the signed momentum strategy in the chinese stock...

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The signed momentum strategy in the Chinese stock market

Abstract

This paper uncovers the performance of short-term momentum in the Chinese stock market.

we find under the traditional way to construct the momentum strategy, there is no significant

momentum performance for the whole sample. We also find the momentum performance varies

with different market states: when the market continues, the momentum return is significantly

positive and when the market transits, the momentum return is significantly negative. In addition,

we find this special performance might come from the change of beta difference between winner-

decile and loser-decile during the formation period and the holding period with the market transition.

We combine the traditional momentum strategy with the market dynamic and give a signed

momentum strategy, and this improved strategy gives significant portfolio return, which can’t be

fully explained by the popular asset-pricing factors. Our finding for the relation between short-term

momentum performance and the market dynamic holds during the period of market tremendous

changing and can be a universal explanation for other markets’ momentum performance.

JEL classification: G10, G11, G12, G14

Keywords: short-term momentum, market dynamic, signed momentum

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1. Introduction

A large body of literature draws attention to the momentum anomaly in the US

stock market, since Jegadeesh and Titman (1993). 1 There is a long history for

momentum effects: Chabot, Ghysels and Jagannathan (2009) argue that the momentum

anomaly dates back to the Victorian era; Geczy and Samonov (2016) use a sample from

1801 and find the momentum profits still remain positive and statistically significant in

the pre-1927 data in the US. Similar to the US, the significant momentum profit still

exists around world.2 Besides stocks, momentum strategy is widely found in other

assets.3

However, Asian stock markets are exceptions. Fama and French (2012), Asness,

Moskowitz and Pederson (2013) show that there is no significant momentum effect in

the Japan stock market. Hameed and Kusnadi (2002) find no evidence to support the

momentum profits in the Hong Kong, Korean, Taiwan and other Asia Pacific stock

markets. Griffin, Ji and Martin (2003) and Chui, Titman and Wei (2010) extend the

scope of Asian markets and still cannot find momentum effects. Consistent with most

Asian markets, there is also no momentum effect in the Chinese stock market.

Studies try to find the reason for the insignificant momentum performance in the

Asian stock markets. Cooper, Gutierrez, and Hameed (2004) repost that the short-term

momentum return in the US stock market is conditioned by market states, and they find

the momentum return exclusively following UP markets. Motivated by this finding,

1 See Asness (1995), Jegadeesh and Titman (2001), Israel and Moskowitz (2013) and Huang et al (2018),

among others.

2 See Liu et al (1999), Nijman et al (2004), Forner and Marhuenda (2003), Mengoli (2004), and Baltzer,

Jank and Smajlbegovic (2019), among others, for the European stock markets; Rouwenhorst (1998,

1999), Chan, Hameed and Tong (2000), Grundy and Martin (2001), Lewellen (2002), Griffin, Ji and

Martin (2003), Patro and Wu (2004), Chui, Titman and Wei (2010), Fama and French (2012), Asness,

Moskowitz and Pederson (2013), and Li et al (2014) for the international stock market.

3 See Okunev and White (2003), Serban (2010), Menkhoff et al (2012) for currency; Erb and Harvey

(2006), Moskowitz, Ooi and Pedersen(2012) for exchange futures contracts; Miffre and Rallis (2007),

Bianchi, Drew and Fan (2015, 2016) for commodity futures; Gebhardt, Hvidkjaer and

Swaminathan(2005), Jostova et al (2013), Haesen, Houweling and van Zundert (2017) for government

bond.

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Asem and Tian (2010) divide the US stock market into the market continuation

subsample and the market transition subsample, and find the momentum profits are

higher when the market continues in the same state than when it transits to the different

state. After that, Hanauer (2014) presents that the relationship between momentum

performance and market dynamic is not a special case for the US stock market, and

gives some supportive evidence from the Japan, Korea, Taiwan, and Turkey stock

markets. Hanauer (2014) believes that the historically low return of traditional

momentum strategy in the Japan stock market is from the offsetting of positive

momentum returns during the market continuation periods and the negative momentum

returns during the market transition periods, and the more market transition periods in

Japan than the US stock market is the key factor for the different momentum

performances in two markets. What’s more, Lin et al (2016), Cheema and Nartea (2014,

2017b) also give the evidence of momentum states and the momentum performance

from the Taiwan stock market and the Chinese stock market.

Even though the Chinese stock market has relatively short history, it has become

the second largest stock market and the biggest emerging market. And the fact that

Chinese stock market is dominated by the individual investors especially the

uninformed retail investors could bring more significant momentum than the developed

stock market like the US stock market, considering the momentum performance reflects

the inefficiency of information diffusion. However, there is no significant evidence for

the short-term momentum in previous studies, which indicates some other disturbance

factor existing in the Chinese stock market. In this paper, we want to figure out this

disturbance factor and give an improved momentum strategy for the Chinese stock

market.

Similar to the finding in other stock markets, we find the market dynamic can be

the main disturbance factor for performance of short-term momentum in the Chinese

stock market. Using a comprehensively monthly data of all A-shares from 1997 to 2018,

we form the momentum strategy by using the cumulative returns from 12 months before

to one month before the holding month (𝑡 − 12 to 𝑡 − 2, which is the most common

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formation period used in momentum studies) and the cumulative returns from 12

months before to 7 months before the holding month (𝑡 − 12 to 𝑡 − 7, which is

motivated by the finding in Novy-Marx (2012) who find momentum is primarily driven

by firms’ performance during this period) to rank stocks, and study the performance of

short-term momentum in the holding period (𝑡) based on the difference of portfolio

return between stocks in the group 1(that means the average value of stock return in

this group is the lowest in the formation period) and stocks in the group 10 (that means

the average value of stock return in this group is the highest in the formation period).

As we have found, the performance of short-term momentum varies with different

markets states. When we divided the market into two subsamples based on the sign of

excess market return during the formation period, we find the difference of short-term

momentum performance in the subsample following the Down market (with negative

sign during the formation period) and that following the Up market (with non-negative

sign during the formation period) is significant positive from both two formation

periods (with the difference of 5.67% and the T value of 2.32 for 𝑡 − 12 to 𝑡 − 2

formation period and the difference of 4.49% and the T value of 2.36 for 𝑡 − 12 to 𝑡 −

7 formation period ). This finding is just opposite with Cooper, Gutierrez, and Hameed

(2004), and indicates the Down market may play a greater role on the performance of

short-term momentum in the Chinese stock market.

When we divide the market into four subsamples based on the sign of excess

market return during the formation period and the holding period, we find different

momentum performance in each subsample. Our subsamples include the Up & Up

subsample (constitution by the Up market in the formation period (𝑡 − 12 to 𝑡 − 2/ 𝑡 −

12 to 𝑡 − 7) and the Up market in the holding period ( 𝑡)), Up & Down subsample,

Down & Down subsample and Down & Up subsample. And we also find the different

performance in the market continuation subsample (constitution by Up & Up and Down

& Down) and the market continuation subsample (constitution by Up & Up and Down

& Down).

We take the results based on the 𝑡 − 12 to 𝑡 − 2 formation period as an example,

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and find when the market changes from Up to Down, there is significantly negative

momentum return (-8.63%, with the T value of -3.65). However, when the market sign

continues from Up to Up, the momentum performance is insignificantly positive

(0.65%, with the T value of 0.27). Results from the Up to Down subsamples dominate

the performance of momentum strategy during the Up market, so the comprehensive

result of two subsamples is significantly negative. When the market changes from

Down to Up, there is significantly negative momentum return (-15.83%, with the T

value of -6.70). And when the market sign continues from Down to Down, the

momentum performance is significantly positive (20.30%, with the T value of 8.40).

Results from these two subsamples have the similar size but in the opposite sign, so the

comprehensive result during the Down market is insignificantly positive. Results based

on the 𝑡 − 12 to 𝑡 − 7 formation period are similar (but with the smaller magnitude in

average value and T value).

In addition, we find the significant short-term momentum effect exists when the

market continues and the significant reversal effect exists when the market transits.

When we take the results based on the 𝑡 − 12 to 𝑡 − 2 formation period as an example,

result from market continuation subsample gives the significantly positive momentum

return (9.07%, with the T value of 5.25), and based on our previous result, the

significant momentum return is mainly from the Down & Down subsample. Result

from market transition subsample gives the significantly negative momentum return (-

12.06%, with the T value of -7.22), and this significant reversal effect is from both the

Up & Down and the Down & Up subsamples, but the Down & Up subsamples plays a

more important role (This finding is consistent with the study of Daniel and Moskowitz

(2016), who find the momentum effect in the US stock market suffers serious crashes

when the market rebounds from the panic states). Results from the 𝑡 − 12 to 𝑡 − 7

formation period give a similar but weaker conclusion, so in the Chinese stock market,

the traditional 𝑡 − 12 to 𝑡 − 2 formation period is a better choice to form a momentum

strategy, which is inconsistent with the result in the US stock market (see in Novy-Marx,

2012).

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We extend the study of momentum performance and the market dynamic by

studying the change of stock characteristics during different market states. The three

stock characteristic proxies used in this paper are the beta, capitalization and B/M. The

idea to relate the stock characteristics with the momentum strategy is from Kothari and

Shanken (1992) and Grundy and Martin (2001), who argue that past-return sorted

portfolio/the momentum strategy has significant time-varying exposure to systematic

factors. By longing past winner stocks and shorting past loser stocks, momentum

portfolio has positive/negative loadings on factors which have had a positive/negative

realization during the formation period. Daniel and Moskowitz (2016) furtherly give

the intuition that: when the market has fallen significantly over the momentum

formation period, a good chance exists that the firms that fall in tandem with the market

are high-beta firms, and those that performed the best are low-beta firms. Thus,

following market declines, the momentum portfolio is likely to be long low-beta stocks

and short high beta stocks. Daniel and Moskowitz (2016) find that betas for the past-

loser-decile rise above 3 and past-winner-decile fall below 0.5 after the major market

deciles, that’s, when the market rebound from panic states, the beta difference between

the past-winner group and past-loser group changes from positive in strategy formation

period into the negative (-2.5 in their paper) in the holding period. Momentum crashes

in the US stock market during the market rebound period is from the conditionally large

negative beta. Thus, in this paper, we also give the similar summary as the Daniel and

Moskowitz (2016) in more market states of the Chinese stock market to find the

explanation for the changeable momentum performance with the market states.

