The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
Look beyond the Liquidity:
An Investment Strategy Focusing on
Large Stocks Based on Trading-Volume
Factor
Xuan Zhang* & Xin Cheng**
*Assistant Professor, School of Business Administration, China University of Petroleum, Beijing, CHINA.
E-Mail: zhangxuan816{at}139{dot}com
**Consultant, Bain & Company, Beijing, CHINA. E-Mail: chengxin611{at}gmail{dot}com
Abstract—An investment strategy based on the trading-volume factor could be quite compelling, which has
already been recommended by some researchers in the past. However, China stock market is quite special.
Could such a strategy also work in China? In this paper, we try to answer this question based on the data of
China stock market. Specifically speaking, in this paper, we have developed an investment strategy that works
in China stock market based on the relationship between trading volume and stock return. Results suggest that
investors should (a) long small trading volume stocks and short large trading volume stocks during the bull
market period, and (b) long large turnover stocks and short small turnover stocks during the bear market
period. This research not only contributes to the existing literature through showing the significant effect of the
trading-volume factor in China stock market, but also provides implications to investors by empirically
proposing an investment strategy that could be effective in China stock market.
Keywords—Bear Market; Bull Market; Investment Strategy; Stock Market; Trading Volume.
Abbreviations—Capital Asset Pricing Model (CAPM); High Book/Price Minus Low Book/Price (HML);
Market Risk Premium (RmRf); Small-Minus-Big Market-Capitalization (SMB); Winner-Minus-Loser PriceMomentum (UMD).
I.
F
INTRODUCTION
OR a long time, in the stock market, investors have
always been trying hard to improve their returns.
Different people or groups apply various investment
strategies. Some investors depend on fundamental analysis,
some do technical analysis, some follow recommendations by
security analysts, while still some others just make random
investments. Many people may trust analysts, because
they’ve got professional knowledge about investments.
However, few strategies could bring significant positive
abnormal return. For example, past research has shown that a
strategy of buying the most highly recommended stocks
would yield negative abnormal returns [Barber et al., 10].
Can we apply information in firms’ financial statements to
form our trading strategies? Some scholars support it,
emphasizing the importance of analyzing the accrual and cash
components of current earnings in the assessment of future
earnings [Graham et al., 1]. However, based on the past
research [eg., Sloan, 6], the stock prices don’t fully reflect
publicly available information, especially financial
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information. In conclusion, past literature has told us that
financial statement analysis may also be not that useful. If the
above investment strategies may not be effective as expected,
then is there any strategy that could really work? The
motivation of this research is to answer this question.
If investment strategies based on public information or
analysts’ recommendations couldn’t bring common investors
significant abnormal returns, then what kind of strategy
would be better? Researchers have tried hard to find some.
For example, to trade stocks on special days sometimes may
bring investors surprises. According to Hirshleifer &
Shumway [11], stock returns and sunshine are significantly
positively correlated with each other. Another investment
strategy discussed and recommended by a number of
researchers is based on liquidity measures [eg., Karpoff, 2;
Campbell et al., 3; Conrad et al., 4; Blume et al., 5; Datar et
al., 8; Lee & Swaminathan, 9; Pástor & Stambaugh, 12; Chan
& Faff, 13].
In previous studies, many scholars care about tradingvolume measures which could proxy for liquidity [eg., Fama
& French, 15]. For example, investors commonly track
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
“activity” as measured by trading volume and many technical
analysts use measures related to trading volume to predict
whether a stock will rise or fall in the short term or the
midterm. In this paper, our objective is to develop an
investment strategy based on the trading-volume factor that
would work in China stock market. This strategy follows
Brown et al., [16]’s work “Trading volume and Stock
Investments”, but in order to make it competitive in China,
we make major adjustments based on our data.
This research has contributions both theoretically and
empirically. Firstly, although some scholars have shown in
their research that investment strategies based on the tradingvolume factor could be effective, they didn’t test their
strategies in China stock market. This research makes up this
gap and contributes to the existing literature through showing
the significant effect of the trading-volume factor in China
stock market. Secondly, by empirically proposing an
investment strategy that could be effective in China stock
market, this research provides implications to investors who
are trying to get abnormal returns in China stock market.
II.
RELATED WORKS
Brown et al., [16] extend the investment strategy based on
trading volume. From their perspective, trading-volume
measures proxy for more than liquidity: They are as much of
momentum as of investor interest or information content.
