Is the Chinese Stock Market Really Different?

Evidence from Stock Splits in the U.S. and China

Sheridan Titman1

University of Texas at Austin

Chishen Wei2

Singapore Management University

Bin Zhao3

Shanghai Advanced Institute of Finance

Shanghai Jiao Tong University

10 October 2017

                                                            1 Department of Finance, University of Texas at Austin (email: sheridan.titman@mccombs.utexas.edu) 2 Department of Finance, Singapore Management University (email: cswei@smu.edu.sg) 3 Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University (email: bzhao@saif.sjtu.edu.cn) We thank Harrison Hong, Yao Lu, Garry Twite, Xintong Zhan, Zhen Zhou, and conference and seminar participants at the CICF (Xiamen), NYU-Volatility Conference, and Renmin University for helpful comments. We especially thank the Shanghai Stock Exchange for valuable assistance with the exchange data. Wei acknowledges research support from Nanyang Technological University.

 

Is the Chinese Stock Market Really Different?

Evidence from Stock Splits in the U.S. and China

Abstract

We compare stock price reactions around stock split announcements in the U.S. and Chinese markets. We find positive initial stock price reactions and positive post-split return drift in both markets. However, about half of the Chinese splits are “suspicious,” and these exhibit significant reversal of the initial post-split drift within a year. Using transaction data from the Shanghai Stock Exchange, we find that in general, retail investors are net buyers after split announcements, while mutual funds and institutional investors are net sellers, and this effect is strongest for the “suspicious” splits..

1

Most observers view the U.S. and the Chinese stock markets very differently. The U.S.

stock market, which tends to be dominated by sophisticated institutions, is considered among the

most efficient in the world. The China market, which is now the second largest in the world, is

very different. It is dominated by relatively unsophisticated retail investors1 (e.g., Neftci and

Menager-Xu, 2006; Mei, Scheinkman, and Xiong, 2009), and for the most part, Chinese stocks

cannot be sold short. According to the 2014 China Household Finance Survey, approximately one

third of Chinese investors lack a high school education and more than half of the new investors

who opened accounts in 2014 do not have a high school education.2 The mixture of unsophisticated

investors and short sale constraints creates an environment where stocks can trade substantially

above fundamentals, providing potential opportunities for manipulation.

Anecdotal evidence of mispricing in the Chinese market tends to fuel the perceived

differences between the U.S. and Chinese markets. Perhaps, the best example in the academic

literature is the study by Xiong and Yu (2011), which documents high prices for way out-of-the

money Chinese put warrants that were effectively worthless. Of course, there is also anecdotal

evidence of extreme overpricing of U.S. stocks that can also be tied to constraints on short-selling

(see for example Lamont and Thaler, 2003). These anecdotes have informed the actions of China

regulators and raise three fundamental questions regarding the stock market. Is such behavior

pervasive? Are there systematic differences in information efficiency across the two markets?

Are Chinese retail investors susceptible to manipulation?

                                                            1 Using complete account level trading data from the Shanghai Stock Exchange, we find that retail investors generate 88.6% of average daily trading volume during 2013-2015. 2 The 2014 China Household Finance Survey covers approximately 4,000 households across the country. http://www.bloomberg.com/news/articles/2015-03-31/china-s-big-stock-market-rally-is-being-fueled-by-high-school-dropouts

2

To explore these questions, we provide new evidence on the reaction of stock prices to stock

split announcements in the U.S. and China. We choose the stock split setting for this comparison

because stock splits are purely cosmetic events and have no direct effect on cash flows in both

countries. Moreover, since stock splits are a simple numeric change, the event should be easily

understood by investors regardless of language differences. Other corporate announcements are

not as simple and not well-suited for cross-country comparisons. For example, cash dividends are

taxed at different rates around the world and differences in accounting standards make earnings

announcements difficult to compare across countries. Perhaps, because splits provide such a clean

information event, Fama, Fisher, Jensen, and Roll (1969) chose stock split events as the original

experiment to study informational efficiency.

Splits, however, can be used as a vehicle to confuse naive investors. The Chinese media

often portray stock splits as a game of hot potato intended to attract retail investors to bid up the

stock price when insiders seek to exit their positions. 3 The presence of short-sellers can make this

type of manipulation more difficult in the United States. However, short selling on the Chinese

A-share market was not allowed prior to 2010, and by 2016 only around 30% of the stocks could

be shorted (Figures 1 and 2). Regulators understand the potential for manipulation in this

environment; the Shanghai Stock Exchange announced in its 55th media conference on November

25, 2016 that they will pay special attention to stock split announcements in order to prevent stock

price manipulators from taking advantage of retail investors.4 As we discuss shortly, we explore

this issue using complete account level trading data from the Shanghai Stock Exchange from 2013-

2015.

                                                            3 Media websites often warn retail investors of the possibility that stock splits are used to attract new investors to bid up the stock price for the benefit of existing investors. We provide examples in the Appendix. 4 http://www.sse.com.cn/aboutus/mediacenter/conference/c/c_20161125_4206987.shtml

3

We examine stock splits in both China and the United States during the 1999 to 2015

period. The 1999 starting point is somewhat arbitrary, however, prior to 1999 the Chinese market

was relatively small and illiquid. The recent period may also be of independent interest given

changes in the U.S. market since the earlier split studies. For example, the U.S. market experienced

a change in the minimum tick size from eighth to pennies. As Brennan and Copeland (1988) and

others have discussed, splits change the effective minimum tick size, potentially affecting liquidity.

This effect is likely to be less important after decimalization. Second, McLean and Pontiff (2016)

find that the returns associated with most anomalies uncovered in the academic literature tend to

deteriorate out of sample. Hence, we expect the post-split abnormal returns to also diminish.

Finally, in the pre-1999 period, it may have been difficult to distinguish the post-split price run up

from the price momentum effect since recent good return performance tends to trigger stock splits.

If the post-split abnormal returns are simply a manifestation of the Jegadeesh and Titman (1993)

momentum effect, we expect to observe much weaker post-split abnormal returns in the more

recent period in which the abnormal returns from the momentum strategy is weak.

We find that the returns around the initial announcement of U.S. stock splits are similar

before and after 1999. We find abnormal announcement returns of 3.14%, which compares to the

3.31% abnormal return reported in the 1967-1976 sample examined by Grinblatt, Masulis, and

Titman (1984). In addition to the announcement effect, we observe a 4.20% abnormal return over

the next 12 months, which is less than the 9% abnormal return reported during the 1988-1997

sample in Ikenberry and Ramnath (2002). The reduction, but not elimination of excess returns, is

consistent with the more general findings in McLean and Pontiff (2016) that anomaly returns have

weakened in recent years. It is surprising, however, that such a simple strategy continues to be

4

profitable decades after its initial discovery because it suggests that U.S. investors have continued

to underreact to this information over a protracted time period.5

The Chinese stock return evidence shares some similarities to the United States evidence,

but also exhibits important differences. Stocks in the Chinese market also experience a positive

unconditional average size-adjusted return around the announcement of a stock split (1.84%)

during the sample period 1999–2015. 6 This suggests that the market is surprised by the split

announcement. In the subsequent 60 days, we find a significant unconditional average size-

adjusted return of 1.93%. However, unlike the U.S. evidence, the drift stops after the initial 3

months and in some cases exhibits a reversal in the subsequent 9 months.

Since split announcements in China are often concurrent with dividend and annual earnings

announcements, such confounding events potentially obscure our inferences. Our analysis of

market reactions to dividend and earnings announcements suggest that these events are unlikely to

be the cause of either the announcement effect or the post-split drift. In contrast to the positive

average reaction to split announcements, periodic dividend announcements do not surprise the

market on average. Unlike the U.S. evidence, we do not find a significant response to

announcements of dividend increases. The split announcements effect may also be related to

positive concurrent earnings announcements and the post-earnings announcement drift (PEAD)

phenomenon (e.g., Ball and Brown, 1968). Yet, we find that during our sample period in China,

PEAD is relatively small (1.17% over three months) and notably non-existent for firms with

positive earnings surprises, but do not split. Hence, the post-split drift does not seem to be driven

by PEAD.

                                                            5 McLean and Pontiff (2016) do not include stock splits in their anomalies study. 6 Since risk benchmarks are not yet well-established in the China market, we adjust returns based on size deciles for our sort analysis.

5

To explore whether the post-split announcement drift is due to the alleged manipulation

depicted in the Chinese media, we identify a subsample of Chinese split events that are potentially

suspicious using ex-ante firm and split characteristics. The number of splits that we classify as

suspicious is actually quite large – about 42% of the sample – which is the likely reason why splits

have attracted the attention of regulators.

Our classification first identifies firms that announce stock splits after poor recent stock

performance or announce splits at unusual times (i.e., outside of earnings announcements). Split

announcements after poor recent stock performance are unusual because a common rationale for

stock splits is to restore the stock price to an optimal trading range, usually after recent good stock

performance (e.g., Baker and Gallagher, 1980). In addition, we attempt to identify those Chinese

firms that are most likely to exploit small, minority shareholders. Our first measure is a proxy for

tunneling based on suspicious balance sheet activity developed in Kim et al. (2015). We also use

forthcoming restricted share lockup expirations of certain insiders (i.e., institutional investors)

because insiders at these firms may be motivated to exit their positions, and thus have the greatest

incentive to manipulate their share prices. 

Our evidence is consistent with manipulation among these suspicious split-issuers.

Specifically, Figure 5 shows that this subsample of splits exhibits positive abnormal returns in the

first four months following a split, which mirrors the entire sample. But over the next eight months,

the returns of stocks with suspicious splits significantly reverse – suspicious splitters experience

negative annualized abnormal returns of −6.81%. It is also notable that after excluding firms that

ex ante are most likely to manipulate their share prices, the remaining firms in the China sample

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experience positive one-year abnormal post-announcement drift that is comparable to the

unconditional average in the U.S. market. 7

These suspicious splits have not gone away as the Chinese market has matured. Indeed, the

number of suspicious splits has increased in the recent five years (2011–2015) as shown in Figure

4. In this latter sample period, the pattern of return reversal after the initial four month return drift

is much stronger than in the earlier sample period (−11.5% annualized versus −2.75% annualized).