We give a summary of beta difference between stocks in the winner-decile and

loser-decile ranked by their return in the formation period and holding period. As we

have found, the sign of beta difference during two periods is critical for the performance

of short-term momentum in the Chinese stock market: when there is the same sign

during two periods, stocks in the winner-decile and loser-decile during formation period

have this similar performance of stocks in the winner-decile and loser-decile during

holding period, and the momentum return is likely to be positive. However, when the

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sign is opposite during two periods, stocks in the winner-decile and loser-decile during

formation period have different beta performance of stocks in the winner-decile and

loser-decile during holding period, and the momentum return is likely to be negative.

We also give the similar summary of capitalization difference and B/M (book to market)

difference, and find the signs of these two variables are unrelated to the performance of

short-momentum, so the change of beta difference is the only key factor for the special

performance of momentum in the Chinese stock market.

Based on these findings, we give an improved signed momentum strategy, which

is a combination of traditional momentum strategy and the market dynamic.

Specifically, we use the traditional way to construct the momentum strategy during the

market continuation period by ranking stocks based on their return in the formation

period and longing stocks in the winner-decile and shorting stocks in the loser decile.

But during the market transition period, we use the opposite way to construct the

momentum strategy by ranking stocks based on their return in the formation period and

longing stocks in the loser-decile and shorting stocks in the winner decile.

Our signed momentum strategy has good performance in the Chinese stock market.

When we use 𝑡 − 12 to 𝑡 − 2 as the formation period, we find an approximately

increasing trend, and the excess return between group 10 and group 1 (i.e., the strategy

profit) is significant both in the economic level and the statistics level (with the average

annual return as 10.50%, and the T value as 8.74, significant at 1% level). And we also

find the excess return from the signed momentum strategy can’t be fully explained by

these three models (with the average value of 0.90 and T value of 2.70 for the CAMP

mode, the average value of 1.04 and T value of 3.31 for Fama-French three factors

model and the average value of 0.94 and T value of 2.84 for Fama-French five factors

model, respectively), and this finding furtherly proves the efficiency of our signed

momentum strategy. Results based on the 𝑡 − 12 to 𝑡 − 7 formation period is similar

but in the smaller size, which indicates after considering the influence of market

dynamic, the 𝑡 − 12 to 𝑡 − 7 is still not a better alternation of the 𝑡 − 12 to 𝑡 − 2

formation period in the Chinese stock market.

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By giving some summary about stocks in the winner-decile and the loser-decile

during the formation period are in the same group or the counterparty group during the

holding period, we do the robustness test and confirm our finding about the momentum

performance and market dynamic. And in our further analysis about the market

dynamic and the existing conclusion about the momentum performance in other stock

markets, we find the market dynamic has a universe influence on the momentum

performance: markets with the significant momentum performance are generally with

more market continuation periods and the average absolute values of market return are

relatively smaller (therefore, the market dynamic has smaller influence on the change

of beta difference and the momentum performance is less influenced) and those with

the insignificant momentum performance are just on the opposite situation.

The possible contribution of this paper is at threefold. Firstly, we give a detailed

result about the performance of traditional momentum in the Chinese stock market and

the different performance of momentum in the different market states, compared to

those who also study about the Chinese stock market (see Kang, Liu and Ni (2002),

Pan, Tang and Xu (2013)), we give a more comprehensive result. Compared to the study

of Cheema and Nartea (2014, 2017b), we give a better way to define the Up and Down

market by using the sign of excess return of the market return to the risk-free return

during the strategy formation period, considering the fact that in all asset pricing model,

market return plays a great role by using the form of the difference between market

return and the risk-free return, that is, the market premium. Even though the value of

risk-free return is relatively low during our sample period, we believe it would be better

to define the market states after eliminating this influence. Secondly, by studying the

change of beta difference, capitalization difference and B/M difference between the

winner-decile and the loser-decile during the formation period and the holding period,

we find the important role of beta difference on the relationship between momentum

performance and the market dynamic, and that is the reason for the changed momentum

performance with market states. Finally, we give a multi-market comparison about the

market dynamic and the existing results of momentum performance, and find markets

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with significant momentum performance generally have different market dynamic to

those with the insignificance momentum performance, so our finding based on the

Chinese stock market could be universally applicable.

The remainder of this paper is organized as follows: Section 2 documents the data

and momentum strategy used in this paper. Section 3 provides the baseline results of

traditional short-term momentum in the Chinese stock market for the full sample and

during different market states. Section 4 presents our explanation for the relationship

between momentum performance and market dynamic, and gives the performance of

our improved signed momentum strategy. Section 5 performs a battery of robustness

tests and further analyses. Section 6 concludes the paper.

2. Data and Momentum Strategy

2.1. Data Sources

We get a comprehensive dataset of the Chinese A-shares over the sample period

from January 1991 to September 2018. The monthly data is retrieved from the CSMAR

Database4 . We reuuire a stock to have at least a full year’s data (12 months) to be

included in our sample. To ensure the adeuuacy and validity of data, we don’t include

the ST (special treatment) stocks, stocks with price less than 1 CNY5 at the beginning

of each month, and the financial stocks in our sample. After applying these filter rules,

our final sample contains 2825 stocks of public firms listed in the Chinese stock market.

Data used in this paper includes the monthly stock return, the risk-free return6, the

capitalization value and book to market ratio of stocks, and so on.

2.2. Momentum Strategy

In this paper, we use two universal ways to form the momentum strategy. The first

4 CSMAR Database is widely used for the Chinses studies, which is the only Chinese database used by

Wharton Research Data Services. Website of this data is http://www.gtarsc.com/.

5 The face value of stocks in the Shanghai Stock Exchange and the Shenzhen Stock Exchange.

6 Proxied by the one-year national debt coupon rate, and monthly data is converted from the yearly data

based on compound interest calculation method.

http://www.baidu.com/link?url=klfj0tjrnWkOZa1ZlR1gmtW_TV89Q2KMlKS9SHtEsE4A2VnjbeaGoDMv-p3JEqTT

http://www.baidu.com/link?url=klfj0tjrnWkOZa1ZlR1gmtW_TV89Q2KMlKS9SHtEsE4A2VnjbeaGoDMv-p3JEqTT

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one is based on the cumulative returns from 12 months before to one month before the

holding month (i.e., the 𝑡 − 12 to 𝑡 − 2 month returns) to rank stocks, consistent with

the literature of Jegadeesh and Titman (1993), Asness (1995), Fama and French (1996)

and the recent study from Asness, Moskowitz and Pederson (2013), Daniel and

Moskowitz (2016). The reason to give a one-month gap between the formation period

and the holding period is to avoid the short-term reversals shown by Jegadeesh (1990)

and Lehmann (1990), and the same way is also used in the recent papers. The alternative

method is based on the cumulative returns from 12 months before to 7 months before

the holding month (i.e., the 𝑡 − 12 to 𝑡 − 7 month returns) to rank stocks, which is

motivated by the finding in Novy-Marx (2012) that “Momentum is primarily driven by

firms’ performance 12 to seven months prior to portfolio formation, not by a tendency

of rising and falling stocks to keep rising and falling.”, and they find momentum

strategy based on this formation period is more profitable than the traditional way in

the US stock market and the international euuity indices, commodities, and currencies.

All data is placed into one of ten decile portfolios based on this ranking, where the

portfolio 10 includes all the winner stocks (those with the highest past return in our two

formation periods) and the portfolio 1 includes all the loser stocks. We use the euual-

weighted way to calculate the average value of holding period returns of each decile

portfolio to mitigate the bias caused by the small stock returns. All stocks in the holding

period are same to the formation periods, except the delisting.

In order to ensure the statistical validity of the results, we only retain the portfolios

with more than 30 stocks in each decile, and considering the loss of sample size caused

by using the previous 12 months data to form the momentum strategy, the final sample

period of this paper to study the short-term momentum effect in the Chinese stock

market is from July 1997 to September 2018, including 255 months.

3. Traditional Short-term Momentum

3.1. Baseline Results

In this section, we test the performance of traditional short-term momentum

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strategy in the Chinese stock market. We rank stocks by their cumulative return during

the 𝑡 − 12 to 𝑡 − 2 and 𝑡 − 12 to 𝑡 − 7 formation periods and sort them into 10

groups. The average return of stocks in the group 1 to group 10 has a monotonously

increasing trend during the formation period. If the momentum effect is significant in

the Chinese stock market, we can also see the increasing trend of the average return of

stocks in the group 1 to group 10 during the holding period (𝑡), and the difference of

average return between stocks in the group 10 and stocks in the group 1 should be

significantly positive. The baseline results of this part are given in the table 1.

[ Insert table 1 here]

As we can see from the table 1, there is no significant momentum effect in the

Chinese stock market. When we use the traditional 𝑡 − 12 to 𝑡 − 2 formation period

to form the short-term momentum strategy, the difference of portfolio return between

stocks in the group 1(that means the average value of stock return in this group is the

lowest in the formation period) and stocks in the group 10 (that means the average value

of stock return in this group is the highest in the formation period) is insignificantly

negative (-1.04%, with the T value of -0.85), which is inconsistent with the performance

of momentum effect. Result based on the 𝑡 − 12 to 𝑡 − 7 formation period gives the

similar conclusion: the difference of portfolio return between stocks in the group 1 and

stocks in the group 10 is insignificantly positive (1.00%, with the T value of 1.06), we

can’t find the evidence of short-term momentum effect in the Chinese stock market.

In addition, Cooper, Gutierrez, and Hameed (2004) find the short-term momentum

effect in the US stock market is conditioned by market states, and find momentum

returns exclusively follow the up markets. In this paper, we also give the results of

short-term momentum performance following the Up and Down market in the Chinese

stock market. Up and Down markets are defined by the sign of excess market return

(the difference between value-weighted market return and the risk-free return) during

each formation period, and if the sign of excess market return is non-negative during

the formation period then we define it as a Up market. Vice versa, if the sign of excess

market return is negative during the formation period then we define it as a Down

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market7.

However, we still can’t find the significant momentum effect in these two

subsamples. When we use the traditional 𝑡 − 12 to 𝑡 − 2 formation period to form the

short-term momentum strategy, the difference of portfolio return between stocks in the

group 1 and stocks in the group 10 following the Down market is insignificantly

positive (2.08%, with the T value of 1.19), and the result based on 𝑡 − 12 to 𝑡 − 7

formation period following the Down market is significantly positive (3.45%, with the

T value of 2.27).Performance of short-term momentum following the Down market has

the (significantly) positive return in the Chinese stock market, and the results following

the Up market is just opposite. When we use the traditional 𝑡 − 12 to 𝑡 − 2 formation

period to form the short-term momentum strategy, the difference of portfolio return

between stocks in the group 1 and stocks in the group 10 following the Up market is

significantly negative (-3.59%, with the T value of -2.13), and the result based on 𝑡 −

12 to 𝑡 − 7 formation period following the Up market is insignificantly negative (-

1.04%, with the T value of -0.88).

