Financial Analysts and Speculative Bubble in Emerging Stock Market*

Chi-Wen Jevons LeeTulane University and Zhejiang University

Chang LiuTsinghua University

August 31, 2008

* We appreciate comments from Kevin Chen, Xiaoyu Bai, Clive Lennox, and participants at Tsinghua，Tulane, and HKUST Accounting Workshop. Contact author: Chi-Wen Jevons Lee, A.B. Freeman School of Business, Tulane University, New Orleans, LA 70118-5669, USA; Jevons .lee@tulane.edu .

Financial Analyst and Speculative Bubble in Emerging Stock Market

AbstractThis paper examines the speculative bubble regularly set off by financial analysts in China.

We document significant positive two-day excess returns right before analyst’s buy

recommendation and then significant reversals afterward. The securities with greater initial price

rise suffer greater subsequent reversals. After controlling the structure of analyst market, we find

that the initial price rise and subsequent reversal are positively correlated with the liquidity of the

security. Due to the peculiar trading restriction, we find a way to document naive trader ’s

behavior. Our results suggest that analyst’s recommendations incite intensive noise trading by

naive investors as well as convey fundamental information. Thinness of market and associated

illiquidity create bubble and profitable trading opportunity for informed traders. This research

captures the rare episode of speculative bubbles associated with market learning toward

information efficiency.

Keywords: financial analyst, stock recommendation, price pressure, market inefficiency

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I. Introduction

This paper studies the market activities measured by stock returns and trading volumes around the

publication of analysts' stock recommendations in China.1 Based on a large sample of

recommendations in the weekly column "Stocks Recommended by The Most Analysts This Week"

in Shanghai Securities News on every Monday during 2000 to 2005, we find apparent pattern of

surging and receding of stock prices around the recommendation. Positive abnormal return begins

the day before the analyst recommendation. Cumulative abnormal return reaches the peak on the

announcement date, declines sharply the next day, and never rebounds thereafter. Almost half of

the two-day initial rise is reversed in the subsequent 5 days after announcement. We find evidence

that dealers primarily trade with naïve traders, not between themselves. The trading activities are

negatively associated with liquidity. This paper finds repetitive 5373 episodes of mini-bubbles set

off by analyst recommendation. The magnitude of bubbles is positively associated with the

number of analysts recommending the stocks. We provide large sample evidence to the bubbles

predicted by Allen, Morris and Postlewaite (1993) and Abreu and Brunnermeier (2003)

Detailed analyses reveal that the post-announcement cumulative abnormal returns are

negatively correlated with the surge before the announcement, i.e., securities with greater price

surge suffer larger reversals. Moreover, we find a positive correlation between abnormal returns

and abnormal trading volumes on the announcement date. More active trading is associated with

greater swing of returns surges and reversals. Using market value of stocks, dollar volume,

standard deviation of daily raw returns and ratio of absolute stock return to dollar volume as

liquidity measures, we find that, with informational effect on returns being controlled for, the on-

announcement returns decrease, while post-announcement returns increase, with the liquidity of

the recommended security. Less liquid securities suffer greater fluctuation, larger initial rise and

larger subsequent fall. These phenomena suggest speculative bubbles ignited by analyst forecasts

due to market illiquidity. Sudden and large shifts in demand can lead to a temporary order-flow

1 Starting from Lee (1986, 1987), studies of financial analyst behavior evolves into a cottage industry. Numerous articles have been devoted to various issues of financial analysts in securities market. More relevant ones to this paper are Bushan (1989), Barber and Loeffler (1993), Stickel (1995), Womack (1996), Greene and Smart (1999), Liang (1999), Michaely and Womack (1999), Jackson (2005), Frankel, Kothari, and Weber (2006). Most studies have been devoted to this issue in well-developed markets such as U.S. and Britain, little is known about the market reactions to analyst reports in emerging market. For preliminary studies of this issue in emerging market, see Lin (2000) and Zhu and Wang (2001). These two papers all documented the mean-reversion phenomenon in the stock market reactions, but failed going deeper. For example, their data are all less than one year and none explored the issue of speculative bubble. They simply measured market reactions without theory.

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imbalance and price is adjusted to encourage investors to accommodate the demand shifts. Our

empirical evidence reveals price pressure around the announcement of stock recommendation. We

find speculative bubbles regularly set off by the “coarse and stale” information of analyst

recommendations.

Price pressure drives price away from fundamental value. The existence of price pressure

suggests that not all the abnormal returns are information driven. Price adjustments with little

information content are characterized as bubble. By bubble, we mean that price systematically

deviates from its fundamental value; market overreacts beyond what fundamentals would imply.2

Price pressure can arise from noise trading and naïve herding.3 Shleifer and Summers (1990)

suggested that noise traders regard the analysts as market gurus whose recommendations stimulate

herding behavior. The noise traders herd after the public signal and the actions of one another.

Their bursting sentiment shifts the demand curve. Allen, Morris, and Postlewaite (1993) suggested

that short-sales constraints and symmetric information can create finite bubbles. These two

attributes are much more prominent in an emerging market such as China than those developed

market documented in the literature.

On the announcement date for a buy recommendation, noise traders rush to buy the stocks,

pushing up prices. Due to the "t+1" trading rule in China, they cannot sell the stocks on the same

day as they buy them, but can do so on or after the next day. Therefore, they sell these stocks in

haste after the announcement date to realize the profits. The consequence of their speculation

activities is the temporary imbalance between demand and supply for the recommended securities.

In order to resolve the order imbalance, prices have to be adjusted away from the equilibrium

value so that some investors would be willing to provide liquidity service. Thus the mean-

reversion of the stock returns comes about. Liquidity measures a security's vulnerability to short-

run demand shocks. Less liquid security prices are more subject to temporary demand shifts,

resulting in larger price fluctuations.

In addition, we also find that the magnitude of the on-announcement run-up is declining as

years go by, greatest in year 2000 (the first sample year) and smallest in year 2005 (the last sample

2 For a theoretical analysis of bubbles, see Abreu and Brunnermeier (2003). Bradley, Jordan, and Ritter (2007) studied bubbles associated with analyst behavior following IPOs.3By "noise investor" we mean the investors with no private information to exploit when trading. Such investors are also referred to as "uninformed investors" or "liquidity investors." Grossman and Stiglitz (1980) showed that noisy traders and associated price pressure are essential for assets market equilibrium with rational expectation.

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year). The year-median of the post-announcement reversals, in general, also declines with time,

largest in year 2000 and second smallest in year 2005, suggesting that investors are learning from

past experience and thus initial enthusiasm fades over time. This research captures the rare episode

of bubbles associated with market learning toward information efficiency.

As Grossman and Stiglitz (1980) argued, the existence of price pressure is antithetical to the

efficient market hypothesis, but crucial for market equilibrium under rational expectation. There

are two critical elements for assets market equilibrium and an additional one for bubbles. The first

is the existence of noise traders. In China's security market, most individual investors lack the

knowledge of investment and the ways of obtaining information. They trade according to their

individual sentiment or by herding. The second element is the informed traders. In this study, we

can capture the information as the analysts' recommendations on the newspapers. In the youthful

China stock market, listed corporations' disclosure and transparency are so opaque that individual

investors have to rely on public analysts' recommendations. One would find it difficult to provide

significant evidence to Grossman-Stiglitz Theorem in the U.S. and Britain. In this paper, we

clearly identify the evidence of price pressure in China. Beyond the first two elements for

Grossman-Stiglitz Theorem, a crucial component for bubble is the limitation of arbitrage. In

China, short-arbitrageurs aiming at arbitrage profits have to own the stock before the

announcement date. Long-arbitrage needs large amount of cash, which is quite difficult to acquire

in China's rigidly regulated financial system. Hence, informed traders are only able to arbitrage to

the extent of cash in pocket. This paper provides a unified empirical study for Grossman and

Stiglitz (1980) and Abreu and Brunnermeier (2003).

A rational market cannot be totally blind. Price pressure in Grossman-Stiglitz Theorem

required fundamental innovations. We find that the market reactions begin before the information

release, implying information leakage, even perhaps manipulation. On-announcement returns

increase with the number of recommending analysts, and decrease with the frequency of being

recommended and firm size, consistent with the information hypothesis that analyst's

recommendation releases relevant information for fundamental revaluation. The number of

recommending analysts measures the precision of information. More analysts' recommendations

indicate higher information precision. Frequency of being recommended measures the amount of

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information already released to the market before recommendation. Similarly, firm size is a

measure of information environment and the extent of market attention. The information of

frequently recommended securities and large firms is gathered in greater detail, processed with

greater care, and disseminated more widely. Hence the shock caused by a single piece of

information at its announcement would be small. Price pressure, arising from investors'

incapability of accurate valuation, is closely associated with the nature of information.

In China, institutional traders can trade when the market is closed whereas individual traders

can only trade when the market is open. Institutional traders are better informed and more

sophisticated. We have micro data to show trading behavior within trading day and between

trading date. These data allow us to show the different trading behavior of informed traders and

noise traders. We find that the regular speculative bubbles in our case are driven by the informed

traders extracting rent of information from the noise traders. In this paper, we provide empirical

linkage between speculative bubbles and noisy rational expectation.

This paper proceeds as follows. The next section is a review of the related literature, followed

by hypotheses development. Section III provides evidences of speculative bubbles. Section IV

conducts series of empirical rests to figure out the driving force behind speculative bubbles.

Conclusions are provided at the end.

II. Literature Review and Hypotheses Development

Financial analysts, serving in brokerage houses, independent research institutes, fund

corporations and banks, play important roles in allocating resources in capital markets. They are

primary information intermediaries in the market: collecting private information, forecasting

firms’ prospects, and conducting retrospective analysis that interprets past events (Beaver (1998),

p.10). Their labors enhance the informational efficiency of the capital markets (Frankel et al.

