 analysts. The amount of stocks recommended in
each group always decrease as the number of analysts increases. In order to study the impacts of
different levels of analysts’ concentration on recommended stocks, I classify the recommended
stocks into three groups, which are recommended by less than 6 analysts, 6-7 analysts and more than
7 analysts. The groups represent low level, medium level and high level of analysts’ concentration on
recommended stocks, respectively.
The final sample have 494 stock recommendations, including 42 recommendations made by more
than 7 analysts, 183 recommendations made by 6-7 analysts and 269 recommendations made by less
than 6 analysts. For each recommendation published in the column, daily returns before and after the
dates are calculated using daily closing and opening prices provided by the CSMAR Database— the
most accurate database which aim at financial and economic fields in China. The daily closing prices
of Shanghai A-share Index (SAI) are used to calculate the normal returns. The sample stocks of SAI
are the whole A-shares listed in Shanghai Stock Exchange. Recommended stocks that are not
recorded in the CSMAR Database are excluded.
4
Methodology
The reactions of the stock prices to analysts’ recommendations can be inspected by the methodology
of event study.
3.1 The determination of the event time and the event window.
The publication date of this column will be treated as the event day
, meanwhile, the event
window is between 15 trading days before and after the publication day (from
to
).
Kerl and Walter (2007) defined the period [-20, 20] as the event period. Considering the large effects
of external factors and over-speculation in China stock market, it is hard to tell whether the stock
price changes are caused by recommendations in more than one month. In order to focus on the
8
impacts of recommendations, I choose a shorter event window of 31 days.
3.2 The calculation of normal returns and abnormal returns.
is the daily return of the stock i on day t, it calculated as:
There are three important models to calculate normal return: Market Adjusted Returns, Mean
Adjusted Returns and Market and Risk Adjusted Returns. Chen and Jiang (2005) collected all
A-shares data from December 1990 to December 2003 to demonstrate that ‘Market Adjusted Returns’
for calculating the normal returns were better than ‘Market and Risk Adjusted Returns’ and ‘Mean
Adjusted Returns’ under different situations in China market. Therefore, I use this model to calculate
normal returns:
Where
1
is the return of Shanghai A-share Index for day t,
for day t, and
is the closing price of the index for day
Then the abnormal return
is the closing price of the index
.
for stock i on day t is the difference between daily SAI return and
daily stock return:
=
For a sample of N stocks, the average abnormal returns for day t,
And the cumulative average abnormal returns from day
to day
is calculated as:
,
is calculated as:
3.3 Test statistic
To test if the average abnormal returns are significant on a specific day in the event window, the
1
The daily returns of Shanghai A-share Index are provided in CSMAR Database.
9
AARs are tested using following t-test:
Test statistic AAR=
Where:
If it is assumed that the abnormal returns have finite variance and are independent, the t-statistic for
large N approximately has a standard normal distribution (Central Limit Theorem). In event studies,
N>30 is typically needed for this approximation to be reasonably good. To test if the cumulative
average abnormal returns are significant on a specific day in the event period, the CAARs are tested
using the following formula:
Test statistic CAAR
Where:
5
Empirical results
In this part the results of the event study will be presented. The event study will show that pattern of
abnormal returns the average recommendation follows. The results of price movements (
and
for event day T) of the whole stock recommendations, the recommendations made by
more than 7 analysts, the recommendations made by 6-7 analysts and the recommendations made by
less than 6 analysts are shown in four tables (Table 1, Table 3, Table 4 and Table 5 respectively). The
corresponding graphs of the
are presented in Figures 1 through 4. And Table 2 will
report the results for the whole sample associated with the price pressure hypothesis and the
information hypothesis.