Another finding from the table 1 is that, the difference of short-term momentum

performance in the subsamples following the Down market and Up market is significant

positive from both two formation periods (with the difference of 5.67% and the T value

of 2.32 for 𝑡 − 12 to 𝑡 − 2 formation period and the difference of 4.49% and the T

value of 2.36 for 𝑡 − 12 to 𝑡 − 7 formation period ). This finding is just opposite with

Cooper, Gutierrez, and Hameed (2004), and indicates the Down market may play a

greater role on the performance of short-term momentum in the Chinese stock market.

To sum up, we can’t find the evidence for short-term momentum pattern in the

Chinese stock market and the performance in the subsamples following the Up and

Down market is different.

7 Our definition is similar to Cooper, Gutierrez, and Hameed (2004) and Asem and Tian (2010) who use

the past 36-month /12-month value-weighted market return to classify the up and down markets in the

US stock market, and considering we have two formation periods in this paper, we give the corresponding

classification based on each period length.

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3.2. Results during different market states

As we can also see from the table 1, the proportion of Up market and Down market

is similar in the Chinese stock market. In our whole sample of 255 months, 115/116

months are defined as the Down market and 140/130 months are defined as the Up

market based on the 𝑡 − 12 to 𝑡 − 2/ 𝑡 − 12 to 𝑡 − 7 formation period. This finding

gives the different market performance of the Chinese stock market compared to the

US stock market, taking the study of Asem and Tian (2010) as an example, who find

699 Up market and 249 Down market from January 1927 to December 2005 based on

the past 12-month value-weighted market return to classify the US stock market states.

The similar ratio of the Up market and Down market indicates there might be more

freuuent market transitions in the Chinese stock market than the US stock market

(which has the superior ratio of the Up market).

In this part, we want to study the short-term momentum effect based on the

subsamples of different market states. We have four subsamples in this part, Up & Up

gives the subsample combined by the up market in the formation period (𝑡 − 12 to 𝑡 −

2/ 𝑡 − 12 to 𝑡 − 7) and the up market in the holding period ( 𝑡); Up & Down gives the

subsample combined by the up market in the formation period and the down market in

the holding period; Down & Down gives the subsample combined by the down market

in the formation period and the down market in the holding period; Down & Up gives

the subsample combined by the down market in the formation period and the up market

in the holding period. The situation with same market states in two periods is considered

to be the market continuation (Up & Up and Down & Down) and the situation with

different market states in two periods is considered to be the market transition (Up &

Down and Down & Up). We give the results of short-term momentum during these

subsamples in the table 2.


When we take the results based on the 𝑡 − 12 to 𝑡 − 2 formation period as an

example, results from different market states subsamples are uuite different. When the

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market changes from Up to Down, there is significantly negative momentum return (-

8.63%, with the T value of -3.65). However, when the market sign continues from Up

to Up, the momentum performance is insignificantly positive (0.65%, with the T value

of 0.27). Results from the Up to Down subsamples dominate the performance of

momentum strategy during the Up market, so the comprehensive result of two

subsamples is significantly negative (given in the table 1). When the market changes

from Down to Up, there is significantly negative momentum return (-15.83%, with the

T value of -6.70). And when the market sign continues from Down to Down, the

momentum performance is significantly positive (20.30%, with the T value of 8.40).

Results from these two subsamples have the similar size but in the opposite sign, so the

comprehensive result during the Down market is insignificantly positive. Results based

on the 𝑡 − 12 to 𝑡 − 7 formation period are similar (but with the smaller magnitude in

average value and T value), so we don’t give them for brief.

Our finding of momentum performance in the Up market subsample is

inconsistent with the Cooper, Gutierrez, and Hameed (2004), who find the momentum

profits in the US stock market are in fact confined to periods following Up markets.

And our finding is contrary the behavioral models of Hong and Stein (1999) who think

the increased wealth during the Up market reduces investors’ risk aversion and leads to

higher momentum profits following Up markets. But considering the special investors

constitution of the Chinese stock market, our results are reasonable. Chinese stock

market is considered to be dominated by the retail investors (more than 50% account

belongs to investors with asset less than 0.1 million CNY), and these investors are

presumably more prone to behavioral biases. Retail investors’ trading behavior during

the Up market could be more risk-seeking8. Those investors treat the loser stocks as a

8 Taking the strange performance in 10 trading days after the 2019 Spring Festival as an example. During

this period, the Shanghai Composite Index rose 7.1%, Shenzhen Component Index rose 9.7%, and the

SME Board and the Growth Enterprise Market rose 13-14%. In all 3575 stocks of two stock markets,

only 57 fell, accounting for about 1.6%, more than 98% of the stocks were rising, and the average daily

trading amount was near to 600 billion CNY in this period. An extreme “demon stock” --Eastern

Communication rose from 3.70 CNY to 34.64 CNY, soared 9 times in 4 months, and rose 145% in 10

days; the second demon stock also rose 105% in 10 days.

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kind of lottery or a gamble during the Up market, which induces both the past winner

stocks and the loser stocks are attractive for the retail investors, and their trading

diminishes the return difference of these stocks and negating the profitability of the

momentum trading strategy9.

Even though there are successive short-sale ban lifts since 2010 in the Chinese

stock market10, the threshold of short sale is 0.5 million CNY, which is relatively high

for the individual investors (especially for the retail investors). And the trading of short

sale only accounts less than 1% of the total credit trading (margin trading and short

sale), which plays a tiny influence on retail investors’ trading. The undeveloped short

sale in the Chinese stock market limits the retail investors’ risk-seeking trading during

the Down market, and the short-term momentum effect performs well in the Down &

Down subsample.

Furtherly, we also find the significant short-term momentum effect exists when

the market continues and the significant reversal effect exists when the market transits.

When we take the results based on the 𝑡 − 12 to 𝑡 − 2 formation period as an example,

result from market continuation subsample gives the significantly positive momentum

return (9.07%, with the T value of 5.25), and based on our previous result, the

significant momentum return is mainly from the Down & Down subsample. Result

from market transition subsample gives the significantly negative momentum return (-

12.06%, with the T value of -7.22), and this significant reversal effect is from both the

Up & Down and the Down & Up subsamples, but the Down & Up subsamples plays a

more important role (This finding is consistent with the study of Daniel and Moskowitz

(2016), who find the momentum effect in the US stock market suffers serious crashes

when the market rebounds from the panic states). Results from the 𝑡 − 12 to 𝑡 − 7

formation period give a similar but weaker conclusion, so in the Chinese stock market,

the traditional 𝑡 − 12 to 𝑡 − 2 formation period is a better choice to form a momentum

9 The explanation of the short-term momentum performance in the Up & Up market states is similar to

Cheema and Nartea (2017 a).

10 See the details in Xiong, Gao and Feng (2017).

16

strategy, which is inconsistent with the result in the US stock market (see in Novy-Marx,

2012).

3.3. Results when the market changes greatly

As we have found, the short-term momentum performance relies on the market

states, so we want to furtherly study whether the extent of market continuation or

market transition has influence on this performance.

Based on the average absolute value of market excess return during each formation

period ( 𝑡 − 12 to 𝑡 − 2 / 𝑡 − 12 to 𝑡 − 7 ) and the holding period ( 𝑡 ), we get the

subsample of market with great changes. Specifically, we separate the whole sample

into two parts based on the absolute value of market excess return in each month and

the average value of absolute market excess return in the whole sample. The months

with larger absolute value of market excess return than the average level in the whole

sample during the formation period or the holding period are considered to have the big

changes. We give the results based on these largely changed subsamples in the table 3.


As we can see from the table 3, results based on the largely changed subsamples

are similar but more significant than those in the table 2. We still take the results based

on the 𝑡 − 12 to 𝑡 − 2 formation period as an example. When the market changes

from Up to Down, there is significantly negative momentum return (-24.43%, with the

T value of -7.76), which is almost tripled in size than that in the table 2. However, when

the market sign continues from Up to Up, the momentum performance is significantly

negative (-5.82%, with the T value of -1.77), and this finding proves our intuition that

the gamble behavior of retail investors is more serious in the continuous bull market.

When the market changes from Down to Up, there is significantly negative momentum

return (-25.65%, with the T value of -7.47). And when the market sign continues from

Down to Down, the momentum performance is significantly positive (26.35%, with the

T value of 6.83).

In addition, we also find the momentum performance in the market continuation

17

subsample during the largely changed periods is less significant than in the whole

sample of table 2: When we take the results based on the 𝑡 − 12 to 𝑡 − 2 formation

period as an example, results from market continuation subsample during the largely

changed periods give the significantly positive momentum return (4.47%, with the T

value of 1.72), almost half in size than that in the whole sample of table 2. Results from

market transition subsample during the largely changed periods give more significantly

negative momentum return (-24.87%, with the T value of -10.86), more than doubled

in size than that in the whole sample of table 2. Results in the subsamples of table 3

give an important finding that the momentum return in market continuation subsample

during the largely changed periods makes less contribution to the short-term momentum

in the Chinese stock market, but the reversal return in market transition subsample

during the largely changed periods has great influence on the short-term momentum

(the average reversal return is about 6 times than the momentum return), performance

in the market transition subsample during the largely changed periods maybe the main

reason for the “Chinese special short-term momentum”. Results from the 𝑡 − 12 to 𝑡 −

7 formation period give a similar but weaker conclusion, which is reasonable

considering the big changes based on the 𝑡 − 12 to 𝑡 − 2 formation period imply the

longer persistent influence from the big changes of market continuation and market

transition on investors’ behavior and the stock return.

To sum up, we give the performance of short-term momentum in the different

market states subsamples. We find the momentum return is significant during the

market continuation period, and the Down & Down subsamples dominates this result.

During the market transition period, momentum performance changes to reversal, and

the Down & Up subsamples play a more important role for this reversal performance.

The similar proportion of market continuation period and market transition period

causes the internal insignificant short-term momentum performance in the Chinese

stock market. Study in the market largely changed subsamples gives a more significant

result: The momentum effect is weaker in this market continuation period but the

reversal effect performs especially significant in the market transition period, so the

18

performance in the market transition subsample during the largely changed periods

plays an important role in the Chinese short-term momentum performance.