Perhaps because of the increase in suspicious split activity, Chinese stock market regulators issued

a press release urging investors to carefully evaluate companies that split their stock.8

To better understand the trading dynamics of retail investors around stock split

announcements, we use complete account level trading data from the Shanghai Stock Exchange

from 2013 to 2015. We supplement our analysis with an additional sample of trading data around

all stock transfers from 1999 to 2015.9 Retail investors dominate the market, comprising of 88.6%

of daily trading volume from 2013 to 2015. Consistent with the idea that retail investors are

attracted to attention-grabbing events (e.g., Seasholes and Wu, 2007; Barber and Odean, 2008),

we find that retail investors are aggressive net buyers of split-announcing stocks, while

institutional investors and mutual funds are net sellers. For example, the unconditional average

total net trading imbalance (buy minus sells) for retail investors with an account size of less than

$5,000,000RMB is more than five times the daily average volume over the post announcement

t=+1 to t=+120 period. The traders of these retail accounts are particularly attracted to split

announcements by the identified group of “suspicious” firms, which is consistent with the view

that these stocks are being actively marketed to individual investors.

                                                            7 It is noteworthy that we also document that there are no subsequent drift patterns among the subset of U.S. firms that announce stock splits after poor recent stock performance. 8 http://www.sse.com.cn/aboutus/mediacenter/conference/c/c_20170317_4252156.shtml 9 Stock transfers are the preferred method of splitting shares (84%) in the China market.

7

Our study adds to a growing literature on the link between stock returns and firm

fundamentals in the China market.10 An understanding of these return characteristics is important

for benchmarking the long-term post announcement returns in our study. Our study is related to

two concurrent papers on the China market that documents short-term market reactions to dividend

announcements (Fang, Hu, and Wang, 2015) and stock dividends (He, Li, Shi, and Twite, 2016).

Our contribution is to exploit the simple and relatively clean stock split setting to compare and

contrast investor behavior between the China market and the more developed U.S. market. Our

analysis indicates that the market reaction to stock split announcements in China is similar to the

U.S. evidence after excluding the suspicious splits that may have been used to manipulate the

splitting firm’s stock price. The similarity of the evidence in these markets is somewhat remarkable

given the access to past research about the U.S. market.

Our findings also add to the growing literature on the expropriation of minority shareholders

in China. By carefully examining the balance sheet, prior studies uncover evidence of corporate

tunneling and propping activities through related party loans, related party transactions, and SEOs

(e.g., Jiang, Lee, and Yue, 2010; Jian and Wong, 2010; Peng, Wei, and Yang, 2011; Kim et. al,

2015). We take a different approach by analyzing the potential for stock price manipulation using

the stock split setting. Consistent with anecdotes from the popular press, we present evidence that

Chinese stock splits attract the attention of small retail investors.

1. Literature review and the China stock market setting

                                                            10 Chen, Kim, Yao, and Yu (2010) and Carpenter, Lu, and Whitelaw (2016) document strong value effects in the China market. Chen, Hu, Shao, and Wang (2015) find that the value effects are only concentrated during 1995 and 1996 years, and are non-existent outside that period.

8

We briefly discuss three leading explanations for the stock split decision: 1) costly attention, 2)

liquidity, and 3) catering/clientele.11 Then, we discuss the stock split decision in the China setting.

1.1 Theories of stock splits

The costly attention hypothesis states that managers split shares when they want to attract

more attention (Grinblatt, Masulis, and Titman, 1984). The signal is credible because managers

welcome more attention when they are doing well, but would prefer to avoid scrutiny if they have

something to hide. Consistent with this hypothesis, studies find that firms that issue stock splits

experience strong future earnings (Lakonishock and Lev, 1987; Asquith, Healy, Palepu, 1999) and

greater analyst coverage (e.g., Arbel and Swanson, 1989; Ikenberry and Ramanth, 2002).

Liquidity explanations for stock splits are based on the economics of market marking.

Before decimalization of all U.S. stock exchanges in 2001, the minimum tick size was 1/8 of a

dollar pre-1997 and 1/16 between 1997 and 2000. Managers may split shares to target an optimal

tick size with an eye on attracting market making activity (e.g., Angel, 1997; Schultz, 2000; Lin,

Singh, and Yu, 2009). The evidence on liquidity improvement, however, is mixed. Studies find

that liquidity post-split both decreases (e.g., Copeland, 1979; Lakonishok and Lev, 1997;

Lamoureux and Poon, 1987; Easley, O’Hara, and Saar, 2001) and increases (e.g., Amihud,

Mendelson, and Uno, 1999). Clientele/catering theories assert that managers target a certain type

of shareholder clientele (e.g., Green and Hwang, 2009; Baker, Greenwood, and Wurgler, 2009).

One rationale is that high stock prices may limit retail investors with limited capital from

purchasing round lots. This friction is likely to be unimportant in the recent period given that odd

lots are now cheaply and easily executed.

                                                            11 For a comprehensive review, see He and Wang (2012).

9

1.2 China stock market setting and manipulation hypothesis

There are many institutional similarities between the Chinese and U.S. stock markets. Both

Shanghai and Shenzhen markets are modern electronic systems without designated market makers.

Buy orders are placed in round lots of 100 shares, but sell orders have no lot size requirements.12

Starting in 2002, firms are required to report quarterly earnings similar to the U.S. market. Firms

in China typically announce stock splits during annual earnings announcement, while U.S. firms

tend to announce stock splits independently of earnings announcements. Since we have the exact

dates of these corporate announcements, we address potential confounding effects of earnings

announcements on stock splits in Section 3.2.

The similarities in market regulations between the China and the U.S. market allow for a

direct comparison for stock splits across the two markets. Evidence suggests that the determinants

of stock splits in China are similar to those in the U.S. market. He, Li, Shi, and Twite (2015) find

that stock dividend issuers in China are larger in size (assets), have higher asset growth, are more

profitable (ROA), and retain more earnings. Companies that issue stock dividends also gain analyst

coverage, consistent with the attention hypothesis.

The main difference is the prevalence of unsophisticated retail investors in the China market

and the lack of short selling. Approximately one third of Chinese investors lack a high school

education and more than half of the new investors who opened accounts in 2014 do not have a

high school education (Source: 2014 China Household Finance Survey). Short selling on the

Chinese A-share market was not allowed prior to 2010. Figure 1 shows the fraction of companies

                                                            12 The mean/median closing stock price as of December 2015 is $17.99/$24.32RMB in our sample. The maximum price is $218.19RMB. Therefore, it is unlikely that round lot constraints would affect most investors.

10

that can be shorted on the Shanghai and Shenzhen stock exchange. By 2016, only around 30% of

the stocks could be shorted.

Figure 2 shows that average short interest remains well below 1% of tradable shares during

the sample period. From 2010 to 2015, short interest grew along with the fraction of companies

that could be shorted. Short interest falls to close to zero in 2016 following the tightened trading

rules regarding short selling on securities market and index futures. The financial media has raised

public concerns of stock splits as a vehicle for market manipulation. Appendix 1 contains two

examples of warnings by the media that stocks splits are used for insiders to offload shares.

Regulators of the Shanghai Stock Exchange announced in its 55th media conference on November

25, 2016 that they will pay special attention to stock split announcements in order to prevent stock

price manipulators from taking advantage of retail investors.

We hypothesize that stocks splits in China may be used to manipulate the stock price to

allow insiders to exit their positions at attractive prices. The market conditions in China described

above create an environment where such manipulation is more likely to occur. The news of stock

splits may catch the attention of retail investors (e.g., Barber and Odean, 2008), who bid up the

share price. Moreover, constraints on short-selling prevent informed investors from short-selling

these shares, which provides an opportunity for insiders to exit their positions. We test this

hypothesis using ex-ante scenarios where stocks splits raise suspicions. Specifically, manipulation

scenarios should display strong stock price increases, but subsequent return reversals.

2. Sample, data, and summary statistics

We obtain daily stock return and split announcement data from CSMAR for China A-shares. Only

domestic investors are allowed to trade in A-shares. Our sample starts in January of 1999 since the

Chinese stock market is relatively undeveloped before then, and we are less confident about the

11

accuracy of the pre-1999 data. We end our sample of split announcements in June 2015 because

we require at least 12 months to measure post-announcement returns. Therefore, our analysis of

post-split announcement stock returns ends in June 2016.

We start with a sample of 4607 stock dividend and stock transfer announcements from the

CSMAR database. Chinese firms issue two types of splits, “stock dividend” and “stock transfer.”

In terms of accounting, “stock dividends” come from earnings while “stock transfers” come from

the capital reserve fund. Both types of stock splits are technically the same, they have no impact

on firms’ earnings or operations, but stock transfers are the preferred method of splitting shares in

China.13 84% of the splits in our sample are performed using stock transfers. In both cases, the

splits are typically proposed in the annual report (85% of the sample), but must be approved at the

shareholder meeting. On the content date, the ex-date is disclosed. So there are three relevant

information dates; the date the split is announced in the annual report, the content date to announce

the results, and the ex-date. 99.7% of the splits announced in our sample period were approved.

We screen out stock splits that fail to report trading in the three-day window around the split

announcement date, consistent with prior event studies in the China market (e.g., Liu, Shu, and

Wei, 2017). This eliminates confounding events, such as trading halts that occur around

information sensitive events, which may bias the estimates. To be included in our sample, we

require that stocks have complete accounting information and at least one year of prior stock

returns for benchmarking purposes. We also exclude stocks with abnormal financial conditions

designated as “special treatment” (codes ST, ST-plus, and PT) by the stock exchange because these

stocks face trading and financial restrictions (Peng, Wei, and Yang, 2011). Special treatment (ST)

stocks have daily price limits of +/− 5% and are carefully monitored by the stock exchange. The

                                                            13 He, Li, Shi, and Twite (2016) find that in China, stock dividends are substitutes for stock splits.

12

stock exchange audits the interim company reports of ST-status stocks, and requires investors who

wish to trade ST stocks to sign a risk acknowledgement contract. The company will face delisting

if it cannot return to profitability in the near future. Our sample consists of 3,786 stock splits after

implementing these screens.

We additionally gather data from the following sources:

Restricted shares lock up ending information from the WIND database for the 2006 to 2015

period.14

United States stock market data from CRSP. We collect ordinary common stocks with CRSP

share code 10 or 11, which excludes certificates, ADRs, SBIs, and Unit Trusts. Stock splits

are identified using the CRSP stock split distribution code 5523.

Stock split data from the CRSP header file. We focus on stock splits and exclude reverse

splits. We collect both announcement date and payment date. To be included in the split

sample, each stock must report market capitalization, book value, and past 12 months return

at the previous June end. We require this screen in order to appropriately benchmark using

DGTW adjusted returns. In total, there are 2,381 U.S. splits in our sample.