They found that (1) for stocks that are more liquid, there
would be a positive relationship between the trading volume
and stock returns; and (2) for stocks that are less liquid, there
would be a negative relationship between the trading volume
and stock returns, because investors may demand a liquidity
premium.
Now let’s firstly have a brief review of the basic
procedure used by the article to get the investment strategy
and what are their key findings.
They began their study by defining two simple measures
related to the trading volume: (1) average daily trading
volume, measured on a three-month basis; and (2) turnover,
measured by the percentage of annualized trading volume to
shares outstanding. Firstly, they averaged the above two
measures among each of five quintile portfolios in their
samples (100 stocks for the S&P 500 and 200 stocks for the
Largest 1,000) and sorted them on three metrics/investment
styles commonly used: the PB ratio (price per share divided
by the book value of equity), which captures the valueversus-growth styles; the market capitalization of equity,
which captures small-versus-large stock styles; and pricemomentum winners-versus-losers styles. Their results
showed monotonic relationships among the first two metrics
and a U-shaped relationship for the last metric.
Next, they tried to investigate the potential profitability
of long–short portfolios sorted based on trading volume and
turnover. Portfolio deciles were formed on the basis of the
trading-volume measures, and their returns over the
subsequent 1-month, 3-month, 6-month, and 12-month
periods were compared. Their results were contrary to much
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of the past work by other researchers, showing that for larger
stocks, those traded more (with bigger trading volume and
turnover) had higher returns, and vice versa. Furthermore,
they found the alpha to be significant for the most heavily
traded portfolio, when using CAPM (the traditional capitalasset-pricing model), that is, regressesing excess (T-bill)
returns on market excess returns. The above results could
even be exaggerated when applying the three-factor FamaFrench model (1992, 1993), considering the factors of RmRf
(market risk premium), SMB (small-minus-big: return on
portfolios of small stocks minus return on portfolios of big
stocks), and HML (high-minus-low: return on portfolios of
high-book/price stocks minus return on portfolios of lowbook/price stocks), and when applying the four-factor FamaFrench model, adding an extra factor of UMD (winnerminus-loser price-momentum, measured by returns over the
previous six months) into consideration [eg., Carhart, 7;
Avramov & Chordia, 14; Fama & French, 17]. The alpha was
also positive and significant for the highest turnover
portfolio.
Finally, they tested the effect of trading-volume factors
by investigating the difference between the return of hightrading-volume portfolios and that of low-trading-volume
portfolios, and examined the correlations between the
trading-volume factors and other well-known factors. Their
results showed that the trading-volume factors were often
significant and could have bigger effect than other factors.
So we give a brief summary of their findings: Consistent
with previous research, their findings suggest that tradingvolume measures proxy for more than liquidity: They are as
much measures of momentum as of investor interest or
information content. Investors may demand a liquidity
premium for less liquid stocks, leading to a negative
relationship between trading volume and stock returns. But
for stocks that are more liquid—the focus of our current
study—their results show a positive relationship between
trading volume and stock returns.
III.
INITIAL INVESTMENT STRATEGY AND
DATA
3.1. Initial Investment Strategy
Following the essential idea of Brown et al., [16], we try to
form a similar investment strategy in China stock market in
this paper, and find out if the investment strategy would be
also competitive in China. Our initial investment strategy is
as follows:
When forming a portfolio which only contains
large stocks (with great liquidity), long the heavily and
frequently traded stocks and short the less heavily and
less frequently traded stocks. (The portfolio is
constructed based on equal-weighted method.)
In order to obtain a wise investment strategy, we have
done the following work in this paper: we consider to firstly
have a general replication of this articles procedure and to see
whether the same pattern exists in China market. Secondly
© 2015 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
we would make reasonable adjustment based on the data and
propose an adjusted investment strategy for China market.
Then we try to test whether there are significant returns,
Alpha and whether the strategy is sensitive to the nature of
the market (bull or bear). Finally, we try to prove the
existence of “trading-volume factors” in China market.
market. This reflects that China stock market is less mature
than US stock market and that it’s an emerging market.
3.2. Data and Sample
4.1.1. Relationships between Trading-Volume Factors and
Three Commonly Used Metrics/Investment Styles
Just as Brown et al., [16] did in their paper, we also choose
big companies in the China stock market as our samples to
test the investment strategy. We use Hushen 300 portfolio,
and because of the restriction of data completeness, we select
200 stocks from Hushen 300 portfolio, excluding those with
little information available.