10
Table 1
Average Abnormal Returns and Cumulative Average Abnormal Returns for the Whole Stock
Recommendations
Event Day
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Notes:
t-statistic >
t-statistic >
t-statistic >
AAR
T-test AAR
CAAR
T-test CAAR
0.54%
0.42%
0.22%
-0.08%
-0.04%
0.54%
0.92%
0.42%
0.16%
0.20%
0.45%
0.50%
0.60%
0.73%
4.03%
0.20%
0.44%
0.32%
-0.37%
0.14%
0.11%
0.20%
0.39%
0.12%
0.07%
-0.06%
0.12%
-0.15%
-0.16%
0.05%
0.13%
4.876
3.574
1.819
-0.747
-0.369
4.370
7.931
3.269
1.331
1.631
3.433
4.210
4.598
5.224
21.899
1.241
3.477
2.309
-2.782
0.976
0.823
1.538
3.197
0.927
0.564
-0.462
0.938
-1.229
-1.533
0.433
1.180
0.54%
0.96%
1.18%
1.10%
1.06%
1.60%
2.52%
2.94%
3.10%
3.31%
3.76%
4.26%
4.86%
5.59%
9.62%
9.82%
10.26%
10.58%
10.21%
10.36%
10.46%
10.66%
11.05%
11.17%
11.24%
11.18%
11.30%
11.15%
10.99%
11.03%
11.16%
4.876
5.846
6.081
4.917
4.290
5.664
8.186
8.592
8.617
8.345
9.118
9.838
10.455
11.431
17.591
16.988
17.223
16.744
16.038
15.703
15.849
15.973
16.073
15.880
15.587
15.329
15.220
14.499
14.135
13.881
13.973
: Significant at 10% level;
: Significant at 5% level;
: Significant at 1% level.
11
N
493
494
494
494
494
494
494
493
494
493
494
494
492
492
494
494
494
491
493
493
492
493
494
493
492
494
494
494
494
494
492
Table 1 reports average abnormal returns (AARs) and cumulative average abnormal returns (CAARs)
for the whole stock recommendations within the period [-15, 15]. As a whole, it shows positive
AARs on trading days -10 through 2; they are statistically significant except for day -7, day -6 and
day 0. The AARs fluctuate in following trading days being positive or negative. Picking a five days
period [-2, 2] around the publication date, the cumulative AAR is 5.72%. Thus, the recommendations
published in the column result in a revaluation of stocks in short-term period around the event. The
maximum value of AAR is 4.03% on trading day -1, while on the publication date the market
reaction is 0.20%. However, there is a significant negative value of AAR at -0.37% for day -3. It is
consistent with the findings of Ding and Sun (2001). Considering the publication date is Monday and
day -1 is Friday, the results may be affected by the Day of the Week Effect. Feng (2000) discovered
that unlike most of the stock markets in developed countries and emerging markets, China stock
market own significant positive Friday Effect.
Since prices always reflect new information gradually, examining cumulative average abnormal
returns can give a clear picture of how the entire market reacts to the stock recommendations. We can
see from Table 1, all CAARs are statistically significant, the highest value of 11.30% is displayed
over the period [-15, 11]. As shown in Figure 1, the CAAR of the stock recommendations does not
follow a particular spike and reversal pattern. The CAARs from day -1 on experience a significant
increase, which indicates that individual investors may follow the recommendations and earn
abnormal profits.
The values for AAR and CAAR in Table 1 and the pattern of
increase in stock prices during the period [-10, -1]. The
in Figure 1 show an
is 9.62%, statistically
significant. The price movements before publication date may due to the release of first-hand
information to brokerages’ clients. After those recommendations are published in the column, prices
continue to increase. It suggests that the stock recommendations in the column provide new
information that has not been reflected by stock prices.
12
Figure 1
The CAARs on Days [-15, 15] for All Recommendations
12.00%
10.00%
8.00%
CAAR
6.00%
4.00%
2.00%
0.00%
-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Event Day
Table 2
Cumulative Average Abnormal Returns for Recommendations Separated by the Information Effect
and the Price Pressure Effect
Total Price Reaction
Information Effect
CAAR[-2, 2]
t-stat
CAAR[-2, 15]
5.72%
15.843
6.30%
Notes:
t-statistic >
: Significant at 10% level; t-statistic >
t-statistic >
: Significant at 1% level.
t-stat
11.073
Price Pressure Reversal
Effect
CAAR[3, 15]
t-stat
0.58%
1.449
: Significant at 5% level;
I use the whole sample to analyze the levels of information effect and price pressure effect on price
reactions. Table 2 reports CAARs in three different periods for segregating information effect from
price pressure effect. Most recommendations in the column are nailed down to publish on trading day
−3; therefore, the price reaction should start when the recommendations list is fixed. The CAARs for
the period [-2, 2] shows the total price reaction around the publication date, because both effects
work at the same time. The information effect is represented by the CAAR [-2, 15] because it
includes the total price reaction around the publication date and also the following movement until
trading day 15. The new information should affect stock prices permanently. The CAAR [3, 15]
13
represents the reversal price pressure effect. It is suggested that rising buying pressure around the
publication date is temporary and will be reversed after day 0; the prices will go down during this
period. Since Table 1 shows that the AAR starts to be negative at day 3, the period [3, 15] can be
chosen to examine price pressure effect.