2.1 Summary statistics

Table 1 presents a tally of the number of stock splits in our U.S. and Chinese samples. In

the Chinese market, stock split announcements typically occur in the first two calendar quarters,

often coinciding with annual earnings announcements (85% of our sample). Few splits are

                                                            14 Share lockups are restricted shares held by institutional investors, employees, and other large shareholders. A split share structure was established at the inception of the China’s stock market, where approximately two-thirds of A-shares were not tradable. The split share structure reform started in 2005. Non-tradable shares became tradable one year after reform completion and the number of newly tradable shares cannot exceed 5% of the A-market float shares. The number of newly tradable shares cannot exceed 10% of the A-market float shares two years after the completion of the reform. All shares that were not tradable prior to the freeform become fully tradable three years after the completion of the reform. (See: China Securities Regulation Committee (2005), Li, Wang, Cheung, and Jiang (2011))

13

announced in the last quarter of the year. Stock split announcements in the U.S. sample are more

evenly distributed throughout the calendar quarters, with slightly more occurring in the first half

of the year (59%). Overall, stock splits remain popular in recent years in China. In contrast, we

have observed very few U.S. stock splits in recent years.

Panel B presents the number and percentage of firms that announce splits, cash dividends,

or both split and cash dividends at least once each year for the China market. Cash dividends are

more common compared to stock splits, as the annual average of firms that announce cash

dividends is 41.6% compared to 13.1% for stock splits. Nearly 2/3 of the splits are also announced

with a concurrent cash dividend, which we analyze in more detail in Section 3.2.

Panel C presents summary statistics of the characteristics of firms that announce stock splits.

For each month with a stock split announcement, we calculate the median stock characteristics for

stocks with and without a stock split announcement and report the time-series averages. In China,

firms that announce stock splits are generally smaller, with higher past stock returns, and lower

book-to-market ratios than firms without split announcements. The patterns are generally similar

in the U.S., although firms that announce stock splits tend to be slightly larger.

2.2 Shanghai stock exchange trading data

We obtain two samples of account level trading data from the Shanghai Stock Exchange.

Our first sample contains all daily trades of all stocks for a three year period from January 2013

through December 2015. Our second sample consists of the record of all account level trades

around all stock transfers events15 from January 1999 through December 2015. The trading data is

recorded by trade, with security code, encrypted account identifier, trade price, trade volume, trade

                                                            15 84% of splits in our sample are performed using stock transfers.

14

direction, and the date and time of the trade. The record also shows whether the trade order is a

limit order or market order, although more than 99% of the trades are market orders.

The Shanghai Stock Exchange classifies account into twelve types as listed below:

Individual account with wealth level: i.) less than 100,000 RMB; ii.) between 100,000RMB to

1,000,000 RMB; iii.) between 1,000,000 to 5,000,000 RMB; iv.) between 5,000,000 to

10,000,000 RMB; v.) wealth level over 10,000,000 RMB.

Mutual fund; Broker asset management; Broker self-account which the brokers use to trade for

themselves; Insurance company; Other general institutional investors.

Qualified Foreign Investor (QFII); Social security.

From these twelve classifications, we make our analysis tractable by creating the following

six groups: “small individual investors” for individual investors with accounts less than 5,000,000

RMB; “large individuals investors” for individual investors with accounts greater than 5,000,000

RMB; mutual fund; other institutional investors including broker asset management, broker self-

account, insurance company, and other general institutional investors; Qualified Foreign Investors

(QFII); and social security.

To measure trading activity, we create a measure of net trading imbalance within each of the

six investor groups as follows. For each stock i, on each day t, within each investor group j, we

define net trading imbalance as:

, , , ,, ,

, ,

i t j i t ji t j

i t j

DollarBuy DollarSell

Net Trading ImbalanceAverage DailyVolume

1

where average daily volume is the average daily volume over the past trading year. For our

analysis, we accumulate net trading imbalances over various windows for each investor group

around split announcement dates.

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3. Market Reactions Around Split Announcements

This section presents evidence on market reactions around split announcements. We first present

evidence for the Chinese and U.S. market during the 1999 to 2015 sample period. Then, we

explore the possibility that stock splits are used to manipulate share prices.

3.1 Comparing stock splits across the China and U.S. markets

We start by reporting abnormal returns around stock split announcements in the Chinese and

United States markets during the 1999 to 2015 sample period. For shorter horizons, we compute

for each stock the daily buy and hold abnormal return during the announcement window (days [−1,

0, 1]) and several periods around the announcement date, correcting for the possible effects of

return clustering during each calendar month using robust (White) standard errors. For longer

horizons, we use monthly data to calculate buy and hold abnormal returns.

For the Chinese market, we calculate the abnormal return as the difference between each

stock’s buy and hold return minus its corresponding size-decile value-weighted benchmark

portfolio matched at the prior December year end. We choose return benchmarks based on size

deciles because size effects are strong, but other characteristics, including momentum and value

effects, are not robust throughout our sample period in the China market (e.g., Fang, Xu, and

Wang, 2015). Including these characteristics would introduce noise in our analysis. We calculate

abnormal returns in the U.S. market by subtracting buy and hold returns from its corresponding

daily DGTW benchmark (i.e., size-value-momentum 5x5x5) following Daniel, Grinblatt, Titman,

and Wermers (1997).

Table 2 reports daily and monthly average returns and corresponding t-statistics. Panel A

reports positive abnormal daily returns around the split-announcement. Both markets exhibit large

pre-announcement run ups. The [−10, −2] average buy and hold abnormal return is significantly

16

positive in both the China market (2.65%, t=11.51) and United States market (2.74%, t=8.16). If

we extend the pre-announcement window from month t−3 to t−1, we find a substantially larger

pre-announcement run up in the U.S. market, nearly 20% on average (19.64%, t=7.92) compared

to the three month pre-announcement abnormal return of 4.68% (t=6.06) in China. These run ups

could reflect information leakage, or alternatively management’s tendency to choose to split shares

only if the recent performance is favorable. It is also noteworthy that the abnormal returns during

the previous twelve-month period of the Chinese split announcers are on average only 16.27%,

which contrasts with the much higher abnormal returns average of 89.96% in the U.S. market. The

small pre-split returns in the China market suggests that adjusting a firm’s stock price back to a

preferred trading range may be a less important motive for stock splits in China.

Stock splits in the U.S. market experience a three-day announcement return [−1, +1] that is

nearly twice as large as the stock splits in the Chinese market (3.10% vs. 1.84%). The patterns are

similar using monthly returns. Firms that announce stock-splits exhibit 4.41% (t=14.07) abnormal

returns during the announcement month in the China market compared to 7.88% (t=10.12) in the

U.S. market. This, however, should not be interpreted as an announcement affect because the

month t=0 return also includes the pre-split run up.

Figure 3 presents event time buy and hold abnormal returns for the U.S. and China market

during the sample period 1999 to 2015. Visual inspection shows that the post-announcement drift

patterns during the first 60 days after the initial announcement is slightly greater in the U.S. market

compared to the Chinese market. Panel A in Table 2 shows that during the first t=2 to t=60 days,

the stock splits in the Chinese market experience on average a 1.93% return (t=3.14) while the

average return in U.S. market is 3.07% (t=4.06). It is interesting to note that for the initial post-

17

announcement period [t=+2, t=+10], the abnormal returns for stock splits are insignificant in the

Chinese market (−0.05%, t= −0.32), but significantly positive in the U.S. market (1.52%, t=6.52).

We also track abnormal returns up to one year after split announcements using monthly data.

In the U.S. market, the buy and hold abnormal 12 month return is 4.20% (t=3.25) which is less

than half the magnitude of the drift effect reported in Ikenberry and Ramanth (2002).16 In the

Chinese market, the unconditional buy and hold abnormal 12 month return is 1.72% (t=1.72).

Unlike the U.S. market, this return is not statistically significant and suggests that some split

announcing firms experience a reversal after the initial t+2 to t+60 positive return drift. We explore

reasons for this finding in the next subsection.

After a split announcement, there are a few notable post-announcement events related to the

mechanics of the stock split process. The first event is the shareholder meeting date (if the split is

announced with an earnings announcement). In the U.S., stock transfers require shareholder

approval while stock dividends only require the approval of the board. After the split is approved

in the U.S. market, the record date determines which stockholders are entitled to receive shares. In

China, the split is approved and the exact ex-date of the stock split is announced on the content

date. As mentioned earlier, 99.7% of stock split proposals were approved during our sample

periods. The ex-date represents the date after the distribution of shares.

Panel B examines the three-day cumulative abnormal return around each of these event days.

The unconditional average return on the shareholder meeting date is small but positive in the

Chinese market. The shareholder meeting occurs on average around 25 trading days after the split

announcement. We do not report returns for the U.S. market on such dates because we do not

                                                            16 In unreported results, we find similar magnitudes to Ikenberry and Ramanth (2002) in their sample using DGTW adjusted returns.

18

observe shareholder meeting dates in our sample. The average return around the record date in the

U.S. market is not significantly different zero.

There is a large positive return around the Content date announcement in the China market

of 2.24% (t=10.48). The U.S. market does not have an announcement that corresponds to the

Content date. The returns around the ex-date are considerably different in the two markets. In the

U.S., there is a significantly positive ex-date return of 0.85% (t=5.16), which is consistent with the

findings in the earlier sample periods (e.g., Eades, Hess, and Kim, 1984; Grinblatt, Masulis, and

Titman, 1984). However, in China, the ex-date return is significantly negative (−1.19%,

t=−6.26). We find both observations surprising since the ex-dates are known in advance. The

U.S. finding is particularly puzzling as ex-date returns continue to exist long after publication of

the anomalous pattern (e.g., Charest, 1978). A possible microstructure explanation is that on and

after ex dates, the price tends to cluster at the ask (e.g., Maloney and Mulherin, 1992; Conrad and

Connelly, 1994). Our evidence that the effect has not diminished despite the narrowing of bid-ask

spreads and the fact that the effect goes in the opposite direction in China is inconsistent with this

story. We note that in China the ex-date tends to occur on average less than 5 days after the Content

date and the average cumulative abnormal returns from the Content date through the ex-date is

1.01% (t=3.35), which is similar to the magnitude of the ex-date return in the U.S. market stated

above.

This initial comparison between the U.S. and China market provides new out-of-sample

evidence on the market reactions to stock splits. First, we report positive market reactions to stock

splits in China and find that the U.S. market continues to react positively to stock split

announcements in the post-1998 sample. Second, our pre-announcement return evidence suggests

that the effect of recent stock return performance on the decision to announce stock splits may be

19

different across the two markets. In the Chinese market, stock splits occur after relatively modest

past returns, whereas stock splits are triggered only after large past returns in the U.S. market.

