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William A. Orme

Xuanjuan Chen, Kenneth A. Kim, Tong Yao, and Tong Yu

On the predictability of Chinese stock returns

On the predictability of Chinese stock returns

Xuanjuan Chen, Kenneth A. Kim, Tong Yao, and Tong Yu*

January 2009

* Chen is from Kansas State University. Email: jchen@ksu.edu. Kim is from State University of New York at Buffalo and PACAP. Email: kk52@buffalo.edu. Yao is from University of Iowa. Email: tong_yao@uiowa.edu. Yu is from University of Rhode Island. Email: tongyu@uri.edu. All errors are our own.

On the predictability of Chinese stock returns

Abstract

In this study, we take eighteen firm characteristic variables documented to predict cross-

sectional stock returns in the U.S. market, and examine their relation with stock returns in the

emerging Chinese equity market for the sample period from 1995 to 2007. We find relatively

weak predictability for Chinese stocks. When forming sorted portfolios, six out of eighteen firm

characteristics generate top-bottom decile return spreads that are statistically significant; the

return spreads across the sorted portfolios are generally smaller relative to those in the U.S.

market. We consider potential explanations for the cross-country difference in return

predictability. While return predictability is often considered an indication of market

inefficiency, it is unlikely that weaker predictability in the Chinese market is due to higher

efficiency. In addition, Chinese stocks are more homogeneous in many of the firm characteristics

examined; however, difference in heterogeneity does not completely explain the difference in

return predictability between the two markets. Finally, we examine the effect of stock return

synchronicity, which is measured by the market model R-square and is considered an indication

of price inefficiency (Morck, Yeung, and Yu, 2000). The average synchronicity for Chinese

stocks is substantially higher than that for U.S. stocks. Further, return predictability is stronger

for stocks with lower synchronicity. Our results suggest a more intricate relationship between

market efficiency and stock return predictability than conventionally perceived.

1

On the predictability of Chinese stock returns 1. Introduction

The stock market in China has grown rapidly in recent years. It plays an increasingly

active role in China’s economic growth, and is increasingly accessible to international

investors. Naturally, understanding the economic forces and individual firm characteristics

driving stock price movements in this market becomes an important issue. However, to date,

academic research in this area is still nascent. The purpose of this study is to provide a

systematic analysis on the relationship between firm characteristics and cross-sectional stock

returns in this emerging yet already quite large stock market.

The fact that stock returns can be predicted by various firm characteristics such as

size, book-to-market ratio, and past stock returns – which is generally termed “cross-sectional

return predictability” – has been well-documented for the U.S. market and many other equity

markets around the world. The cause of such predictability has also been long debated.

Unexplained by standard asset pricing models such as CAPM, cross-sectional return

predictability is viewed by many as prima facie evidence of market inefficiency. Meanwhile,

other researchers attribute predictability to unmeasured risk factors or rational dynamics in

conditional risk-return tradeoff. In this study, we take a large set of return-predictive firm

characteristic variables that are documented in the U.S. market, and examine whether these

variables can predict stock returns in the Chinese market. While our approach to document

return predictability is systematic, our goal is modest. We do not intend to provide a definite

answer on whether such predictability is a rational asset pricing effect or due to market

inefficiency (answering this question is an ambitious task that we leave for future studies).

However, some of our empirical results are informative in evaluating a subset of explanations.

Moreover, we believe our findings can serve as valuable basis for the further discussions on

the causes and explanations of stock return predictability in the emerging markets.

To the extent that return predictability is caused by investor misreaction to information

or irrational preferences, there are reasons to anticipate strong predictability in the Chinese

market. Several studies have documented strong behavioral biases among investors in this

market, such as over-confidence, disposition effect, representativeness bias, and herding; see,

e.g., Chen, Kim, Nofsinger, and Rui (2007), Feng and Seasholes (2005), and Shumway and

Wu (2006). In addition, stock trading is dominated by individual investors (e.g., Chen, Kim,

2

Nofsinger, and Rui, 2007), who are potentially subject to stronger biases relative to

sophisticated institutional investors. Further, short-sales are not allowed in China, making it

difficult for mispricing to be quickly arbitraged away.1

There are several studies on the predictability of Chinese stock returns; however, these

studies typically focus on a small set of predictive variables. Wang and Xu (2004) find that

firm size explains cross-sectional differences in returns but that the book-to-market ratio does

not. Kang, Liu, and Ni (2002) document price momentum in the Chinese market while Wang

(2004) finds the contrary. Wang and Chin (2004) further document the return predictive

power of trading volume. Perhaps the most comprehensive study so far in this area is Eun and

Huang (2007). They find that that although stock beta is not priced in cross-sectional stock

returns in the Chinese market, a few firm characteristics, including size, book-to-market ratio,

firm-specific risk, dividend-yield, and the existence of offshore shares (B and H shares), can

be used to predict returns. They argue that many of these effects are related to market

imperfections, and that “[g]iven imperfections, stocks are priced rather rationally in China,

despite the widespread perception to the contrary.”

We specifically identify 18 firm-specific variables, known to be predictive of cross-

sectional stock returns at the annual horizon in the U.S. market, and test their predictability on

Chinese stock returns. The variables we include in our study are as follows: (i) market

capitalization, book-to-market ratio, and momentum (i.e., past 12-month stock returns), which

are three return predictors most extensively scrutinized in the U.S. literature; (ii) three

conventional value indicators: earnings-to-price ratio, cash flow-to-price ratio, and sales

growth; (iii) two measures for earnings quality: accounting accruals and net operating assets;

(iv) measures of firms’ tangible and intangible investments, including capital expenditure,

research and development expenditure, and advertising expenditure; (v) asset growth and

change in gross profit margin, which summarize the firms’ growth in size and profit margins,

(vi) equity financing and debt financing for firms’ external financing activities, and finally,

(vii) idiosyncratic return volatility, trading turnover, and Amihud’s (2002) illiquidity ratio as

variables related to information uncertainty or liquidity of stocks.

1 To the extent that stock return predictability is driven by rational asset pricing effect, the existence of such predictability is interesting on its own; it suggests that there are similar risk factors or similar dynamics in risk-return tradeoff in play in both the U.S. and Chinese markets.

3

We find that during the period from 1995 to 2007, almost all of the proposed

predictors predict the direction of subsequent one-year stock returns in a way consistent with

their predictive patterns in the U.S. market. To be specific, after adjusting for the sign to make

each variable positively related to stock returns in the U.S. market, the return difference

between top and bottom decile portfolios sorted by each individual return predictor is mostly

positive in the Chinese market. Six of the 18 proposed predictors are statistically significant in

explaining the cross-sectional variation of subsequent returns. The variables with significant

return predictability include the book-to-market ratio, accruals, net operating assets, R&D

spending, asset growth, and illiquidity. When performing the same tests on U.S. stocks during

the same period, we find significant return spreads between top and bottom decile portfolios

for 10 out of 18 variables, suggesting a weaker anomaly effect in the Chinese market than in

the U.S. market. For robustness we examine the alphas of stock portfolios sorted on firm

characteristics, based on the Fama-French three factor model. Five out of the 18 variables

produce significant alphas for the top-bottom decile return spread in the Chinese market while

10 predictors produce significant alphas in the U.S. market. The overall message is that, stock

return predictability exists in the Chinese market, but the effect is weaker relative to the U.S.

market.

What explains the weaker predictability of Chinese stock returns? To start with, we

first point out an intriguing contradiction of our empirical evidence with the conventional

perception. It is often considered that, because return predictability is an indication of market

inefficiency (conditional on behavioral explanations), in cross-country comparisons, the

market with higher return predictability should be the one with higher degree of market

inefficiency. This, however, is unlikely the case because the Chinese market is usually

perceived to be much less efficient.

One possible explanation we consider is that Chinese firms are more homogeneous in

the characteristics examined. If there is not much cross-sectional dispersion in a return-

predictive variable, the cross-sectional dispersion in stock return associated with the variable

will also be small. To examine this hypothesis, we compare the cross-sectional differences in

the return predictors themselves between the two markets. We find that indeed return

predictors are more homogeneous in China. However, we also find that the sensitivity of

stock returns to per unit cross-sectional difference in many of the variables is weaker in

4

China. We further confirm such results by using Fama-MacBeth (1973) cross-sectional

regressions. Therefore, lack of cross-sectional dispersion of firm characteristics is not the

whole reason for weaker return predictability in China.

We next consider a hypothesis that is opposite to conventional wisdom. That is, the

lack of return predictability in China is actually due to long-lasting market inefficiency. Under

the behavioral asset pricing framework, return predictability depends on two factors –

investors’ initial misreaction and subsequent price correction. If initial mispricing is large, but

stock price is persistently noisy, to the extent that mispricing is seldom corrected, then return

predictability will be weak. This is likely for an emerging market such as China, where stock

prices are very noisy and at the same time fundamental information is often not reliable. For

example, earnings management and accounting manipulation have been found to be prevalent

(e.g., Chen and Yuan, 2004; Haw, Qi, Wu, and Wu, 2005; Jian and Wong, 2004); there is also

evidence of rampant market manipulation (e.g., He, 1998; Shenzhen Stock Exchange, 2005).

To test this hypothesis, we resort to a well-documented measure of valuation

efficiency – stock return synchronicity, or the R-square of regressing individual stock returns

onto market returns. 2 A number of studies have concluded that return synchronicity is

inversely related to stock price informativeness; that is, lower return R2 is associated with

greater capitalization of firm-specific information (Roll 1988, Morck, Yeung, and Yu 2000,

and Durnev, Morck, Yeung, and Zarowin 2003). It is also well-known that in less developed

capital markets, R2 tends to be particularly high, suggesting low pricing efficiency.

Our empirical evidence is consistent with this hypothesis. During our sample period,

the average R2 in China is 0.46 while it is 0.12 in the U.S. market, indicating that stock prices

in the Chinese market incorporate firm-specific information to a substantially less degree.

Further, when we divide Chinese stocks into subsamples based on synchronicity, we find that

return predictability is stronger among stocks with low R2, relative to stocks with high R2.

Therefore, weaker predictability in China does not mean that Chinese stocks are more

rationally priced; rather, it is a symptom of persistent noisy valuation and persistent

mispricing. An interesting implication that one can generalize, at least in the context of

2 Admittedly, our test here is indirect. An ideal direct test of the hypothesis would involve identifying an “intrinsic value” for a stock based on fundamental information, and then examining the joint process of the observed stock price and the intrinsic value. However, this approach is difficult to implement due to lack of reliable fundamental information in the Chinese market.

5

comparing international financial markets, is that there exists an intricate relationship between

return predictability and market efficiency. It would be naïve to label a market more efficient

simply because there is less return predictability.

The remainder of the paper is organized as the following. Section II provides a review

of the existing literature on stock return predictability. Section III discusses data and

methodology for constructing stock return predictors for both the U.S. and Chinese markets.

Section IV and V provide empirical results. Section VI concludes.