(2006)).

Lee (1986, 1987) found preliminary evidence that the value of analyst’s recommendation is

positively associated with information costs, consistent with Grossman-Stiglitz Theorem.

However, considerable evidence suggests that not all price changes are fully justified by

information. Shleifer and Summers (1990) proposes the noise trader approach to finance, as an

alternative to the efficient market paradigm. This approach rests on two critical assumptions:

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investor irrationality and limited arbitrage. First, some investors are not fully rational, subjecting

to the influence of sentiments. Second, arbitrage is limited because of the risk arbitrageurs have to

assume. They developed several implications consistent with the evidence, such as closed-end

funds changes, price pressure, and historical episodes. One of the implications suggests that some

demand changes may be a response to changes in sentiment not justified by information or

pseudo-signals perceived to convey information, and thus are irrational. If many investors base

their trading strategies on pseudo-signals, e.g., advice of financial gurus, aggregate demand shifts

will result.

Barber and Loeffler (1993) analyze returns and volume around the announcement of analysts'

recommendations appearing in the monthly "Dartboard" column of the Wall Street Journal. The

stocks selected by analysts experience a 4.06 percent (t=10.77) abnormal return over the two-day

period consisting of the journal's publication day and the subsequent day, and a -2.08 percent (t=-

1.56) return from day 2 through day 25. Firms experiencing positive abnormal volume on the

announcement have significant post-announcement price reversal, while those with no positive

abnormal volume on the announcement have no reversal. Less liquid firms have a larger price

reaction on days 0 and 1. Their evidence suggests that the price response on the announcement of

recommendations is at least partially driven by buying pressure in the recommended securities.

Greene and Smart (1999) argue that "Dartboard" column generates temporary price pressure by

increasing noise (i.e., uninformed) trading. This conclusion comes from two pieces of evidence:

most of the abnormal returns following the column's publication disappear within a few weeks,

and abnormal trading volume and temporary abnormal returns are greater for stocks recommended

by analysts with successful records, which are not at all predictor of their future success and long

term performance. Securities with the greatest initial price run-up and the greatest increase in

trading volume experience the largest price reversals. Liang (1999) also documents the reversion

pattern in the "Dartboard" column stock prices. The two-day announcement abnormal return of

3.52 percent starts to reverse from the third day. Higher trading volume and reduced bid-ask

spread are indicators of more noise trading by naive investors, which further supports the price

pressure hypothesis. Stickel (1995) finds that the magnitude of change in recommendation, analyst

reputation，and broker size have temporary effects on stock prices.

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Nevertheless, identifying price pressure caused by analysts' recommendations may be

inconclusive because these events usually convey new information to the market and it is quite

difficult to distinguish the price pressure from the informational effect. Harris and Gurel (1986)

avoid this objection by examining price reactions to changes in the composition of Standard and

Poor's list of 500 stocks, an event that does not plausibly reflect any new fundamental information.

Changes in the composition of S&P 500 should not reveal new information about performance

potential because the changes are based only on publicly available information and on well-known

criteria, not on forecast returns. But they do shift demand as many large index funds' portfolio

holdings just represent the S&P index. Harris and Gurel (1986) find that immediately after an

inclusion into the index is announced, price increases by more than 3 percent, which is nearly fully

reversed after 2 weeks. Besides, the magnitude of these increases has risen over time, paralleling

the growth of index funds. Ritter (1988) studies the January effect, another situation not

confounded by the informational problems. January effect, one of the anomalies in stock returns,

refers to the phenomenon that low-capitalization stocks have unusually higher returns than large

ones in every January. Ritter finds that individuals have a below-normal buy/sell ratio in late

December and an above-normal ratio in early January because small stocks usually held by

individuals are sold during December to realize losses for tax purposes and reinvested in later in

early January.

In this article, two joint hypotheses are empirically tested: the information hypothesis and the

price pressure hypothesis. The information hypothesis asserts that analysts' recommendations

reveal relevant information, with which the market revaluates the security, and thus the abnormal

performance on the announcement represents a fundamental revaluation of the security. In

contrast, the price pressure hypothesis maintains that the recommendations create temporary

buying or selling pressure by naive traders in the recommended securities, and this pressure leads

to the observed abnormal performance.

The information hypothesis suggests that analysts provide useful assessments to market

players. The price pressure hypothesis suggests that investors who accommodate demand shifts

must be compensated for the transaction costs and portfolio risks that they bear when they agree to

immediately buy or sell securities which they otherwise would not trade (Harris and Gurel

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(1986)). Liquidity and transaction costs are the root of price pressure. The noise traders

compensate the informed traders through price pressure. This phenomenon is commonly called as

speculative bubbles. The joint evidence for information hypothesis and price pressure hypothesis

will support Grossman-Stiglitz Theorem. The evidence of price pressure without the support of

information hypothesis will favor the sentiment argument made in Shleifer and Summers (1990)

Financial analysts can generate both useful information service and speculative bubbles,

especially in emerging markets. As pointed out in Aharony, Lee, and Wong (2000) and Lee

(2001), because of high transaction costs, opaque information system, and evolving market

structure, China provides better setting to examine speculative bubbles than all other markets of

same economic significance.

III. Trace of Speculative Bubbles

The weekly column "Stocks Recommended by Most Analysts This Week" has been published

on every Monday in Shanghai Securities News since January 10, 2000. This column summarizes

the securities recommended by the largest number of analysts in major security newspapers in

China on former Friday, such as Shanghai Securities News itself, China Securities Journal,

Securities Times, and Information Morning Post. Each security in a column receives somewhat

favorable comments from at least 2 analysts. In a typical column, each security's name is, in

sequence, followed by the number of recommending analysts, analysts' names and short

quotations of their opinions. Most of the opinions are technical analyses with little fundamental

contents. There is scarcely an analyst claiming to have access to inside information.4 These reports

are “coarse and stale.” Market reactions to them are viewed as speculative bubbles by most

observers.

Analysts expend considerable resources to work out the research reports. In a competitive

and rational market, this costly activity must be compensated in the form of underwriting fees,

trading profits, and commissions from trading (Womack (1996)). Analysts publicly disclose their

4 Its original name is "Space of Individual Stocks." The column explicitly presents the names of the newspapers quoted and a statement that the publication of the recommendations in this column does not represent an endorsement by the publisher. The format and newspapers quoted are slightly different over time. In more recent years due to the growth of analyst's industry and the space limitation of the column, only the security recommended by the largest number of analysts is followed by analysts' names and opinions, while others have just their names and the number of recommending analysts reported.

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reports only if the expected benefit of doing so is greater than the cost of reports. In China, the

analysts may have earned such benefits as trading commissions from investors stimulated by their

analyses, good relationship with management to maintain the supply of private information and

investment banking business, and even profits sharing with insiders who realize arbitrage gains

with the aid of analysts. For example, an IPO-company needs good signal on the offering date and

a good analyst report can help. The reports in this study are simply a secondary dissemination of

profitable service provided by analysts to their clients. These reports are stale. Though the

publication time of the newspapers quoted by this column is different, it is certain that they are

publicly available to investors prior to the opening trade on Monday, the event day of our study.

Trading based on this type of information is certainly naïve.

Data on stock prices and financial information are derived from Genius Financial Database.

The sample consists of 5373 recommended security-dates made on 1291 different securities on

239 dates between January 10, 2000 and June 30, 2005. The following criteria are used in the

sample selection process: (1) date is before June 30, 2005; (2) only the first appearance in the

column is included in the sample when a security appears more than once on the same date; (3) the

security is Common A share; (4) the security is not recommended within the preceding and

subsequent two weeks, i.e., the security is not recommended more than twice in one month

centred on the event date; (5) the security has price data in Genius Financial Database both before

and after 28 days, and has more than 2 price data during both the preceding and the subsequent 7

days.

Criterion (1) is used to avoid the huge confounding effect from the stock trading reform

beginning in April 29 and speeding up in September, 2005. All of the non-negotiable shares of

the listed corporations, approximately two thirds of the total shares, became negotiable in the

market. Criterion (2) is used to eliminate double reporting. Criterion (3) excludes stocks

outstanding in Hong Kong (H share), stocks issued only to foreign investors (B share), and funds.

Only A-shares, common stocks traded by Chinese investors in Shanghai and Shenzhen stock, are

used in this study. A-share traders are mostly naïve new investors, good crowd for speculative

bubbles. Criterion (4) is used to reduce confounding effect. Multiple recommendations of the

same firm can be found in the database. The two-week interval is selected because the column is

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published every week and the price response of a recommendation will last for two week

centered on the event date. Criterion (5) excludes those stocks that do not have enough data for

the event study or subject to confounding effect due to IPO and other trading events.

Table 1 Panel A presents the extent to which each criterion affects the sample size. Some

securities are recommended quite often, as many observations (3014/8826=34%) are discarded

after criterion (4) is applied. But with the first time of being recommended (1335 observations)

excluded from the sample before criterion (4), the average time lag with last recommendation is

166 (median 70) calendar days, or more than 5 (median 2) months. It means that the securities in

the sample before criteria (4) and (5) are not repetitively recommended. The time distribution of

the final sample is in Table 1 Panel B.