Third, we find that the post-split return drift is substantially smaller than in earlier samples. In the

Chinese market, the average post-split drift is around 2% after one year and is statistically

insignificant. In the U.S. market, the approximate 4% one-year drift is larger and more significant

than in the Chinese market, but about half the size of the return drift in earlier periods (e.g.,

Ikenberry and Ramanth, 2002).

3.2 Do confounding announcements (i.e., earnings and dividends) explain our findings?

Confounding events such as cash dividend and earnings announcements are potential

explanations for the post-announcement split patterns that we document in the previous section.

For example, the market may underreact not to the split announcement, but to cash dividend

announcements as nearly 2/3 of the split announcements are accompanied by cash dividend

announcements in the China market. Additionally, nearly our entire sample of split announcements

occur concurrently with earnings announcements, which raises the possibility that the post-split

announcement drift patterns are related to the post-earnings announcement drift (PEAD)

phenomenon (e.g., Brown and Ball, 1968). We analyze these possibilities by calculating the market

reactions to dividend and earnings announcements in the Chinese market.

Table 3 shows no significant market reaction to the full sample of 12,921 cash dividend

announcements. The initial market return is insignificant (−0.09%, t=−1.38) and exhibits no return

drift over the next 60 days, six months, and one year. An interesting question is whether the market

perhaps reacts to increases in dividend payout differently. Michaely, Thaler, and Womack (1995)

find that announcements of increases in dividend payout in the United States are associated with

positive initial market reaction and future return drift. However, the next row shows that for

20

dividend increase announcements, the initial and future market returns are also insignificant, which

is similar to the full sample of dividend announcements. The results indicate that dividend

announcements have little effect on returns and suggests that the periodic announcements of

dividends are not surprising to the market on average.

In contrast, stock splits have large announcement effects, regardless of whether they are

announced concurrently with a dividend. The third row shows a statistically positive initial market

reaction (1.89%, t=12.38) which is comparable to the unconditional split announcement return

(1.84%, t=13.96) in Table 2. These stocks also experience positive return drift over the next 60

days (2.25%, t=2.87), six month (1.97%, t=2.46), and one year (2.91%, t=2.34). The return

patterns are also generally similar for instances when the dividend announcement represents an

increase in dividend payout. The bottom row shows a positive initial reaction (2.00%, t=11.65)

and drift over the subsequent 60 days (2.33%, t=2.91) and six months (2.25%, t=2.60), while the

one year return is positive, but not reliably different than zero (1.12%, t=0.85).

Given the stark difference in market reactions to dividend and split announcements, it is

unlikely that dividends, or dividend increases, explain split announcement effects. Although

dividends are frequently issued concurrently with stock splits, the evidence suggests that the

market is likely reacting to surprise of the split announcement rather than the dividend.

We also check the possibility that the post-earnings announcement drift is a viable

explanation for our findings. Each quarter, we sort stocks into five groups based on three-day

abnormal market reaction breakpoints from the prior eight quarters of earnings announcement.

Then we calculate the average returns and t-statistics from 2002 to 2015. We begin this analysis

in 2002 because firms in China were required to report quarterly earnings only starting in 2002.

21

Panel A of Table 4 presents average buy and hold returns from t=+2 to t=+60 of stocks sorted

in quintiles based on three-day abnormal return around earnings announcement (reported in the

bottom row). The results indicate that stocks with the most extreme negative earnings surprise

exhibit significant post earnings announcement drift in the 60 days after announcement, realizing

a return of −1.12% (t=−4.70). Notably, stocks with the most extreme positive earnings surprise

exhibit no post earnings announcement drift (0.05%, t=0.16). Positive-Negative represents the

average difference in returns between the Positive and Negative surprise portfolios each quarter.

Since PEAD is a relatively small economic phenomenon in the Chinese markets, it appears

an unlikely explanation for the stock split announcement returns patterns. For comparison, the

PEAD phenomenon is about four times larger in the U.S. market (Hirshleifer, Lim, and Teoh,

2009). Perhaps more importantly, firms that announce stock splits tend to have positive earnings

surprises, but stocks with positive earnings surprise do not in general experience post-earnings

announcement drift in the China market. In unreported tests, we also verify that split announcers

experience strong fundamental performance. We find that of firms that announce splits, 53% have

experienced increases in ROA and 76% increased net profits year over year. In comparison, only

45% of unconditional earnings announcements experience increases in ROA and 58% increased

net profits year over year.

Panel B of Table 4 presents stock split announcements that occur during and independent of

earnings announcements. The majority of splits are concurrently announced with earnings. Firms

that announce splits unaccompanied with earnings announcements are quite rare (<8%). These

suspicious announcements experience large initial reactions (4.68%, t=7.70) as well as large drift

over the first 60 days (6.51%, t=2.91). However, the returns reverse subsequently over the year as

the total one year post-split return is negative but insignificant (−3.47%, t=−0.88). This result is

22

consistent with investors reacting positively to the surprising announcement, then potentially

overreacting to the news.

Overall, the evidence suggests that PEAD is an unlikely explanation for the stock split return

patterns. First, PEAD is a relatively small economic phenomenon in the Chinese markets with

abnormal returns of 1.17% (t=2.85) over the next 60 days, which is about a quarter the size of the

PEAD phenomenon in the U.S. market (Hirshleifer, Lim, and Teoh, 2009). Second, there is no

post-earnings announcement drift among stocks with positive earnings surprise. As we document,

the market reacts favorably to splits and experience positive three-day announcement returns.

3.3 Suspicious stock split announcements: Manipulation hypothesis

To explore the potential for stock price manipulation, we track stock split announcement that

have characteristics that may arouse suspicion. We are guided by anecdotal evidence from the

Chinese financial press, which refer to stock splits as the ‘pass the parcel’ game (a cultural

equivalent to the game of hot potato). Appendix 1 presents examples of media warnings for

investors to be wary of the ‘greater fool’ theory. Managers may split shares in an attempt to boost

stock valuations if they believe that expanding the shareholder base can push up prices. By

attracting the trading interest of relatively uninformed individual investors, they may create the

necessary liquidity for the more informed insiders to exit their shares. We explore these motives

by examining the market reaction to stock splits that may seem either suspicious in the sense that

they are surprising or are issued by firms with motives for insiders to exit.

3.3.1 Suspicious announcement: Poor prior stock returns

Our first category of suspicious split announcements are firms with poor prior stock returns

during the recent past three months. These splits are very unusual in the U.S., where a common

rationale for stocks splits is to restore the stock price to an optimal trading range, typically after

23

recent strong stock performance (e.g., Baker and Gallagher, 1980). We calculate the past three

month stock return and identify split-announcing firms that reside in the bottom quintile of past

three month return. We first report the results in the U.S. sample and later compare it with the

China sample.

The results in Panel A of Table 5 shows that split announcers are rarely in the bottom quintile

of recent past three month returns in the U.S. market. The first row shows that there are only 92

split observations (less than 4% of the sample) with poor recent stock performance, but these

announcements generate large initial three-day market reactions (6.92%, t=4.11). This may reflect

the possibility that these events are less anticipated. In contrast to the total sample, the subsequent

return drift over the next six to 12 months for splits in this subsample is negative, but not reliably

different than zero (−3.66%, t=−0.95). Since the bottom quintile cut-off only captures 92 split

observations, we expand the cut-off to include the bottom two quintiles of returns over the prior

three months. The results are similar using this alternative specification ‒ the market responds

positively to the initial split announcement (4.05%, t=6.43), but the subsequent return drift patterns

are negative, although not different than zero over the next six to 12 months.17 The bottom row

shows that after excluding these suspicious split-announcing firms from the sample, the average

one-year post-announcement buy and hold abnormal return is 5.07% (t=3.33).

The first row of Panel B of Table 5 presents the stock returns around split announcement of

Chinese firms with poor recent stock returns, defined as firms in the bottom quintile of past three

month returns. It is noteworthy that relative to the U.S. market, a larger fraction of firms in the

Chinese market (i.e., 603 firms) perform stock splits after poor recent stock performance. Unlike

the U.S. subsample, the initial market reaction for these splits is similar to the unconditional

                                                            17 The lack of statistical power may be due to the small sample size of firms that split after poor recent stock performance.

24

average (2.07% vs. 1.84%). We find subsequent return drift patterns similar to those in the U.S.

market. The returns are negative, but not statistically different than zero over the next 12 months

(‒2.54%, t=‒0.95). One possible interpretation for the lack of return drift for these subsamples is

that since the market finds these stock splits more surprising, they attract more scrutiny and thus

more fully incorporate the relevant information on the announcement date.

3.3.2 Traces of tunneling

Our second category of suspicious split announcements is motivated by the pyramidal

ownership structures of Chinese firms that can allow for tunneling of assets by controlling

shareholders. We adopt an ex-ante measure of tunneling activity based on high levels of accounts

receivable and prepaid expenses following Kim et al. (2015).18 Specifically, we identify a group

of split-announcing firms that may potentially suffer from such agency issues (i.e. traces of

tunneling) as firms in the top quintile of accounts receivable and prepaid expense each year.

We present the results of the “traces of tunneling” sub-sample in the second row of Panel B

in Table 5. The initial three-day market reaction is slightly smaller than in the unconditional

sample (1.58% vs. 1.84%). Over the next t+2 to t+60, the stock price drifts upwards with the buy

and hold abnormal return totaling 3.27% (t=2.95), which is similar to the unconditional average

(3.21%, t=4.71). However, these returns reverse over the next year. The one year buy and hold

abnormal return is an insignificant −0.36 (t=−0.12). A potential interpretation of this finding is

that most of the information in the stock split is incorporated at the announcement. While investors

may subsequently somewhat overreact, the price eventually reverts back to announcement levels.

3.3.3 Suspicious announcement: Atypical split timing

                                                            18 This was revealed through the authors’ discussions with CFOs and former employees of major auditing firms in China.

25

Our third category of suspicious split announcements is based on the atypical announcement

timing. As discussed earlier, only 8% of split declarations occur outside of earnings

announcements. The third row of Panel B in Table 5 repeats the results of these splits. The initial

three-day market reaction is larger than in the unconditional sample (4.68% vs. 1.84%), suggesting

that the market is surprised by the announcement. The subsequent return drift patterns similar to

those from the poor-stock return sample. The post-split one year returns are negative, but not

statistically different than zero.