We obtained each stock’s monthly data of the period
from January 2002 to December 2009 from Wind and CCER
data base. We select this period of time because the China
stock market experienced both a bear market period from
January 2002 to May 2005, and a bull market period from
June 2005 to January 2008. After that, the China stock market
began to experience a long period of downturn. In our
following analysis, we divided our data into two subgroups
according to time periods, one represents bear market period
and the other represents bull market period.
We measure the recent trading volume using the variable
of Dvol3mo, which is calculated as the total trading volume
of the past 3 months on a rolling basis. And we measure
turnover using the variable of Turno, which is calculated as
the average turnover ratio of the past 3 months also on a
rolling basis. Rolling window of the two variables is 3
months and they are adjusted for each month. Table 1 shows
the descriptive analysis of our data.
Table 1: Summary Statistics, 2002-2009
Mean
Median
72.03
43.04
PE
4.87
4.05
PB
11.75
11.62
ROE
1.69
1.59
Ret1mo
5.01
4.62
Ret3mo
9.95
9.28
Ret6mo
18.49
17.02
Ret12mo
6460.60
4053.59
Mktcap(million)
546.54
343.46
Shs(million)
569.01
418.92
Dvol3mo(million)
35.73
35.78
Turno
Variables in Table 1 are: price-to-earnings ratio (PE),
price-to-book ratio (PB), return on equity (ROE); 1-month
return (Ret1mo), 3-month return (Ret3mo), 6-month return
(Ret6mo), 12-month return (Ret12mo), these are calculated
on a rolling basis and adjusted for each month; market
capitalization of equity in A stock market (Mktcap), common
shares outstanding in A stock market (Shs), total trading
volume of the past 3 months measured on a rolling 3-month
basis (Dvol3mo), and average turnover ratio of the past 3
months measured on a rolling 3-month basis (Turno).
As Table 1 shows, PE, trading volume and turnover of
stocks in China market are much larger than those in US
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IV.
RESULTS
4.1. A Simple Replication in China Stock Market
As the procedure of the original article [Brown et al., 16], we
first compared these measures averaged among each of five
quintile portfolios in our samples and sorted on three
metrics/investment styles commonly used: the PB ratio
(capturing the value-versus-growth styles), the market
capitalization of equity (capturing small-versus-large stock
styles),
and
winners-versus-losers
price-momentum.
However, the pattern found by us is quite different from that
in US. Please see Table 2. The US data shows monotonic
relationships among the price-to-book ratio and marketcapitalization variables and a U-shaped relationship with the
momentum variable. However, in China market, monotone
decreasing relationships are found among marketcapitalization and momentum variables and an inverted Ushaped relationship is found with the price-to-book variable.
The relationships found here suggest some complex
interactions among various investment styles and trading
volume and the relationships may be quite different compared
with the US market.
Table 2: Trading Volume and Turnover, 2002-2009
Trading Volume (Dvol3mo)
Turnover(Turno)
617,020,033
35.244
PB1
541,935,090
34.689
PB2
659,933,322
36.275
PB3
584,536,273
37.084
PB4
441,631,387
35.353
PB5
423,459,914
40.891
MKT1
674,816,017
38.192
MKT2
597,512,199
38.347
MKT3
528,666,483
32.720
MKT4
620,601,494
28.495
MKT5
636,553,445
32.653
MOM1
640,575,589
33.583
MOM2
680,324,642
34.779
MOM3
481,594,383
34.675
MOM4
406,008,047
42.956
MOM5
4.1.2. Performance of Portfolios Sorted by Trading Volume
and Turnover
We formed portfolio deciles on the basis of the tradingvolume measures and compared returns over the subsequent
1-month, 3-month, 6-month, and 12-month periods.
Specifically, the 200 stocks are divided into 10 groups based
on Dvol3mo and Turno separately, with D1 (D10) being the
lowest (highest) measure. Then we calculate the returns for
the subsequent 1-month, 3-month, 6-month, and 12-month
holding periods for each group from D1 to D10 for both the
Dvol3mo measurement and Turno measurement. Moreover,
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
since our investment strategy is to long the heavily and
frequently traded stocks and short the less heavily and less
frequently traded stocks, we also calculate the returns for the
subsequent 1-month, 3-month, 6-month, and 12-month
holding periods for the portfolio D10-D1. A t-test for each
return has then been done to see whether they are
significantly different with 0. The results are shown in Table
3 and Table 4. Table 3 is performance of portfolios sorted by
trading volume. Table 4 is performance of portfolios sorted
by turnover.