Insert Table 1 Here

The objective of this paper is to analyze the speculative bubbles set off by analyst

recommendations. But due to the difficulty of identifying the exact publication time, we set the

event day on the column's publication date, i.e., Monday, before which time all information is

certain to be available to investors. Our method is biased against finding trace of bubbles. We

measure bubbles by abnormal returns and abnormal trading volumes. Each security's abnormal

return is calculated by subtracting the daily return on market index from that security's with-right

daily return, using Shanghai (Shenzhen) A-share market index5 for stocks listed on Shanghai

(Shenzhen) Stock Exchange. Specially,

where ARi,t represents the abnormal return of security i on event day t, r i,t represents security i's

with-right daily return on event day t, and rm,t represents market index's return on event day t,

which is market index's close price on day t divided by close price on day t-1 then less one. If

security i is listed on Shanghai (Shenzhen) Stock Exchange, then Shanghai (Shenzhen) A-share

market index is used as the market index. The event window is [-20,+20], i.e., 41 days centred on

the announcement date. Security i's cumulative abnormal return on day t is the sum of abnormal

returns from day -20 to day t, that is,

5In China, there are two stock exchanges, Shanghai and Shenzhen, but no index exists for the whole market.10

where CARi,t represents security i's cumulative abnormal return from day -20 to day t.

We calculate abnormal return by simply subtracting market index's daily return from each

security's daily return because of three reasons. The first is to avoid possible bias from analysts

basing their recommendations on past performance (Greene and Smart (1999)). Secondly, since

many securities are repetitively recommended, as revealed by criterion (4) in Table 1 Panel A, the

period used to estimate beta in market model usually contains other events, which will affect the

estimation accuracy. Thirdly, some empirical evidence suggests that the betas of Chinese listed

companies are approximately equal to one6, which makes beta irrelevant.

Insert Table 2, Figure 1 and Figure 2 Here

The results are presented in Table 2 and Figure 1. Positive abnormal return begins well before

publication. The abnormal return reaches its maximum on day -1, which is the publication day of

the newspapers quoted by this column, with mean of 2.232% and median of 1.851%. Significantly

positive return still exists on day 0, but not as high as day -1. This implies that investors rush to

buy stocks as soon as they are recommended, which results in the shoot in the return. However,

highly significantly negative return (mean of -0.237% and median of -0.461%) appears on day +1

and lasts for quite a number of days. Almost half of the two-day initial rise is reversed in the

subsequent 5 days after announcement. The calculation is: (5.499-6.756)/(6.756-3.677)=-40.8%

for mean, and (4.705-6.108)/(6.108-3.281)=-49.6% for median. Even 10 days after day 0, negative

return still exists and is significant. This pattern is not caused by a few outliers in the sample

because the mean, median and percentage of positive numbers are all consistent.

Figure 2 clearly shows trace of speculative bubbles. After several days' rising and reaching its

peak on day 0, cumulative abnormal return declines dramatically on day +1 and never rebounds

thereafter. This mean-reversion phenomenon is consistent with the price pressure hypothesis, a

sign of speculative bubbles discussed in the literature, such as Barber and Loeffler (1993), Greene

and Smart (1999), Liang (1999), Lin (2000) and Li (2003). It seems that not all price changes are

rational or fully justified by information. Although a lot of returns are reversed, cumulative

abnormal return does not decline to zero, which suggests that the recommendations contain useful

6The reason is that Chinese security market is to a large extent influenced by Chinese government. Thus, stock price changes usually result from the same macroeconomic factors, such as the government's policy.

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fundamental information.

To determine whether trading activity increases around the publication of recommendations,

we analyze trading volumes following the approach used in Harris and Gurel (1986). Each

security's abnormal trading volume is calculated by dividing the ratio of that security's event-time

volume to market index's event-time volume by the median ratio in the 6 weeks preceding the

announcement week then less one. Specially,

where

where AVi,t represents the abnormal trading volume of security i on event day t, Vi,t and Vm,t are the

trading volumes of security i and market index on event day t, respectively, their ratio is VR i,t, and

is the median value of this ratio in the 6 weeks preceding the announcement week, i.e., the

period [-50,-21]. The abnormal volume, AVi,t, is a standardized measure of security's trading

volume on day t, adjusted for market variation to remove the effects of market-wide events on the

individual security's volume. Its expected value is zero if there is no change in volume during

event period relative to the prior 6 weeks.

Insert Figure 3 Here

As Figure 3 present, volume increases greatly around the publication of recommendations,

consistent with the results in Greene and Smart (1999). 7 On average, volume on day -1 is 2.92

(median 1.35) times larger than the median volume during the preceding 6 weeks, and volume on

day 0 is 3.11 (median 1.37) times larger. Similar to the abnormal return, trading volume begins to

increase long before the announcement date, indicating the information is released prior to its

publication. The reason is that analysts publish their reports only after their clients have received

or even benefited from the information (Davies and Canes (1978), Bhushan (1989), Stickel

(1995), Lin (2000)). In addition, volume is above normal level for quite a long time after the

announcement, that is, the trading of recommended securities is active in a long period of time.

7 Numerical results for abnormal trading volumes surrounding the publication of analysts' recommendations are provided upon request.

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This result on dispersive volume movement indicts herding behavior associated with speculative

bubbles.

IV. Driving Forces of Speculative Bubbles

As an emerging market, most investors in China are uninformed and unsophisticated. They

view stock market as casino and make random decisions to obtain speculative profits. They

usually rely on advice from people they believe to have inside information or are capable of

precise prediction, such as professional financial analysts. Analysts' advice is published in

newspapers, which increases its credibility and range of influence. Investors follow these

perceived market gurus whose advice acts as pseudo-signal that investors believe others will

follow. Since investors' trading strategies are based on a common signal, market trading activities

are correlated although each individual trades randomly. In the literature, this is called herding,

defined as a group of investors trading in the same direction over a period of time. Most herding

models suggest that investors follow some common signal (Nofsinger and Sias (1999)). Such

herding leads to aggregate demand shift; some may not be motivated by fundamentals.

A majority of readers of recommendations in newspapers are likely to be uninformed

individuals who rely heavily on advice from others. They therefore trade on sentiment by herding.

Sophisticated investors, such as mutual funds and financial institutions, employ their own analysts

and do not rely on newspapers for investment decisions.

Another reason for the bubble may be the profit-seeking activities of arbitrageurs who know

the fundamental value of stock. When noise traders are herding, it is profitable for arbitrageurs to

walk along with this irrational wave of enthusiasm for a while and then against it. They buy stocks

when prices rise to stimulate the interest of noise traders and then sell out near the top to take the

profits. In the short term, their arbitrage contributes to the movement of prices away from

fundamentals, thus feeds the bubble rather than helps it to dissolve (Shleifer and Summers

(1990)).

Short selling is not allowed in China. Without short sales, an arbitrageur, the fully rational

investor not subject to sentiment, can sell only stocks already in his holdings. To profit from the

overvalued stock, an arbitrageur has to own the recommended stocks prior to the announcement.

In addition, even in cases where informed traders do possess the private information and hold the

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recommended stocks in advance, they are only able to arbitrage to the extent that their capital can

afford. Arbitrage trading certainly requires a large amount of money, which is not easy to obtain in

a rigidly regulated financial system in China. Besides, arbitrage is risky in a world of uncertainty.

It is always possible that future price runs against an arbitrageur's expectation. Moreover, one has

to make his portfolio unbalanced to do arbitrage. The increasing level of idiosyncratic risk can

prevent arbitrage from sustaining itself to bring the overvalued stock back to equilibrium.

Therefore, short-selling forbiddance, capital constraint, and idiosyncratic risk make arbitrage

limited. Together with noise investors' herding and sentiment-based trading, which may probably

be reinforced by arbitrageurs' jumping on the bandwagon, the speculative bubbles caused by price

pressure cannot be completely eliminated by arbitrage on the disclosure date of fundamental

information. The mean-reversion phenomenon in the stock return of the recommended securities

caused by investors' speculative trading is very similar to the January effect caused by individual

investors' tax-motivated selling small stocks in December and buying back in early January (Ritter

(1988)), except that the time interval is much shorter.

Many economic factors can influence the magnitude of the bubbles. Price pressure is referred

to price adjustments resulting from a temporary imbalance between supply and demand for a

security. To resolve the order imbalance, marketmakers may revise quotes to induce some investor

to alter his inventory away from the desired level. Those investors who accommodate demand

shifts are compensated for their immediate liquidity service.

We can list a few important ones as follows.

(1) Liquidity of the recommended security.

Illiquidity reflects the impact of order flow on price-the discount that a seller concedes

or the premium that a buyer pays when executing a market order-that results from

adverse selection costs and inventory costs (Amihud (2002)). If price reaction is caused

by price pressure, larger price responses would be observed for less liquid firms (Barber

and Loeffler (1993)).

Larger firms' stocks are more widely held and thus are more liquid. Small firms are

more vulnerable to temporary demand-shift shocks and thus will have greater runs-up at

announcement and more reversals subsequently. Barber and Loeffler (1993) find that

small size firms have a larger price reaction to analysts' recommendations.

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(2) Proportion of institutional or sophisticated investors in shareholders.

The more sophisticated investors in shareholders, the less noise trading happens, and the

less bubbles are generated.

(3) Strength of the signal on which noise traders rely to make investment decisions.

More analysts recommending the same security is a strong signal, causing more investors

to buy and price to go higher.

Since analysts' recommendations are information-bearing events, we must separate price

pressure from informational effect on returns. The amount of information released is associated

with two economic factors as follows.

(1) Precision of information

The variance-shifting effect of information suggests that return is high when information

is precise.

The number of recommending analysts is indicative of the precision of information.8

(2) Information environment

Firm size measures a firm's information environment, or the number of alternative

sources of relevant information about firm value, 9

Frequency of recommendations measures the attention market pays and the amount of

pre-announcement information dissemination.10

To provide further evidence of existence of price pressure, we closely analyze the abnormal

returns over the event window [-5,+5], i.e., from the preceding one to the subsequent one week.