3.3.4 Insider exits: Share lockup expiration

Next, we test the possibility that stock splits are an opportunity for insiders to exploit less

sophisticated retail investors seeking to exit their positions. We identify share lockup expirations

in the period month t−1 to t+6 around split announcements using public records of expiration of

share lockup periods. We separate share lockups into two groups: a) share lockup expirations for

institutional investors holding private placements, IPOs, and SEOs and b) those for privatizations,

employees, and other large shareholders. We speculate that there may be additional pressure to

please institutional investors who funded at times when management required external financing.

We begin the analysis in 2006 because the data on privatization lockups starts then (e.g., Liao, Liu,

and Wang, 2014). We note that this sample represents potential insider exits, as we do not have

actual records of insider trades.

The fourth row of Panel B of Table 5 reports the results of the institutional investor lockup

sample. The market reacts favorably to the initial split announcement during the three-day window

(1.62%, t=3.64), but the stock price begins to drift lower after the announcement. The buy and

hold abnormal returns are negative over the initial t+2 to t+60 period, month +1 to +6, and month

+1 to +12, although they are not significantly different than zero due to a lack of power (n=210).

26

In contrast, we find strong and persistent price drift among non-institutional investor lockups. In

unreported tests, the average buy and hold abnormal one year return is 6.19% (t=2.18).

3.3.5 Manipulation hypothesis: Overall evidence

To visualize the prevalence of splits classified as suspicious, we plot in Figure 4 the number

of suspicious splits each year (grey bar) and annual percentage of all suspicious splits (dotted line)

in our sample. The first year of the sample, 1999, had nearly 120 suspicious splits, which amounts

to over 60% of splits in 1999. The figure shows that the fraction and number of suspicious splits

drops off during the 2000s. In the recent five years, we have witnessed a steady rise in the number

and fraction of suspicious splits, which have exceeded the levels in 1999. In 2015, there were 135

suspicious split. The upward trend is partially due to the additional of institutional lockups, but

even omitting those types of suspicious splits, the number of suspicious have increased. For

example, from 2011 to 2015, 47% of all splits are classified as suspicious, which is similar to 45%

of all splits classified as suspicious if we exclude the institutional lockups.

Figure 5 plots the +1 to +12 month buy and hold abnormal returns for the suspicious stock-

split sample (solid red line) and for the rest of the split announcers (dashed blue line). The non-

suspicious sample exhibits higher cumulative abnormal returns in each of the subsequent 12

months than the suspicious sample. The 12 month size-adjusted abnormal return is 3.90% (t=3.71),

reported in the first row of Panel C in Table 5. This suggests that the return patterns around split

announcements in the U.S. (4.20%) are similar among the sample of non-suspicious stock splits in

China.

In contrast, the suspicious sample exhibits return patterns that are consistent with

manipulation reported in the financial media. The second- row of Panel C in Table 5 reports a

negative, but insignificant 12 month size-adjusted abnormal return ‒2.55% (t=‒1.33). However,

27

the figure reveals that in the year after the split announcement, the share price peaks in four month

after announcement (1.98%), but drops monotonically over the next few months. This return

reversal is significant as the size-adjusted abnormal return from month five to twelve is −4.54%

(t=−2.62), which is approximately −6.81% annualized. In unreported tests, we find that these

stocks continue to exhibit poor performance for up to 24 months.

The pattern of initial price run-up and subsequent reversal exists primarily in the latter five

years of the sample. The third row of Panel C in Table 5 shows no significant price drift after the

initial split announcement during the 1999–2010 period. In contrast, the bottom row reports

significant return drift during the first 60 days (2.64%, t=2.34), which subsequently reverses over

the entire year (−6.49%, t=−1.86) during the recent 2011-2015 period. The return reversal from

the month t+5 to t+12 is −7.66% (t=−3.12).

The positive initial market reaction and continued return drift in the first four months after

split announcement provide an extended opportunity for existing shareholders to exit their

positions at favorable prices. New shareholders who purchase shares of suspicious split announcers

soon experience poor relative stock performance. One possible interpretation is that naïve retail

investors are unable to distinguish between suspicious and non-suspicious split announcers, where

the latter do not experience future return reversals. Companies seeking to temporarily boost the

stock price in the near-term may try to pool with non-suspicious split announcers. Perhaps because

of the increase in suspicious split activity, Chinese stock market regulators issued a press release

urging investors to carefully evaluate companies that split their stock.19

In the next section, we test whether these suspicious splits announcers indeed grab the

attention of retail investors.

                                                            19 http://www.sse.com.cn/aboutus/mediacenter/conference/c/c_20170317_4252156.shtml

28

4. Do stock splits attract retail investors?

It is a commonly believed that stock splits attract the attention of small retail investors (e.g., Baker

and Gallagher, 1980). Circumstantial evidence supports this view; the announcement of stock

splits are followed by subsequent increases in volatility, volume, and smaller lot sizes, which are

price dynamics frequently associated with the trading activity of retail investors (e.g., Lamoureux

and Poon, 1987; Schultz, 2000). However, the evidence is merely suggestive because these studies

do not report the actual trading activity of the investors themselves.

Using two samples of complete account level trading data from the Shanghai Stock

Exchange, we directly examine the trading dynamics of all investors around split announcements.

Our setting is of interest because, as we show in the next section, trading in the Shanghai market

during our sample is dominated by retail investors. Our analysis also exploits heterogeneity among

retail investors and, perhaps most importantly, we search for evidence relating to regulatory

concerns that stock price manipulators may use stock splits to take advantage of unsophisticated

retail investors.

4.1 Trading volume by investor group

We start by analyzing the average daily trading volume for all stocks by each investor

group on the Shanghai stock exchange during the period 2013–2015. We examine six types of

investors: small individual investors with account wealth less than five million Chinese RMB;

large individual investors with account wealth above five million Chinese RMB; domestic mutual

fund; non-mutual fund institutional investors; qualified foreign institutional investors (QFII), and

social security. To compare across groups, we calculate the % of daily trading volume for each

investor group.

29

Table 6 shows that individual investor accounts generate the majority of total trading volume

over this sample period. Small individual accounts generate 77.2% of the trading volume on

average, while large individual accounts generate 11.4%. Together, individual investors in the

market generate nearly 90% of the total trading volume. The next largest investor group are other

institutional investors (7.4%) and mutual funds (4.2%). Social security accounts and qualified

foreign investors comprise of the remaining total trading volume.

The results also suggest that individual investors with small accounts tend to trade smaller

stocks, consistent with the findings in Hong, Jiang, Wang, and Zhao (2014). They are also more

likely to trade past losers relative to winners, which suggests they have contrarian trading

tendencies. This is consistent with patterns found in Ng and Wu (2006) using an earlier sample of

stocks (4/2001–4/2002) from the SSE exchange. In contrast, individual investors with large

accounts are more similar to mutual funds and institutional investors in their preference for trading

larger stocks that tended to have high past returns.20 As we will soon show, the overall tendency

of small individual investors to trade small losers does not carry over to their attraction for stock

splits, which are on average past winners.

Overall the results indicate that the Chinese market during the 2013–2015 is predominantly

dominated by individual investors with small trading accounts. These retail investors have a

preference for trading small capitalization stocks and stocks with low past returns. The

professional money managers, including mutual funds and other institutions, exhibit a preference

for large stocks and stocks with high past returns.

4.2 Which investor groups are attracted to stock splits?

                                                            20 Ng and Wu (2007) find similar evidence that individual investors with large accounts tend to trade like institutional investors in the China market using a sample of stocks from the SSE from 4/2001 to 8/2002.

30

Figure 6 presents the plot for the six investor groups. Stock splits announcements clearly

grab the attention of retail investors with smaller trading accounts. The net sellers are both mutual

funds and other institutional investors. Visual inspections show that in the 30 days prior to the split

announcement, the net trading imbalance for individuals with small accounts is flat and is not

significantly different from zero (0.09, t=0.31) as shown in Panel B of Table 7. Immediately after

split announcement, small individual investors rapidly accumulate shares over the next 120 days.

Panel A of Table 7 shows that the total net trading imbalance from t=+1 to t=+120 is 5.34 (t=4.27).

This implies that individuals with small accounts accumulate more than five times the daily

average trading volume, which on average is about 5% of the stocks’ market capitalization.

We next examine whether it is split announcement that grabs the attention of retail investors

or some other firm characteristics, such as earnings surprise. Using the same sample of stock splits,

we match each stock split announcement to a benchmark group of stocks with earnings news

during the 2013/1 to 2015/6 sample period. Specifically, we match based on stocks in the same

quintiles of three-day CAR around earnings announcement during the quarter, market

capitalization, and past three month return. Then, we calculate the cumulative net trading

imbalance starting from day t=+1 to t=+60 and day t=+1 to t=+120 around stock split

announcement and earnings announcement (for the benchmark).21

Panel A of Table 7 shows that firms announcing stock splits are considerably more likely to

attract the purchase activity of small individual investors compared to those making regular

earnings announcements in the first 60 days around the announcements. The net trading imbalance

is 3.95 (t=3.27) for stock splits compared to 0.62 (t=1.57) for the matched sample of non–split

earnings announcements. The difference is a significant 3.33 (t=3.60).

                                                            21 Our sample starts in 2013 and ends in June of 2015 because we require a benchmark group of stocks for comparison in this test.

31

Large individual investors are not attracted to stock splits but are attracted to regular earnings

announcements. For split announcements, the net trading imbalance is insignificant 0.18 (t=1.21)

for split announcements compared to a significant 0.66 (t=4.21) for regular earnings

announcements.

The results indicate that mutual funds and institutional investors are the main sellers of

shares both around stock split announcements and earnings announcements. For example, the net

trading imbalance for mutual funds is −1.59 (t=−4.56) around split announcements and

−0.147(t=−2.27) around the matched earnings announcement. The difference is a significant −1.42

(t=−4.85). The net trading imbalance for institutional investors is −2.42 (t=−3.17) around split

announcement and −1.17 (t=−3.02) around the matched earnings announcement. The difference is

a significant −1.25 (t=−2.36). Qualified foreign investors and social security accounts are much

smaller traders in the market. Qualified foreign investors are less likely to purchase stocks with

split announcements compared to a matched sample of earnings announcement firms. Social

security accounts exhibit no difference between net imbalance between splits and matched sample

firms.

The results are similar when we measure the total cumulative net imbalance period out to

120 days. The evidence suggests that small individual investors continue to accumulate shares in

companies with split announcements while the institutions are net sellers. Overall, the evidence

suggests that stock splits attract the attention and buying activity of smaller individual investors.