2. Brief discussion of stock return predictors

The literature on the relation between firm-specific variables and the cross-section of

expected stock returns is vast. Within this literature, we attempt to identify the most important

firm-specific return predictor variables. We end up with 18 such variables, and group them

into the following categories: (i) three return predictors most extensively scrutinized in the

U.S. literature: market capitalization, the book-to-market ratio, and momentum (i.e., past 12-

month stock returns); (ii) three conventional value indicators: earnings-to-price ratio, cash

flow-to-price ratio, and sales growth; (iii) two measures for earnings quality: accounting

accruals and net operating assets; (iv) measures of firms’ tangible and intangible investments,

including capital expenditure, research and development expenditure, and advertising

expenditure; (v) asset growth and change in gross profit margin, which summarize the firms’

growth in size and profit margins, (vi) equity financing and debt financing for firms’ external

financing activities, and finally, (vii) variables related to information asymmetry or liquidity

of stocks including idiosyncratic return volatility, trading turnover, and Amihud’s (2002)

illiquidity ratio. In this section, we offer a brief literature review of these variables.

The size effect and the value effect are among the earliest proposed stock return

predictors (e.g., Banz, 1981; Basu, 1977; Reinganum, 1981). Smaller market capitalization

firms and firms with higher earnings-to-price ratios tend to earn higher returns. Similarly,

Fama and French (1992) show that firms with higher book-to-market ratios subsequently have

higher returns. Lakonishok, Shleifer, and Vishny (1994) report that future returns are

positively correlated with cash flow-to-price ratio and negatively correlated with past sales

growth. They suggest that investors overvalue firms’ past performances, so these variables are

often jointly regarded as value or contrarian indicators.

6

Jegadeesh and Titman (1993) show that stocks with better past returns subsequently

earn higher returns, at horizons ranging from 3 to 12 months. The buying of past winners and

the selling of past losers are widely known as momentum strategies. As shown by Fama and

French (1996), the momentum effect is not subsumed by the size or value effects. Several

behavioral models have been proposed to explain the momentum effect based on investor

underreaction or overreaction to information (Barberis, Shleifer, and Vishny, 1998; Daniel,

Hirshleifer, and Subrahmanyam, 1998; Hong and Stein, 1999) or irrational investor

preferences (Grinblatt and Han, 2005).

Sloan (1996) reports an accruals anomaly. Stocks with higher accounting accruals

tend to have lower future returns. His explanation for the anomaly is investors overvalue

firms with high accruals thinking that accruals will persist. Hirshleifer, Hou, Teoh, and

Zhang (2004) find that firms with higher net operating assets (NOA) have lower future returns,

suggesting that marginal investors do not understand that high NOA implies decreasing

returns to assets scale. These two anomalies are somewhat related as net operating assets is

the accumulation over time of the difference between net operating income and free cash

flows, plus capitalized investments. However, despite their potential relation, Hirshleifer, et

al. (2004) show the explanatory power of accruals and NOA do not subsume each other when

used jointly to predict returns. These two effects are often referred to as earnings quality

anomalies.

Titman, Wei, and Xie (2004), and Beneish, Lee, and Tarpley (2001) find that firms

with high capital investments subsequently have low returns. Titman, et al. (2004) attribute

this finding to an overinvestment tendency of corporate managers and investor underreaction

to information. In contrast to evidence on tangible capital investments, Chan, Lakonishok and

Sougiannis (2001) find that ratios of corporate R&D spending and advertising spending to

market capitalization are positively correlated with future returns. They point out that

intangible investments on R&D and advertising are expensed rather than capitalized in

accounting treatment (to the effect of depressing current earnings at the benefit of future

earnings) and that investors appear to misreact to this accounting effect. The effects of capital

expenditure, R&D, and advertising can be referred to as corporate investment anomalies.

A large number of corporate event studies have shown that future stock returns are

abnormally low in the years following initial public offerings (Ritter, 1991), seasoned equity

7

offerings (Loughran and Ritter, 1997), debt offerings (Spiess and Affleck-Graves, 1999), and

bank borrowings (Billett, Flannery, and Garfinkel, 2006). Conversely, future stock returns are

abnormally high following stock repurchases (Ikenberry, Lakonishok, and Vermaelen, 1995;

Lakonishok and Vermaelen, 1990). Bradshaw, Sloan and Richardson (2006) summarize

firms’ external financing activities into two variables: external equity financing and debt

financing. They report that these two variables are negatively correlated with future stock

returns, and they attribute this pattern to investor optimism and firms’ efforts to time the

market in raising capital. We categorize all of these variables and findings as financing-

related anomalies.

Related to the corporate investment and financing anomalies is the asset growth

anomaly reported by Cooper, Gulen and Schill (2007). They find a strong inverse relation

between firm asset growth and future stock returns. They associate their finding to investor

misreaction to assets growth. Because firms in their early growth stages experience a positive

relation between assets growth and subsequent returns, investors might assume this relation

will persist into the future thus underappreciating that return on assets diminishes as asset size

increases.

Abarbanell and Bushee (1998) view changes in gross profit margins as part of a

fundamental analysis strategy. Greater changes in gross margins indicate an improvement in

the firm’s terms of trade, which, in turn, leads to higher expected operating performance and

future stock returns.

Finally, information uncertainty and stock liquidity have also been found to be related

to future returns. Ang, Hodrick, Xing, and Zhang (2006) show that stocks with high

idiosyncratic volatility risk have low subsequent returns. Datar, Naik, and Radcliffe (1998)

and Lee and Swaminathan (2000) show that stocks with high trading volume earn lower

future returns. Jiang, Yao, and Xu (2006) link both anomalies to adverse selection in

corporate disclosure and investor underreaction. In addition, Amihud (2002) finds that stock

liquidity inversely predicts stock returns.

From the above discussion, we identify 18 firm-specific return predictor variables.

We list these variables in Table 1. For those variables that have been posited to be negatively

related to subsequent returns in the U.S. market, we transform them by changing their signs

and we indicate this transformation by putting a “–” sign in front of variable. All of our

8

variables are posited to be positively related to future returns. We do not attempt to develop

unique ex ante hypotheses for each predictor on Chinese stock returns as our study is

exploratory in nature. Instead, we accept the hypotheses that have been developed for U.S.

markets as the default applicable hypotheses for Chinese markets.

[Insert Table 1 Here]

3. Data and variables construction

3.1. Data

For data on Chinese firms, information on stock price, return, and trading volume, as

well as corporate financial statement information, is obtained from the PACAP-CCER

Greater China database, a joint product of the PACAP Research Center at the University of

Rhode Island and SINOFIN Information Service Ltd. There are different share types in China.

In this study, we only include A-shares, which account for more than 85% of the tradable

market value of Chinese stocks at year-end of 2007.3 Our sample covers financial statement

data from 1994 to 2005 and stock return data from July 1995 to June 2007.4 We eliminate

banks, close-end funds, real estate firms, and investment companies. Approximately 5% of

Chinese stocks have a price below ¥1.5 To avoid potential market microstructure related

issues in measuring returns, we require that a stock have end-of-June price of no less than ¥1

to be included in our sample for year t.6 Further, a stock must have available information on

stock price, market capitalization, and at least one valid stock return predictor in addition to

firm size at the end of June in order to be retained in our sample.

3 Stocks of a typical Chinese firm may consist of state shares (those owned by the central or local governments), legal-entity shares (those held by domestic legal entities such as listed companies, state owned enterprises, and banks), and tradable shares, with the restriction that state and legal-entity shares cannot be traded publicly. Tradable shares are further classified into tradable A- and B-share classes. Tradable A-shares are ordinary shares primarily made available to Chinese citizens and institutions, whereas B-shares are primarily made available to foreign investors. A recent regulatory change makes A-shares available to a small group of qualified foreign institutional investors. Since February 2001, B-shares are available to domestic investors. 4 In our tests, return predictors are constructed using accounting information at the end of fiscal year t-1. The holding period for stock portfolios in the subsequent year is from July of year t to June of year t+1. More specifically, the return holding period corresponding to accounting information reported at the end of fiscal year 2005 (December 2005 as December is the fiscal year end for all Chinese firms) is from July of 2006 to June 2007. 5 The official abbreviation for the Chinese currency, the yuan, is CNY, but it is commonly abbreviated as RMB (renmindi, which literally translates to “people’s currency”). The Latinized symbol for the yuan is ¥. 6 Our results remain similar without this minimum price threshold and when we set the minimum price at ¥5.

9

Panel A of Table 2 presents summary statistics for the Chinese market. In June of

1994, a total of 287 stocks are traded in the Shanghai Stock Exchange and the Shenzhen Stock

Exchange, and the number substantially increases to 1,516 in June of 2007. In June of 2007,

the aggregate market capitalization of tradable A-shares is ¥5294.6 billion (equivalent to

USD756.37 billions using an exchange rate of ¥7 for USD1), about 40% of the aggregate

nominal market capitalization of ¥13,465 billion (USD 1,923.57 billion).

[Insert Table 2 Here]

For data on U.S. firms, information on stock price, return, and trading volume is from

CRSP. Information on corporate financial statements is from COMPUTSTAT. As with the

Chinese sample period, the U.S. sample period also covers 1994-2005 for financial statement

data and July 1995 to June 2007 for stock returns data. Our filters on U.S. data are as follows.

First, we select all common stocks traded in NYSE, AMEX, and NASDAQ at the end of June

in each year t. Second, we eliminate primes, close-end funds, real estate investment trusts,

American Depository Receipts, and foreign companies. Third, we require stocks to have a

minimum price of $1 at the end of June.

Panel B of Table 2 shows summary statistics for our U.S. sample. From Panels A and

B, we note some interesting differences between China and the U.S. First, we see that

Chinese stocks experience far more trading. During our sample period, the average annual

stock turnover ratios, defined as the ratio of trading volume to firm’s market capitalization,

are 503% in Shanghai Stock Exchange and 509% in Shenzhen Stock Exchange, whereas the

corresponding average annual turnovers are 133% for NYSE/AMEX and 177% for NASDAQ.

In addition, Chinese stocks are much smaller in size with respect to price: the time-series

average stock price in the Chinese market is ¥10.77 for A shares, which is roughly $1.5 when

using the exchange rate of ¥7 for US$1, much smaller relative to the average U.S. stock price

of $27.75.

3.2. Construction of stock return predictor variables

Based on our discussion in section 2, we construct 18 stock return predictor variables.

Here, we briefly describe the construction of each of those variables. More detailed

information on all variables can be found in the Appendix.

1. Firm Size (SIZE)

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SIZE is the natural logarithm of the firm’s market value of equity, which is the firm’s

market price multiplied by its common shares outstanding at the end of June of year t. For

our Chinese firms, only the outstanding A-shares are used in the market value computation.

2. B/P Ratio (B/P)

B/P is the book value of equity, taken as the ratio of the firm’s book value to its

market capitalization for the year-end at calendar year t – 1.

3. Price Momentum (MOM)

MOM is the cumulative return of a stock in month –12 through –1 preceding June of

year t. We skip one month between portfolio formation and holding period to avoid the

effects of bid–ask spread, price pressure, and any lagged reaction (Jagadeesh and Titman,

1993).

4. E/P Ratio (E/P)

E/P is the ratio of earnings for the fiscal year to the market capitalization at the year-

end of calendar year t – 1. The earnings measure for U.S. firms is earnings before

extraordinary items and depreciation, whereas it is net income for Chinese firms.