Table 3: Performance of Portfolios Sorted by Trading Volume, 2002-2009
Overall
Sub1 (Bear)
20.69039
-15.1031
D1
21.16298
-23.7443
D5
1 Month Holding Period
16.8586
-12.504
D10
(Annualized Return)
-3.83179
2.599151
D10-D1
t-Test p-value
0.515
0.605
20.23732
-15.2832
D1
21.378
-21.163
D5
3 Month Holding Period
16.55275
-13.408
D10
(Annualized Return)
-3.685
1.875
D10-D1
t-Test p-value
0.294
0.472
19.77198
-15.1653
D1
20.981
-20.160
D5
6 Month Holding Period
15.407
-13.630
D10
(Annualized Return)
-4.365
1.535
D10-D1
t-Test p-value
0.030**
0.312
19.91425
-11.9416
D1
21.52311
-13.5438
D5
12 Month Holding Period
15.335
-11.882
D10
(Annualized Return)
-4.5793
0.05955
D10-D1
t-Test p-value
0.000***
0.953
Note: ***p<0.01, **p<0.05, *p<0.1
Sub2 (Bull)
85.17642
84.15479
65.16861
-20.0078
0.141
80.69134
82.376
62.74062
-17.951
0.024**
72.51954
75.409
57.190
-15.329
0.000***
57.58178
60.03543
47.24229
-10.3395
0.000***
Table 4: Performance of Portfolios Sorted by Turnover, 2002-2009
Overall
Sub1 (Bear)
19.345
-20.145
D1
14.252
-13.768
D5
1 Month Holding Period
15.912
-7.930
D10
(Annualized Return)
-3.432
12.214
D10-D1
t-Test p-value
0.500
0.003***
16.216
-24.306
D1
22.161
-15.340
D5
3 Month Holding Period
18.813
-21.445
D10
(Annualized Return)
2.596
2.861
D10-D1
t-Test p-value
0.617
0.418
13.506
-24.868
D1
19.959
-14.562
D5
6 Month Holding Period
20.791
-16.749
D10
(Annualized Return)
7.285
8.119
D10-D1
t-Test p-value
0.031**
0.000***
14.10892
-20.3848
D1
20.16963
-8.92242
D5
12 Month Holding Period
19.47326
-14.6217
D10
(Annualized Return)
5.364332
5.763105
D10-D1
t-Test p-value
0.006***
0.000***
Note: ***p<0.01, **p<0.05, *p<0.1
Sub2 (Bull)
86.866
63.000
61.753
-25.113
0.038**
82.379
78.572
74.410
-7.96835
0.483
74.517
68.829
71.904
-2.61281
0.693
61.12756
56.66311
59.34164
-1.78592
0.638
Generally speaking, we could hardly say the strategy that
long the heavily and frequently traded stocks and short the
less heavily and less frequently traded stocks works in China
market. Many returns of D10-D1 are even significantly
negative. However, it seems that there are alternative ways to
interpret the return data and there may be some new trading
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strategies. We don’t do a detailed analysis here and we will
leave to it for later discussion when we take a second look at
Table 3 and Table 4 later. Generally speaking, we can argue
here that the strategy of longing the heavily and frequently
traded stocks and shorting the less heavily and less frequently
traded stocks doesn’t work in China market.
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
4.1.3. Any Significant Superior Performance?
We further apply the CAPM, regressing monthly portfolio
returns on market excess returns (the RmRf variable
measured by Fama-French), to find out if there’s any
significant superior performance. The intercept return here
can be interpreted as the traditional Jensen’s alpha. We also
measured alpha after controlling for RmRf, SMB, HML, and
UMD in the three- and the four-factor Fama-French models.
The results are presented in Table 5, Table 6 and Table 7.