We also examine whether investors groups are more likely to accumulate shares in

anticipation of the splits announcement in Panel B of Table 7. Both groups of retail investors (small

and large) tend to accumulate shares before earnings announcements, perhaps in anticipation of

good news. However, the results indicate that the cumulative net imbalance in the pre-

32

announcement period is not significantly different for the split-announcement companies and

matched stocks. This suggests that investors are not anticipating the split announcement.

4.3 Are individual investors more likely to buy suspicious splits?

In Section 3.3, we identified stock splits that are more likely to be motivated by a desire of

insiders to manipulate stock prices. Our conjecture is that the more sophisticated investors may

avoid these stocks, suggesting that in these cases we should observe more accumulation by

individual investors. To examine this question more carefully, we accumulate total net trading

imbalance for each investor group across what we classify as suspicious announcements. For this

analysis, we use our second sample of complete account level trading data from the Shanghai Stock

Exchange around all stock transfer events from January 1999 through December 2015. This larger

sample of stock splits allows us to compare between suspicious and non-suspicious

announcements. We calculate the within group averages and differences across groups, taking care

to cluster standard errors by quarter.

Panel A of Table 8 presents the results. The evidence indicates that individual investors with

smaller accounts are more likely to purchase shares in split announcing stocks of the suspicious

announcement variety; the total trading imbalance is 4.60 for the suspicious/institutional lockup

group compared to 3.25 for the rest of the split announcers. The difference is 1.34 (t=2.55). For

individual investors with large accounts, there is no statistical difference between their trading

activities between the two groups of stocks. This suggests that the large individual investors are

relatively more informed than the individual investors with smaller accounts.

While the net sellers (i.e., mutual funds and other institutional investors) are more inclined

to sell shares in the suspicious /institutional lockup group of stocks, the differences are not

33

statistically significant. Qualified foreign investors and social security accounts also do not exhibit

statistical differences between the two types of splits.

While the sorts suggest that individual investors with small accounts tend to accumulate

shares in suspicious splits, this finding can potentially be generated by other firm characteristics

that tend to be linked to suspicious splits. To address this concern, we estimate a panel regression

to directly control for these characteristics. We create a dummy variable, suspicious, which equals

one if the stock had poor prior stock returns over the past three months, atypical timing, traces of

tunneling, or have impending institutional share lockup expirations, and zero otherwise. We

include the book to market ratio (B/M), market capitalization (Size), and three-day cumulative

abnormal return around announcement (CAR). Our regressions include quarter fixed effects to

capture macroeconomic time trends and standard errors are clustered each quarter. The dependent

variable is the cumulative net imbalances from day t=−1 to to=+120 in order to capture

announcement effects.

The results in Panel B of Table 8 show that even after controlling for these other firm

characteristics that small individual accounts tend to accumulate the stocks of firms that announce

suspicious splits. The coefficient estimate on the suspicious variable is significantly positive (0.85,

t=2.61). For the control variables, the coefficient on size is significantly positive (0.01, t=2.19).

This is interesting because it suggests a slightly stronger preference around stock splits for larger

stocks for small investors, who typically prefer smaller companies. The coefficient estimates on

the suspicious variable from the regression of the other investor groups are all negative, but they

are not statistically significant.

Overall, our analysis using account level trading data form the Shanghai Stock Exchange

shows that split announcements grab the attention of individual investors with small trading

34

accounts. Splits of the suspicious variety are particularly attractive to these retail investors. This is

consistent with our prediction that most sophisticated investors may tend to avoid these types of

splits. It is also possible that these splits are marketed towards unsophisticated investors, perhaps

to provide liquidity for existing shareholders who wish to exit their positions.

5. Conclusion

The U.S. and Chinese stock markets differ along a number of dimensions. The U.S. market has a

much longer history, and in contrast to the Chinese market, trading in the U.S. market is dominated

by sophisticated institutional investors. The premise of this paper is that stock splits provide a

particularly good experiment for studying how these differences affect the transmission of

information. Splits have no direct effect on corporate cash flows, which makes it easy to compare

these events across markets. Yet, extensive research on stock splits on U.S. data prior to our

sample period suggests that splits seem to convey information to market participants. Therefore,

our analysis of splits provides a useful comparison between the markets, as well as additional out-

of-sample evidence in the U.S. market.

We uncover important differences between the markets that are consistent with the idea

that the Chinese investors are less sophisticated. In particular, we classify a much larger percentage

of Chinese stock splits as “suspicious” and find that the return patterns of these events are

consistent with the presence of manipulation. However, when we exclude the suspicious events,

the returns patterns around stock splits in the U.S. and Chinese markets are remarkably similar.

Specifically, we find post-split cumulative one-year abnormal returns of 4% to 5% in both markets

among non-suspicious split-issuers. We find this evidence somewhat surprising because the U.S.

market consists mainly of sophisticated institutional investors, and there was substantial published

research on stock splits in the U.S. prior to our sample period.

35

Our results also provide an out-of-sample examination of stock splits in the U.S. market.

During the recent 1999–2015 period, stock splits continue to earn abnormal returns over the post

12-month period. The magnitude is lower than earlier periods, which is consistent with the more

general findings in McLean and Pontiff (2016) that anomaly returns have weakened in recent years.

However, it is surprising that such a simple strategy continues to be profitable. The results suggest

that investors in the U.S. continue to underreact to the information conveyed by splits, long after

the discovery of the pattern.

 

36

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39

Appendix 1.

Examples of media reports on the dangers of stock splits in the China market

Headline translation: “The hidden secret of high stock split for public companies: be aware of being the last person in pass the parcel’s game” Tencent (http://finance.qq.com/a/20130711/001123.htm)

Headline translation: “The game of stock split on the financial market”

Xinmin News (http://xinmin.news365.com.cn/ljzjrc/201503/t20150327_1792873.html)

40

Figure 1.

Percentage of Firms that are can be shorted in Chinese A-Share Market from 2010-2016

This figure shows the % of total shares that are can be shorted in the Chinese A shares market from 2010 to 2016. Prior to 2010, short selling was banned in the market. Source: Shanghai and Shenzhen Stock exchange.

0%

10%

20%

30%

40%

50%

60%

2010 2011 2012 2013 2014 2015 2016

Shanghai Shenzhen Entire A Share

41

Figure 2. Average Monthly Short Interest as a Percentage of Tradable A-Shares

This figure shows the average monthly short interest as a percentage of tradable Chinese A shares from 2010 to 2016. Prior to 2010, short selling was banned in the market. Source: Shanghai and Shenzhen stock exchange.

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

2010 2011 2012 2013 2014 2015 2016

Shanghai Shenzhen Entire A Share

42

Figure 3. Market Reactions Around Split Announcements

This figure presents average buy and hold abnormal returns from t=−1 to t+60 days around split announcement (t=0) in the China and U.S. market. The sample period are stock splits that occurred during the period 1999/01–2015/06. In China (U.S.), the buy and hold abnormal return is the buy and hold return minus the daily size-decile (daily DGTW) benchmark.

0%

1%

2%

3%

4%

5%

6%

7%

8%

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

U.S. Market China Market

43

Figure 4. Suspicious Splits in the Chinese market

This figure presents the number of suspicious splits (grey bars/left axis) and the average fraction of suspicious splits (dotted blue line/right axis) in the Chinese market. Suspicious splits without institutional lockups (solid red line) are splits announcements by firms with poor stock performance in the previous 3 months (bottom 2 quintiles of the market), announcements that occur outside of earnings announcement periods, or firms with traces of tunneling (i.e., abnormally high levels of accounts receivable and pre-paid expense). From 2006 onwards, we include split announcements of firms that experience lockup expiration of institutional shares in the month-1 to month+6 period around split announcement (dotted blue line). The sample period for the lock-up analysis is from 2006/01–2015/06 because reporting on lockups begins in 2006. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

20

40

60

80

100

120

140

160

180

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

# of suspicious splits % suspicious splits

# of suspicious splits % of suspicious splits

44

Figure 5. Market Reactions Around Suspicious Splits Announcements

This figure presents average buy and hold abnormal returns from month t+1 to t+12 after split announcement in the China market. Suspicious splits are splits announcements by firms that meet one of the following criteria: poor recent stock performance, splits announcement outside of earnings announcements periods, firms with traces of tunneling (i.e., high levels accounts receivable and pre-paid expense), or impending share lockup by institutional investors. The sample period is 1999/01–2015/06.

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

1 2 3 4 5 6 7 8 9 10 11 12

Suspicious Rest of the splits

45

Figure 6. Net Trading Imbalance around Split Announcement

This figure presents average cumulative net trading imbalance by investor type around the announcement of a stock split for stocks on the Shanghai Stock Exchange. Net trading imbalance is the total buy minus sell volume for each investor group divided by average daily volume over the past year. The sample is 2013/01–2015/06. We examine six types of investors: individual investors with account wealth less than or equal to five million Chinese Yuan; large individual investors with account wealth above five million Chinese Yuan; domestic mutual funds; non-mutual fund institutional investor, social security, and qualified foreign institutional investors (QFII).

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

-30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 110 120

Small_Ind Large_Ind Other_InstMutualFund QFII SocialSecurity

46

Table 1. Summary Statistics

This table presents summary statistics of stock splits for the China and U.S. markets. Panel A reports the number of splits each calendar quarter from Q1 1999 to Q2 2015. Due to the prevalence of trading halts in China, the stock must trade in the three day period (t−1 to t+1) around split announcement to be included in the sample. Panel B reports the number and percentage of firms to issue splits, dividends, or both split and dividends at least once in the calendar year. Panel C reports median values of firm characteristics of stocks that announce splits and those that do not. Size is the market capitalization at December of year t−1 in either RMB or USD. Ret (t−2, t−12) is the return from month t−12 to t−2. Ret (t−1, t−3) is the three-month stock return from month t−3 to t−1. B/M is the book-to-market ratio at Dec of year t−1.Turnover is the share turnover the prior quarter. Idiosyncratic volatility is the idiosyncratic risk measured from a market model of daily returns during prior year.