This short window is chosen for further analysis because most price reactions take place during

this period and extending the window will bring in more noise from other events. The window [-

5,+5] is partitioned into three intervals: [-5,-2], [-1,0] and [+1,+5]. The cumulative abnormal

returns over these three intervals, represented by CAR[-5,-2], CAR[-1,0] and CAR[+1,+5],

measure the market reactions before, at and after the announcement, respectively.

Liquidity is an elusive concept. The literature has used many measures to capture

8 If several analysts recommend the same security simultaneously, that security is more likely to be valuable for investment. Higher returns are expected for stocks recommended by more analysts.9 Many studies find an inverse relation between firm size and information content of earnings announcements and researchers argue that larger firms are followed by more analysts, resulting in more private information exploration. Analysts have incentives to focus on large firms because their stocks are liquid and widely held by many investors. Stimulating the interest of a large number of investors can generate lots of transactions business for analysts' company, which is one of analysts' objectives.

10 The information of frequently recommended securities is gathered and processed frequently before recommendations, and hence the impact of a single information release on the announcement is smaller.

15

unobservable liquidity. Widely used measures are size or market value of stock, volume or

turnover, bid-ask spread, and standard deviation of returns. Amihud (2002) employs daily ratio of

absolute stock return to its dollar volume, averaged over some period, as a measure of illiquidity.

This ratio can be interpreted as the daily price response associated with one dollar of trading

volume, or the daily price impact of the order flow. In this paper, we use market capitalization,

dollar and share volume, standard deviation of returns, and the illiquidity measure in Amihud

(2002) as proxies for liquidity of a security. Bid-ask spread is not used due to unavailability of

microstructure data. The natural logarithm of market value of A shares outstanding (LnAValue) is

used as the proxy for market value of stock by multiplying the median close price during the event

window [-50,-21] by the number of A shares outstanding at the end of the year preceding the

recommendation. The natural logarithm of median dollar volume (LnVolD) and share volume

(LnVolS) during [-50,-21] are proxies for volume. The standard deviation of returns (StdRet) is the

standard deviation of daily raw returns over period [-50,-21]. Following Amihud (2002), stock

illiquidity (Illiq) is measured by the median ratio of the daily absolute raw return to the dollar

trading volume on that day over period [-50,-21]. This ratio gives the absolute price change per

dollar of daily trading volume, or the response of price to order flow.

Data on the proportion of sophisticated investors in shareholders are not available. Since such

sophisticated investors as institutions usually hold lots of shares, shareholding concentration can

serve as a rough measure of the proportion of sophisticated investors in shareholders. The natural

logarithm of the average number of shares held by each A-share account at the end of last year

(LnSPA) is used as a proxy for shareholding concentration. Data on the number of recommending

analysts (NAnalyst) are obtained from the column. The natural logarithm of total assets at the last

year-end (LnAsset) is the proxy for firm size. Since two thirds of shares are non-negotiable and A

shares are negotiable in market, the market value of A shares is a better proxy for market value of

stock and liquidity than the value of total assets. The number of times of being recommended in

the event year (NRec) is chosen as a proxy for the amount of attention paid by market or the

frequency of being recommended. The reciprocal of calendar time lag between this and last

recommendations (InvLag) is the proxy for the amount of pre-announcement information

dissemination. Note that the last two variables (NRec, InvLag) are calculated based on samples

16

before the selection criteria (4) and (5) are applied. Year dummies are used in regressions to

control for fixed time effects.

For robustness, in the correlation and regression analyses, we express each variable in terms

of its ordinal ranking scaled to lie between zero and one. This method is proposed in Chan et al.

(1996) to account for possible nonlinearities in the relation and to allow direct comparison of all

variables which are expressed on a common scale. In consideration of the growth of analyst

industry over time, we calculate the rank values of NRec in each year and NAnalyst on each

recommendation date, to make their ranks comparable across time. The definitions of the variables

used in subsequent analyses are presented in Table 3.

Insert Table 3 Here

One case is eliminated due to lack to complete price data. Our empirical test is based on the

final sample of 5372 security-date observations. Descriptive statistics (not reported here) for

various variables reveal that, the mean (median) of CAR[-1,0] is 3.08% (2.53%). The mean

(median) of CAR[+1,+5] is -1.27% (-1.38%). Approximately half of the two-day run-up is

reversed in the following 5 days.

Cross-correlations of the variables are in Table 4. CAR[-1,0] exhibits significant negative

correlation with CAR[+1,+5], that is, securities with greater on-announcement returns experience

larger reversals later on. Consistent with price pressure hypothesis, on-announcement return

CAR[-1,0] is negatively correlated with liquidity (LnAValue, LnVolD, StdRet, Illiq) and

shareholding concentration (LnSPA), while post-announcement return CAR[+1,+5] is positively

related to liquidity and shareholding concentration. Less liquid securities suffer greater

fluctuation, larger initial rise and larger subsequent fall. Also, as information hypothesis predicts,

on-announcement return is positively correlated with the number of recommending analysts

(NAnalyst), and negatively with frequency of being recommended (NRec) and firm size

(LnAsset). The high correlations among all the liquidity measures indicate that on the whole they

represent the same stock characteristic. A positive relation between firm size and frequency of

being recommended is consistent with the fact documented widely by previous research that

analysts tend to follow large firms in order to get more transactions business. The rank

correlations, though not reported here, reinforce these results.

Insert Table 4 Here

17

Next, we examine the relationships between on- and post-announcement returns and on-

announcement abnormal trading volume and market value of stocks. First, the whole sample is

partitioned into 10 equal-size portfolios by the on-announcement return CAR[-1,0]. If all of the

abnormal return is information driven, the return should be permanent and there will be no post-

event drift. Contrary to this prediction, Table 5 demonstrates that the post-event abnormal returns

of all portfolios are negative. Furthermore, portfolios with larger on-announcement returns have

more negative post-announcement returns, i.e., greater reversals. The F- (Chi-square) statistic for

testing equal means (medians) rejects the null hypothesis that all the portfolio means (medians) are

equal. The t- (z-) statistic of two-sample t test (Wilcoxon rank sum test) indicates that the mean

(median) of the highest on-announcement return portfolio is significantly lower than that of the

lowest portfolio. These results suggest that prices deviate from their equilibrium to higher levels

first and then regress to the equilibrium.

Similarly, we create 10 portfolios based on the two-day on-announcement abnormal volume

CAV[-1,0]. The relationship between return and trading volume is studied because the information

and price pressure hypotheses have different implications about their correlation. Information

hypothesis asserts that trading volume arises from heterogeneous beliefs prior to information

arrival or different understanding of the same information. It predicts differential on-

announcement returns in the trading volume portfolios, but no post-event reversals. In contrast, the

existence of price pressure implies a greater initial price run-up and subsequent reversal in the

portfolios with higher trading volume.

The results in Table 5 support the predictions of price pressure hypothesis. The on-

announcement return and subsequent reversal both increase with on-announcement volume. The

highest volume portfolio has significantly larger initial return and subsequent reversal than the

lowest volume portfolio. This evidence is similar to the one documented by Barber and Loeffler

(1993) and Greene and Smart (1999). Finally, the whole sample is sorted into 10 portfolios by the

market value of stocks (LnAValue), which measures a security's liquidity or its sensitivity to

temporary demand impulse. Although market value can also be a proxy for firm size and measure

information environment and thus a negative correlation between market value and on-

announcement return is predicted under the information hypothesis, there should not be post-event

18

reversals. The results of Table 5 reveal that, against information hypothesis but as price pressure

hypothesis predicts, all portfolios have negative returns after announcement, more negative for

small firms and less for large firms. In a word, the mean-reversal of returns is common in the

sample because it exists in each portfolio formed by several different classification methods. The

relations between post-event reversal and initial price rise, on-announcement trading volume, and

market value of stocks all support the price pressure hypothesis.

Insert Table 5 Here

To determine whether the speculative bubbles change over time, we analyze the time factor.

Each year's returns and the return differences between every two adjacent years are reported in

Table 6. It is apparent that the magnitude of speculative bubbles declines gradually over time, a

sign of learning effect. In 2000, the first sample year, the two-day on-announcement return is as

high as 7%. It falls much in 2001 to about 3%, and drops in every subsequent year though not so

sharply, and to only 2% in 2005, the last sample year. There are two possible explanations. One is

information environment change. As time passes, investors have more and more alternative

sources of information, and rely less on analysts' recommendations, so that the effect of a single

piece of advice on price is smaller. Growth of analyst industry and media, and improvement in

disclosure regulation can all enlarge investors' approaches to information. The other reason is

investors' learning. In the beginning, analyst advice columns are new to investors who have great

expectations for the profits these columns can bring to them. This enthusiasm induces a great

many investors to buy the recommended stocks, resulting in prices rising beyond what

fundamental information supports. Price overreaction and hence the subsequent reversal make

analyst columns fall short of investors' expectations. Some late-buying investors even suffer losses

by following their advice. Such unpleasant experience keeps these investors from doing it second

time. Thus, the initial enthusiasm fades over time. Consistent with the latter explanation, the year-

median of post-event reversal is, in general, also decreasing with time, largest in year 2000 and

second smallest in 2005. But the year-mean of post-event return does not have a quite clear

relation with time, maybe due to some extreme values. Since the initial runs-up in all years are

larger than the subsequent reversals, information environment change is also one cause of the

over-time decline of on-announcement return.

19

Insert Table 6 Here

Learning effect reduces noise trading in later time. Indeed, some noise traders can wise up to

their errors evolving into rational investors. Others may fade away. However, investors in the

market are changing all the time. As old noise traders disappear, those new ones entering the

market are subject to the same biases. Hence, noise traders as a group do not disappear from the

market in an emerging market.