As can be seen in Table 2, there is a significant growth in stock prices of 6.30% during the days [-2,
15] due to permanent information effect. The price pressure reversal effect is 0.58%, statistically
insignificant. Hence, I assume that price pressure effect contributes 0.58% to the increase of stock
prices around the publication date. Since the CAAR [−2, 2] for total price reaction is 5.72%,
information hypothesis is supported, and the price pressure hypothesis is supported to a lesser extent
due to the small reversal.
Figure 2
The CAARs on Days [-15, 15] for Recommendations Made By More Than 7 Analysts
16.00%
14.00%
CAAR
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Event Day
Table 3 and Figure 2 show that, comparing to the whole stock recommendations; stocks
recommended by more than 7 analysts have a higher level of impact on stock prices. The AARs and
CAARs experience a larger fluctuation. The cumulative AAR during days [-2, 2] is 7.95%. The AAR
for day 1 after the reporting of the recommendations made by more than 7 analysts is 1.15%,
statistically significant, which is approximately 2.6 times as the
of the whole sample.
However, from day 2 through 4, AARs start to show negative value, which are all statistically
14
Table 3
Average Abnormal Returns and Cumulative Average Abnormal Returns for the Stock
Recommendations Made By More Than 7 Analysts
Event Day
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Notes:
t-statistic >
t-statistic >
t-statistic >
AAR
T-test AAR
CAAR
T-test CAAR
N
0.80%
-0.24%
-0.18%
0.31%
0.34%
0.70%
2.29%
1.61%
-0.39%
0.37%
-1.07%
0.51%
0.87%
0.48%
6.96%
-0.04%
1.15%
-0.60%
-0.66%
-0.37%
0.03%
-1.27%
1.21%
0.26%
1.58%
-0.89%
-0.26%
-0.23%
-0.02%
-0.30%
0.71%
2.751
-0.745
-0.535
0.732
0.818
1.719
4.471
2.625
-0.952
0.881
-1.877
1.067
2.767
1.130
11.823
-0.062
2.096
-1.198
-1.840
-0.979
0.074
-2.680
2.932
0.571
2.816
-2.221
-0.399
-0.817
-0.048
-0.830
2.099
0.80%
0.56%
0.38%
0.69%
1.03%
1.74%
4.02%
5.63%
5.24%
5.61%
4.54%
5.05%
5.93%
6.41%
13.38%
13.33%
14.48%
13.88%
13.23%
12.86%
12.89%
11.61%
12.83%
13.09%
14.67%
13.78%
13.52%
13.30%
13.28%
12.98%
13.70%
2.751
1.234
0.776
0.909
1.192
1.787
3.417
4.073
3.740
3.567
2.989
2.977
3.252
3.682
7.014
6.777
6.683
6.711
6.500
6.897
6.655
6.370
6.657
6.518
6.219
5.930
5.676
5.793
5.956
5.808
5.665
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
: Significant at 10% level;
: Significant at 5% level;
: Significant at 1% level.
15
insignificant. On day 6, it falls dramatically to -1.27% but then immediately move upwards, and
reaches the highest level at 1.58% on day 9 after the publication day, and then followed by
continuous negative values. When looking back, from day -4 to -1, AARs have relatively significant
positive values. And AAR for day -1 peaks at 6.96% and is statistically significant. The CAAR
before the recommendations reported in the column is 13.38% (t-statistic=7.014); it has the
maximum value of 14.67% during the period [-15, 9].
As can be seen from Table 4 and Figure 3, the degree of impacts of the stocks recommended by 6-7
analysts is lower than the recommendations made by more than 7 analysts. Table 4 shows positive
AARs on trading days -10 through 2; they are statistically significant except for days -8 to -6, day 0
and day 2. In days [-2, 2], the AARs add up to 6.70%. The AAR for day -1 is extremely high at
4.55%. During the following days, only day 3, day 5, day 10, day 12 and day 13 have negative AARs.