4. Signed Momentum

4.1. Reason for the special performance

Results in the prior subsections present that the performance of short-term

momentum in the Chinese stock market is based on the market states: momentum exists

when the market continues and reverses when the market transits, and the results are

different based on the market states during the formation periods and the holding

periods. In this section, we want to find the reason(s) for this special performance.

Our explanation for the changeable momentum performance with the market states

is consistent with the Daniel and Moskowitz (2016), who find the momentum crashes

in the US stock market when the market rebounds from the panic states, except that we

use that idea for more market states in the Chinese stock market.

In this part, we extend Daniel and Moskowitz (2016)’s intuition into all market

states, and give some summary statistics of beta difference between the winner group

and loser group based on return ranking during the formation period and the holding

period in the table 4.

[Insert table 4 here]

Our study emphasizes on all four different classifications of the market states: Up

& Down, Up & Up, Down & Down and Down & Up. Beta used in this part is calculated

by the CAPM model using the previous 36-momth data (for the month 𝑡 , beta is

calculated by using data from 𝑡 − 35 to 𝑡). Considering our summary of momentum

performance is from July 1997, we have enough data for the beta calculation and the

sample numbers of four market state classifications are same to those in the table 2.

As we can see from the table 4, the beta difference between the winner-decile and

loser-decile is heavily influenced by the sign of market return during ranking period.

Specifically, when we rank stocks based on their return in the 𝑡 − 12 to 𝑡 − 2

19

formation period, the beta difference in the holding period of Up & Down subsample

is significantly positive (0.094, with the T value of 2.83). However, the beta difference

between the winner-decile and loser-decile ranked by stock return in the holding period

is significantly negative (-0.154, with the T value of -7.26), which indicates the beta

difference based on ranking of stock return in the formation period has the opposite

sign with the profitable portfolios (the 10 − 1 ranked by stock return in the holding

period must have the positive excess return considering it is the return difference

between stocks in the winner group and the loser group). The beta difference

comparison during the Down & Up subsample gives a similar result: when stocks are

ranked by their return in the 𝑡 − 12 to 𝑡 − 2 formation period, the beta difference in

the holding period is significantly negative (-0.114, with the T value of -5.96), and the

beta difference between the winner-decile and loser-decile ranked by stock return in the

holding period is significantly positive (0.146, with the T value of 5.50). In this

subsample, the beta difference based on ranking of stock return in the formation period

has the opposite sign with the profitable portfolios. The opposite signs of beta

differences based on ranking of stock return in the formation period and the profitable

portfolios in these two market states are consistent with the fact that the short-term

momentum has negative portfolio return during these periods.

However, in the market continuation subsamples, the situation is uuite different.

The both beta differences in the holding period of Up & Up subsample are significantly

positive, whether ranked by stock returns in the 𝑡 − 12 to 𝑡 − 2 formation period or

based on the winner-decile and loser-decile in the holding period (Specifically, the beta

difference is 0.166 with the T value of 5.37 versus 0.103 with the T value of 4.12,

respectively). And the both beta differences in the holding period of Down &Down

subsample are significantly negative, whether ranked by stock returns in the 𝑡 −

12 to 𝑡 − 2 formation period or based on the winner-decile and loser-decile in the

holding period (Specifically, the beta difference is -0.126 with the T value of 6.42 versus

-0.146 with the T value of 8.31, respectively). The same sign of beta difference based

on ranking of stock return in the formation period and the profitable portfolio is

20

consistent with the positive portfolio return of short-term momentum during these

periods. The summary based on the 𝑡 − 12 to 𝑡 − 7 formation period is uuite similar,

so we don’t give it for brief.

To sum up, we find the sign of beta difference based on ranking of stock return in

the formation period and the profitable portfolio in the holding period is critical for the

performance of short-term momentum in the Chinese stock market: when there is the

same sign during two periods, the momentum return is likely to be positive, and when

the sign is opposite during two periods, the momentum return is likely to be negative.

Therefore, we make a primary conclusion that the changed beta difference is the reason

for different performance of momentum effect during different market states.

In addition, we also give the summary of the capitalization difference and B/M

(book to market ratio) difference during the different market states, and find if there is

similar trend in these two stock characteristic variables. Results are given in the table 5

and table 6.



As we can see from table 5, the capitalization difference based on the return

ranking in the 𝑡 − 12 to 𝑡 − 2 formation period and that based on the winner-decile

and loser-decile in the holding period have no similar correlation like the beta difference:

only two capitalization differences based on the return ranking in the 𝑡 − 12 to 𝑡 − 2

formation period are significantly positive (in the Up & Down and Down & Down

subsamples), and one capitalization difference based on the winner-decile and loser-

decile in the holding period is significantly negative (in the Down & Up subsample).

The relationship of the capitalization difference from two ranking bases is inconsistent

with the performance of short-momentum, so the capitalization difference is not the

reason for the special performance of momentum in the Chinese stock market.

The summary of B/M difference tells a similar story. Almost all B/M differences

based on the return ranking in the formation period or the winner-decile and loser-decile

21

in the holding period are (significant) negative, and this phenomenon is not changed

with different market states. The relationship of the B/M difference from two ranking

bases is inconsistent with the performance of short-momentum, so the B/M difference

is also not the reason for the special performance of momentum in the Chinese stock

market.

To sum up, we give some summary of the beta difference, capitalization difference

and B/M difference based on ranking of stock return in the formation period and from

the winner-decile and loser-decile in the holding period, and find only the change of

beta difference is correlated to the performance of short-term momentum in the Chinese

stock market. When there are same signs of beta difference from two ranking bases, the

momentum return is likely to be positive, correspondingly, when there are opposite

signs of beta difference from two ranking bases, the momentum return is likely to be

negative. The change of beta difference is the reason for different performance of

momentum effect in different market states.

4.2. Performance of Signed Momentum Strategy

Based on finding in our previous study, the momentum effect exists when the

market continues and changes to reversal effect when the market transits. Motivated by

this special performance, we want to give a signed momentum strategy, which is a

combination of traditional momentum strategy and the market dynamic.

Specifically, during the market continuation period (Up & Up and Down & Down),

we use the traditional way to construct the momentum strategy: After ranking stocks

based on their return in the formation period, we long stocks in the winner-decile and

short stocks in the loser decile. But during the market transition period (Up & Down

and Down & Up), we use the opposite way to construct the momentum strategy: After

ranking stocks based on their return in the formation period, we short stocks in the

winner-decile and long stocks in the loser decile.

We give the performance of our signed momentum strategy in the table 7.


22

As we can see from the table 7, the signed momentum strategy has good

performance in the Chinese stock market. When we use 𝑡 − 12 to 𝑡 − 2 as the

formation period, we find an approximately increasing trend, and the excess return

between group 10 and group 1 (i.e., the strategy profit) is significant both in the

economic level and the statistics level (with the average annual return as 10.50%, and

the T value as 8.74, significant at 1% level).

Table 7 also reports the alphas (intercepts) and their T values from the time-series

regressions of strategy profits on the CAPM model, Fama-French three factors model

and Fama-French five factors model. According to the model specification, alpha gives

the unexplained part by the explanatory variables in each model, and a significant alpha

indicates the special information that can’t be explained by the asset-pricing models.

Alphas in the table 7 give the coincident result that excess return from the signed

momentum strategy can’t be fully explained by these three models (with the average

value of 0.90 and T value of 2.70 for the CAMP mode, the average value of 1.04 and T

value of 3.31 for Fama-French three factors model and the average value of 0.94 and T

value of 2.84 for Fama-French five factors model, respectively), and this finding

furtherly proves the efficiency of our signed momentum strategy. Results based on the

𝑡 − 12 to 𝑡 − 7 formation period is similar but in the smaller size, which indicates after

considering the influence of market dynamic, the 𝑡 − 12 to 𝑡 − 7 is still not a better

alternation of the 𝑡 − 12 to 𝑡 − 2 formation period in the Chinese stock market.

In summary, we give a signed momentum strategy in this part, which is a

combination of the traditional momentum strategy and the market dynamic. This new

strategy has the similar performance as other countries, and can earn significant

portfolio return in the Chinese stock market.11

11 In this part, we don’t give the performance of signed momentum strategy in different market states,

considering the performance of signed momentum strategy is same to the traditional momentum in the

market continuation subsample, Up & Up subsample, and Down & Down subsample and the

performance of signed momentum strategy is just opposite in sign with the traditional momentum in the

market transition subsample, Up & Down subsample and Down & Up subsample. And the similar

23

4.3. Signed momentum following Up and Down market

In this part, we give some additional evidence about the performance of signed

momentum following the Up and Down markets. The way to define the Up and Down

market is same as the section 3.1: If the sign of excess market return is non-negative

during the formation period then we define the holding month as following the Up

market. Vice versa, if the sign of excess market return is negative during the formation

period then we define the holding month as following the Down market. And the

numbers of Up market and Down market are same as the table 1. We give the results of

signed momentum strategy following Up and Down market in the table 8.


As we can see from the table 8, the signed momentum strategy performs more

significant when following the Down market, whether for annualized return of the

momentum strategy (10 − 1) or for the 𝐴𝑙𝑝ℎ𝑎𝑠 from the regression of momentum

return on the market factor (𝐶𝐴𝑃𝑀), Fama-French three factors (𝐹𝐹3) and the Fama-

French five factors (𝐹𝐹5) are larger in size and significant at 1% level. While for the

signed momentum strategy following Up market, the value of signed momentum return

is smaller in size and insignificant after controlling the influence of market factor,

Fama-French three factors and the Fama-French five factors.

This finding is consistent the performance of traditional momentum strategy and

reasonable for the special investor constitution in the Chinese stock market: Retail

investors are more aggressive during the Up market, and might buy more loser-decile

stocks for speculative purpose, therefore, the abnormal return of these stocks can

influence the performance of momentum. However, limited to the practical short sale

limits in the Chinese stock market, retail investors’ gambling behavior is restricted, so

the short-term momentum performs better.

5. Robustness and Further Analyses

situations are suitable for the summary of beta difference, capitalization difference and B/M difference

in different market states.