To test whether the initial price response is at least partially driven by price pressure from

buying, we run regressions of on- and post-announcement returns on several liquidity measures,

controlling for the informational effect on returns. If price pressure exists, larger on-announcement

return and larger subsequent reversals will be observed for less liquid securities. Market value of

stocks outstanding, dollar volume, daily ratio of absolute stock return to dollar volume, and

standard deviation of raw returns are used as proxies for liquidity of a security. To avoid potential

multicollinearity, the former three measures are respectively rather than together regressed with

the fourth one. Shareholding concentration measures the proportion of sophisticated investors in

shareholders. The number of simultaneously recommending analysts, the number of times (dates)

of being recommended in this year, total assets, and year dummies are explanatory variables

chosen as proxies for the arrival of information, information environment, and fixed time effects.

Table 7 reports the regression results. The coefficients on the four liquidity measures are all

statistically significant and have signs consistent with the hypothesis that less liquid securities'

prices suffer greater swings around announcements. The two-day on-announcement return

decreases with market value of stocks outstanding and dollar volume, but increases with the daily

ratio of absolute stock return to dollar volume and standard deviation of raw returns. This result is

consistent with that documented by Barber and Loeffler (1993), who find that less liquid firms

have a larger on-announcement price reaction to analysts' recommendations. Contrarily, the post-

announcement return has just the opposite relationships with these variables. This evidence

strongly supports the hypothesis that, at announcement, buying pressure causes stock price to

deviate from its full-information value so as to induce some passive demanders to offer their

shares and resolve the temporary order imbalance. Less liquid securities are more sensitive to

short-run demand shifts, and thus have more fluctuation around such event, greater initial price

20

run-up and larger subsequent reversal. The positive relation between post-announcement return

and shareholding concentration is consistent with our prediction that more sophisticated investors

in shareholders will produce less noise trading. But on-announcement return also increases with

shareholding concentration, inconsistent with our prediction.

Insert Table 7 Here

The regression results also suggest that recommendations embody valuable information. In

all regressions of on-announcement return, the coefficients on the number of recommending

analysts are significantly positive; while none of the coefficients are significant in regressions of

post-event return. Consistent with the information hypothesis, on-announcement return is

negatively related to frequency of recommendations, firm size and time, indicating that

infrequently recommended securities, small firms, and recommendations in anterior years have a

larger on-announcement price reaction; while post-event drift has no relationship with any of these

variables, suggesting that the relative excess returns over their corresponding counterparts, that is,

frequently recommended stocks, large firms, and advice in posterior years, are warranted by

fundamentals and thus not retracted subsequently. In the regressions of on-announcement return,

the coefficients on the year dummies, though all significantly positive, decrease with time, parallel

with the improvement in information environment over time. A comparison of the regressions of

on- and post-announcement returns reveals that, both price pressure and information have impact

on initial return at announcement, while post-event drift is only related to price pressure. The

regression results provide supports for both the price pressure and information hypotheses. In fact,

these two hypotheses are not mutually exclusive.

As sensitivity tests, we express each variable, except dummies, in terms of its ordinal ranking

scaled to fall between zero and one, and then run the regressions again. The rank regression

results, presented in Table 8, reinforce all the above results, with only a few exceptions. First, the

relation between shareholding concentration and on-announcement return, though still positive, is

not significantly different from zero. Second, unlike the insignificant relations in the level

regressions, post-event return significantly decreases in the number of recommending analysts,

and increases with frequency of recommendations. The securities recommended by many analysts

or for many times are associated with large initial price runs-up followed by large reversals. It

21

seems that these variables are also correlated with price pressure. However, this may not be just

coincidence if we make the conjecture that the intensity of price pressure is positively related to

the amount of information released by the recommendation. It is possible that, more information

released at announcement will stimulate more investors to engage in speculative trading, and

produce more price pressure around the announcement date. The more information contained in

the recommendation, the more intense price pressure will be. Besides, the number of

simultaneously recommending analysts can also measure the strength of the signal relied on by

noise traders to take actions. More analysts recommending the same security means a stronger

signal. In addition, price pressure results from some investors' judgment biases. Hence, less

frequently recommended securities are more difficult to evaluate due to information scarceness,

and thus the bubble caused by noise trading is probably larger.

Insert Table 8 Here

Before leaving this section, we would like to touch the issue of price manipulation. If price

rise is caused by pure manipulation, it will eventually revert to its former level. The price reversals

of our sample are on average less than the prior runs-up. Hence, our results are not driven by pure

manipulation. The analysts in our sample do provide useful information.

Price manipulation can be done by brokerage houses themselves. As member of emerging

profession, many stock brokers in China do not have their own core competence. Their services to

unsophisticated customers are simply buy/sale recommendation. To keep their clients and keep

them trading, some brokers manipulate price of the stock they recommend. After advising their

clients to buy a certain stock, they boost the price using large amount of capital and withdraw

afterward causing a clash. In other cases, analysts may collude with wheel-dealers, helping them

to gain at the expense of noisy traders. Analyst and dealers can collude to generate speculative

bubbles. Since regulation and enforcement in China are still relatively loose, such fraudulent

activities can exist.

In 2005, China Securities Regulatory Commission issued a new regulation: brokerage houses

and analysts are prohibited from explicitly or implicitly assuring investors of any investment

profits, from colluding with affiliated entities to manipulate stock prices, from providing relevant

information to affiliated entities before making evaluations, predictions or recommendations, from

making any recommendations that are in the interest of affiliated entities but at the expense of

22

other investors, and so on. The stipulation of regulation proves the existence of those misdeeds

within, because regulations are often done under pressure of public complaints. However, a direct

test of price manipulation by brokers is difficult.

V. Behavior of Noise Traders and Institutional Traders

In China, institutional traders can trade among themselves after the market is closed whereas

individuals can trade only when the market is open. Hence trading activities during the market

close indicate the trading activities of institutional traders only. The activities of noise traders can

only be found during the intra day.

In Table 9 and Figure 4, intra-day return is a security's close price divided by its open price of

the same day then minus market index's close/open price ratio. Overnight return is a security's

open price divided by its last day's close price then minus market's overnight change of price.

Inter-day return is a security's open price divided by its previous day's open price then minus

market's open price change. Table 9 shows that it is profitable (mean 0.365% and median 0.218%)

to buy in at the open price on day 0 (Monday) then sell out at the open price on day 1 (Tuesday).

In Figure 4, the figures on the horizontal axis represent the event days relative to the

announcement day. The vertical bars on integers (i.e., -1, 0, 1, etc.) represent intra-day returns. The

intra-day trading can be done by both dealers and naïve traders. Bars on half-integers (i.e., -0.5,

0.5, etc.) are overnight returns between previous day's close price and the subsequent day's open

price. Overnight trading can only be done by dealers. We can see that when institutional traders

conduct business among themselves, the expected profits are zero. When noise traders conduct

business with institutional traders, the expected profits are negative. The largest expected loss

takes place when the noise traders conduct business with institutional traders on the day before the

publication of analyst recommendation, where some institutional traders can have the information

but no noise traders can.

Time -2.0, -1.0, These trading profits can be decomposed into two components: the trading

between institutional investors and the trading between institutional investors and noise traders.

This result is consistent with that in Greene and Smart (1999), who analyze the post-

publication trading of the securities recommended in the Dartboard column contests using intraday

23

data. They find that most event-day abnormal returns are realized within the first hour of trading,

and thus traders following the column's advice would not earn abnormal profits unless their orders

were executed very early in the day, which is one piece of evidence suggesting that post-

announcement trading is primarily noise.

Insert Table 9 and Figure 4 Here

The above analyses reveal that investors cannot get any profit if the recommended stocks are

bought in late on day 0 (Monday) or sold out late on day 1 (Tuesday). Does this result change

when more analysts are recommending the same security? To investigate whether the timing

strategy differs when the securities in consideration are recommended by different number of

analysts, we partition the sample into 5 equal-size portfolios based on the number of analysts

recommending the security. As Figure 4 shows, overnight and intra-day returns are all negative

across the 5 analyst portfolios after day 0. Further, securities recommended by more analysts

suffer greater depreciation after announcement. Consequently, late action in the securities

recommended by more analysts will bring greater losses to investors rather than any profits. The

evidence is consistent with the hypothesis that analysts' advice induces intensive noise trading by

naive individual investors, resulting in bubble burst.

Insert Figure 5 HereFigure 5 provides an intuitive expression of the bubble busting. Specifically, we partition the

sample into 5 equal-size portfolios based on the on-announcement rise CAR[-1,0] and then

examine the post-announcement drift for each portfolio. To lessen the impacts of any outliers in

returns, we use the median value of returns for each portfolio in the plot. As Figure 5 reveals,

abnormal returns are all negative in the 5 portfolios. Furthermore, returns are getting more

negative when cumulated over time, especially for those securities with greater initial rise on

announcement, which supports the hypothesis that investors overreact on announcements of stock

recommendations.

Table 5 and Figure 5 together show that when more analysts are involved in recommendation

a stock, the bigger is the bubble and the more spectacular is the bubble burst. Hence analyst

pumping can affect noise traders’ sentiment. An alternative explanation is that when an event is

more complicated, more analysts will devote time to analyze the issue. The higher price pressure

is thus generated to compensate the informed traders for their information gathering ability and

24

effort. This study cannot discriminate these two possibilities.