5. C/P Ratio (C/P)

C/P for U.S. firms is the sum of earnings before extraordinary items and depreciation

over the firm’s market capitalization at the fiscal year-end of year t – 1. This definition

follows Fama and French (1993). For Chinese firms, similarly, C/P is the sum of net income

and depreciation over its market capitalization at the fiscal year-end of year t – 1.

6. Sales Growth (SG)

SG is the sales revenue for the fiscal year ending in calendar year t – 1 over the sales

revenue from the fiscal year-end in year t – 2.

7. Accruals (ACC)

Accounting accruals is the noncash component of earnings. Following Sloan (1996),

we estimate ACC as the change in noncash current assets less the change in current liabilities

(excluding debt in current liabilities and income tax payable) and less depreciation, during the

fiscal year ending in year t – 1, scaled by the average total assets at the beginning and end of

that fiscal year.

8. Net Operating Assets (NOA)

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Following Hirshleifer et al. (2004), we estimate NOA as the difference between

operating assets and operating liabilities for the fiscal year ending in calendar year t – 1,

scaled by the average total assets at the beginning and end of that fiscal year.

9. Capital Expenditure (CAPEX)

Following Jegadeesh et al. (2004), CAPEX for U.S. firms is the capital expenditure of

the firm for the fiscal year ending in calendar year t – 1 over the average total assets at the

beginning and end of that fiscal year. For Chinese firms, the numerator of the CAPEX

variable is the change in net fixed assets plus the change in accumulated depreciation from the

fiscal year ending in calendar year t – 2 to the fiscal year ending in calendar year t – 1.

10. R&D Expenses (RD)

Following Chan et al. (2001), RD is the ratio of R&D expenditure over market

capitalization. R&D is not separately reported in China but is included in management

expenses. RD for Chinese firms is, thus, approximated by the ratio of management expenses

for the fiscal year ending in calendar year t – 1 to market capitalization at the end of year t –

1.

11. Advertising Costs (ADV)

Following Chan et al. (2001), ADV is advertising expenses over market capitalization.

Advertising costs are not separately reported in China but are included in sales and marketing

expenses. ADV for Chinese firms is, thus, approximated by sales and marketing expenses for

the fiscal year ending in calendar year t – 1 over market capitalization at the end of year t – 1.

12. Assets Growth (AG)

Following Cooper et al. (2007), AG is measured as the percentage change in total

assets from the fiscal year ending in calendar year t – 2 to the fiscal year ending in calendar

year t – 1.

13. Change in Gross Profit Margin (∆GPM)

Consistent with Abarbanell and Bushee (1998), GPM is the difference between net

sales and cost of goods sold divided by net sales. ∆GPM is the percentage change in GPM

from the fiscal year ending in calendar year t – 2 to the fiscal year ending in calendar year t –

1.

14. External Equity Financing (∆EQ)

12

Following Bradshaw et al. (2006), ∆EQ is the net cash received from the sale (and/or

purchase) of common and preferred stock less cash dividends paid for the fiscal year ending

in calendar year t – 1.

15. External Debt Financing (∆DT)

Following Bradshaw et al. (2006), ∆DT is the net cash received from the issuance (or

reduction) of debt for the fiscal year ending in calendar year t – 1.

16. Idiosyncratic Risk (STDR)

Similar to Ang et al. (2006), STDR is the standard deviation of the residuals in the

regression of daily stock return on daily value-weighted market return with five lags and five

leads for the period from month –12 through month –1 preceding June of year t.

17. Trading Turnover (TURN)

Following Jegadeesh, Kim, Krische, and Lee (2004), TURN is the percentile rank of

the average daily volume turnover in the 12 months preceding June of year t, where daily

volume turnover is the ratio of the number of shares traded each day to the number of shares

outstanding at the end of the day. Because trading volume is measured differently in

NASDAQ than in NYSE and AMEX, a percentile ranking is performed separately for

NASDAQ and for NYSE/AMEX.

18. Illiquidity (ILLIQ)

Following Amihud (2002), ILLIQ is the percentile rank of the average daily ratio of

the absolute stock return to its dollar volume, across month –12 through month –1 preceding

June of year t. Again, as with the trading turnover measure, a percentile ranking is performed

separately for NASDAQ and for NYSE/AMEX.

Table 3 summarizes the cross-sectional distributions of the 18 stock return predictors,

for both our Chinese data (Panel A) and our U.S. data (Panel B), including 25 and 75

percentiles, mean, median, standard deviation, skewness, and kurtosis.

[Insert Table 3 Here]

Recall that among these 18 variables, 10 are posited to be negatively correlated with

future stock returns: SIZE, SG, ACC, NOA, CPX, AG, ∆EQ, ∆DT, STDR, and TURN. In the

rest of analysis, we add a negative sign in front of each of these variables (see Table 1) so that

the resulting variables positively predict returns.

13

4. Empirical results

4.1. Raw returns of sorted portfolios

We first examine the return-predictive performance of our 18 variables using sorted

decile portfolios. In June of each year t, we rank stocks into deciles based on each predictor

using year t – 1 financial statement data. We form equally weighted portfolios in each decile

and hold the positions from July of year t to June of t + 1.7 That is, we test whether our

predictors can explain the cross-sectional variation in subsequent one-year returns. We first

calculate the cross-sectional average return in each portfolio and then compute the time-series

averages of the cross-sectional mean for each predictor. D10 and D1 deciles contain stocks

with the largest and smallest measures of each return predictor, respectively. For example, for

firm size (SIZE), D10 stocks contain the decile of the largest firms, and D1 contain the decile

of the smallest firms. For momentum (MOM), D10 contain firms with the largest past returns,

and so forth. Table 4, Panel A, reports the average portfolio returns for each decile for the

Chinese market and also the mean return difference (i.e., spread) between D10 and D1 stocks,

in Panel B we report the return spread between D10 and D1 stocks for the U.S. market, and in

Panel C we report the spread difference between China and U.S. markets.

[Insert Table 4 Here]

In looking at the second-to-last row in Panel A of Table 4, where mean differences

between D10 and D1 stocks are reported, we see that except for MOM, E/P and C/P

predictors, the signs of the mean differences for all other predictors are positive, consistent

with their hypothesized signs (recall that when hypothesized signs are negative we transform

those variables so that their hypothesized signs become positive). This observation is

somewhat surprising, as it suggests that variables that have been found to predict returns in

mature markets, like U.S. markets, are also potentially useful in predicting returns in

emerging markets such as China’s markets, confirming the argument in Rouwenhorst (1998,

1999) that similar return factors are present around the world.

The bottom row in Panel A of Table 4 presents t-statistic for the return difference

between D10 and D1 stocks. It shows that only five return predictors significantly predict

stock performance in the subsequent year. More specifically, reported in column (2), the top

B/P-sorted portfolio outperforms the bottom B/P-sorted portfolio by 0.74% per month (t = 7 We obtain consistent, slightly weaker, results when using value-weighted returns.

14

1.80), consistent with a finding in Eun and Huang (2007). In columns (7) and (8), we see that

high –ACC and NOA firms outperform low –ACC and -NOA firms by 0.35% and 0.56% per

month, significant at the 10% and 5% levels, respectively. In column (10), we see firms with

high R&D outperform those with low R&D by 0.87% per month (t=2.52). The statistically

significant results for ACC, –NOA and R&D is consistent with the interpretation that Chinese

investors are largely uninformed and/or un-sophisticated; they underreact to salient

information provided in firms’ financial statements regarding firms’ accruals, net operating

assets (accumulated accruals) and R&D expenses (related to firm intangible assets) (Sloan,

1996; Hirshleifer et al. 2004; Chan et al., 2004). Further, in columns (12), we see that firms

with high asset growth have higher subsequent returns. For Illiquidity rank-sorted portfolios,

D10 stocks outperform D1 stocks by 0.66% per month (t=1.65), indicating that Chinese

investors are willing to pay a significant premium for more liquid stocks.

While it is hard to provide a consistent story to justify why some predictors have

significant return predictive power in the Chinese market while others do not, interesting

points arise. First, trading on momentum, perhaps the most important stock characteristic

predicting future stock performance in the U.S. and in other developed markets, is

unprofitable in the Chinese market. The return difference between D10 and D1 stocks sorted

by prior 12-month momentum is 0.05% with a t-statistic of 0.16. This finding is consistent

with Wang (2004). Second, firm size, return standard deviations, and turnover do not

significantly predict stock performance in the subsequent year.8 This is different from the

results reported in Eun and Huang (2007). The difference may be attributable to the different

research designs employed in these two papers: the analysis performed in Eun and Huang

(2007) involves regression analysis involving multiple return predictors while ours is decile-

portfolio analysis for each firm attribute.

Panel B of Table 4 reports U.S. results. For brevity, we simply provide mean return

differences between D10 stocks and D1 stocks sorted by respective firm characteristics. Over

the same sample period 1994-2007, the return differences for all the return predictors are

positive, but some firm characteristics, such as firm size, R&D, return standard deviations,

and stock turnover are not reliably return predictive. That is, return differences for 11 of the

8 We alternatively measure firm size using firm total market capitalization, including market capitalization for A and B shares and total book value of assets. We obtain similar results.

15

18 predictors are statistically significant. Overall, however, these results show that, relative to

the Chinese stock market, more predictors are statistically significant in explaining U.S.

returns.

In Table 4, we also examine the return predictive power of the 18 firm-specific

variables jointly. We create a combined return predictor measure using the average ranking of

the 18 return predictors weighted by their sensitivities to subsequent returns. Specifically, the

combined predictor measure for stock j in year t is expressed as

18/18

1,∑

=

=i

jti

jt RankCOMBO (1)

where jtiRank , is the cross-sectional percentile rank of return predictor i for stock j. Stocks are

ranked into deciles in each year based on jtCOMBO . That is, firms with the lowest and

highest COMBO measures are denoted as D1 and D10 stocks, respectively. Reported in the

last column of Panel A, Table 4, we see that the average return for COMBO-sorted D1 stocks

is 1.52% per month and the average return for COMBO-sorted D10 stocks is 2.24% per

month. The return difference between D10 and D1 is 0.72%, with a t-statistic of 1.98. This

result suggests that a combined rank measure of all predictor variables is effective in

predicting future stock returns in the Chinese market. For the U.S. market, the return

difference between D10 and D1 stocks for the COMBO-sorted decile portfolios is 0.89%

(t=2.88).

In Panel C, we report differences between D10-D1 return spreads between China and the

U.S. Most differences are negative, indicating that the return spread is larger in the U.S.

market than in the Chinese market. The differences in return spreads between China and U.S.

are statistically significant negative for 8 out of 18 predictors, including MOM, E/P, C/P, -SG,

ADV, -AG, -∆EQ, and -∆DT.

4.2. Alphas of sorted portfolios

An important question that needs to be raised whenever any pattern of stock return

predictability is found is whether the pattern is related to risk. To control for the risk–return

relation, we use the Fama–French (1993) three-factor model to obtain alphas for all decile

portfolios. Our analysis is motivated by Fama and French (1998), who show that the value

16

effects in international markets can be explained by their three-factor model. The three-factor

regression model takes the following form:

titititiitit HMLhSMBsRMRFbRFR ,εα ++++=− , (2)

where tiR , – RFt is the monthly returns of portfolio i in excess of the monthly risk free rate.