24

5.1. Summary of same group proportion

In this subsection, we give a summary about stocks in the winner-decile and the

loser-decile during the formation period are in the same group or the counterparty group

in the holding period. Intuitively, if the short-term momentum exists, stocks in the loser-

decile/ winner-decile during the formation period are supposed to in the same group of

the holding period. Absolutely, it is an ideal story. But we still want to study the

proportion of stocks are in the same deciles during two periods, and use this as the

robustness test for the traditional grouping study. Results of this part are given in the

table 9.


As we can see from the table 9, the proportion of stocks in the same deciles during

two periods is relatively small in the Chinese stock market. When we use the 𝑡 −

12 to 𝑡 − 2 as the formation period, the average value of the same decile proportion in

the whole sample is 0.119, which is consistent with the integral insignificant

performance of short-term momentum. The proportion varies with the different market

states, and just like the results from grouping study, the same decile proportion in the

Down & Down subsample is the largest in four market states, so the short-term

momentum, if existing, is mainly derived from this subsample. The same decile

proportions in other three subsamples (Up & Down, Up & Up and Down & Up) are

smaller than the whole sample, but there is no significant difference among them. The

same decile proportion in market continuation subsample is larger than the market

transition subsample, the proportion difference is 0.019 and significant at 1% statistics

level. This finding is consistent with our previous result, momentum performance is

stronger when the market continues than the time when market transits. The result get

from the 𝑡 − 12 to 𝑡 − 7 formation period is uuite similar, so we don’t give for brief.

In this subsection, we study the proportion of stocks in the same deciles during the

formation period and the holding period, and find the proportion is small in the Chinese

stock market, consisting with the integral insignificant momentum performance. And

25

the proportion is different in each market state: the proportion is higher during market

continuation period than the market transition period, and in the Down & Down

subsample, the proportion is highest. Our finding in this part is consistent with the

section 3.1 and 3.2, and can be the additional supportive evidence for the robustness

test.

5.2. Based on the top 90% stocks?

When study the performance of momentum, Asness, Moskowitz and Pederson

(2013) limit stocks in each market to a very liuuid set by ranking stocks based on the

market capitalization value at the beginning-of month, and keep the biggest stocks that

account cumulatively for 90% of the total market capitalization. By this way, they keep

only 17% largest firms in the US stock market, 13%, 20%, and 26% of firms in the UK,

Europe, and Japan stock market, respectively.

In this paper, we also do the same filtration in the Chinese stock market and find a

very different result. For the whole sample of 2825 stocks and 419363 data, after doing

the filtration, 2622 stocks and 245090 data are kept in the top 90% subsample, there is

not a special group that dominates the whole market capitalization with very small

number of stocks included.

In addition, based on our previous study in the section 3.3, stock market

capitalization is not the reason for special performance of short-term momentum in the

Chinese stock market, this top 90% filtration will not make significant change in our

results.

5.3. Prediction of market return

The classification bases of the different market states are the signs of excess market

return during the formation period and the holding period, that is, our study for the

performance of short-term momentum under different market situations is an ex post

study, considering we use the sign of market return at the holding period.

The intuition to bring our study to the practical use is to find the prediction of market

return, specifically, prediction for the (sign of) market return in the holding period. The

26

potential predictor variables we think might relate to the market return and we can get

from the stock market include: the market return on previous months, the market

turnover calculated by the ratio of total market trading volume and the total market

capitalization of all stocks, the monthly variance calculated by the sum of suuare of A-

share index’s daily return, the increase ratio of total trading volume. And we also use

some macro-economic variables as the predictors, including the increase ratio of M0

(currency in circulation) and M1 (currency in circulation and the commercial banks’

demand deposit), the inflation ratio calculated from the CPI (consumer price index), the

increasing ratio of taxation, the change of exchange rate of CNY against USD, and the

change of Chinese Leading Economic Index. The prediction period is previous 36/48

months.

By using the data of these predictors in the previous months as the explanatory

variables, we do the univariate and multivariate regressions. As we have found, even

some variables have the significant influence on the market return and the regressions

have relatively high explanatory power, the fitted values from the regression models

are lowly correlated to the real market return (the highest correlation of the fitted value

and the real market return is 0.2127; the highest correlation of the sign of fitted value

and the sign of real market return is 0.2584).

Failure to find the accuracy prediction for the (sign of) market return leaving the

ex-ante study of the short-term momentum with market dynamic is still a potential area.

5.4. Summary of market states in other stock markets

In this part, we give some summary of market states in other stock markets for

comparison, including the US stock market (using the data of S&P 500 index as

example), the UK stock market (using the data of FTSE ALL-Share index as example),

the German stock market (using the DAX index as example), the Korean stock market

(using the KOSPI index as example), the Japan stock market (using the Nikkei 225

index as an example), the Taiwan stock market (using the Taiwan Weighted index as

example) and the Chinese stock market (using value weighted-A shares index as

example). Results of summary of market states in these stock markets are given in the

27

table 1012.


According to previous studies of other countries, the US stock market, the UK

stock market and the European stock market have significant short-term momentum

performance, but in the Asian stock markets (the remaining part of our sample), there

is no significant momentum performance. The summary of market states in these

countries (city) provides some supportive evidence for this finding.

The number of market continuation subsample is relatively larger than the number

of market transition subsample in markets having significant momentum, and for

markets with insignificant momentum performance, the numbers of these two

subsamples are closer. And the conclusion still holds when market changes greatly 13,

so we can draw a primary conclusion that the proportion of market continuation in

different markets has influence on the momentum performance, and the high proportion

of market continuation is likely related to the significant momentum performance.

And we also give the summary for the average value of absolute market return

during the formation period and the holding period, based on the intuition that the

bigger average value of market change might have bigger influence on the momentum

performance. We give this result on the table 11.


Results in the table 11 also support our intuition: markets with the significant

momentum performance (the US, UK and Germany stock markets) have relative

smaller absolute market return during the formation period and the holding period than

those with the insignificant momentum performance. That is, the average value of

market change has larger influence on the momentum performance on the Korean,

12 We give the results based on the 𝑡 − 12 to 𝑡 − 2 formation period as example, and results based on

𝑡 − 12 to 𝑡 − 7 formation period are quite similar, so we don’t give for brief.

13 The period having the larger absolute value of market return during the formation period/ holding

period than the average absolute value of the whole sample is defined as with large market changes, and

if momentum performance is related to the market dynamic, these periods may play a more important

role.

28

Japan, Taiwan and Chinese stock markets, combining our finding in the table 10 that

those stock markets have relatively smaller proportion of market continuation, we can

draw our conclusion that short-term momentum is influenced by the market dynamic,

which could be a general conclusion not only for the Chinese stock market. The reason

for insignificant momentum performance in the Asian stock markets is the big changes

of market return and the less proportion of market continuation.

6. Conclusion

Based on a comprehensively monthly data of all A-shares from 1997 to 2018, we

study the performance of short-term momentum in the Chinese stock market.

Consisting with the previous studies about China, we also can’t find the evidence for

significant momentum return by using the traditional way to construct the momentum

strategy. In addition, we find the momentum performance is related to the market

dynamic, and the momentum return is significantly positive when the market continues

but changes to significantly negative when the market transits.

By studying the change of beta difference, capitalization difference and the B/M

difference between the winner-decile and the loser-decile during the formation period

and holding period, we find the change of beta difference is the key factor for the

changed momentum performance along with market dynamic. Based on these findings,

we give an improved signed momentum strategy, which is a combination of the

traditional momentum strategy and the market dynamic, and find this new momentum

strategy performs well in the Chinese stock market: from the past loser-decile to the

past winner-decile, there is an approximately increasing trend, the portfolio return is

significantly positive and can’t be fully explained by the existing asset-pricing factors.

Besides, we also find traditional momentum strategy and the improved momentum

strategy perform better following the Down market, which is caused by the retail

investors’ gambling behavior during the Up market but failure to do the opposite

aggressive trading during the Down market, limited by the short sale constraint.

By giving some summary about stocks in the winner-decile and the loser-decile

during the formation period are in the same group or the counterparty group during the

29

holding period, we do the robustness test and confirm our finding about the momentum

performance and market dynamic. And in our further analysis, we find the market

dynamic has a universe influence on the momentum performance: markets having the

significant momentum performance are generally with more market continuation

periods and the absolute value of market return is relatively smaller (therefore, the

market dynamic has smaller influence on the change of beta difference and the

momentum performance is less influenced) and those having the insignificant

momentum performance are just on the opposite situation.

Our study contributes to find that the short-term momentum performance in the

Chinese stock market is related to the market dynamic, and only when the market

continues from the formation period to the holding period, the momentum exists.

Considering the short-term momentum is a reflection of investors’ underreaction to the

public/private information, the extent of momentum performance can be considered to

be a signal of information diffusion efficiency. However, the insignificant momentum

performance makes this signal weaker, which implying the importance of keeping

market stability (therefore, increase the number of market continuation periods) for the

Chinese stock market.

30

Table 1. Baseline results of the traditional short-term momentum strategy

Results in this table present the performance of short-term of momentum strategy in the whole

sample, Up market and Down market based on 𝑡 − 12 to 𝑡 − 2 and 𝑡 − 12 to 𝑡 − 7 formation

periods. 𝐷𝑖𝑓𝑓 gives the difference between momentum performance following the Down market

and the Up market. 𝑁 in the table gives the number of each sample. 𝑀𝑒𝑎𝑛 is the equal-weighted

stock return of stocks in the group 1 and group 10, and the difference of stock return in these two

groups. 𝑆𝑡𝑑 is the standard deviation of stock return in each group, T is T value of each portfolio's

excess return, and the 𝑆𝑃 is the Sharp Ratio of each portfolio. 𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the annualized

results of the monthly return, and values are given as the percentage value. Significance at the 1%,

5%, and 10% level is denoted as ***, **, and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

𝑁 𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃 𝑁 𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃

Whole

sample

1

255

11.20 34.97 5.11*** 0.32

255

9.88 35.38 4.46*** 0.28

10 10.13 34.20 4.73*** 0.30 10.72 34.13 5.02*** 0.31

10 - 1 -1.04 19.42 -0.85 -0.05 1.00 15.12 1.06 0.07

Down

market

1

115

5.60 34.96 1.72* 0.16

116

7.16 33.12 2.33** 0.22

10 7.68 30.97 2.66*** 0.25 10.31 30.03 3.70*** 0.34

10 - 1 2.08 18.78 1.19 0.11 3.45 16.36 2.27** 0.21

Up

market

1

140

15.79 35.04 5.33*** 0.45

139

12.16 37.27 3.85*** 0.33

10 12.14 36.74 3.91*** 0.33 11.07 37.32 3.50*** 0.30

10 - 1 -3.59 19.97 -2.13** -0.18 -1.04 14.03 -0.88 -0.07

𝐷𝑖𝑓𝑓 5.67 19.44 2.32** 4.49 15.13 2.36**

31

Table 2. Short-term momentum in the subsamples of different market states.