VI. Conclusions

Financial analysts, as information intermediaries, play an important role in the allocation of

resources in capital markets. Analysts have to make an effort to work out financial analysis

reports. Some even get access to private information from management. The considerable costs

associated with these activities are all cost of information, which implies that price does not reflect

all available information and analysts are necessary to the market. Analysts' costly activities are

compensated by underwriting fees, security trading commissions, and profits from investment

advice or other service they provide to clients. Analysts sometimes publicly disclose their reports

to stimulate trading or advertise for their costly advice services. The published report is probably

just a by-product or a secondary dissemination of some profitable service analysts provide to their

clients. Whether such a report contains information is empirically examined in this paper.

Using the data of the weekly column "Stocks Recommended by Most Analysts This Week" in

Shanghai Securities News from 2000 to 2005, we find that there is market reaction surrounding its

publication. Trading volume is above normal around the announcement day. Positive market-

adjusted abnormal returns begin very early before publication, indicating that there is information

leakage and some insiders, such as analysts' clients, have received information and taken

advantage of it. The average abnormal return reaches its maximum of 2.232% on day -1, the

publication day of the newspapers quoted by this column. However, not all of this return is

supported by fundamentals because we observe significant and long-lasting return reversals since

day +1. Almost half of the two-day initial rise is reversed in the subsequent 5 days after

announcement. This bubble phenomenon exists in each partitioned sample. Besides, the securities

with greater initial price rise suffer greater subsequent reversals. The securities with greater on-

announcement trading volume or with lower market value have larger initial price rise followed by

larger retractions. After controlling for the informational effect on returns, we find that, the initial

price rise and subsequent reversal both are positively correlated with the liquidity of a security,

measured by market value of stocks outstanding, dollar volume, daily ratio of absolute stock

return to dollar volume, and standard deviation of raw returns. These results all suggest that price

pressure exists around recommendation announcements.

25

Readers of this column are mostly uninformed individual investors who rely on analysts'

advice to make investment decisions. These investors are more subject to estimation biases or

impact of sentiment. With this irrationality in judgment, they follow analysts' recommendations

with great enthusiasm. These naive traders intensively buy and sell the recommended securities,

resulting in abnormal trading around announcement. Their overly optimistic herding drives prices

away from fundamental values. The price deviation is possibly reinforced by the speculation or

even wheel-dealing activity of some informed investors who intend to stimulate others' interest

and gain a profit. Moreover, arbitrage, limited in the short run by short-selling forbiddance, capital

constraint and risk, cannot eliminate all of this arbitrage opportunity. For these reasons, price

retraction occurs subsequently as it eventually reverts toward fundamental value. It is certain that

some naive investors lose in this process. Although investors can learn over time, learning does

not get all noise traders to disappear as new investors are entering the market all the time.

Noise traders' intensive trading in the same direction causes a temporary imbalance between

supply and demand for a security, referred to commonly as price pressure. To resolve this order-

flow imbalance, price is adjusted to induce some investors to accommodate the demand shift by

immediately buy or sell shares which they otherwise will not trade. These passive suppliers of

liquidity are compensated for their liquidity service when price regresses to its full-information

level (Harris and Gurel (1986)). Speculative bubbles are more prominent in less liquid securities

because they are less actively traded and thus more sensitive to such demand shift and order

imbalance in the short term. Therefore, less liquid securities experience more price swings around

announcements.

Though speculative bubbles exist, our results do not reject the information hypothesis that

analyst recommendations embody valuable information. The mean reversion is not complete, that

is, initial price rise is not fully offset by subsequent reversal, so analysts' advice has a long-lived

impact on price. Those securities with abundant information from alternative sources or paid more

attention to by the market yield lower on-announcement returns than those with rare information.

We also find that, traders following the column's advice cannot earn abnormal profits unless their

orders are executed very early. They have to take actions quickly; otherwise, they will incur a loss

rather than gain a profit.

26

Speculative bubbles may also exist in other situations in China. An example is IPO in

Chinese security market. Xiu (2002) documents that secondary-market price and turnover ratio of

IPO firms decline after the first day of public offering, especially sharply in the initial a few days,

and that the downward movement is long-lasting, indicating that the aim of investors' purchasing

new stocks is to get speculative profits. Since Chinese government strictly controls the IPO

process, the issuing price of IPO is restricted by government in a narrow low range, which results

in the striking abnormal return on the first day of public offering (Li and Xiu (2006)). It is widely

known that the price will rise on the first day of IPO and purchasing newly-issued stocks can

certainly get profits. Under its guide, all investors dart to buy as long as there are newly-issued

stocks. However, this trend chasing behavior is probably beyond rationality and thus leads to the

movement of prices away from fundamentals. Since the institutional background of Chinese

capital market is different from that of developed market, this difference will certainly induce

different behavior of participators. Whether price pressure and bubbles exist in situations other

than the one addressed in this paper is an interesting issue worthy of future research.

27

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29

Table 1 SampleThe sample collection is based on the column "Most Popular Stock This Week" in Shanghai Securities News between January 10, 2000 and June 30, 2005. Data on stock prices and financial information are derived from Genius Financial Database.

Panel A: Sample Selection ProcessSample Selection Criterion Number of SamplesTotal data available before June 30, 2005 8826Less:Data with error 1Data which is not related to an A-share firm 13Data with recommendation more than once in given month

3014

Data without enough price observation 425Final Sample size 5373

Panel B Time Profile of Sample

Year Jan Feb Mar Apr May

Jun Jul Aug

Sep Oct Nov

Dec Total

%

2000

18 19 36 32 28 27 36 31 33 24 39 31 354 6.59

2001

10 40 37 87 28 89 75 68 56 26 46 145 707 13.16

2002

69 27 73 93 54 75 123 93 72 58 91 82 910 16.94

2003

79 57 124 87 42 111 83 69 77 48 66 143 986 18.35

2004

95 113 153 163 88 68 125 163 170 62 214 138 1552

28.89

2005

192 110 178 116 139 129 0 0 0 0 0 0 864 16.08

Total

463 366 601 578 379 499 442 424 408 218 456 539 5373

100

% 8.62

6.81

11.19

10.76

7.05 9.29

8.23

7.89

7.59

4.06

8.49

10.03

100

30

Table 2 Market Reactions to Analysts Recommendation: Abnormal ReturnsThe table presents abnormal returns and cumulative abnormal returns surrounding the publication of analysts' recommendations. Abnormal returns (AR) are calculated by subtracting market index's daily return from each security's daily return. Cumulative abnormal returns (CAR) are calculated from day -20 through day +20. Event window is [-20,+20], i.e., 41 days centred on the publication date.

Event Day

NAR CAR

Mean Median

t-stat Percent Positive

Mean

Median

t-stat Percent Positive

-20 5365

0.093 -0.010 3.47*** 0.497 0.093

-0.010 3.47*** 0.497

-15 5371

0.022 -0.040 0.82 0.491 0.359

0.173 5.55*** 0.523***

-10 5372

0.209 0.082 7.68*** 0.523***

0.817

0.642 9.38*** 0.556***

-5 5372

0.456 0.196 15.33*** 0.554***

1.669

1.447 15.98***

0.592***

-4 5372

0.546 0.299 18.86*** 0.587***

2.214

1.884 20.48***

0.626***

-3 5373

0.553 0.237 19.49*** 0.571***

2.767

2.418 24.78***

0.653***

-2 5373

0.910 0.535 29.64*** 0.644***

3.677

3.281 32.03***

0.689***

-1 5373

2.232 1.851 61.25*** 0.829***

5.909

5.394 49.83***

0.787***

0 5373

0.847 0.528 25.20*** 0.621***

6.756

6.108 53.92***

0.804***

1 5373

-0.237

-0.461 -8.15*** 0.376***

6.519

5.841 50.74***

0.782***

2 5373

-0.291

-0.427 -10.52***

0.390***

6.228

5.521 48.19***

0.771***

3 5373

-0.222

-0.324 -7.59*** 0.410***

6.006

5.247 45.22***

0.757***

4 5373

-0.456

-0.417 -17.20***

0.379***

5.549

4.837 41.67***

0.740***

5 5373

-0.050

-0.121 -1.78* 0.468***

5.499

4.705 40.11***

0.726***

10 5373

-0.040

-0.078 -1.40 0.480***

5.000

4.381 34.56***

0.698***

15 5373

-0.065

-0.097 -2.44** 0.470***

4.674

3.883 29.91***

0.660***

20 5372

-0.043

-0.091 -1.64 0.474***

4.317

3.383 26.55***

0.641***

31

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectivelyValues of AR and CAR are in percentage. The t-statistic is based on the null hypothesis that the mean is zero.

32

Table 3 Definitions of VariablesThe table presents the definitions of variables used in the subsequent analyses and the calculations of their ranks. The year t is the recommendation year. Each variable's rank is its ordinal ranking scaled to lie between zero and one.

Variable DefinitionCAR[a,b]

Bubble in prices, cumulative abnormal return over the event window [a,b]

CAV[a,b] Bubble in trading activity, cumulative abnormal trading volume over the event window [a,b]

LnAValue

Size measure, the natural logarithm of market value of A shares outstanding, calculated by multiplying the median close price during the event window [-50,-21] by the number of A shares outstanding at the end of year t-1

LnVolD Size measure, the natural logarithm of median dollar volume during the event window [-50,-21]

LnVolS Size measure, the natural logarithm of median share volume during the event window [-50,-21]

StdRet Volatility measure, the standard deviation of daily raw returns during the event window [-50,-21]

Illiq Illiquidity measure, the median ratio of the daily absolute raw return to the dollar trading volume on that day during the event window [-50,-21]

LnSPA Shareholding concentration measure, the natural logarithm of the average number of shares per A-share account at the end of year t-1

LnAsset Size measure, the natural logarithm of total assets at the end of year t-1NAnalyst Analyst pumping measure, the number of simultaneously recommending analysts,

rank calculated on each recommendation dateNRec Analyst pumping measure, the number of times (dates) of being recommended in

year t, calculated based on samples before the selection criteria (4) and (5) are applied, rank calculated in each year

InvLag Analyst pumping measure, the reciprocal of calendar time interval between this and last recommendations, with value of 0 for the first time of being recommended, calculated based on samples before the selection criteria (4) and (5) are applied

Yj year dummies, with value of 1 if the recommendation is in year j and 0 otherwise, j=2000, 2001, ..., 2004

33

Table 4 Cross-Correlation among VariablesThe table presents the correlation values among variables for analyses. Spearman (Pearson) correlations are above (below) the diagonal.