Panel A. Number of Stock Splits

China Market U.S. Market

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1999 95 52 46 0 1999 124 91 64 88 2000 78 42 41 0 2000 113 64 86 35 2001 101 41 23 0 2001 41 51 44 40 2002 77 42 31 0 2002 53 92 25 20 2003 67 41 22 0 2003 31 43 72 81 2004 119 62 35 1 2004 85 71 51 54 2005 86 40 10 0 2005 89 77 54 53 2006 72 46 28 1 2006 57 92 33 31 2007 75 67 30 0 2007 29 44 34 23 2008 191 107 28 0 2008 13 9 13 2 2009 102 82 13 1 2009 2 4 1 5 2010 164 92 25 0 2010 10 14 4 17 2011 197 123 28 0 2011 15 28 9 4 2012 198 111 17 1 2012 16 12 5 10 2013 147 108 17 2 2013 6 20 10 16 2014 129 105 21 18 2014 13 14 9 7 2015 154 134 - - 2015 6 3 - -

47

Table 1. (Continued)

Panel B. Number and % of firms that announce splits and cash dividends each year

Number of firms % of firms Year Stocks Split Dividend Split+Dividend Split Dividend Split+Dividend 1999 926 193 197 32 20.3% 21.3% 3.5% 2000 1027 161 233 46 15.7% 22.7% 4.5% 2001 1172 158 533 90 13.5% 45.5% 7.7% 2002 1252 144 595 96 11.5% 47.5% 7.7% 2003 1315 129 551 79 9.8% 41.9% 6.0% 2004 1378 215 525 132 15.6% 38.1% 9.6% 2005 1466 136 645 111 9.3% 44.0% 7.6% 2006 1461 147 507 104 10.1% 34.7% 7.1% 2007 1544 166 599 110 10.8% 38.8% 7.1% 2008 1658 325 655 220 19.6% 39.5% 13.3% 2009 1712 197 769 153 11.5% 44.9% 8.9% 2010 1860 278 820 221 14.9% 44.1% 11.9% 2011 2215 347 925 279 15.7% 41.8% 12.6% 2012 2449 327 1183 297 13.4% 48.3% 12.1% 2013 2577 271 1395 252 10.5% 54.1% 9.8% 2014 2621 270 1416 249 10.3% 54.0% 9.5% 2015 2736 288 1252 255 10.5% 45.8% 9.3%

Panel C. Median Firm Characteristics of Stocks that Announce/ Do not announce Splits

China Market U.S. Market

Split firms Non-Split Firms Split firms Non-Split Firms

Size ($10,000 RMB) $6,771 $7,311 Size ($M USD) $3,796 $3,078 Ret (t−2, t−12) 25.98% 12.76% Ret (t−2, t−12) 50.90% 10.71% Ret (t−1, t−3) 8.79% 3.93% Ret (t−1, t−3) 16.10% 4.08% B/M 0.35 0.96 B/M 0.43 0.78 Turnover (qtr) 55.2% 64.0% Turnover (qtr) 48.3% 45.5% Idiosyncratic Volatility 34.2% 35.6% Idiosyncratic Volatility 29.8% 37.5%

n 3786 224,755 n 2432 936,438

48

Table 2. Abnormal returns around split announcement for China and U.S. Market

This table presents average buy and hold abnormal returns around split announcements for the United States and China market using daily and monthly return data for split announcements between 1999/01–2015/06. Panel A reports daily buy and hold abnormal returns around announcement date (t=0) for the China and U.S. market. For the China market (U.S.), the buy and hold abnormal return is calculated as the buy and hold return minus the size–decile (DGTW) benchmark. Panel B reports three-day cumulative abnormal returns around events that occur after initial split announcement (Announcement date). Shareholder meeting date is the date of the shareholder meeting which is available only for in the China market. Record date is the shareholder date of record in the U.S. market. Content date is the date that officially confirms the proposed split and announces the split date. Ex-date is the day after the split distribution. Days after announcement is the number of trading days after split announcement. The t–statistics in parenthesis correct for clustering each calendar month using robust (White) standard errors.

Panel A. Abnormal Returns: U.S. and China Market (1999−2015)

Daily Monthly N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [3 to −1] [month 0] [+1 to +6] [+1 to +12]China 3786 mean 2.65 1.84 −0.05 1.93 16.27 4.68 4.41 1.89 1.72

t–stat (11.51) (13.96) (−0.32) (3.14) (7.19) (6.06) (14.07) (3.04) (1.72) United States 2432 mean 2.74 3.10 1.52 3.07 89.96 19.64 7.88 3.26 4.20

t–stat (8.16) (13.27) (6.52) (4.06) (9.99) (7.92) (10.12) (4.17) (3.25)

Panel B. Abnormal 3 day return around post−split events

N Announcement

Date Shareholder

Meeting Date Record Date (U.S. Only)

Content Date (China Only)

Ex-Date

China 3786 Mean 1.84 0.43 N/A 2.24 −1.19 t–stat (13.96) (4.14) (10.48) (−6.26)

Days after Announcement 0 25.1 N/A 37.0 41.4

United States 2432 mean 3.10 N/A 0.20 N/A 0.85 t–stat (13.27) (1.59) (5.16)

Days after Announcement 0 N/A 14.8 N/A 26.0

49

Table 3. Market reaction to cash dividends compared to splits in China Market (1999-2015)

This table presents average buy and hold abnormal returns around cash dividend and cash dividend + stock split announcements in the China market. Dividend increase are firms that announce cash dividend that are greater than their prior dividend payment, measured over the past 24 months. The subsample are concurrent cash dividend and split announcement events. The sample period is 1999/01–2015/06. The t-statistics in parenthesis are clustered each calendar month using robust (White) standard errors.

Daily Monthly

N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [−3 to −1] [month 0] [+1 to +6] [+1 to +12]

Dividend 12921 mean 0.85 −0.09 −0.02 −0.35 1.68 1.03 0.64 −0.69 −1.04 t–stat (6.94) (−1.38) (−0.15) (−1.24) (1.96) (3.06) (4.59) (−1.25) (−1.19)

Dividend increase 5546 mean 0.63 −0.44 −0.02 −0.54 2.73 1.15 0.06 −0.61 −0.81 t–stat (5.98) (−4.58) (−0.13) (−1.56) (2.73) (3.26) (0.32) (−0.99) (−0.95)

Subsample: Split + dividend announcements

Dividend with Split 2576 mean 2.44 1.89 0.10 2.25 13.62 3.20 4.37 1.97 2.91 t–stat (9.78) (12.38) (0.46) (2.87) (6.82) (4.76) (12.29) (2.46) (2.34)

Dividend increase 1811 mean 2.77 2.00 0.09 2.33 14.25 3.86 4.75 2.25 1.12 with Split t–stat (11.06) (11.65) (0.39) (2.91) (6.35) (4.81) (12.51) (2.60) (0.85)

50

Table 4. Post earnings announcement drift and stock splits

This table presents average buy and hold abnormal returns of stocks around earnings announcements in the China market. The average buy and hold abnormal return is calculated as the buy and hold return minus the size–decile benchmark. Panel A presents average buy and hold returns from t=+2 to t=+60 of stocks sorted in quintiles based on three–day abnormal return earnings announcement. Stocks are sorted into five groups based on three–day abnormal market reaction breakpoints from the prior eight quarters of earnings announcement. The bottom row [−1,+1] reports the three–day abnormal announcement return (the sorting variable). Positive−Negative is the average difference in returns between the Positive and Negative surprise portfolios each quarter. Panel B presents various average buy and hold return periods for stock splits during earnings announcements and without earnings announcements. We report the mean estimates and t–statistics in parentheses, testing the hypothesis of zero abnormal return. The sample period is from 2002–2015 since reporting of quarterly earnings begins in 2002.

Panel A. Initial market reaction to earnings announcement and post earnings announcement drift

Abnormal Returns

Positive Surprise

4 3 2 Negative Surprise

Positive−Negative

[+2,+60] mean 0.05% 0.06% −0.23% −0.63% −1.12% 1.17% t–stat (0.16) (0.23) (−1.00) (−2.91) (−4.70) (2.85)

[−1,+1] Mean 6.01% 1.46% −0.43% −2.39% −6.23% 12.24%    t–stat (16.64) (6.08) (−1.81) (−9.17) (−19.51) (31.84)

Panel B. Splits during and outside earnings announcement periods (1999−2015)

Daily Monthly

N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [−3 to −1] [month 0] [+1 to +6] [+1 to +12]

Split during E.A. 3494 mean 2.58 1.62 −0.04 1.64 16.04 4.54 4.10 1.89 1.52 t–stat (11.20) (14.49) (−0.23) (2.64) (6.71) (5.83) (13.60) (3.14) (1.50)

Split outside E.A. 292 mean 3.45 4.68 −0.03 6.51 19.04 6.37 8.24 1.91 −3.47 t–stat (5.32) (7.70) (−0.07) (2.91) (4.65) (3.01) (6.82) (0.73) (−0.88)

51

Table 5. Suspicious Splits: Abnormal returns around split announcement for China and U.S. Market

This table presents average buy and hold abnormal returns around split announcements for a sample of suspicious split announcements in the China and U.S. market. Panel A presents subsamples of splits by recent stock performance in the United States. Poor stock return are the stocks that were in the bottom quintile/bottom 2 quintiles in returns during the previous three months. Panel B presents subsamples of China split announcements by the type of suspicious activity. Traces of Tunneling are stocks in the top quintile of total net account receivables and net prepaid expenses. Split outside E.A. are split announcements that occur outside of an earnings announcement. Institutional lockups are firms that experience lockup expiration of institutional shares in the month−1 to month+6 period around split announcement. The sample period for the lock-up analysis is from 2006/01–2015/06 because reporting on lockups begins in 2006. Panel C presents subsamples of China split announcements for all suspicious (non-suspicious) are splits announcements that exhibit any (none) of the above suspicious characteristics and different time periods. The average buy and hold abnormal return is calculated as the buy and hold return minus the size-decile benchmark (China) and DGTW benchmark (U.S.), where t=0 is the calendar day of the split announcement. t-statistics are calculated using robust White standard errors and clustered each calendar month. The sample period is from 1999/01–2015/06.