The overall trend of CAAR after day 3 is stable, except for a relatively considerable drop through
day 11 and 13. Its highest value of 11.89% is displayed over the period [-15, 9]. The
has a significant value of 10.09%.
Figure 3
The CAARs on Days [-15, 15] for Recommendations Made By 6-7 Analysts
14.00%
12.00%
CAAR
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Event Day
16
Table 4
Average Abnormal Returns and Cumulative Average Abnormal Returns for the Stock
Recommendations Made By 6-7 Analysts
Event Day
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Notes:
t-statistic >
t-statistic >
t-statistic >
AAR
T-test AAR
CAAR
T-test CAAR
0.62%
0.33%
0.09%
-0.12%
-0.04%
0.50%
0.92%
0.31%
0.06%
0.01%
0.61%
0.35%
1.05%
0.84%
4.55%
0.30%
0.64%
0.37%
-0.53%
0.38%
-0.06%
0.08%
0.21%
0.21%
0.20%
-0.31%
0.20%
-0.34%
-0.41%
0.26%
0.32%
3.178
1.916
0.415
-0.628
-0.176
2.361
4.682
1.468
0.277
0.047
2.880
1.771
4.424
3.259
14.411
1.014
2.883
1.480
-2.289
1.346
-0.219
0.375
1.087
0.954
0.916
-1.352
0.960
-1.478
-2.330
1.295
1.679
0.62%
0.96%
1.05%
0.93%
0.90%
1.39%
2.32%
2.63%
2.69%
2.70%
3.31%
3.66%
4.70%
5.54%
10.09%
10.39%
11.03%
11.40%
10.87%
11.24%
11.18%
11.27%
11.48%
11.69%
11.89%
11.57%
11.77%
11.43%
11.02%
11.28%
11.60%
3.178
3.597
3.143
2.443
2.012
2.789
4.209
4.715
4.463
4.121
4.853
5.095
6.138
6.805
11.248
10.796
11.484
11.229
10.418
10.514
10.462
10.247
10.250
10.224
10.296
9.903
9.975
9.245
8.840
8.821
9.030
: Significant at 10% level;
: Significant at 5% level;
: Significant at 1% level.
17
N
182
183
183
183
183
183
183
182
183
183
183
183
182
182
183
182
183
183
182
183
182
182
183
183
183
183
183
183
183
183
181
Table 5
Average Abnormal Returns and Cumulative Average Abnormal Returns for the Stock
Recommendations Made By Less Than 6 Analysts
Event Day
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Notes:
t-statistic >
t-statistic >
t-statistic >
AAR
T-test AAR
CAAR
T-test CAAR
0.45%
0.58%
0.37%
-0.12%
-0.11%
0.55%
0.71%
0.31%
0.32%
0.31%
0.58%
0.61%
0.25%
0.69%
3.22%
0.17%
0.20%
0.43%
-0.21%
0.06%
0.23%
0.50%
0.38%
0.03%
-0.25%
0.24%
0.12%
0.00%
-0.01%
-0.04%
-0.09%
3.019
3.371
2.399
-0.837
-0.757
3.302
4.983
1.938
2.103
1.894
3.450
3.856
1.503
3.954
14.122
0.887
1.280
2.531
-1.203
0.350
1.598
3.103
2.283
0.188
-1.541
1.356
0.801
-0.031
-0.103
-0.331
-0.591
0.45%
1.03%
1.40%
1.28%
1.17%
1.72%
2.43%
2.74%
3.05%
3.36%
3.94%
4.55%
4.80%
5.49%
8.71%
8.89%
9.09%
9.51%
9.30%
9.36%
9.60%
10.10%
10.49%
10.52%
10.27%
10.50%
10.62%
10.62%
10.60%
10.56%
10.47%
3.019
3.224
3.512
3.348
2.862
3.603
4.612
4.398
4.913
5.090
5.385
5.807
5.654
6.017
8.020
7.443
7.327
7.444
7.381
6.849
6.797
7.005
7.069
6.974
6.765
6.733
6.728
6.402
6.472
6.143
6.076
: Significant at 10% level;
: Significant at 5% level;
: Significant at 1% level.