CAR[-1,0] CAR[+1,+5] LnAValue LnVolD StdRet Illiq LnSPA NAnalyst NRec LnAssetCAR[-1,0] -0.132 -0.101 -0.044 0.051 0.065 -0.084 0.136 -0.144 -0.176CAR[+1,+5] -0.075 0.099 0.075 -0.079 -0.106 0.060 -0.031 0.071 0.075LnAValue -0.115 0.090 0.633 -0.219 -0.729 0.102 -0.085 0.332 0.657LnVolD -0.049 0.073 0.667 0.182 -0.928 0.142 0.102 0.347 0.374StdRet 0.081 -0.050 -0.193 0.203 0.098 0.014 0.128 0.028 -0.159Illiq 0.055 -0.116 -0.584 -0.719 0.049 -0.129 -0.036 -0.335 -0.428LnSPA -0.075 0.052 0.191 0.183 0.002 -0.086 0.111 0.086 0.098NAnalyst 0.202 -0.012 -0.042 0.111 0.093 0.014 0.059 0.092 0.052NRec -0.137 0.046 0.415 0.412 0.007 -0.252 0.123 0.075 0.312LnAsset -0.169 0.073 0.709 0.450 -0.164 -0.320 0.180 0.045 0.430Correlations significant at the 5 percent level, two-tailed, appear in bold. Variable definitions appear in Table 4. Security-dates without complete price data during the event window [-5,+5] are discarded. The statistics are based on a final sample of 5372 security-date observations.

34

Table 5 Trace of Speculative Bubbles The table presents on- and post-announcement returns of portfolios created by sorting the whole sample by several variables, including CAR[-1,0], CAV[-1,0], and LnAValue, and the differences between the lowest and highest value portfolios. Values are in percentage.

CAR[-1,0] CAR[+1,+5]Category Mean Median Mean MedianPanel A: CAR[-1,0]Lowest -2.437 -1.884 -0.177 -0.527Decile 2 -0.270 -0.261 -0.807 -0.824Decile 3 0.663 0.679 -0.722 -0.978Decile 4 1.413 1.418 -1.240 -1.104Decile 5 2.153 2.135 -1.489 -1.378Decile 6 2.889 2.894 -1.548 -1.555Decile 7 3.726 3.724 -1.646 -1.756Decile 8 4.768 4.750 -1.665 -1.667Decile 9 6.463 6.389 -1.734 -1.981Highest 11.397 10.639 -1.638 -2.268F-stat/Chi-sq 6082.90*** 5317.29*** 7.12*** 95.09***Low-High -91.21*** -28.37*** 4.07*** 5.79***Panel B: CAV[-1,0]Lowest 1.192 0.793 -0.578 -0.710Decile 2 1.371 1.149 -0.583 -0.640Decile 3 1.849 1.568 -1.061 -1.161Decile 4 2.243 2.002 -1.410 -1.399Decile 5 2.544 2.474 -1.453 -1.351Decile 6 3.229 2.856 -1.653 -1.881Decile 7 3.267 3.118 -1.198 -1.385Decile 8 4.028 3.378 -1.367 -1.800Decile 9 4.960 4.405 -1.720 -1.803Highest 6.100 5.397 -1.672 -1.799F-stat/Chi-sq 106.90*** 866.72*** 4.49*** 63.74***Low-High -19.79*** -18.27*** 3.77*** 4.70***Panel C: LnAValueLowest 3.626 2.744 -2.035 -1.961Decile 2 3.783 3.147 -1.409 -1.337Decile 3 3.207 2.693 -1.720 -1.933Decile 4 3.272 2.880 -1.569 -1.660Decile 5 3.367 2.914 -1.225 -1.520Decile 6 3.053 2.446 -1.428 -1.490Decile 7 3.202 2.690 -1.320 -1.370Decile 8 2.811 2.364 -0.978 -1.294Decile 9 2.563 2.156 -0.638 -1.085Highest 2.244 1.604 -0.580 -0.777

35

F-stat/Chi-sq 7.58*** 71.65*** 5.14*** 56.85***Low-High 5.43*** 4.70*** -4.39*** -5.23***

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectively. Security-dates without complete price data during the event window [-5,+5] are discarded. The statistics are based on a final sample of 5372 security-date observations. Variable definitions appear in Table 4.The F-statistic of analysis of variance is based on the null hypothesis that all the portfolio means are equal. The Chi-square statistic of Kruskal-Wallis test is based on the null hypothesis that all the portfolio medians are equal. The t-statistic of two-sample t test is based on the null hypothesis that two portfolio means are equal. The z-statistic of Wilcoxon rank sum test is based on the null hypothesis that two portfolio medians are equal.

36

Table 6 Trace of Speculative Bubbles：Evolutions over the YearsThe table presents abnormal returns surrounding the publication of analysts' recommendations (CAR[a,b]) for each year, and the differences between every two adjacent years. Values are in percentage. Variable definitions appear in Table 4.

CAR[-5,-2] CAR[-1,0] CAR[+1,+5]Year N Mean Median Mean Median Mean Median2000 354 4.310 3.323 7.711 6.896 -1.113 -1.8222001 707 2.532 2.039 3.295 2.859 -0.813 -0.9352002 909 1.827 1.584 3.095 2.578 -1.289 -1.4652003 986 1.537 1.171 2.832 2.547 -1.440 -1.5712004 1552 2.894 2.097 2.568 2.026 -1.342 -1.3912005 864 2.609 2.144 2.169 1.743 -1.344 -1.301F-stat 29.29 *** 121.66 *** 133.48 *** 454.96 *** 1.89 * 22.0 1*** Year-Pair Difference00-01 5.32*** 4.54*** 15.26*** 15.40*** -0.78 -3.44***01-02 3.95*** 4.14*** 1.20 1.46 2.55** 3.80***02-03 1.80* 2.65*** 1.81* 1.31 0.87 0.9003-04 -8.24*** -7.37*** 1.91* 3.37*** -0.56 -0.8204-05 1.39 -0.61 2.46** -2.27** 0.01 0.33

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectivelyThe F-statistic of analysis of variance is based on the null hypothesis that all the portfolio means are equal. The Chi-square statistic of Kruskal-Wallis test is based on the null hypothesis that all the portfolio medians are equal. The t-statistic of two-sample t test is based on the null hypothesis that two portfolio means are equal. The z-statistic of Wilcoxon rank sum test is based on the null hypothesis that two portfolio medians are equal. Events without complete price data during the event window [-5,+5] are discarded. The statistics are based on a final sample of 5372 security-date observations.

37

Table 7 Driving Forces of Speculative Bubbles, Ordinary Regression ResultsThe regressions report the effect of liquidity, size, and intensity of analyst following on speculative bubbles. Variable definitions appear in Table 4.

CAR[-1,0] CAR[+1,+5]Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4

Intercept 0.11926 0.04202 -0.02262 0.04784 -0.10488 -0.07245 -0.02744 -0.04491(7.23***) (4.06***) (-2.55**) (3.21***) (-4.94***) (-

5.44***)(-2.42**) (-2.35**)

LnAValue

-7.295e-3 - - - 3.384e-3 - - -

(-8.93***) (3.21***)LnVolD - -4.684e-3 - - - 2.501e-3 - -

(-8.38***) (3.47***)Illiq - - 1.025e+6 0.792e+6 - - -1.222e+6 -1.164e+6

(7.15***) (5.33***) (-6.65***)

(-6.10***)

StdRet 0.23860 0.44511 0.31848 0.26323 -0.21839 -0.32145 -0.23888 -0.22518(4.28***) (7.95***) (5.81***) (4.75***) (-3.04***) (-

4.46***)(-3.40***)

(-3.16***)

LnSPA 3.376e-3 2.858e-3 1.924e-3 2.363e-3 3.605e-3 3.694e-3 3.391e-3 3.282e-3(3.22***) (2.75***) (1.88*) (2.31**) (2.66***) (2.76***) (2.59***) (2.50**)

NAnalyst 3.606e-3 3.659e-3 3.573e-3 3.603e-3 -0.535e-3 -0.566e-3 -0.526e-3 -0.534e-3(12.52***)

(12.68***)

(12.37***)

(12.51***)

(-1.44) (-1.52) (-1.42) (-1.44)

NRec -0.578e-3 -0.659e-3 -1.223e-3 -0.652e-3 0.634e-3 0.595e-3 0.549e-3 0.407e-3(-2.03**) (-2.32**) (-4.64***) (-2.33**) (1.73*) (1.63) (1.63) (1.13)

LnAsset - - - -3.410e-3 - - - 0.846e-3(-5.87***) (1.13)

Y2000 54.174e-3 55.341e-3 53.275e-3 51.224e-3 2.268e-3 1.223e-3 -1.045e-3 -0.536e-3(20.78***)

(20.79***)

(20.30***)

(19.41***)

(0.68) (0.36) (-0.31) (-0.16)

Y2001 22.428e-3 21.246e-3 20.569e-3 18.613e-3 3.970e-3 4.198e-3 2.006e-3 2.491e-3(10.90***)

(10.46***)

(10.11***) (9.06***) (1.50) (1.61) (0.77) (0.94)

Y2002 23.829e-3 21.628e-3 22.293e-3 21.060e-3 -1.442e-3 -0.577e-3 -2.836e-3 -2.530e-3(12.12***)

(11.25***) (11.43***) (10.77***)

(-0.57) (-0.23) (-1.14) (-1.01)

Y2003 19.320e-3 18.302e-3 18.779e-3 18.158e-3 -3.655e-3 -3.316e-3 -5.135e-3 -4.981e-3(10.36***)

(9.89***) (10.04***)

(9.72***) (-1.52) (-1.39) (-2.14**) (-2.08**)

Y2004 6.088e-3 6.454e-3 6.430e-3 5.598e-3 -0.728e-3 -0.999e-3 -1.922e-3 -1.716e-3(3.86***) (4.07***) (4.04***) (3.52***) (-0.36) (-0.49) (-0.94) (-0.84)

38

Adj. R-sq 0.1568 0.1551 0.1519 0.1576 0.0113 0.0116 0.0181 0.0181F-stat 91.91*** 90.79*** 88.56*** 84.18*** 6.57*** 6.74*** 9.99*** 9.20***

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectivelyThe t-statistics are in parentheses below the corresponding coefficients. Events with missing data, and without complete price data during the event window [-5,+5] are discarded. The regressions are based on a final sample of 4891 security-date observations.