Panel A. Suspicious Splits in the United States: 1999-2015

Daily Monthly N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [−3 to −1] [month 0] [+1 to +6] [+1 to +12]

Poor Stock Return 95 mean 2.99 6.92 −0.02 3.03 55.87 −21.82 17.74 −1.12 −3.66 (Bottom quintile) t–stat (1.31) (4.11) (−0.03) (1.36) (3.33) (−16.88) (2.25) (−0.39) (−0.95)

Poor Stock Return 310 mean 1.88 4.05 1.29 2.78 31.30 −12.92 10.81 −0.12 −1.20 (Bottom 2 quintile) t–stat (2.50) (6.43) (2.34) (2.04) (4.91) (−18.07) (4.17) (−0.08) (−0.55)

Rest 2122 mean 2.87 2.96 1.55 3.11 100.00 24.79 7.39 3.79 5.07 t–stat (7.75) (12.20) (6.22) (3.82) (9.93) (8.94) (9.83) (4.35) (3.33)

52

Table 5. (Continued)

Panel B. Suspicious Splits by Type in China: 1999-2015

Daily Monthly N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [−3 to −1] [month 0] [+1 to +6] [+1 to +12]

Poor Stock Return 603 mean 1.94 2.07 −0.23 −0.88 0.25 −17.31 4.34 0.00 −2.54 (Bottom quintile) t–stat (4.42) (7.80) (−0.71) (−0.86) (0.10) (−20.31) (5.79) (0.00) (−0.95)

Traces of Tunneling 739 mean 2.76 1.58 0.15 3.27 23.53 6.28 4.28 3.70 −0.36 t–stat (6.96) (6.28) (0.49) (2.95) (6.96) (3.95) (7.72) (3.27) (−0.12)

Split outside E.A 292 mean 3.45 4.68 −0.03 6.51 19.04 6.37 8.24 1.91 −3.47 t–stat (5.32) (7.70) (−0.07) (2.91) (4.65) (3.01) (6.82) (0.73) (−0.88)

Institutional Lockups 210 mean 2.04 1.62 −0.55 −1.23 13.15 1.78 2.89 −0.42 −4.97 (starts in 2006) t–stat (3.88) (3.64) (−0.86) (−0.82) (3.01) (1.21) (3.35) (−0.21) (−1.86)

Panel C. Aggregated Suspicious and Non-Suspicious Splits in China: 1999-2015

Daily Monthly N [−10,−2] [−1,+1] [+2,+10] [+2,+60] [−12 to −1] [−3 to −1] [month 0] [+1 to +6] [+1 to +12]

Non-suspicious (rest) 2179 mean 2.73 1.72 0.00 2.27 17.61 8.45 4.27 2.24 3.90 t–stat (11.10) (14.37) (−0.01) (3.09) (6.61) (11.87) (12.22) (3.42) (3.08)

All suspicious 1607 mean 2.54 2.01 −0.13 1.48 14.45 −0.43 4.61 1.41 −2.56 t–stat (8.70) (8.71) (−0.60) (1.92) (6.07) (−0.36) (9.45) (1.55) (−1.33)

Suspicious splits: Sub–periods

1999−2010 858 mean 2.91 1.59 −0.36 0.46 19.44 0.19 4.69 1.94 −0.48 t–stat (9.24) (5.76) (−1.26) (0.43) (5.67) (0.10) (9.35) (1.66) (−0.18)

2011−2015 749 mean 2.12 2.48 0.14 2.64 8.73 −1.13 4.53 0.81 −4.94 t–stat (4.28) (6.33) (0.44) (2.34) (3.38) (−0.82) (5.14) (0.56) (−1.86)

53

Table 6. Average % of daily trading volume by investor type

This table presents the average % of daily trading volume by investor type for all stocks on the Shanghai Stock Exchange from 2013–2015. The first row provides the full sample average. Stocks are split into three terciles Small/Mid/Large (30%/40%/30%) stocks based on the Dec t−1 market capitalization, Low/Mid/High B/M terciles (30%/40%/30%) based on the book to market ratio at Dec t−1, and Low/Mid/High terciles (30%/40%/30%) past returns based on past three–month return.

Small

Individual Accounts

Large Individual Accounts

Mutual Fund Other

Institutions QFII Social Security

Daily Average 77.2% 11.4% 4.2% 7.4% 1.1% 2.8%

Small Cap 82.9% 10.4% 2.8% 5.5% 0.6% 4.5% Mid Cap 81.3% 10.4% 3.1% 5.7% 0.6% 4.0% Large Cap 70.8% 12.9% 4.9% 9.8% 1.6% 2.6% Low Past Return 77.9% 11.1% 3.6% 7.3% 1.1% 2.9% Mid 77.6% 11.2% 4.0% 7.4% 1.2% 3.0% High Past Return 75.3% 12.2% 5.5% 7.7% 1.0% 2.7%

Low B/M 77.1% 11.4% 5.3% 7.2% 0.9% 2.9% Mid B/M 78.4% 11.0% 3.8% 7.0% 1.0% 2.9% High B/M 75.8% 11.5% 3.4% 8.4% 1.6% 2.8%

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Table 7. Cumulative net trading imbalance around split announcements

This table presents the average cumulative net trading imbalance by investor type for split announcements and matched earnings announcements without split on the Shanghai Stock Exchange during the period 2013/01–2015/06. Net trading imbalance is the daily buy minus daily sell divided by average daily volume during the past year. Cumulative net trading imbalance is summation of the daily imbalance from t+1 to +60 or t+1 to +120 in Panel A and t=−10 to t=−2 and t=−30 to =−2 in Panel B. Splits are the entire sample of split announcements between 2013/01–2015/06. Match sample is the group of stocks matched on size, B/M, prior three-month return, and three-day market reaction to earnings announcement. The last column, Difference, represents the difference between the two groups. N is the number of stock splits in the sample. t-statistics are presented in parenthesis and are calculated using robust (White) standard errors and are clustered each calendar quarter.

Panel A. Average cumulative net trading imbalance after split announcement

t=+1 to +60 t=+1 to +120

N Splits Matched sample

Non-split Difference

(Splits−Non-split)

Splits Matched sample

Non-split Difference

(Splits−Non-split)

Small Individual 195 3.95 0.62 3.33 5.34 0.63 4.70 Accounts (3.27) (1.57) (3.60) (4.27) (1.50) (4.66)

Large Individual 195 0.18 0.66 −0.48 0.00 1.03 −1.03 Accounts (1.21) (4.21) (−1.92) (0.01) (5.89) (−2.85)

Mutual Funds 195 −1.59 −0.17 −1.42 −2.20 −0.17 −2.03 (−4.56) (−2.27) (−4.85) (−4.36) (−2.61) (−4.16)

Other Institutions 195 −2.42 −1.17 −1.25 −2.96 −1.64 −1.33 (−3.17) (−3.02) (−2.36) (−4.38) (−6.73) (−2.45)

QFII 195 −0.05 0.10 −0.14 −0.04 0.18 −0.22 (−0.46) (1.09) (−3.87) (−0.30) (1.15) (−4.33)

Social Security 195 −0.07 −0.04 −0.04 −0.14 −0.04 −0.10 (−0.67) (−1.06) (−0.30) (−0.90) (−0.96) (−0.58)

55

Table 7. (Continued)

Panel B. Cumulative average net trading imbalance before splits announcement

t=−10 to −2 t=−30 to−2

N Splits Matched sample

Non-splits Difference

(Splits – Non-split) Splits

Matched sample Non-split

Difference (Splits – Non-split)

Small Individual 195 0.13 0.24 −0.11 0.09 0.67 −0.58 Accounts (1.43) (3.18) (−1.25) (0.31) (3.07) (−2.23)

Large Individual 195 0.13 0.14 −0.01 0.29 0.38 −0.09 Accounts (2.72) (4.86) (−0.11) (2.20) (7.51) (−0.60)

Mutual Funds 195 −0.07 −0.11 0.04 0.03 −0.38 0.41 (−1.49) (−4.12) (0.83) (0.19) (−3.19) (1.81)

Other Institutions 195 −0.21 −0.23 0.02 −0.55 −0.64 0.09 (−2.55) (−2.28) (0.24) (−2.69) (−3.27) (0.42)

QFII 195 0.00 −0.02 0.02 0.05 −0.01 0.06 (0.05) (−2.24) (0.87) (1.71) (−0.51) (1.40)

Social Security 195 0.02 −0.02 0.04 0.09 −0.02 0.11 (0.80) (−2.70) (1.70) (1.34) (−1.13) (1.60)

56

Table 8. Trading net imbalance around split announcements

This table presents average total net trading imbalance (t=−1 to t+120) for each investor type for split announcements on the Shanghai Stock Exchange during the period 1999/01–2015/06. All splits are the full sample of split announcements. Suspicious splits are the group of stocks that either had poor prior stock returns over the past three months, occur outside of earnings announcement, exhibited traces of tunneling, or had impending institutional share lockup expirations. Non-suspicious splits are a remaining set of split announcements. The difference is the difference between the two groups. Panel B reports results from a panel regression of net trading imbalance from t=−1 to t+120 for each investors group on a suspicious dummy variable and additional characteristics including book-to-market (B/M), market capitalization (Size), three-day announcement return (CAR). t-statistics are presented in parenthesis and are calculated using robust (White) standard errors and are clustered each calendar quarter.

Panel A. Average trading net imbalance (t=−1 to t+120) by investor type and split type

Investor type All Splits Suspicious

Splits Non-suspicious

Splits

Difference (Suspicious−

Non-suspicious)

Small Individual Accounts 3.72 4.60 3.25 1.34 (6.47) (6.40) (5.67) (2.55)

Large Individual Accounts −0.29 −0.30 −0.29 −0.02 (−2.86) (−1.50) (−2.75) (−0.07)

Mutual Funds −1.40 −2.00 −1.08 −0.92 (−3.80) (−3.67) (−2.86) (−1.76)

Other Institutions −1.91 −2.07 −1.83 −0.25 (−8.17) (−6.96) (−7.29) (−0.90)

QFII 0.00 −0.05 0.03 −0.08 (−0.01) (−0.89) (0.44) (−1.11)

Social Security −0.11 −0.17 −0.08 −0.08 (−1.64) (−1.97) (−1.12) (−1.08)

Panel B. Panel regression of net trading imbalance on stock split characteristics

Dependent variable: Net trading imbalance t–1 to t+120

Small Individual Accounts

Large Individual Accounts

Mutual Funds

Other QFII Social

Security

Suspicious 0.85 −0.02 −0.34 −0.32 0.00 −0.05 (2.61) (−0.21) (−1.20) (−1.31) (−0.06) (−1.11)

B/M −1.78 −0.38 1.58 0.48 0.10 0.05 (−1.66) (−1.75) (1.90) (0.87) (1.22) (0.39)

Size 0.01 0.01 −0.02 0.00 0.00 0.00 (2.19) (3.23) (−5.32) (−0.16) (0.62) (−0.42)

CAR 2.57 −0.57 0.32 −1.10 0.11 0.12 (0.90) (−1.07) (0.15) (−0.63) (0.56) (0.55)

Intercept 3.17 −0.34 −1.53 −0.85 −0.37 −0.03 (7.52) (−3.05) (−4.25) (−3.86) (−9.75) (−0.66)

Adj RSQ 0.1692 0.0777 0.0458 0.1019 0.0277 0.0517 N 866 866 866 866 866 866