18
N
269
269
269
269
269
269
269
269
269
268
269
269
268
268
269
269
269
266
269
268
268
269
269
268
267
269
269
269
269
269
269
Figure 4
The CAARs on Days [-15, 15] for Recommendations Made By Less Than 6 Analysts
12.00%
10.00%
CAAR
8.00%
6.00%
4.00%
2.00%
0.00%
-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Event Day
Referring to the result of the stock recommendations made by less than 6 analysts showed in Table 5
and Figure 4, it has the smallest impacts on stock prices among these three subgroups. Table 5 shows
positive AARs on trading days -10 through 2; they are statistically significant except for day -3, day
0 and day 1. In days [-2, 2], the AARs add up to 4.71%. The AAR for day -1 is 3.22% and also the
highest level in the event window. Moreover, the AAR on day 1 is 0.20%, which is around three
times less than the one in recommendations made by 6-7 analysts and 5.7 times less than the one in
recommendations made by more than 7 analysts. The overall trend of CAAR after day -1 is growing
gradually, expect for a drop on day 9 and slightly decreasing during days [13, 15]. The CAAR before
the recommendations are published in the column is 8.71% (t-statistic=8.020); the CAAR has the
highest value of 10.62% during the period [-15, 12].
For the first question: the stock recommendations in the column “The Best Potential Stocks
Recommended by Analysts This Week” which made by more than 7 analysts provide abundant new
information, and individual investors who follow these recommendations can expect to earn
abnormal profits; the stock recommendations in the column which made by 6-7 analysts and less
than 6 analysts also have new information, however, individual investors will earn less abnormal
profits.
For the second question: the recommendations in the column “The Best Potential Stocks
19
Recommended by Analysts This Week” do provide positive cumulative average abnormal returns
around the publication date. Hence, individual investors can follow those recommendations and earn
abnormal profits.
For the third question: the higher level of analysts’ concentration on recommended stocks show
higher degree of impacts on stock prices and returns. The higher level of analysts’ concentration on
recommended stocks will led to bigger range of information dissemination so that it can generate
more abnormal returns.
For the fourth question: the recommendations in the column “The Best Potential Stocks
Recommended by Analysts This Week” provide support for the information hypothesis, and weak
support for the price pressure hypothesis.
6
Discussion
The first striking fact is that the stock recommendations published in the column “The Best Potential
Stocks Recommended by Analysts This Week” provides persistent positive cumulative average
abnormal returns. To be specific, the prices go up significantly before the recommendations
published. However, after the publication date, the average abnormal returns move downwards, even
show negative values. The CAAR for the period [-15, -3] is shown to be 4.86% in the entire sample
of stock recommendations, almost 43% of the total price reaction during the days [-15, 15]. There are
three probable explanations for the pre-event price reaction. First, the first-hand information has
already been disseminated among brokerages’ clients before the recommendations published in the
column. It is common those brokerages’ clients could access to the latest relevant information ahead
from other investors and follow the recommendations in China. Second, the stocks on
recommendation list might already have good performance before, so that analysts tend to suggest
they will still bring abnormal returns in the future. Thus, high abnormal returns before the
publication date are not caused by the recommendations. Third, some other major events happen
before the publication date can also lead to the pre-event price reaction, such as the change of board
20
of directors, merger and acquisition. The companies with important relevant news tend to be
considered by analysts.
The AARs for day 1 and day 2 add up to 0.76%, statistically significant; but then it shows
considerable negative value on day 3, followed by small positive values. The result is consistent with
the finding of Lloyd Davies and Canes (1978) which analysts’ recommendations have significant
impacts on prices in two days after publication. Grossman and Stiglitz (1980) suggested that new
information which can bring significant profits is costly. We can see from this study, the first-hand
recommendations contain more information than the second-hand ones. So if investors want to
obtain first-hand information, they should pay for the brokerages and become their clients. However,
the information easily getable for few Chinese Yuan by reading the column in newspaper is rapidly
incorporated into stock prices; the readers can still earn certain abnormal returns after transaction
costs 2 . In that case, China stock market is inefficient. Furthermore, for gain profit from the
recommendations, the investors should buy the recommended stocks after they receive the
information as soon as possible and close the deal in really short term. It is suggested that they can
follow the recommendations made by more than 7 analysts, the AAR for day 0 and day 1 add up to
1.11%, which can cover the transaction costs.