Results in this table present the performance of short-term of momentum strategy in subsamples of different market states, including the Up & Down, Up & Up, Down

& Down and Down & Up subsamples. Up & Up gives the subsample combined by the up market in the formation period (𝑡 − 12 to 𝑡 − 2/ 𝑡 − 12 to 𝑡 − 7) and the up

market in the holding period ( 𝑡) and others subsamples have the similar meaning. Market continuation gives the results based on the same market states in two periods

(Up & Up and Down & Down) and the market transition gives the results based on the different market states in two periods (Up & Down and Down & Up). 𝑀𝑒𝑎𝑛

is the equal-weighted stock return of stocks in the group 1 and group 10, and the difference of stock return in these two groups. 𝑆𝑡𝑑 is the standard deviation of stock

return in each group, T is T value of each portfolio's excess return, and the 𝑆𝑃 is the Sharp Ratio of each portfolio. 𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the annualized results of the

monthly return, and values are given as the percentage value. Significance at the 1%, 5%, and 10% level is denoted as ***, **, and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7


Up & Down

1

64

-68.78 24.06 -22.87*** -2.86

61

-88.31 23.87 -28.90*** -3.70

10 -77.41 26.88 -23.04*** -2.88 -93.04 24.27 -29.95*** -3.83

10 - 1 -8.63 18.90 -3.65*** -0.46 -4.73 13.04 -2.83*** -0.36

Up & Up

1

76

87.01 29.19 25.98*** 2.98

78

90.72 29.30 27.35*** 3.10

10 87.56 29.05 26.27*** 3.01 92.49 27.61 29.59*** 3.35

10 - 1 0.65 20.88 0.27 0.03 1.84 14.78 1.10 0.12

Down& Down

1

57

-81.13 22.49 -27.23*** -3.61

60

-67.23 21.47 -24.25*** -3.13

10 -60.83 23.06 -19.92*** -2.64 -50.58 22.10 -17.73*** -2.29

10 - 1 20.30 18.24 8.40*** 1.11 16.65 15.64 8.24*** 1.06

Down & Up

1

58

90.83 26.55 26.05*** 3.42

56

86.86 27.50 23.63*** 3.16

10 75.01 24.91 22.94*** 3.01 75.54 25.76 21.94*** 2.93

10 - 1 -15.83 18.00 -6.70*** -0.88 -10.69 16.26 -4.92*** -0.66

Market Continuation

1

133

14.95 35.78 4.82*** 0.42

138

22.05 34.58 7.49*** 0.64

10 23.96 34.03 8.12*** 0.70 30.29 32.57 10.92*** 0.93

10 - 1 9.07 19.92 5.25*** 0.46 8.28 15.26 6.37*** 0.54

Market Transition

1

122

7.10 34.17 2.30*** 0.21

117

-4.47 36.01 -1.34 -0.12

10 -4.95 33.99 -1.61 -0.15 -12.35 34.86 -3.83*** -0.35

10 - 1 -12.06 18.43 -7.22*** -0.65 -7.58 14.63 -5.61*** -0.52

32

Table 3. Short-term momentum when the market changes greatly

Results in this table present the performance of short-term of momentum strategy in subsamples of different market states when the market changes greatly, including

the Up & Down, Up & Up, Down & Down and Down & Up subsamples. Up & Up gives the subsample combined by the up market in the formation period (𝑡 −

12 to 𝑡 − 2/ 𝑡 − 12 to 𝑡 − 7) and the up market in the holding period ( 𝑡) and others subsamples have the similar meaning. Market continuation gives the results based

on the same market states in two periods (Up & Up and Down & Down) and the market transition gives the results based on the different market states in two periods

(Up & Down and Down & Up). 𝑀𝑒𝑎𝑛 is the equal-weighted stock return of stocks in the group 1 and group 10, and the difference of stock return in these two groups.

𝑆𝑡𝑑 is the standard deviation of stock return in each group, T is T value of each portfolio's excess return, and the 𝑆𝑃 is the Sharp Ratio of each portfolio. 𝑀𝑒𝑎𝑛 and

𝑆𝑡𝑑 are the annualized results of the monthly return, and values are given as the percentage value. Significance at the 1%, 5%, and 10% level is denoted as ***, **,

and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7


Up & Down

1

36

-95.54 27.15 -21.11*** -3.52

37

-111.82 26.79 -25.39*** -4.17

10 -119.77 28.16 -25.52*** -4.25 -122.13 26.89 -27.62*** -4.54

10 - 1 -24.23 18.72 -7.76*** -1.29 -10.31 14.25 -4.40*** -0.72

Up & Up

1

51

113.05 31.85 25.35*** 3.55

42

136.28 33.08 26.70*** 4.12

10 107.13 32.77 23.35*** 3.27 137.12 31.10 28.57*** 4.41

10 - 1 -5.82 23.54 -1.77* -0.25 0.91 17.39 0.34 0.05

Down& Down

1

24

-118.20 26.52 -21.83*** -4.46

26

-109.62 22.30 -25.07*** -4.92

10 -91.85 28.76 -15.64*** -3.19 -91.25 23.45 -19.84*** -3.89

10 - 1 26.35 18.90 6.83*** 1.39 18.37 14.70 6.37*** 1.25

Down & Up

1

30

145.42 26.18 30.42*** 5.55

32

131.34 28.11 26.43*** 4.67

10 119.77 26.95 24.35*** 4.45 111.65 27.35 23.10*** 4.08

10 - 1 -25.65 18.80 -7.47*** -1.36 -19.81 17.09 -6.56*** -1.16

Market Continuation

1

75

39.05 43.44 7.79*** 0.14

68

42.26 45.42 7.67*** 0.93

10 43.45 41.35 9.10*** 0.23 49.80 42.88 9.58*** 1.16

10 - 1 4.47 22.46 1.72* 0.46 7.58 16.48 3.79*** 0.46

Market Transition

1

66

13.99 43.83 2.59** 0.51

69

0.95 44.53 0.18 0.02

10 -10.88 44.21 -2.00** 0.36 -13.71 43.28 -2.63** -0.32

10 - 1 -24.87 18.61 -10.86*** -0.65 -14.72 15.57 -7.85*** -0.94

33

Table 4. Beta difference during the formation period and holding period

Results in this table present the average beta of stocks in the group 1 (the loser decile), the group 10

(the winner decile) and the beta difference of two deciles (10 − 1) during the formation period and

the holding period. “Ranking basis” gives the ranking period for the beta calculation and “market

state” gives the results from four subsamples: Up & Down, Down & Up, Up & Up and Down &

Down. Up & Up gives the subsample combined by the Up market in the formation period (𝑡 −

12 to 𝑡 − 2/ 𝑡 − 12 to 𝑡 − 7) and the Up market in the holding period ( 𝑡) and others subsamples

have the similar meaning. 𝑀𝑒𝑎𝑛 is the equal-weighted beta of stocks in the group 1 and group 10,

and the difference of stock return in these two groups. 𝑆𝑡𝑑 is the standard deviation of beta in each

group, and T is T value of each portfolio's excess return. Significance at the 1%, 5%, and 10% level

is denoted as ***, **, and *, respectively.

Market

state

Ranking

basis

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T

Up &

Down

Formation

period

1 1.065 0.127 1.074 0.119

10 1.159 0.179 1.135 0.146

10 − 1 0.094 0.267 2.83*** 0.061 0.207 2.29**

Holding

period

1 1.143 0.130 1.150 0.127

10 0.989 0.095 0.983 0.092

10 − 1 -0.154 0.170 -7.26*** -0.167 0.159 -8.18***

Up &

Up

Formation

period

1 1.036 0.144 1.030 0.136

10 1.201 0.156 1.194 0.134

10 − 1 0.166 0.269 5.37*** 0.165 0.234 6.23***

Holding

period

1 1.050 0.112 1.037 0.117

10 1.153 0.164 1.148 0.151

10 − 1 0.103 0.218 4.12*** 0.110 0.206 4.73***

Down&

Down

Formation

period

1 1.134 0.092 1.129 0.105

10 1.007 0.107 0.997 0.113

10 − 1 -0.126 0.149 -6.42*** -0.133 0.162 -6.33***

Holding

period

1 1.138 0.084 1.131 0.091

10 0.992 0.099 0.998 0.101

10 − 1 -0.146 0.133 -8.31*** -0.133 0.145 -7.11***

Down

&Up

Formation

period

1 1.146 0.082 1.159 0.104

10 1.033 0.122 1.023 0.115

10 − 1 -0.114 0.145 -5.96*** -0.136 0.160 -6.34***

Holding

period

1 1.003 0.129 1.019 0.127

10 1.150 0.119 1.157 0.140

10 − 1 0.146 0.203 5.50*** 0.138 0.220 4.68***

34

Table 5. Capitalization difference during the formation period and holding period

Results in this table present the average capitalization of stocks in the group 1 (the loser decile), the

group 10 (the winner decile) and the capitalization difference of two deciles (10 − 1) during the

formation period and the holding period. “Ranking basis” gives the ranking period for the

capitalization calculation and “market state” gives the results from four subsamples: Up & Down,

Down & Up, Up & Up and Down & Down. Up & Up gives the subsample combined by the Up

market in the formation period (𝑡 − 12 to 𝑡 − 2/ 𝑡 − 12 to 𝑡 − 7) and the Up market in the holding

period ( 𝑡 ) and others subsamples have the similar meaning. 𝑀𝑒𝑎𝑛 is the equal-weighted

capitalization of stocks in the group 1 and group 10, and the difference of stock return in these two

groups. 𝑆𝑡𝑑 is the standard deviation of capitalization in each group, and T is T value of each

portfolio's excess return. Significance at the 1%, 5%, and 10% level is denoted as ***, **, and *,

respectively.