Portfolio return tiR , in each month is the equally weighted stock returns. In our setting,

stocks are ranked into deciles in each month by a specific return predictor and i refers to the

ith decile portfolio. RMRFt is the market return in excess of the risk free rate; SMBt and HMLt

are the monthly size and book-to-market factors, respectively. Regressions are performed

using average monthly returns of each decile-sorted predictor variable during the entire

sample period.

We follow procedures provided in Fama and French (1993) to compute SMB and

HML factors using Chinese stock return data. As discussed previously, although Chinese

public companies issue multiple shares, we focus on A-shares. The market value of stocks is

taken by multiplying A-shares’ closing prices to their shares outstanding. Book value per

share is book value divided by totals shares issued by the firm. We construct size and B/P

portfolios in June of each year t. In particular, we sort all stocks into small and big size

groups based on the median market capitalization of all stocks in June of year t. We

independently sort all stocks into low, median, and high B/P groups based on the 30% and

70% cutoff points of the book-to-market ratio of all stocks. Six size-B/P portfolios are

defined as the intersections of the two size and three B/P groups. The monthly value-

weighted average return on each portfolio is then computed. SMB is the difference, in each

month, between the simple average of returns on the three small-stock portfolios (S/L, S/M,

and S/H) and the simple average of returns on the three big-stock portfolios (B/L, B/M, and

B/H). Similarly, HML is the difference, in each month, between the simple average of returns

on the two high-B/P portfolios (S/H and B/H) and the average of returns on the two low-B/P

portfolios (S/L and B/L).

Following Wang (2004) and Kang, et al. (2002), we use the monthly yield of the

three-month household deposit interest rate in China as the risk-free rate. We calculate the

monthly market returns in China as the value-weighted average monthly returns for all A-

17

shares traded in the Shanghai and Shenzhen stock exchanges. The risk-adjusted results are

reported in Table 5.

[Insert Table 5 Here]

Table 5 results are consistent with that in Table 4. As reported in Panel A of Table 5,

we see that four return predictors are statistically significant after we control for risk. The

alpha differences between D10 and D1 stocks are significant for -NOA, RD, –AG, -STDR in

the Chinese market. Panel B of Table 5, which reports on U.S. stock returns, shows that, after

making risk adjustments, 10 predictors are statistically significant in explaining subsequent

returns. Consistent with our earlier results on raw returns, after risk adjustments, we find that

some firm-specific variables are able to predict returns in China, but more variables are able

to predict returns in the U.S. For the COMBO measure, the return spread is 0.79% per month

(t=2.42) in the Chinese market while it is 1.07% per month (t=2.94) in the U.S. market. Panel

C reports the difference between three-factor adjusted alphas of stocks in China and the U.S.

Different from the procedure used in the first two panels, here we no longer compute alphas

for stock portfolio deciles. Instead we estimate three-factor adjusted alpha for each stock in

each month based on the following two-step procedure. First, we estimate factor loadings by

regressing prior 12-month stock returns onto corresponding factor premiums. Second, we

subtract the expected returns using the estimated loading from stock returns in the current

month to obtain the three-factor adjusted alpha for the stock. With alphas of individual stocks,

we next compute alpha differences between predictor-sorted D10 and D1 portfolios and then

calculate the differences in differences between China and U.S. in each month. Finally, we

average the monthly differences over time to obtain the numbers reported in Panel C. The

differences in alpha spreads between China and U.S. are statistically significant negative for

10 out of 18 predictors.

4.3. Cross-sectional regressions

In this section, we further check the robustness of our decile-sorted portfolio results by

performing cross-sectional regressions of monthly stock returns on return predictors. We use

the Fama–MacBeth (1973) procedure to compute the time-series averages of the coefficients

on each of our predictors. Panel A of Table 6 reports results when we perform univariate

18

regressions. The dependent variable is the monthly stock return and the explanatory variable

is each individual stock return predictor.

[Insert Table 6 Here]

From Table 6, Panel A, we see that the coefficients on B/P, -NOA, RD, -AG, and

ILLIQ are significantly different from zero, generally validating the sorted-portfolio evidence

reported in Table 4. Also confirming prior results, reported in Panel B of Table 6, we see that

8 firm specific variables have significant coefficients in the return regression in the U.S.

market.

We also conduct two multivariate regression tests, where monthly returns are

regressed on either all 18 predictor variables or on the first 6 principal components. The two

main advantages to estimating a multivariate regression is that we can identify the marginal

effect of each variable on its ability to predict returns and we can estimate the predictive

ability of all of these variables jointly rather than in isolation (i.e., the effect of each variable

can be evaluated while holding constant other variables). For stocks with missing

observations for one or more stock return predictors, we replace the missing predictor with the

annual cross-sectional median value for that predictor to take advantage of the large cross-

sectional data. The results of multivariable regressions are reported in Panel C of Table 6.

We find few variables have significantly positive coefficients. Multivariable regression

results using U.S. data is reported in Panel D of Table 6.

The average adjusted R2s from the monthly multivariate regressions, which are also

reported in Panels C and D, provide a way to quantify the overall return predictability in each

market. As it turns out, the average adjusted R2 for the multivariate regression model is 9%

for the Chinese market, while it is 6% in the U.S. market. If independent variables in a

regression model are correlated, then a multicollinearity problem may inflate the R2s. To rule

out this explanation, we identify the first six principal components of the 18 return predictors,

and we use these six principal component factors as joint regressors. The time-series average

R2 of this principle component-based regression is 5% for both markets. It is interesting to

see that despite there being fewer statistically significant return predictors in China, the

overall explanatory power of firm-specific variables is similar in these two markets as

revealed by their similar R2s. We do not have a good explanation for this observation.

19

4.4. Why are returns less predictable in China?

So far, our results suggest that return predictors have much less power in predicting

future stock returns in China than in the U.S. One possibility for this finding is that return

predictors in China are less heterogeneously distributed than they are in the U.S.; that is, (i)

the difference between average values of a return predictor for D10 and D1 stocks sorted by

the predictor is small in China and (ii) return spreads between D10 and D1 stocks are

accordingly smaller in China than in the U.S. market. To examine this possibility, we

compare the spread of D10 and D1 stocks in the predictors themselves in China and U.S. The

results are reported in Panel A of Table 7.

[Insert Table 7 Here]

The first row in Panel A reports the spread in return predictors between D10 and D1

stocks in China and the second row reports the spread for the U.S. The difference in the

spread between China and U.S., and associated t-statistics, are also shown in Panel A. We see

that while SIZE, -NOA, RD, -AG, -∆EQ, and –STDR spreads in China are significantly lower

than that in U.S., 11 out of the 18 spreads are not significantly different between the two

countries. It seems that less variation in firm-specific measures is unlikely the cause of the

weaker return predictability in China.

We further assess the impact of the variation of return predictors by estimating

standardized stock return spreads between D10 and D1 portfolios sorted by each of the 18

return predictors for the Chinese and U.S. markets. The standardized return spread of a return

predictor is the time-series average of the stock return spreads between D10 and D1 portfolios

scaled by the predictor spreads of that market in corresponding years. The essential

assumption behind the examination of heterogeneity of return predictors across markets is that

return spread per unit of predictor spread holds constant. Thus our specific hypothesis here is

that the standardized return spreads across D10 and D1 deciles sorted by return predictors are

identical in these two markets.

The results, reported in Panel B of Table 7, show that standardized return spreads in

China and U.S. are consistent with those reported in Table 4. We find that China has

significantly lower standardized return spreads in MOM, E/P, C/P, -SG, -ACC, ADV, -AG,

20

and –∆DT. Taken together, Table 7 suggests the weaker return predictability in China is

unlikely due to there being less variation in the predictors themselves.

Another possible reason for low return predictability is that stock prices are

persistently uninformative in the Chinese market. Under the behavioral asset pricing

framework, stock return predictability depends on both initial mispricing and subsequent price

correction. Given initial mispricing, if stock prices are persistently noisy, and correction never

takes place, return predictability could still be weak. It is likely that there is large (initial)

mispricing in the Chinese stock market. However, it is also likely that stock prices are so

noisy that they seldom converge to the fundamental values.

To test this, we resort to a measure well-known in the literature – return synchronicity,

or R2 from regressing individual stock returns on market returns. Price synchronicity is a

measure of informativeness of stock prices. Low synchronicity is associated with greater

incorporation of firm-specific information on individual returns (Roll, 1988; Morck, Yeung,

and Yu, 2000; and Durnev, et al., 2003), thus higher price informativeness.

We test the difference in return synchronicity between the two stock markets. To do

this, we perform market model regressions for all the stocks in the Chinese and U.S. markets

by regressing weekly stock returns onto weekly market returns in each year (July of year t-1

to June of year t) during the period of 1994 to 2005 and obtain the R-square statistics of each

stock in each year. The weekly stock return is the compounded daily return for a calendar

week. We compound daily CRSP value-weighted returns to get weekly market returns.

Within each market, we first average the synchronicity measure across stocks in each year

then aggregate the measure over time. We find that the average market model R2 in China

0.46, while it is 0.12 in the U.S. The much higher R2 in China supports the view that stock

prices are generally much less informative in China.

We then use a double-sort procedure to test whether there is a positive link between

price informativeness and return predictability. In each year, we first sort Chinese stocks into

three groups based on synchronicity. Then, within each synchronicity group, we sort stocks

into equal-weighted decile portfolios based on each of the 18 predictors. Finally, for each

predictor, we calculate the time-series average of the standardized return spreads (return

spread divided by spread in the predictor itself) between the top and bottom portfolios within

21

each synchronicity group. Under our hypothesis, the standardized return spread should be

higher among stocks with lower synchronicity. The results are reported in Table 8.

[Insert Table 8 Here]

From Table 8, we see that for 14 out of 18 predictors, the standardized return spread is

higher in the lowest synchronicity group relative to the highest synchronicity group. The

exceptions are MOM, -SG, -CPX, and ∆GPM. Out of the 14 predictors, 11 work significantly

better predicting returns in low synchronicity groups related to high synchronicity groups, as

inferred by the t-statistics on the difference in standardized return spreads between lowest and

highest synchronicity groups. These variables include B/P, E/P, C/P, -ACC, -NOA, RD, -

∆EQ, -∆DT, -STDR, -TURN, and ILLIQ.

Taken together, our evidence supports the notion that stock prices are less informative

in China, which at least partially explains the weaker return predictability in China.

5. Conclusion

Using Chinese stock returns data, we examine the predictive power of 18 firm-specific

variables that have been hypothesized to predict U.S. stock returns. Using portfolio-sorted

returns and regression analyses, we find that almost all firm-specific variables predict

subsequent one-year returns in their hypothesized direction. However, only 6 firm-specific

variables are statistically significant in their ability to predict raw returns, and 4 firm-specific

variables are statistically significant in their ability to predict risk-adjusted returns. We

conduct the same tests on U.S. stock returns from the same sample period and find that more

predictors are statistically significant in explaining subsequent cross-sectional stock return

variation, indicating that the stock return predictability is weaker in China than it is in the U.S.

market.