Table 5: CAPM Risk-Adjusted Portfolio Performance, 2002-2009
Trading Volume Sorted
Turnover Sorted
Alpha
Overall
Sub1
Sub2
D1
D3
D5
D8
D10
D10-D1
RmRf
0.152
1.042
(0.48)
(32.678)
-0.21
1.088
(-1.079)
(30.536)
0.523
1.009
(0.672)
(14.331)
0.082
1.078
(1.151)
(19.597)
0.162
1.06
(0.414)
(26.786)
0.176
1.052
(0.416)
(24.639)
-0.036
1.033
(-0.086)
(24.262)
-0.167
0.961
(-0.52)
(29.644)
-0.249
-0.117
(-0.51)
(-2.364)
R2
Alpha
0.234
0.842
0.924
(0.551)
(19.611)
-0.011
0.914
(-0.034)
(15.991)
1.345
0.777
(1.235)
(7.903)
0.533
0.815
0.814
(0.895)
(13.53)
0.455
0.826
0.891
(0.896)
(16.087)
0.054
0.846
0.873
(0.125)
(19.409)
0.211
0.861
(0.526)
(21.195)
0.182
0.849
(0.452)
(20.822)
-0.351
0.441
(-0.795)
(0.765)
0.966
0.873
0.87
0.909
0.06
R2
RmRf
0.814
0.886
0.676
0.675
0.746
0.811
0.836
0.831
0.007
Table 6: Fama-French Three Factor Risk-Adjusted Portfolio Performance, 2002-2009
Trading Volume Sorted
Turnover Sorted
Alpha
Overall
Sub1
Sub2
D1
D3
D5
D8
D10
D10-D1
RmRf
SMB
HML
0.509
1.054
0.389
0.032
(1.564)
(37.401)
(4.359)
(0.373)
-0.08
1.09
0.217
-0.058
(-0.424)
(33.397)
(2.977)
(-0.76)
0.848
1.085
0.379
0.163
(0.997)
(17.638)
(2.45)
(1.079)
0.361
1.092
0.485
-0.035
(0.593)
(20.703)
(2.901)
(-0.213)
0.163
1.072
0.474
-0.156
(0.386)
(29.239)
(4.077)
(-1.382)
0.223
1.065
0.51
-0.147
(0.489)
(26.961)
(4.07)
(-1.208)
0.571
1.045
0.35
0.158
(1.254)
(26.512)
(2.801)
(1.301)
0.371
0.968
0.167
0.187
(1.049)
(31.659)
(1.722)
(1.986)
0.09
-0.124
-0.318
0.222
(0.017)
(-2.533)
(-2.053)
(1.472)
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R2
Alpha
0.229
0.85
0.291
-0.099
0.942
(0.467)
(20.03)
(2.161)
(-0.754)
0.391
0.896
0.251
0.186
(1.377)
(18.316)
(2.296)
(1.618)
0.278
0.827
0.362
-0.254
(0.189)
(7.782)
(1.353)
(-0.972)
0.342
0.827
0.516
-0.257
0.834
(0.506)
(14.13)
(2.78
(-1.423)
0.199
0.835
0.408
-0.251
0.909
(0.343)
(16.647)
(2.567)
(-1.62)
0.145
0.852
0.225
-0.033
0.895
(0.29)
(19.606)
(1.633)
(-0.247)
0.298
0.866
0.18
-0.02
(0.634)
(21.286)
(1.395)
(-0.162)
0.259
0.855
0.225
-0.04
(0.552)
(21.077)
(1.754)
(-0.32)
-0.083
0.028
-0.29
0.217
(-0.164)
(0.632)
(-2.08)
(1.596)
0.974
0.922
0.892
0.921
0.106
RmRf
© 2015 | Published by The Standard International Journals (The SIJ)
SMB
HML
R2
0.824
0.924
0.696
0.702
0.765
0.817
0.84
0.838
0.059
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Overall
Sub1
Sub2
D1
D3
D5
D8
D10
D10-D1
Alpha
0.562
(1.904)
-0.064
(-0.323)
0.584
(0.816)
0.448
(0.79)
0.201
(0.486)
0.272
(0.618)
0.608
(1.358)
0.403
(1.17)
-0.045
(-0.081)
Table 7: Fama-French Four Factor Risk-Adjusted Portfolio Performance, 2002-2009
Trading Volume Sorted
Turnover Sorted
RmRf
SMB
HML
UMD
R2
Alpha
RmRf
SMB
HML
1.073
0.299
0.056
-0.458
0.258
0.86
0.241
-0.086
(41.472)
(3.583)
(0.713)
(-4.453) 0.953
(0.529)
(20.137)
(1.754)
(-0.658)
1.088
0.195
-0.041
-0.051
0.509
0.882
0.097
0.311
(32.433)
(2.04)
(-0.443) (-0.355) 0.974
(1.801)
(18.398)
(0.709)
(2.359)
1.097
0.343
0.08
-0.592
0.208
0.83
0.353
-0.276
(21.277)
(2.64)
(0.621)
(-3.611) 0.947
(0.139)
(7.684)
(1.294)
(-1.024)
1.122
0.336
0.004
-0.751
0.339
0.826
0.522
-0.258
(22.585)
(2.1)
(0.028)
(-3.801) 0.858
(0.497)
(13.845)
(2.714)
(-1.421)
1.085
0.408
-0.139
-0.329
0.221
0.843
0.37
-0.241
(29.927)
(3.494)
(-1.258) (-2.285) 0.914
(0.38)
(16.575)
(2.256)
(-1.551)
1.082
0.426
-0.125
-0.424
0.176
0.863
0.172
-0.019
(28.065)
(3.424)
(-1.064) (-2.766) 0.903
(0.354)
(19.753)
(1.221)
(-1.144)
1.057
0.287
0.175
-0.318
0.341
0.881
0.107
-0.001
(26.971)
(2.269)
(1.46)
(-2.037) 0.897