Market

state

Ranking

basis

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7


Up &

Down

Formation

period

1 8.389 7.407 6.853 5.295

10 12.725 11.858 12.043 10.384

10 - 1 4.336 13.572 2.56*** 5.190 10.360 3.91***

Holding

period

1 8.533 7.172 7.550 6.860

10 9.041 7.864 9.376 8.023

10 - 1 0.508 9.126 0.45 1.826 8.837 1.61

Up &

Up

Formation

period

1 9.481 9.244 9.008 9.592

10 11.849 9.556 10.031 7.229

10 - 1 2.368 13.000 1.59 1.024 11.763 0.77

Holding

period

1 10.459 9.818 10.312 9.366

10 9.239 8.674 8.274 7.469

10 - 1 -1.220 12.384 -0.86 -2.037 10.904 -1.65

Down&

Down

Formation

period

1 5.388 6.784 5.958 5.977

10 7.587 4.653 6.877 3.342

10 - 1 2.199 6.981 2.38** 0.920 5.672 1.26

Holding

period

1 5.132 5.067 6.300 6.046

10 6.365 4.637 6.157 4.396

10 - 1 1.233 6.074 1.53 -0.143 6.543 -0.17

Down

&Up

Formation

period

1 7.386 8.173 7.958 8.044

10 8.349 6.153 7.560 6.167

10 - 1 0.963 9.705 0.76 -0.399 9.165 -0.33

Holding

period

1 8.383 7.434 8.514 8.160

10 6.136 5.236 7.369 7.628

10 - 1 -2.247 7.855 -2.18** -1.146 10.333 -0.83

35

Table 6. B/M difference during the formation period and holding period

Results in this table present the average B/M of stocks in the group 1 (the loser decile), the group

10 (the winner decile) and the B/M difference of two deciles (10 − 1) during the formation period

and the holding period. “Ranking basis” gives the ranking period for the B/M calculation and

“market state” gives the results from four subsamples: Up & Down, Down & Up, Up & Up and

Down & Down. Up & Up gives the subsample combined by the Up market in the formation period

( 𝑡 − 12 to 𝑡 − 2 / 𝑡 − 12 to 𝑡 − 7 ) and the Up market in the holding period ( 𝑡 ) and others

subsamples have the similar meaning. 𝑀𝑒𝑎𝑛 is the equal-weighted B/M of stocks in the group 1

and group 10, and the difference of stock return in these two groups. 𝑆𝑡𝑑 is the standard deviation

of B/M in each group, and T is T value of each portfolio's excess return. Significance at the 1%, 5%,

and 10% level is denoted as ***, **, and *, respectively.

Market

state

Ranking

basis

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7


Up &

Down

Formation

period

1 0.917 0.459 0.806 0.348

10 0.504 0.203 0.623 0.283

10 - 1 -0.412 0.484 -6.82*** -0.175 0.394 -3.47***

Holding

period

1 0.704 0.322 0.682 0.284

10 0.618 0.196 0.664 0.238

10 - 1 -0.085 0.342 -2.00** -0.027 0.281 -0.76

Up &

Up

Formation

period

1 0.995 0.492 0.871 0.356

10 0.540 0.224 0.645 0.251

10 - 1 -0.455 0.498 -7.96*** -0.225 0.342 -5.82***

Holding

period

1 0.762 0.334 0.792 0.311

10 0.747 0.347 0.728 0.318

10 - 1 -0.015 0.416 -0.32 -0.055 0.364 -1.35

Down&

Down

Formation

period

1 1.254 0.580 1.137 0.506

10 0.648 0.237 0.690 0.292

10 - 1 -0.587 0.539 -8.23*** -0.436 0.478 -7.07***

Holding

period

1 0.914 0.410 0.936 0.425

10 0.922 0.388 0.871 0.394

10 - 1 -0.020 0.399 -0.38 -0.079 0.444 -1.38

Down

&Up

Formation

period

1 1.303 0.555 1.207 0.515

10 0.644 0.225 0.679 0.288

10 - 1 -0.659 0.532 -9.44*** -0.528 0.465 -8.51***

Holding

period

1 0.906 0.402 0.868 0.436

10 0.917 0.361 0.948 0.382

10 - 1 0.026 0.387 0.51 0.080 0.440 1.35

36

Table 7. Signed momentum in the Chinese stock market

Results in this table present the performance of momentum strategy after considering the influence

of market dynamic (i.e., the signed momentum strategy), including the portfolio return from the

group 1 (the loser decile) to group 10 (the winner decile), the difference of stock return between

group 10 and group 1, and the 𝐴𝑙𝑝ℎ𝑎𝑠 from the regression of signed momentum return on the

market factor (𝐶𝐴𝑃𝑀), Fama-French three factors (𝐹𝐹3) and the Fama-French five factors (𝐹𝐹5).

𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the equal-weighted stock return of each portfolio and the standard deviation of

stock return in each group, T is T value of each portfolio's excess return, and the 𝑆𝑃 is the Sharp

Ratio of each portfolio. 𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the annualized results of the monthly return (except

for three 𝐴𝑙𝑝ℎ𝑎𝑠), and values are given as the percentage value. Significance at the 1%, 5%, and

10% level is denoted as ***, **, and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃 𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃

1 5.45 34.72 2.51*** 0.16 6.73 34.70 3.10*** 0.19

2 7.69 33.58 3.66*** 0.23 9.50 33.39 4.54*** 0.28

3 11.97 34.14 5.60*** 0.35 13.04 33.77 6.17*** 0.39

4 12.91 33.45 6.16*** 0.39 11.41 33.11 5.50*** 0.34

5 13.57 33.27 6.51*** 0.41 14.23 32.81 6.93*** 0.43

6 14.34 33.19 6.90*** 0.43 13.08 32.98 6.33*** 0.40

7 14.54 33.39 6.95*** 0.44 13.99 33.19 6.73*** 0.42

8 13.63 33.70 6.46*** 0.40 13.43 33.02 6.50*** 0.41

9 14.91 33.78 7.05*** 0.44 15.76 34.00 7.40*** 0.46

10 15.95 34.37 7.41*** 0.46 13.77 34.80 6.32*** 0.40

10 − 1 10.50 19.19 8.74*** 0.55 7.04 17.00 5.97*** 0.37

𝐴𝑙𝑝ℎ𝑎(𝐶𝐴𝑃𝑀) 0.90 -- 2.70*** -- 0.58 -- 2.35** --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹3) 1.04 -- 3.31*** -- 0.64 -- 2.35** --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹5) 0.94 -- 2.84*** -- 0.49 -- 1.78* --

37

Table 8. Signed momentum following Up and Down market

Results in this table present following the Up market and the Down market, the performance of

momentum strategy after considering the influence of market dynamic (i.e., the signed momentum

strategy), including the difference of stock return between the group 10 (the winner decile) and

group 1 (the loser decile), and the 𝐴𝑙𝑝ℎ𝑎𝑠 from the regression of signed momentum return on the

market factor (𝐶𝐴𝑃𝑀), Fama-French three factors (𝐹𝐹3) and the Fama-French five factors (𝐹𝐹5).

𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the equal-weighted stock return of each portfolio and the standard deviation of

stock return in each group, T is T value of each portfolio's excess return, and the 𝑆𝑃 is the Sharp

Ratio of each portfolio. 𝑀𝑒𝑎𝑛 and 𝑆𝑡𝑑 are the annualized results of the monthly return (only for

10 − 1), and values are given as the percentage value. Significance at the 1%, 5%, and 10% level

is denoted as ***, **, and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃 𝑀𝑒𝑎𝑛 𝑆𝑡𝑑 T 𝑆𝑃

Follow

Up

Market

10 − 1 4.30 19.96 2.55** 0.22 2.87 13.86 2.46** 0.21

𝐴𝑙𝑝ℎ𝑎(𝐶𝐴𝑃𝑀) 0.39 -- 0.76 -- 0.26 -- 0.76 --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹3) 0.70 -- 1.41 -- 0.44 -- 1.12 --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹5) 0.82 -- 1.44 -- 0.54 -- 1.38 --

Follow

Down

Market

10 − 1 18.04 1.97 10.76*** 1.00 14.25 15.93 9.55*** 0.89

𝐴𝑙𝑝ℎ𝑎(𝐶𝐴𝑃𝑀) 1.51 -- 3.41*** -- 1.18 -- 2.98*** --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹3) 1.48 -- 3.17*** -- 1.08 -- 2.52** --

𝐴𝑙𝑝ℎ𝑎(𝐹𝐹5) 1.45 -- 3.00*** -- 1.03 -- 2.39** --

Table 9. Ratio of stocks in the same group during the formation period and holding period

Results in this table present the ratio of stocks in the same group during the formation period and

holding period. Summary includes the result from the whole sample, the Up & Up subsample, the

of Down & Down subsample, the of Up & Down subsample, the of Down & Up subsample, the

market continuation subsample, the market transition subsample, and the difference (𝐷𝑖𝑓𝑓) of result

from the market continuation subsample and the market transition subsample. 𝑁 gives the number

of months in each subsample, 𝑀𝑒𝑎𝑛, 𝑀𝑒𝑑𝑖𝑎𝑛 and Std are the average value, median value and

standard deviation of the ratio of stocks in the same group during the formation period and holding

period in each subsample. Values in the parentheses are the T values of the difference of the ratio of

stocks in the same group during the formation period and holding period between the market

continuation subsample and the market transition subsample. Significance at the 1%, 5%, and 10%

level is denoted as ***, **, and *, respectively.

𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

𝑁 𝑀𝑒𝑎𝑛 𝑀𝑒𝑑𝑖𝑎𝑛 Std 𝑁 𝑀𝑒𝑎𝑛 𝑀𝑒𝑑𝑖𝑎𝑛 Std

Whole sample 255 0.119 0.107 0.057 255 0.116 0.108 0.047

Up & Down 64 0.109 0.103 0.046 61 0.110 0.102 0.041

Up & Up 76 0.110 0.099 0.049 78 0.112 0.104 0.044

Down & Down 57 0.151 0.157 0.067 60 0.136 0.123 0.054

Down & Up 58 0.108 0.099 0.055 56 0.104 0.099 0.043

Market continuation 133 0.128 0.117 0.061 138 0.123 0.111 0.050

Market transition 122 0.109 0.101 0.050 117 0.107 0.101 0.042

𝐷𝑖𝑓𝑓 -- 0.019

(2.72***)

-- 0.056 -- 0.015

(2.61***)

-- 0.046

38

Table 10. Summary of market states in other countries (based on 𝒕 − 𝟏𝟐 to 𝒕 − 𝟐)

Results in this table present the summary of market states from other countries, including the US

stock market, the UK stock market, the German stock market, the Korean stock market, the Japan

stock market, the Taiwan stock market and the Chinese market for comparison. Summary in this

table is given by using the 𝑡 − 12 to 𝑡 − 2 formation period as an example. Summary includes the

number of total trading months, the months of market continuation, the months of market transition,

the months of Up & Up subsample, the months of Down & Down subsample, the months of Up &

Down subsample and the months of Down & Up subsample in the whole sample and the period

when the market changes greatly.