We test two explanations for the cause of weak return predictability in China. One

possible explanation is that return predictors are more homogeneously distributed in the

Chinese market than they are in the U.S. market. While we find evidence of greater

homogeneity in the Chinese market, we also find the stock return sensitivity to return

predictors is lower in the Chinese market than in the U.S. Another possible explanation for

low return predictability in China is that there is high price inefficiency in China. We find

that the market model R2, a measure of stick price uninformativeness, is much higher for

22

China than for the U.S. Further, across Chinese stocks, when comparing the return spread for

high and low R2 stocks, we find that more predictors work better for the bottom R2 stocks

than for top R2 stocks. This evidence suggests that weak return predictability is related to low

price informativeness. Our results also question the validity of the conventional perception of

the positive relationship between return predictability and market efficiency, at least in the

context of cross-country comparison.

23

References

Amihud, Y., 2002, Illiquidity and stock returns: Cross-section and time-series effects, Journal of Financial Markets 5, 31–56.

Ang, A., R.J. Hodrick, Y. Xing, and X. Zhang, 2006, The cross-section of volatility and

expected returns, Journal of Finance 61, 259–299. Abarbanell, J. and B. Bushee, 1998, Abnormal stock returns to a fundamental analysis

strategy, Accounting Review 73, 19-45 Banz, R.W., 1981, The relationship between return and market value of common stocks,

Journal of Financial Economics 9, 3–18. Basu, S., 1977, Investment performance of common stocks in relation to their price-earnings

ratios: A test of the efficient market hypothesis, Journal of Finance 32, 663–682. Barberis, N., A. Shleifer, and R. Vishny, 1998, A model of investor sentiment, Journal of

Financial Economics 49, 307–343. Beneish, M.D., C.M. Lee, and R.L. Tarpley, 2001, Contextual fundamental analysis through

the prediction of extreme returns, Review of Accounting Studies 6, 65–189.

Billett, M.T., M.J. Flannery, and J.A. Garfinkel, 2006, Are bank loans special? Evidence on the post-announcement performance of bank borrowers, Journal of Financial and Qualitative Analysis 41, 733-751.

Bradshaw, M.T., S.A. Richardson, and R.G. Sloan, 2006, The relation between corporate financing activities, analysts’ forecasts and stock returns, Journal of Accounting and Economics 42, 53–58.

Chan, L., J. Lakonishok, and T. Sougiannis, 2001, The stock market valuation of research and

development expenditures, Journal of Finance 56, 2431–2456. Chen, G., K.A. Kim, J.R. Nofsinger, and O.M. Rui, 2007, Trading performance, disposition

effect, overconfidence, representativeness bias, and experience of emerging market investors, Journal of Behavioral Decision Making, in press.

Chen, K.C.W. and H. Yuan, 2004, Earnings management and capital resource allocation:

Evidence from China’s accounting-based regulation of rights issues, Accounting Review 79, 645–665.

Collins, D.S., S.P. Kothari, and J. Rayburn, 1987, Firm size and the information content of

prices with respect to earnings, Journal of Accounting and Economics 9, 111-138.

24

Cooper, M.J., H. Gulen, and M.J. Schill, 2008, Asset growth and the cross-section of stock returns, Journal of Finance 63(4), 1609-1651

Daniel, K.D., D. Hirshleifer, and A. Subrahamanym, 1998, Investor psychology and security

market under- and over-reaction, Journal of Finance 53, 1839–1886. Datar, V.T., N.Y. Naik, and R. Radcliffe, 1998, Liquidity and stock returns: An alternative

test, Journal of Financial Markets 1, 203–219. Durnev, A., R. Morck, B. Yeung, and P. Zarowin, 2003, Does greater firm-specific return

variation mean more or less informed stock pricing?, Journal of Accounting Research 41, 797-836.

Eun, C.S. and W. Huang, 2007, Asset pricing in China’s domestic stock markets: Is there a

logic?, Pacific-Basin Finance Journal 15, 452-480 Fama, E.F. and K.R. French, 1992, The cross-section of expected stock returns, Journal of

Finance 47, 427–465. Fama, E.F. and K.R. French, 1993, Common risk factors in the returns on stocks and bonds,

Journal of Financial Economics 33, 3–56. Fama, E.F. and K.R. French, 1996, Multifactor explanations of asset pricing anomalies,

Journal of Finance 51, 55–84. Fama, E.F. and K.R. French, 1998, Value versus growth: The international evidence, Journal

of Finance 53, 1975–1999. Fama, E.F. and J. MacBeth, 1973, Risk, return, and equilibrium: Empirical tests, Journal of

Political Economy 81, 607-636.

Feng F. and M. Seasholes, 2005, Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets? Review of Finance 9, 305-351.

Grinblatt, M. and B. Han, 2005, Prospect theory, mental accounting, and momentum, Journal of Financial Economics 78, 311–339.

Haw, I. D. Qi, D. Wu, and W. Wu, 2005. Market consequences of earnings management in

response to security regulations in China, Contemporary Accounting Research 22, 95–140.

He, X., 1998, Stock market manipulation behavior and regulatory suggestions, Soft Science in

China, 80-84. Hirshleifer, D., K. Hou, S. Teoh, and Y. Zhang, 2004, Do investors overvalue firms with

bloated balance sheets? Journal of Accounting and Economics 38, 297–331.

25

Hong, H. and J. Stein, 1999, A unified theory of underreaction, momentum trading and

overreaction in asset markets, Journal of Finance 54, 2143–2184. Ikenberry, D., J. Lakonishok, and T. Vermaelen, 1995, Market underreaction to open market

share repurchases, Journal of Financial Economics 39, 181–208. Jegadeesh, N., J. Kim, S. Krische, and C. Lee, 2004, Analyzing the analysts: When do

recommendations add value? Journal of Finance 59, 1083–1124. Jegadeesh, N. and S. Titman, 1993, Returns to buying winners and selling losers: Implications

for stock market efficiency, Journal of Finance 48, 65–91. Jiang, G., T. Yao, and D. Xu, 2006, The information content of idiosyncratic volatility,

Journal of Financial and Quantitative Analysis, forthcoming Jian, M. and T.J. Wong, 2004, Earnings management and tunneling through related party

transactions: Evidence from Chinese corporate groups, Working paper, Chinese University of Hong Kong.

Kang, J., M. Liu, and S. Ni, 2002, Contrarian and momentum strategies in the China stock

market: 1993–2000, Pacific-Basin Finance Journal 10, 243–265. Lakonishok, J., A. Shleifer, and R.W. Vishny, 1994, Contrarian investment, extrapolation,

and risk, Journal of Finance 49, 1541–1578. Lakonishok, J. and T. Vermaelen, 1990, Anomalous price behavior around repurchase tender

offers, Journal of Finance 45, 455–477. Lee C. and B. Swaminathan, 2000, Price momentum and trading volume, Journal of Finance

55, 1217–1269. Loughran, T. and J. Ritter, 1997, The operating performance of firms conducting seasoned

equity offerings, Journal of Finance 52, 1823-1850. Morck, R, B. Yeung, and W. Yu, 2000, The information content of stock markets: shy do

emerging markets have synchronous stock price movements? Journal of Financial Economics 59, 215–60.

Reinganum, M., 1981, Misspecification of capital asset pricing: Empirical anomalies based on

earnings’ yields and market values, Journal of Financial Economics 9, 19–46. Ritter, J., 1991, The long-run performance of initial public offerings, Journal of Finance 46,

3–27. Roll, R., 1988, R2, Journal of Finance 43, 541–66.

26

Rouwenhorst, K.G., 1998, International momentum strategies, Journal of Finance 53, 267-

284. Rouwenhorst, K.G., 1999, Local return factors and turnover in emerging stock markets,

Journal of Finance 54, 1439-1464. Shengzhen Stock Exchange, 2005, The major illegal behaviors in the Chinese stock market:

Case study on insider trading and price manipulation. Shengzhen. Shumway, T. and G. Wu, 2006, Does disposition drive momentum? Working Paper,

University of Michigan and University of Houston. Sloan, R., 1996, Do stock prices fully reflect information in accruals and cash flows about

future earnings? Accounting Review 71, 289–315. Spiess, D.K. and J. Affleck-Graves, 1999, The long-run performance of stock returns

following debt offerings, Journal of Financial Economics 54, 45–73. Titman S., J. Wei, and F. Xie, 2004, Capital investments and stock returns, Journal of

Financial and Quantitative Analysis 39, 677–700. Wang, C and S. Chin, 2004, Profitability of return and volume-based investment strategies in

China’s stock market, Pacific-Basin Finance Journal 12, 541–564. Wang, C., 2004, Relative strength strategies in China’s stock market: 1994–2000, Pacific-

Basin Finance Journal 12, 159–177. Wang, F. and Y. Xu, 2004, What determines Chinese stock returns? Financial Analysts

Journal 60, 65–77.

27

Appendix Constructing Stock Return Predictors

We provide the variable name, description, computation details for the eighteen return predictors used in this paper. Predictors are constructed in June of year t. For all the financial statement items, year t refers to the fiscal year ending in the calendar year t. For the U.S. data, we provide data item numbers in the CRSP and COMPUSTAT datasets. Variable Description Computation Detail: China Computation Detail: United States 1. SIZE Natural logarithm of

market capitalization ln(closing price at the end of June multiplied by A shares outstanding at the end of June)

ln(closing price at the end of June [CRSP] multiplied by common shares outstanding at the end of June[CRSP])

2. B/P Book to price 1-t

1-t

MV BV , where BV of A Shares = Book Value of

Equity*(Number of outstanding A Shares in December/Total Shares Outstanding); MVt-1 is the market value of A shares at the end of year t-1.

Decemberin MV BV

1-t

1-t , where BV is the book value of

stockholders’ equity [D216], plus balance sheet deferred taxes and investment tax credit [D35, if available], minus the book value of preferred tax [D56, D10, D130, in that order].

3. MOM Cumulative market-adjusted return for the preceding 12 months

112

−−=∏m

mi (1+monthly returnt) –1

12−−=∏m

mi (1 + market monthly returnt), where m is June of year t

112

−−=∏m

mi (1+monthly returnt) –1

12−−=∏m

mi (1 + market monthly returnt), where m is June of year t

4. E/P Earnings to price 1-t

1-t

MVIncomeNet

, where MVt-1 is market capitalization of A

shares at the end of year t – 1

D25]*[D24 MV[D18] Itemary Extraordin before Earnings

1-t

1-t

5. C/P Cash flow to price 1-t

1-t1-t

MVonDepreciatiIncomeNet +

, where MVt-1 is market

capitalization of A shares at the end of year t – 1 D25]*[D24 MV

[D14] onDepreciati[D18] Itemary Extraordin before Earnings

1-t

1-t1-t +

6. SG Sales growth 2

1

−

−

t

t

SalesSales

]12[]12[

2

1

DSalesDSales

t

t

−

−

7. ACC Accruals [(ΔCA – ΔCASH– ΔSI)t–1 – (ΔCL – ΔSTD – ΔLTDC – ΔTP)t–1 – DEPt–1]/ATAt–1, where ΔCA is the change in current assets from previous fiscal year; ΔCash is the change in cash, ΔSI is the change in net short term investment; ΔCL is the change in current liabilities; ΔSTD is the change in short-term debt; ΔLTDC is the change in long term debt included in current liabilities; ΔTP is the change in taxes payable; DEP is change in accumulative depreciation; and ATAt-1 is the average of the beginning and ending total assets (TA)of the reporting year t-1.