We have two hypotheses to explain the cause of abnormal returns: one is the information hypothesis;
another one is the price pressure hypothesis. The price pressure hypothesis states that the
recommendations cause temporary buying pressure by individual investors, which lead to abnormal
returns that quickly reverse. The information hypothesis states that the recommendations disclose
relevant information to the market, resulting in permanent reevaluation of the recommended stocks.
If the information hypothesis is true, and assuming the market is perfect competition, the stock’s
fundamental equilibrium value would be permanently affected by analysts’ recommendation. The
result of this study shows that the CAAR during the period [-2, 15] is 6.3%, statistically significant,
which strongly support the information hypothesis. With respect to the price pressure hypothesis,
analysts’ recommendations indeed influence many ordinary investors; their decisions of buying those
2
The transaction cost in Shanghai Stock Exchange including commission fee, stamp tax and transfer fee is 0.7%.
21
recommended stocks give rise to buying pressure. This pressure drives the abnormal increase of
stock prices in short term, abnormal returns show positive value. As time goes by, the information
that cannot influence stock equilibrium value will gradually eliminate its impacts on the market, and
then buying pressure will be reduced, stock prices will back to normal, abnormal returns turn to be
negative. However, the data shows the reversal is small; the CAAR [3, 15] is 0.58%, statistically
insignificant. The results of this study do not provide strong support for the price pressure
hypothesis.
In fact, China stock market and investors’ current situation can provide evidence as well. As
everyone knows, China has the most stock investors; a majority of them do not have any investment
expertise and have no access to inside information. Their investment decisions are influenced by all
kinds of “information” to a large extent. They actually do not know whether the “information” is true
or not, however, they just simply trust the “information” is real and follow it. Black (1986) called this
kind of investors “Noise Trader”. De Long et al. (1990) used DSSW model to conclude that noise
traders can earn positive expected returns. When a specific stock is recommended by analysts in an
authoritative column, these noise traders will consider this recommendation as very important
information and follow it. Consequently, it induces abnormal increase of stock price in short-run
after the recommendation. Hence, the noise traders in the market and their noise trading cause the
price pressure, and the result of positive abnormal returns in two days after the recommendation is
attributed to the price pressure hypothesis.
In the investigation of abnormal returns classified by different levels of analysts’ concentrations,
stocks which have better prior performance could attract more analysts to give recommendation. It
illustrates that analysts have obvious herd behavior. And the stocks with high level of concentrations
perform better after the recommendation, which means those individual investors who follow the
recommendations have very low degree of rationality.
22
7
Conclusion
Individual investors always depend on investment recommendations made by financial analysts to
make decisions. This research investigates the China A-share market’s reactions to the publishing of
stock recommendations made by financial analysts in the weekly column “The Best Potential Stocks
Recommended by Analysts This Week” of the Shanghai Securities News. While brokerages’ clients
have first-hand information, the readers of the column obtain second-hand information of
recommendations.
The stock price movements show that they have not reflected all the first-hand information of stock
recommendations before it published in the column. The reaction is significant on the day -1 before
the publication day (AAR= 4.03%, t-statistic= 21.899). The abnormal returns for day 1 and day 2
after the publication date can cover the transaction costs. The stock recommendations generate
positive cumulative average abnormal returns. During a five days period around the publication date,
days [-2, 2], a significant cumulative average abnormal return of 5.72% is discovered. The CAAR
during the period [-2, 15] has significant value at 6.3%, and it has a value of 0.58% in days [3, 15].
The results consistent with the information hypothesis and to a lesser extent with price pressure
hypothesis since the reversal is small. The different levels of analysts’ concentration on
recommended stocks show different degree of impacts on stock prices and returns. To be specific, the
higher level of analysts’ concentration on recommended stocks can help investors generate more
abnormal returns.
For most individual investors in China, analysts’ recommendations remain valuable. They can profit
from these recommendations if they buy the recommended stocks as soon as possible. Moreover, as
the abnormal returns will show negative value on day 3, followed by small positive values which can
be negligible after transaction costs, investors should only trade in very short term.
23
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