39

Table 8 Driving Force of Speculative Bubbles, Rank Regression ResultsThe table presents the coefficients, t-statistics, adjusted R-square, and F-statistics for the rank regressions.

CAR[-1,0] CAR[+1,+5]Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4

Intercept 0.39190 0.36920 0.26923 0.35440 0.47010 0.48011 0.54495 0.52657(20.76***)

(19.59***)

(11.32***)

(12.76***)

(23.62***) (24.26***)

(21.80***)

(17.98***)

LnAValue

-0.13580 - - - 0.05403 - - -

(-8.62***) (3.25***)LnVolD - -0.09832 - - - 0.06052 - -

(-6.17***) (3.62***)Illiq - - 0.11324 0.06953 - - -0.07328 -0.06385

(7.17***) (4.00***) (-4.42***) (-3.48***)StdRet 0.02840 0.07167 0.04302 0.03121 -0.07406 -0.09478 -0.07674 -0.07420

(1.97**) (5.01***) (3.04***) (2.19**) (-4.88***) (-6.30***) (-5.15***) (-4.93***)LnSPA 0.02462 0.01743 0.02136 0.01389 0.05486 0.05319 0.04999 0.05160

(1.64) (1.16) (1.42) (0.92) (3.48***) (3.37***) (3.16***) (3.25***)NAnalyst 0.15960 0.15972 0.15878 0.16024 -0.02753 -0.02738 -0.02675 -0.02706

(10.58***)

(10.54***)

(10.50***)

(10.63***)

(-1.73*) (-1.72*) (-1.68*) (-1.70*)

NRec -0.04643 -0.06011 -0.05419 -0.04391 0.05277 0.05028 0.04530 0.04309(-2.97***) (-3.82***) (-3.45***) (-2.79***) (3.20***) (3.05***) (2.75***) (2.59***)

LnAsset - - - -0.09493 - - - 0.02049(-5.89***) (1.21)

Y2000 0.39171 0.39127 0.39893 0.36761 -0.02965 -0.03743 -0.04354 -0.03678(20.08***)

(19.57***)

(19.90***)

(17.79***)

(-1.44) (-1.78*) (-2.07**) (-1.69*)

Y2001 0.14703 0.13134 0.13826 0.11312 0.03503 0.03584 0.03057 0.03600(9.38***) (8.45***) (8.85***) (7.01***) (2.12**) (2.20**) (1.86*) (2.11**)

Y2002 0.11768 0.09903 0.10393 0.09011 -0.01086 -0.00592 -0.00945 -0.00646(8.36***) (7.17***) (7.50***) (6.43***) (-0.73) (-0.41) (-0.65) (-0.44)

Y2003 0.09622 0.08711 0.09076 0.08304 -0.03171 -0.03017 -0.03284 -0.03117(7.07***) (6.42***) (6.68***) (6.10***) (-2.21**) (-2.12**) (-2.30**) (-2.17**)

Y2004 0.05169 0.05038 0.05195 0.04739 -0.00925 -0.01169 -0.01313 -0.01215(4.31***) (4.16***) (4.30***) (3.92***) (-0.73) (-0.92) (-1.03) (-0.95)

Adj. R-sq 0.1195 0.1130 0.1154 0.1214 0.0205 0.0210 0.0222 0.0223F-stat 67.34*** 63.28*** 64.78*** 62.45*** 11.21*** 11.46*** 12.12*** 11.15***

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectivelyIn the calculation, each variable, except dummies, is expressed in terms of its ordinal ranking scaled to lie between zero and one. Security-dates with missing data, and without complete price data during the event window [-5,+5] are discarded. The regressions are based on a final sample of 4891 security-date observations.The t-statistics are in parentheses below the corresponding coefficients. Variable definitions appear in Table 4.

40

Table 9 Behavior of Naïve Traders and Institutional TradersThe overnight trading can be done only by institutional traders and naïve traders can trade only intra-day. The table presents intra-day, inter-day and overnight abnormal returns surrounding the publication of analysts' recommendations. Intra-day return is a security's close price divided by its open price of the same day then minus market index's close/open price ratio. Inter-day return is a security's open price divided by its previous day's open price then minus market's open price change. Overnight return is a security's open price divided by its previous day's close price then minus market's overnight change of price. Values of returns are in percentage. The t-statistic is based on the null hypothesis that the mean is zero.

Inter-Day: opent/opent-1

Institutional TradersOvernight: opent/closet-1

Naïve TradersIntra-Day: closet/opent

Even

t Day

N Mea

n

Median t-stat Percent

Positive

Mea

n

Median t-stat Percent

Positive

Mean Median t-stat Percent

Positive

-2 5373 0.577 0.304 17.45*** 0.578*** 0.042 -0.007 2.52** 0.485** 0.856 0.545 28.72*** 0.630***

-1 5373 1.070 0.715 29.35*** 0.661*** 0.210 0.059 10.91*** 0.581*** 2.005 1.636 58.21*** 0.807***

0 5373 2.524 2.069 56.24*** 0.826*** 0.502 0.218 21.34*** 0.678*** 0.328 0.181 10.70*** 0.538***

1 5373 0.365 0.218 9.69*** 0.547*** 0.033 -0.005 1.60 0.490 -0.290 -0.453 -10.17*** 0.383***

2 5373 -0.397 -0.525 -11.99*** 0.367*** -0.108 -0.050 -7.21*** 0.428*** -0.195 -0.340 -7.32*** 0.412***

3 5373 -0.359 -0.378 -9.36*** 0.395*** -0.167 -0.040 -6.42*** 0.436*** -0.121 -0.239 -4.36*** 0.436***

***, **, and * represent statistical significance at the 1, 5, and 10 percent levels, respectively Significance on "Percent Positive" is based on Wilcoxon signed rank test for the null hypothesis that the median is zero.

41

Figure 1 Speculative Bubble: Abnormal Return around Publication DayThe figure plots mean and median of abnormal returns for 41 days centred on the publication date of analysts' recommendations, i.e., event window [-20,+20]. Abnormal returns are calculated by subtracting market index's daily return from each security's daily return. Values are in percentage.

42

Figure 2 Speculative Bubbles: Cumulative Abnormal Returns around Publication DayThe figure plots mean and median of cumulative abnormal returns for 41 days centred on the publication date of analysts' recommendations, i.e., event window [-20,+20]. Abnormal returns are calculated by subtracting market index's daily return from each security's daily return. Values are in percentage.

43

Figure 3 Herding Behavior: Abnormal Trading Volumes around Publication DayThe figure plots mean and median of abnormal trading volumes for 41 days centred on the publication date of analysts' recommendations, i.e., event window [-20,+20]. Each security's abnormal trading volume is calculated by dividing the ratio of that security's event-time volume to market index's event-time volume by the median ratio in the 6 weeks preceding the announcement week (period [-50,-21]) then less one. Values are in percentage.

44

Figure 4 Noise Traders versus Informed Traders: Intra-Day and Overnight Abnormal Return by Number of Recommending AnalystsThe figure plots median value of intra-day and overnight abnormal returns surrounding the publication of analysts' recommendations for portfolios created by sorting the whole sample by the number of recommending analysts. Intra-day and overnight returns are defined as those in Table 3. The numbers on the horizontal axis represent the event days relative to the announcement day. The vertical bars on integers (i.e., -1, 0, 1, etc.) represent intra-day returns. The intra-day trading can be done by both dealers and naïve traders. Bars on half-integers (i.e., -0.5, 0.5, etc.) are overnight returns between previous day's close price and the subsequent day's open price. Overnight trading can only be done by dealers. This figure shows that most speculative trading is done by naïve traders who can trade only intraday.

45

1 1 12 2 23 3 34 4 45 5 5

1

11

1 11

1

1

1 1

2

2

2

22 2

2

2 22

3

3

3

33

3 3

33 3

4

4

4

4

4 4

4

4 44

5

5

5

5

55

5

5

5 5

Figure 5 Bubble Momentums and Bursting: Post-Announcement Drift Grouped by Intensity of Analyst PumpingThe figure plots median value of post-announcement returns of portfolios created by sorting the whole sample by the on-announcement rise CAR[-1,0]. This figure shows that the naïve traders are affected by the intensity of pumping in the market. The more analysts pump the stock, the bigger is the bubble and the more spectacular is the bubble burst.

46