[(ΔCA – ΔCASH)t–1 – (ΔCL – ΔSTD – ΔTP)t–1 – DEPt–1]/ATAt–

1, where ΔCA is the change in current assets [D4] from previous fiscal year; ΔCash is the change in cash/cash equivalents [D1]; ΔCL is the change in current liabilities [D5]; ΔSTD is the change in debt included in current liabilities [D34]; ΔTP is the change in income taxes payable [D71]; DEP is depreciation and amortization expense [D14]; and ATAt-1 is the average of the beginning and ending total assets [D6] of the reporting year t-1.

Appendix continues

28

Appendix (continued) Variable Description Computation Details: China Computation Details: United States 8. NOA Net operating assets [Operating Assets – Operating Liabilities]t–1/ATAt–1, where

operating assets = TA – cash – short-term investment; operating liabilities = total asset – STD – LTDC – LTD – MI – CE; STD = short-term debt; LTDC = long-term debt included in current liabilities; LTD = total long-term debt; MI = minority interests; CE = common equity; and ATA = the average of the beginning and ending total assets of the reporting year. The values of short-term debt, taxes payable, long-term debt, and minority interest are set as zero if they are missing.

[Operating Assets – Operating Liabilities]t–1/ATAt–1, where operating assets = TA [D6] – cash and short-term investment [D1]; operating liabilities = total asset – STD – LTD – MI – PS– CE; STD = debt included in current liabilities [D34], LTD = long-term debt [D9]; MI = minority interests [D38]; PS = preferred stocks [D130]; CE = common equity [D60]; and ATA = the average of the beginning and ending total assets [D6] of the reporting year. The values of short-term debt, taxes payable, long-term debt, minority interest, and preferred stock are set as zero if they are missing.

9. CAPEX Capital expenditure to total assets

1

1

−

−

t

t

ATACapEx , where CapExt–1 is the change of net fixed assets

in fiscal year t – 1 plus the change in accumulated depreciations in year t –1, and ATA is the average of the beginning and ending total assets (TA) of the reporting year.

1

1

−

−

t

t

ATACapEx , where CapExt-1 is capital expenditure [D128] in

year t-1, and ATA is the average of the beginning and ending total assets (TA, [D6]) of the reporting year t-1.

10. RD Research and Development expenses to market value of equity

1

1SGAE

−

−

t

t

MV, where SGAE t-1 is the selling, general and

administrative expenses at the end of year t-1; MVt-1 is market capitalization of A shares at the end of year t-1

1

1&

−

−

t

t

MVDR , where R&D is research and development expenses

[D46]; market value is market capitalization at the end of year t-1

11. ADV Advertising expenses to sales

1

1-t Expenses Sales

−tMV, where MVt-1 is market capitalization of

A shares at the end of year t – 1. 1

1

−

−

t

t

MVADV , where ADVt–1 advertising expenses [D45] in year t –

1; MVt-1 is market capitalization at the end of year t – 1. 12. AG Change in total assets

2

21

−

−− −

t

tt

TATATA , where TA is total assets.

2

21

−

−− −

t

tt

TATATA , where TA is total assets [D6].

13. ∆GPM Change in gross profit margin 2

22

1

11

−

−−

−

−− −−

−

t

tt

t

tt

SCGS

SCGS , where St-1 and CG are net sales

and the cost of goods sold in year t-1. 2

22

1

11

−

−−

−

−− −−

−

t

tt

t

tt

SCGS

SCGS , where St-1 and CGt-1 are net sales

[D12] and cost of goods sold [D41] in year t-1. 14. ∆EQ Net cash flow

received from external equity financing

2

11

−

−− Δ+Δ

t

tt

ATACSCT , where ∆CT is the change in common

stock, ∆CS is the change in capital surplus, and ATA is the average of the beginning and ending total assets of the reporting year.

2

121

−

−−− −−

t

ttt

ATADIVCPECRE , where CRE is cash received from the

sale of common and preferred stock [D108], CPE is cash paid for purchase of common and preferred stock [D115], DIV is cash dividends paid [D127], and ATA is the average of the beginning and ending total assets [D6] of the reporting year.

Appendix continues

29

Appendix (continued) Variable Description Computation Detail: China Computation Detail: United States 15. ∆DT Net cash flow

received from external debt financing

2

121

−

−−− Δ−Δ+Δ

t

ttt

ATASTDLTNLTD , where ∆LTD is the change in

long-term debt, ∆LTN is the change in long term note, ∆STD is the change in total short term debt, and ATA is the average of the beginning and ending total assets of the reporting year.

2

121

−

−−− −−

t

ttt

ATACDCPDCRD , where CRD is cash received from the

issuance debt [D111], CPD is cash paid for reduction of debt [D114], ∆CD is change in current debt [D301], and ATA is the average of the beginning and ending total assets [D6] of the reporting year.

16. STDR Idiosyncratic risk STDR is the standard deviation of the error term in daily data market model regression:

∑=

−=+ ++=

5

5,,,, *

k

ktikmikii RR εβα ττ

, where Ri,τ is daily stock

return in the 12 months preceding June of each year; Rm,τ+k is the value-weighted average of the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchanges (SZSE) value-weighted daily index return from 5 lags and 5 leads.

STDR is the standard deviation of the error term in daily data market model regression:

∑=

−=+ ++=

5

5,,,, *

k

ktikmikii RR εβα ττ

, where Ri,τ is daily stock

return in the 12 months preceding June of each year, Rm,τ+k is the value-weighted CRSP market daily returns from 5 lags and 5 leads.

17. TURN Average daily volume turnover Percentile rank

n

t

t∑−

−

1

12gOutstandin resVolume/ShaDaily

in

SHSE and SZSE, respectively, where n is number of days available for 12 months preceding the end of June in each year.

Percentile rank n

t

t∑−

−

1

12gOutstandin resVolume/ShaDaily

in

NASDAQ and NYSE/AMEX, respectively, where n is number of days available for 12 months preceding the end of June in each year.

18. ILLIQ Amihud illiquidity measure Percentile rank

n

VOLDt

ti∑−

−

1

12,ti, /|R|

τ in SHSE and SZSE,

respectively, where |Ri,τ| is the return on stock i on dayτ within 12 months preceding June of each year,

,iVOLD τ is the respective daily volume in RMB, and n is

number of days available for 12 months preceding the end of June in each year.

Percentile rank n

VOLDt

ti∑−

−

1

12,ti, /|R|

τ in NASDAQ and

NYSE/AMEX, respectively, where |Ri,τ| is the return on stock i on dayτ within 12 months preceding June of each year,

,iVOLD τ is the respective daily volume in dollars, and n is

number of days available for 12 months preceding the end of June in each year.

30

Table 1 Hypothesized Signs of Stock Return Predictors

For each stock market predictor (i.e., variable), we test its relation to subsequent stock returns. For variables that are hypothesized to be negatively correlated with subsequent stock returns in the U.S. market, we adjust them so they can positively predict returns. For example, because firm size (SIZE) and subsequent returns are posited to have a negative correlation, we transform the SIZE variable to be: –SIZE. A brief description of each variable is provided. Variable Description -SIZE -Firm size (market capitalization) B/P Book-to-price ratio MOM Momentum (past returns) E/P Earnings-to-price ratio C/P Cash flow-to-price ratio -SG -Sales growth from prior year -ACC -Accruals-to-total assets ratio -NOA -Net operating assets-to-total assets ratio -CPX -Capital expenditures to total assets RD Research and development expenditures-to-market value of equity ratio ADV Advertising expenditures-to-market value of equity ratio -AG -Assets growth from prior year ΔGPM Change in gross profit margin -ΔEQ -Net cash flow received from external equity financing -ΔDT -Net cash flow received from debt financing -STDR -Idiosyncratic risk -TURN -Trading volume turnover ILLIQ Illiquidity

31

Table 2 Market Overview

This table reports market characteristics in China and the U.S. in June of the sample years from 1994 to 2007. Panel A is for the Chinese market and Panel B is for the U.S. market. N is the number of listed firms. Turnover is annual trading volume scaled by year-end shares outstanding. Price is the stock price in June of the sample year. For the Chinese market, it is respectively for A-, B-, and H-share price. Market values for A-, B- and H-shares are the closing prices multiplied by shares outstanding in June. For the Chinese market, the total market value is the sum of the market value of A-, B-, H-shares, and non-floating shares, where non-floating share price is set at the price of the A-shares. We use equal weight for the year-end cross-sectional averages. Panel A: The Chinese Market N Turnover Price

(RMB) Market Value (RMB billions)

Total SHSE SZSE SHSE SZSE A B H A B H Total 1994 287 169 118 12.12 7.74 5.52 4.08 2.28 56.48 12.98 8.56 475.50 1995 311 184 127 5.04 2.96 6.92 2.89 2.33 80.35 12.28 13.58 322.08 1996 514 287 227 7.99 14.40 7.65 2.95 1.69 148.17 16.20 12.04 634.68 1997 720 372 348 6.75 8.00 12.76 4.38 2.61 459.83 39.60 14.72 1597.72 1998 825 425 400 4.90 4.66 12.54 2.14 1.19 588.54 23.04 9.61 2018.66 1999 923 471 452 4.51 4.25 13.85 2.95 1.81 880.56 33.43 21.77 2325.03 2000 1060 559 501 5.25 5.21 15.49 2.72 1.38 1283.87 31.52 14.15 3595.05 2001 1139 637 502 2.43 2.06 16.90 9.88 2.42 1747.55 113.16 25.38 4999.48 2002 1206 705 501 2.18 2.06 12.19 6.21 1.79 1455.05 79.75 61.65 4320.93 2003 1266 772 494 2.37 2.11 8.97 4.55 2.22 1262.28 64.64 85.93 4010.72 2004 1362 831 531 3.36 3.25 7.03 3.87 3.31 1179.52 63.67 147.83 4880.63 2005 1365 829 536 3.51 4.15 4.80 3.04 3.49 934.71 56.64 188.52 3693.71 2006 1417 835 582 6.28 6.44 6.70 3.71 4.65 1561.23 75.13 1355.59 4352.54 2007 1516 852 664 10.87 10.79 14.85 8.13 8.00 5294.60 176.38 3101.86 13464.99

Total/Average 1584 876 708 5.03 5.09 10.77 4.48 3.63 -- -- -- -- Panel B. The U.S. market N Turnover Price Market Value (US$ billions)

Total NYSE/ AMEX NASDAQ

NYSE/ AMEX NASDAQ (US$)