Market Total

number

Market

continuation

Market

transition

Up &

Up

Down &

Down

Up &

Down

Down

& Up

Whole sample

US 1078 597 481 445 152 191 290

UK 407 231 176 175 56 61 115

Germany 335 194 141 139 55 55 86

Korean 346 184 162 107 77 70 92

Japan 368 195 173 109 86 87 86

Taiwan 610 327 283 226 101 108 175

China 255 133 122 76 57 58 64

Market changes greatly

US 654 348 306 253 95 123 183

UK 247 131 116 101 30 40 76

Germany 210 122 88 89 33 36 52

Korean 200 108 92 56 52 38 54

Japan 247 128 119 63 65 65 54

Taiwan 314 168 146 122 46 54 92

China 141 75 66 51 24 30 36

Table 11. Average absolute value of market return

Results in this table present the average absolute value of market return from other countries,

including the US stock market, the UK stock market, the German stock market, the Korean stock

market, the Japan stock market, the Taiwan stock market and the Chinese market for comparison.

Results include the average absolute value of market return in the holding period (𝑡 ) and two

formation periods (𝑡 − 12 to 𝑡 − 2 and 𝑡 − 12 to 𝑡 − 7).

Market 𝑡 𝑡 − 12 to 𝑡 − 2 𝑡 − 12 to 𝑡 − 7

US 0.038 0.014 0.018

UK 0.033 0.011 0.014

German 0.044 0.017 0.021

Korean 0.055 0.018 0.024

Japan 0.046 0.016 0.021

Taiwan 0.063 0.022 0.029

China 0.060 0.023 0.030

39

Reference

[1] Asem, E., & Tian, G. Y. (2010). Market dynamics and momentum profits. Journal

of Financial and Quantitative Analysis, 45(6), 1549-1562.

[2] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum

everywhere. The Journal of Finance, 68(3), 929-985.

[3] Asness, C.S. (1995). The power of past stock returns to explain future stock returns.

Working Paper.

[4] Baltzer, M., Jank, S., & Smajlbegovic, E. (2019). Who trades on

momentum?. Journal of Financial Markets, 42, 56-74.

[5] Bianchi, R. J., Drew, M. E., & Fan, J. H. (2015). Combining momentum with

reversal in commodity futures. Journal of Banking & Finance, 59, 423 - 444.

[6] Bianchi, R. J., Drew, M. E., & Fan, J. H. (2016). Commodities momentum: A

behavioral perspective. Journal of Banking & Finance, 72, 133-150.

[7] Chabot, B., Remy Ghysels, E., & Jagannathan, R. (2009). Momentum cycles and

limits to arbitrage - evidence from Victorian England and post-De- pression US

stock markets. Northwestern University, Evanston, IL. Working paper.

[8] Chan, K., Hameed, A., &Tong, W. (2000). Profitability of momentum strategies in

the international euuity markets. Journal of Financial and Quantitative Analysis,

35(2), 153-172.

[9] Cheema, M. A., & Nartea, G. V. (2014). Momentum returns and information

uncertainty: Evidence from China. Pacific-Basin Finance Journal, 30, 173-188.

[10] Cheema, M. A., & Nartea, G. V. (2017a). Momentum, idiosyncratic volatility and

market dynamics: Evidence from China. Pacific-Basin Finance Journal, 46, 109-

123.

[11] Cheema, M. A., & Nartea, G. V. (2017b). Momentum returns, market states, and

market dynamics: Is China different?. International Review of Economics &

Finance, 50, 85-97.

[12] Chui, A. C., Titman, S., & Wei, K. J. (2010). Individualism and momentum around

the world. The Journal of Finance, 65, 361–392.

[13] Chui, A. C., Titman, S., & Wei, K. J. (2010). Individualism and momentum around

the world. The Journal of Finance, 65(1), 361-392.

[14] Cooper, M. J., Gutierrez Jr, R. C., & Hameed, A. (2004). Market states and

momentum. Journal of Finance, 59(3), 1345-1365.

[15] Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial

Economics, 122(2), 221-247.

[16] Demir, I., Muthuswamy, J., &Walter, T. (2004). Momentum returns in Australian

euuities: the influences of size, risk, liuuidity and return computation. Pacific-Basin

40

Finance Journal, 12(2), 143-158.

[17] Erb, C., & Harvey, C. (2006). The tactical and strategic value of commodity futures.

Financial Analysts Journal, 62, 69-97.

[18] Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks

and bonds. Journal of Financial Economics, 33(1), 3-56.

[19] Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing

anomalies. Journal of Finance, 51(1), 55-84.

[20] Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international

stock returns. Journal of Financial Economics, 105(3), 457-472.

[21] Forner, C., &Marhuenda, J. (2003). Contrarian and momentum strategies in the

Spanish stock Market. European Financial Management, 9(1), 67-88.

[22] Gebhardt, W. R., Hvidkjaer, S., & Swaminathan, B. (2005). Stock and bond market

interaction: Does momentum spill over?. Journal of Financial Economics, 75(3),

651-690.

[23] Geczy, C. C., &Samonov, M. (2016). Two centuries of price-return

momentum. Financial Analysts Journal, 72(5), 32-56.

[24] Griffin, J. M., Ji, X., & Martin, J.S. (2003). Momentum investing and business

cycle risk: evidence from pole to pole. The Journal of Finance, 58(6), 2515-2547.

[25] Grundy, B., &Martin, J.S. (2001). Understanding the nature and the risks and the

sources of the rewards to momentum investing. Review of Financial Studies, 14(1),

29-78.

[26] Haesen, D., Houweling, P., & van Zundert, J. (2017). Momentum spillover from

stocks to corporate bonds. Journal of Banking & Finance, 79, 28-41.

[27] Hameed, A., & Kusnadi, Y. (2002). Momentum strategies: Evidence from Pacific

Basin stock markets. Journal of Financial Research, 25(3), 383-397

[28] Hanauer, M. (2014). Is Japan different? Evidence on momentum and market

dynamics. International Review of Finance, 14(1), 141-160.

[29] Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum

trading, and overreaction in asset markets. Journal of Finance, 54 (6), 2143-2184.

[30] Huang, D., Li, J., Wang, L., & Zhou, G. (2018). Time-Series Momentum: Is it

There?. Washington University in St. Louis, Working paper.

[31] Israel, R., & Moskowitz, T. J. (2013). The role of shorting, firm size, and time on

market anomalies. Journal of Financial Economics, 108(2), 275-301.

[32] Jegadeesh, N.. (1990). Evident of predictable behavior of security returns. The

Journal of Finance, 45(3), 881-898.

[33] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers:

Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.

41

[34] Jegadeesh, N., &Titman, S. (2001). Profitability of momentum strategies: an

evaluation of alternative explanations. Journal of Finance 56(2), 699-720.

[35] Jostova, G., Nikolova, S., Philipov, A., & Stahel, C. W. (2013). Momentum in

corporate bond returns. The Review of Financial Studies, 26(7), 1649-1693.

[36] Kang, J., Liu, M. H., & Ni, S. X. (2002). Contrarian and momentum strategies in

the China stock market: 1993-2000. Pacific-Basin Finance Journal, 10(3), 243-265.

[37] Kothari, S. P., & Shanken, J. (1992). Stock return variation and expected dividends:

A time-series and cross-sectional analysis. Journal of Financial Economics, 31(2),

177-210.

[38] Lehmann, B. N. (1990). Fads, martingales, and market efficiency. Quarterly

Journal of Economics, 105(1), 1-28.

[39] Lewellen, J. (2002). Momentum and autocorrelation in stock returns. Review of

Financial Studies, 15(2), 533-563.

[40] Li, B., Stork, T., Chai, D., Ee, M. S., & Ang, H. N. (2014). Momentum effect in

Australian euuities: Revisit, armed with short-selling ban and risk factors. Pacific-

Basin Finance Journal, 27, 19-31.

[41] Lin, C., Ko, K. C., Feng, Z. X., & Yang, N. T. (2016). Market dynamics and

momentum in the Taiwan stock market. Pacific-Basin Finance Journal, 38, 59-75.

[42] Liu, W., Strong, N., &Xu, X., (1999). The profitability of momentum investing.

Journal of Business Finance and Accounting, 26, 1043-1091.

[43] Mengoli, S. (2004). On the source of contrarian and momentum strategies in the

Italian euuity market. International Review of Financial Analysis, 13(3), 301-331.

[44] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012). Currency

momentum strategies. Journal of Financial Economics, 106(3), 660-684.

[45] Miffre, J., & Rallis, G. (2007). Momentum strategies in commodity futures markets.

Journal of Banking and Finance, 31(6), 1863-1886.

[46] Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series

momentum. Journal of Financial Economics, 104(2), 228-250.

[47] Nijman, T., Swinkels, L., & Verbeek, M. (2004). Do countries or industries explain

momentum in Europe?. Journal of Empirical Finance, 11(4), 461-481.

[48] Novy-Marx, R. (2012). Is momentum really momentum?. Journal of Financial

Economics, 103(3), 429-453.

[49] Okunev, J., & White, D. (2003). Do momentum-based strategies still work in

foreign currency markets? Journal of Financial and Quantitative Analysis, 38(2),

425-447.

[50] Pan, L., Tang, Y., & Xu, J. (2013). Weekly momentum by return interval

ranking. Pacific-Basin Finance Journal, 21(1), 1191-1208.

42

[51] Patro, D.K., &Wu, Y. (2004). Predictability of short-horizon euuity returns in

international euuity markets. Journal of Empirical Finance, 11(4), 553-584.

[52] Rouwenhorst, K. G. (1998). International momentum strategies. The Journal of

Finance 53(1), 267-284.

[53] Rouwenhorst, K. G. (1999). Local return factors and turnover in emerging stock

markets. The Journal of Finance, 54(4), 1439-1464.

[54] Serban, A. F. (2010). Combining mean reversion and momentum trading strategies

in foreign exchange markets. Journal of Banking & Finance, 34(11), 2720-2727.

[55] Xiong, X., Gao, Y., & Feng, X. (2017). Successive short‐selling ban lifts and

gradual price efficiency: evidence from China. Accounting & Finance, 57(5),

1557-1604.

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