1994 8084 3718 4366 0.78 1.38 16.75 4959.53 1995 8242 3723 4519 0.84 1.53 19.06 6052.35 1996 8765 3819 4946 0.99 1.66 21.37 7734.33 1997 9058 3860 5198 1.02 1.62 24.04 9922.80 1998 9014 3857 5157 1.42 1.81 28.32 12856.57 1999 8455 3706 4749 1.03 2.31 26.87 15279.38 2000 8284 3533 4751 1.18 2.32 25.79 17898.83 2001 7663 3361 4302 1.49 1.70 26.32 15053.20 2002 7140 3299 3841 1.64 1.44 25.96 12656.96 2003 6690 3235 3455 1.40 1.85 28.61 12756.17 2004 6603 3280 3323 1.58 2.41 35.15 15597.49 2005 6651 3382 3269 1.70 2.16 35.48 16906.57 2006 6645 3421 3224 2.08 2.10 38.08 18497.99 2007 6749 3578 3171 3.26 2.26 44.29 21835.03

Total/Average 15486 6477 9009 1.33 1.77 27.75 --

32

Table 3 Summary Statistics for Stock Return Predictors

This table presents the time-series averages of cross sectional averaged statistics of return predictors for China and the U.S. market for our sample period. Details of these variables are provided in the Appendix. Equal weight is used to compute cross-sectional averages. Panel A: Time-series average of cross-sectional distribution of stock return predictors in China 25% Mean Median 75% Std. Dev. Skewness Kurtosis SIZE(Mil RMB) 442.477 986.909 674.261 1105.336 1244.608 8.140 108.236 B/P 0.701 1.365 1.022 1.535 1.419 5.640 65.871 MOM -0.054 0.259 0.160 0.450 0.555 3.814 52.734 E/P 0.045 0.096 0.088 0.150 0.214 -4.130 69.194 C/P 0.064 0.161 0.118 0.217 0.348 1.679 61.232 SG -0.024 0.366 0.132 0.358 2.273 13.346 292.576 ACC -0.057 0.003 0.004 0.063 0.137 -0.632 11.333 NOA 0.590 0.687 0.705 0.802 0.178 -1.382 13.181 CPX 0.008 0.074 0.047 0.120 0.140 1.514 20.441 RD 0.038 0.121 0.070 0.132 0.182 6.594 82.099 ADV 0.009 0.055 0.023 0.060 0.098 7.419 96.267 AG 0.026 0.196 0.129 0.287 0.334 4.134 38.287 ΔGPM -0.097 -0.013 -0.007 0.085 0.697 0.599 104.805 ΔEQ 0.000 0.040 0.008 0.048 0.097 0.610 40.086 ΔDT -0.012 0.042 0.026 0.088 0.113 0.537 8.368 STDR 0.016 0.019 0.019 0.022 0.006 0.740 4.681 TURN 0.335 1.425 0.733 1.503 7.881 12.111 16.923 ILLIQ( *10-7) 3.771 13.052 9.415 16.674 17.116 6.320 131.524

Panel B: Time-series average of cross-sectional distribution of stock return predictors in United States SIZE(Mil USD) 94.032 2272.371 288.693 972.618 11370.977 14.652 296.271 B/P 0.291 0.793 0.509 0.788 14.548 1.386 993.820 MOM -0.088 0.115 0.107 0.307 0.419 0.362 3.624 E/P 0.016 0.099 0.049 0.075 5.921 -15.330 1270.846 C/P 0.042 0.188 0.081 0.125 7.699 -4.370 1126.556 SG 0.022 0.494 0.122 0.296 13.419 39.575 2091.711 ACC -0.069 -0.026 -0.028 0.012 0.105 0.526 25.047 NOA 0.325 0.546 0.592 0.756 0.296 -0.301 0.369 CPX 0.011 0.226 0.036 0.082 7.868 37.804 2014.799 RD 0.142 0.349 0.235 0.344 1.754 34.712 1617.333 ADV 0.006 0.048 0.017 0.047 0.109 7.901 106.583 AG 0.014 0.465 0.108 0.288 4.115 29.451 1328.820 ΔGPM -0.025 -0.008 -0.002 0.020 0.352 -1.532 213.056 ΔEQ -0.015 0.051 -0.001 0.012 0.231 4.462 35.806 ΔDT -0.015 0.017 0.000 0.025 0.127 2.430 26.952 STDR 0.015 0.026 0.023 0.034 0.015 2.303 19.852 TURN 0.283 0.443 0.398 0.561 0.228 1.510 3.957 ILLIQ ( *10-7) 0.473 9.362 2.343 9.930 34.453 3.012 53.385

33

Table 4 Returns to Decile Portfolios Sorted on Stock Return Predictors

This table reports the average monthly returns of stock portfolio deciles in China and the U.S. Panel A shows the decile returns, as well and the return spread between D10 and D1 stocks, sorted by each of the 18 return predictors and by a combined return predictor (COMBO) of all 18 variables for China market. Panel B shows the return spread between D10 and D1 stocks for the U.S. market. Panel C shows the difference in the return spread between the China market and the U.S. market. In June of each year t, stocks are sorted into deciles based on each predictor, where D10 represents the highest decile of the signed predictor, and D1 is the lowest. Decile portfolios are held unchanged from July of year t to June of year t+1. The 18 predictors are defined in the Appendix. COMBO is the average percentile rank of all 18 return predictors weighted by their sensitivities to subsequent stock returns. To be included in the analysis, stock price is restricted to be no less than ¥1 for China market and $1 for the U.S. market in June of each year. The accounting data is from fiscal year 1994 to 2005 and stock return data is from July 1995 to June 2007.

Panel A: China

-SIZE B/P MOM E/P C/P -SG -ACC -NOA -CPX RD ADV -AG ∆GPM -∆EQ -∆DT -STDR -TURN ILLIQ COMBO

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)

D1 1.84 1.49 1.74 2.27 2.59 1.97 1.93 1.83 1.83 1.42 1.35 1.67 1.76 1.81 2.00 1.67 1.78 1.67 1.52 2 2.13 1.57 2.16 2.10 1.97 1.98 1.84 2.34 1.74 2.01 0.91 1.99 1.88 1.94 1.90 1.97 1.98 1.81 1.61 3 1.91 1.96 2.06 1.92 2.01 2.15 2.13 1.97 2.08 1.90 1.27 1.84 1.82 2.27 1.95 2.14 1.99 2.05 2.00 4 1.76 2.07 2.27 1.88 2.02 2.17 1.93 2.12 2.01 1.74 1.21 2.06 2.19 2.39 1.98 2.19 2.06 2.15 2.05 5 1.99 2.29 2.46 2.06 1.75 2.13 2.06 1.99 1.99 2.27 1.38 2.26 1.95 2.20 2.04 2.18 2.25 2.11 1.89 6 2.04 2.39 2.21 2.02 2.01 2.18 2.37 1.99 2.22 2.31 2.06 2.08 2.08 2.27 2.07 2.19 2.29 1.97 2.36 7 2.23 2.46 2.27 2.10 2.25 2.24 1.98 1.88 2.43 2.18 1.39 2.11 2.56 1.50 2.29 2.18 2.14 2.23 2.37 8 2.20 2.25 2.04 2.11 2.00 2.01 2.19 2.15 2.03 2.46 1.48 2.15 2.56 2.23 2.22 1.99 2.07 2.17 2.28 9 2.23 2.24 1.94 2.15 2.01 2.23 2.32 2.22 2.19 2.24 1.67 2.35 2.33 3.84 2.23 1.88 2.29 2.36 2.43

D10 2.43 2.23 1.79 2.26 2.27 2.06 2.28 2.38 2.33 2.48 1.71 2.33 2.07 2.05 2.20 2.26 2.03 2.33 2.24 10–1 0.59 0.74 -0.05 -0.01 -0.33 0.09 0.35 0.56 0.50 0.87 0.17 0.66 0.31 0.24 0.20 0.59 0.25 0.66 0.72 t-stat 0.95 1.80 -0.16 -0.01 -0.54 0.18 1.62 2.58 1.14 2.52 1.02 1.72 0.82 0.57 0.34 1.35 0.97 1.65 1.98

Panel B: US 10–1 0.22 1.02 0.65 0.91 1.11 0.87 0.73 0.92 0.25 0.37 1.08 1.31 0.09 0.94 0.63 0.28 0.62 0.42 0.89 t-stat 0.79 2.04 1.61 1.97 1.95 2.36 2.30 4.37 0.39 0.51 2.04 3.44 0.59 1.75 3.25 1.01 0.98 1.19 2.88

Panel C: China – US Diff in

D10-D1 0.37 -0.38 -0.70 -0.92 -1.44 -0.78 -0.38 -0.36 0.25 0.50 -1.02 -0.65 0.22 -0.70 -0.43 0.11 -0.36 0.24 -0.17

t-stat 0.52 -0.62 -1.84 -1.65 -1.67 -1.71 -1.49 -1.16 0.33 0.52 -2.01 -1.70 0.82 -1.86 -2.40 0.12 -0.55 0.37 -0.25

34

Table 5 Alphas to Decile Portfolios Sorted on Stock Return Predictors

This table reports the average monthly three-factor alphas of stock portfolio deciles in China and the U.S. Panel A shows the decile returns, as well and the return spread between D10 and D1 stocks, sorted by each of the 18 return predictors and by a combined return predictor (COMBO) of all 18 variables for China market. Panel B shows the alpha spreads between D10 and D1 stocks for the U.S. market. Panel C shows the difference in the alpha spreads between the China market and the U.S. market. In June of each year t, stocks are sorted into deciles based on each predictor, where D10 represents the highest decile of the signed predictor, and D1 is the lowest. Decile portfolios are held unchanged from July of year t to June of year t+1. The 18 predictors are defined in the Appendix. COMBO is the average percentile rank of all 18 return predictors weighted by their sensitivities to subsequent stock returns. The risk-adjusted return is the intercept from time-series monthly regressions of decile portfolio returns on the three factors: tRMRF is the market return in excess of the risk free rate; SMBt and HMLt are the monthly returns on size and book-to-market factors, respectively, computed following Fama-French (1993) procedure. To be included in the analysis, stock price is restricted to be no less than ¥1 for China market and $1 for the U.S. market in June of each year. The accounting data is from fiscal year 1994 to 2005 and stock return data is from July 1995 to June 2007.

Panel A: China

-SIZE B/P MOM E/P C/P -SG -ACC -NOA -CPX RD ADV -AG ∆GPM -∆EQ -∆DT -STDR -TURN ILLIQ COMBO

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)

D1 -0.87 -1.32 -1.40 -1.15 -1.18 -0.91 -1.17 -1.18 -0.92 -1.51 -0.86 -1.11 -1.23 -1.00 -0.98 -1.50 -1.20 -1.09 -1.38 2 -0.85 -1.26 -0.95 -1.46 -1.64 -0.77 -1.30 -0.89 -0.85 -1.16 -1.31 -0.80 -1.02 -1.21 -1.20 -1.20 -0.95 -1.13 -1.30 3 -1.10 -1.15 -0.87 -1.33 -1.22 -0.83 -1.21 -1.28 -1.01 -1.31 -1.06 -1.05 -0.95 -1.14 -1.16 -1.01 -1.10 -1.10 -0.97 4 -1.26 -1.03 -0.98 -1.38 -1.19 -0.92 -1.17 -1.09 -0.96 -1.34 -1.05 -1.12 -0.76 -0.92 -1.10 -0.87 -1.12 -1.00 -1.14 5 -1.07 -0.96 -1.16 -1.22 -1.27 -0.94 -1.26 -1.07 -1.11 -0.95 -1.17 -0.90 -0.92 -1.14 -1.24 -0.90 -0.95 -1.20 -1.13 6 -1.14 -1.02 -1.27 -0.97 -1.07 -1.06 -1.06 -1.17 -0.91 -0.81 -1.00 -1.20 -1.15 -1.04 -0.90 -1.09 -0.82 -1.17 -0.95 7 -1.04 -0.95 -0.92 -0.93 -0.72 -0.94 -0.88 -1.06 -0.98 -1.00 -0.75 -1.06 -1.00 -1.05 -0.90 -0.87 -1.04 -0.94 -0.73 8 -1.04 -0.92 -0.86 -0.65 -0.85 -1.30 -0.75 -1.02 -1.24 -0.82 -0.79 -0.98 -0.97 -0.66 -1.10 -0.97 -0.99 -1.07 -0.88 9 -0.91 -1.00 -0.85 -0.64 -0.71 -1.31 -0.68 -0.92 -1.16 -0.85 -0.58 -0.96 -1.05 -1.32 -0.81 -0.86 -0.86 -0.70 -0.78

D10 -0.59 -0.80 -0.88 -0.70 -0.75 -1.26 -0.96 -0.73 -1.26 -0.67 -0.58 -0.65 -1.42 -1.20 -1.04 -0.64 -1.08 -0.77 -0.59 10–1 0.28 0.52 0.52 0.45 0.43 -0.35 0.21 0.45 -0.34 0.79 0.23 0.45 -0.19 -0.20 -0.06 0.86 0.12 0.32 0.79 t-stat 0.85 1.59 1.14 1.23 1.16 -1.18 0.90 2.05 -1.12 2.48 1.27 2.19 -0.50 -0.88 -0.35 2.72 0.57 1.58 2.42

Panel B: US 10–1 0.10 1.31 0.86 1.30 1.56 1.08 0.32 0.85 0.58 0.19 1.25 1.51 -0.06 1.46 0.67 0.85 0.87 0.40 1.07 t-stat 0.33 2.45 1.62 2.65 2.59 2.99 1.59 3.77 0.86 0.21 2.20 3.71 -0.37 2.30 5.45 0.96 1.30 1.04 2.94

Panel C: China – US Diff in

D10-D1 0.15 -0.70 -0.41 -0.97 -1.08 -1.55 -0.08 -0.37 -0.82 0.45 -1.07 -1.04 -0.23 -1.75 -0.65 0.05 -0.70 0.12 -0.35

t-stat 0.25 -1.76 -0.72 -2.14 -2.25 -2.16 -0.36 -0.79 -1.87 0.93 -2.18 -2.07 -0.49 -2.31 -1.69 0.13 -1.81 0.33 -1.46

35

Table 6 Cross-Sectional Regressions

This table reports the time-series averages of the coefficients in the regression of monthly stock return on stock return predictors in China and U.S. markets. Panel A and B show the result of univariate regressions for China market and U.S. markets. Panel C and D show the result of a joint regression including all the 18 predictors for both markets. AdjR2 is the adjusted R2 for multivariate regressions using all 18 predictors and AdjR2 * is the adjusted R2 for multivariate regression using the first 6 principal components. If a stock return predictor is missing, it is replaced with the annual cross-sectional median. To be included in the analysis, stock price is restricted to be no less than ¥1 for China market and $1 for the U.S. market in June of each year. The accounting data is from fiscal year 1994 to 2005 and stock return data is from July 1995 to June 2007. -SIZE B/P MOM E/P C/P -SG -ACC -NOA -CPX RD ADV -AG ∆GPM -∆EQ -∆DT -STDR -TURN ILLIQ AdjR2 AdjR2*

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

Panel A. Univariate regressions for the Chinese market

Coeff 0.17 0.17 0.35 0.45 0.26 0.08 0.59 0.61 1.10 1.46 1.07 0.61 1.00 0.74 0.27 21.37 0.32 0.61 -- --

t-stat 0.67 2.12 0.67 0.55 0.54 0.64 1.14 1.86 1.30 2.22 0.81 1.77 1.42 0.84 0.61 1.10 1.63 1.83 -- --

Panel B. Univariate regressions for the U.S. market

Coeff 0.03 0.62 0.44 2.04 2.62 0.30 1.34 0.73 0.48 -0.31 1.68 0.27 0.03 0.80 1.86 4.80 0.61 0.48 -- --

t-stat 0.61 2.22 1.74 1.41 1.95 1.61 2.47 3.18 0.88 -0.56 1.79 2.20 0.08 1.16 5.84 0.31 0.90 1.17 -- --

Panel C. Multivariate regressions for the Chinese market

Coeff 0.34 0.01 0.30 0.18 0.16 -0.09 0.60 0.27 0.28 0.20 0.64 0.43 1.07 -0.91 -0.90 20.60 0.00 0.00 0.09 0.05

t-stat 1.47 0.15 0.77 0.20 0.39 -0.99 1.67 0.88 0.43 0.38 0.46 1.50 1.78 -1.27 -1.44 1.72 1.28 -0.26 -- --

Panel D. Multivariate regressions for the U.S. market

Coeff -0.04 0.33 0.33 -0.76 2.05 0.01 0.35 0.81 -0.24 -0.01 -0.26 -0.01 -0.25 0.62 1.20 -4.72 0.00 0.00 0.06 0.05

t-stat -0.70 3.06 1.37 -1.12 2.75 0.12 0.91 5.17 -0.67 -0.03 -0.26 -0.13 -0.91 2.44 4.30 -0.40 0.20 0.88 -- --

36

Table 7 Standardized Return Spreads

This table reports the time-series average of standardized stock return spreads between D10 and D1 portfolios sorted by return predictors for the China and U.S. markets. The standardized return spread (D10-D1) of a return predictor is the stock return spreads scaled by the predictor spreads in a corresponding year. The difference of the standardized return spread between the China and U.S. markets is the difference in the standardized return spreads in these two markets. Decile portfolios are formed in the end of June and held unchanged from July of year t to June of year t+1 for each stock decile. To be included in the analysis, stock price is restricted to be no less than ¥1 for China market and $1 for the U.S. market in June of each year. The accounting data is from fiscal year 1994 to 2005 and stock return data is from July 1995 to June 2007. The t-statistics are included at the bottom of each Panel.

-SIZE B/P MOM E/P C/P -SG -ACC -NOA -CPX RD ADV -AG ∆GPM -∆EQ -∆DT -STDR -TURN ILLIQ

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

Panel A: Spreads of Average Return Predictors between D10 and D1 Stocks

China 2.53 4.33 1.50 0.80 1.17 3.54 0.49 0.63 0.47 0.58 0.39 1.02 0.81 0.28 0.41 0.02 0.90 0.90 US 6.21 3.98 1.54 1.36 1.67 4.05 0.36 0.99 1.84 1.41 0.27 3.72 0.60 0.71 0.43 0.05 0.90 0.90

China-US -3.68 0.35 -0.04 -0.56 -0.50 -0.51 0.13 -0.36 -1.38 -0.88 0.12 -2.70 0.20 -0.43 -0.02 -0.03 0.00 0.00 (t-stat) -25.97 0.17 -0.18 -0.74 -0.48 -0.38 6.79 -6.81 -1.38 -7.30 1.34 -2.65 0.97 -5.61 -0.67 -7.59 0.11 -0.29

Panel B: Standardized Return Spreads between D10 and D1 Stocks

China 0.16 0.23 -0.01 0.30 0.02 -0.10 0.07 0.79 0.90 1.58 0.79 0.52 0.45 -0.59 0.00 9.82 0.77 0.51 (t-stat) 0.70 2.27 -0.04 0.35 0.03 -0.60 0.14 1.94 0.87 2.41 1.28 1.06 0.91 -0.62 0.00 0.44 1.05 1.26

US 0.03 0.48 0.43 1.36 1.48 0.29 1.18 1.04 0.52 -0.10 4.21 1.58 -0.22 1.30 1.66 5.84 0.24 0.51 (t-stat) 0.76 1.82 1.63 1.59 1.60 2.12 2.22 4.89 0.52 -0.19 2.08 3.50 -0.36 1.45 3.02 0.32 0.81 0.95

China-US 0.13 -0.25 -0.44 -1.06 -1.46 -0.39 -1.11 -0.25 0.38 1.77 -4.46 -1.07 0.67 -1.89 -1.65 3.98 -0.52 -0.01 (t-stat) 0.52 -0.89 -1.72 -1.85 -1.65 -1.87 -1.85 -0.54 0.27 1.99 -2.47 -2.13 0.89 -1.56 -2.41 0.14 -0.69 0.00

37

Table 8 Standardized Return Spreads across Stocks Sorted by Synchronicity

We break down firms into three groups based on the R2 from the market model regression: titmttti rr ,,, εβα ++= . This table reports the time-series average of standardized stock return spreads between D10 and D1 portfolios sorted by return predictors for the top, middle, and bottom R2 groups in the Chinese market. The standardized return spread (D10-D1) of a return predictor is the stock return spreads scaled by the predictor spreads in a corresponding year. Decile portfolios are formed in the end of June and held unchanged from July of year t to June of year t+1 for each stock decile. To be included in the analysis, stock price is restricted to be no less than ¥1 for China market in June of each year. The accounting data is from fiscal year 1994 to 2005 and stock return data is from July 1995 to June 2007. The t-statistics are included.

-SIZE B/P MOM E/P C/P -SG -ACC -NOA -CPX RD ADV -AG ∆GPM -∆EQ -∆DT -STDR -TURN ILLIQ

R2 Rank (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

High 0.34 -0.05 0.54 -0.57 -0.47 0.06 0.21 -0.27 1.29 1.29 -0.05 0.71 0.76 -1.12 -0.52 -22.59 -0.82 0.55 t-stat 1.42 -0.38 1.03 -0.70 -0.91 0.28 0.35 -0.49 1.56 1.78 -0.07 1.46 0.95 -0.90 -0.65 -0.91 -2.29 0.99 Mid

0.52 0.26 0.20 0.56 0.17 0.05 0.70 1.20 0.74 1.79 0.31 0.75 0.69 0.50 0.13 20.46 0.46 0.68 t-stat 0.74 1.97 0.75 0.81 0.24 0.54 0.50 1.58 0.92 1.86 0.25 0.30 0.56 0.42 0.23 0.95 1.26 0.71 Low 0.83 0.44 0.12 1.25 0.53 -0.18 0.89 1.60 0.63 2.33 0.91 0.91 0.62 1.00 0.61 35.12 0.90 0.82 t-stat 0.88 3.17 0.26 1.14 0.72 -1.12 0.24 2.16 0.40 2.36 0.56 0.05 0.55 0.45 0.67 1.38 1.69 1.16

High-Low -0.49 -0.49 0.41 -1.81 -1.00 0.23 -0.68 -1.87 0.66 -1.04 -0.96 -0.21 0.14 -2.11 -1.13 -57.71 -1.72 -0.27 t-stat -0.50 -3.35 1.10 -2.90 -2.64 1.30 -1.73 -2.87 0.47 -1.94 -1.62 -0.46 0.12 -1.94 -1.95 -2.50 -3.09 -1.65

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