(0.742)
(21.909)
(0.822)
(-0.009)
0.979
0.111
0.202
-0.282
0.302
0.87
0.151
-0.021
(32.434)
(1.141)
(2.191)
(-2.348) 0.926
(0.661)
(21.723)
(1.172)
(-0.169)
-0.143
-0.225
0.197
0.469
-0.037
0.044
-0.371
0.238
(-2.966) (-1.451)
(1.345)
(2.451)
0.165 (-0.074)
(1.018)
(-2.642)
(1.792)
The investigation of risk-adjusted return performance is
consistent with our prior t-test. We nearly don’t get any
significant D10-D1 Jensen’s alpha. This finding tells us that
the investment strategy doesn’t give us a significant positive
abnormal return in China market.
So far, we could confidently argue that the strategy that
long the heavily and frequently traded stocks and short the
less heavily and less frequently traded stocks doesn’t work in
the China stock market. In the following part, we will review
Table 3 and Table 4 and offer a new investment strategy.
4.2. Adjusted Investment Strategy
Looking at Table 3 and Table 4 again, we can surprisingly
find something else. Since we have split the whole data
sample into two subgroups according to time periods, one
representing bear market period and the other one
representing bull market period, we found that results of
either subgroup are different from each other and also from
the overall. Maybe that’s the reason why we obtained nothing
when applying our investment strategy to our whole data. The
two tables show that:
a. In bull market period, for Dvol3mo, if we long the
most heavily traded stocks and short the least heavily traded
stocks, the average annualized equal weighted returns for the
one-month holding period is -20.01 percent, for the threemonth holding period is -17.95 percent, for the six-month
holding period is -15.33 percent, and for the twelve-month
holding period is -10.34 percent. Therefore we can obtain
significantly negative returns if we long the group of stocks
with largest trading volumes and short the group of stocks
with smallest trading volumes (D10-D1). That means we can
obtain significantly positive returns if we long the group of
stocks with smallest trading volumes and short the group of
stocks with largest trading volumes (D1-D10).
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UMD
-0.248
(-1.464)
-0.366
(-1.766)
-0.155
(-0.452)
0.032
(0.135)
-0.193
(-0.953)
-0.267
(-1.54)
-0.37
(-2.318)
-0.375
(-2.353)
-0.407
(-2.351)
R2
0.828
0.931
0.698
0.702
0.767
0.822
0.85
0.848
0.116
b. In bear market period, for Turno, if we long the most
heavily traded stocks and short the least heavily traded
stocks, the average annualized equal weighted returns for the
one-month holding period is 12.21 percent, for the threemonth holding period is 2.86 percent, for the six-month
holding period is 8.12 percent, and for the twelve-month
holding period is 5.76 percent. Therefore we can obtain
significantly positive returns if we long the group of stocks
with largest turnover and short the group of stocks with
smallest turnover (D10-D1).
Therefore, according to the results above, we conclude
that applying to China market, we can adjust our investment
strategy to the following:
When forming a portfolio which only contains
large stocks, long large turnover stocks and short small
turnover stocks during the bear market period; long
small trading volume stocks and short large trading
volume stocks, during the bull market period. The
portfolio is constructed based on equal-weighted
method.
4.2.1. Testing Significant Jensen’s Alpha
In the following part we investigate the risk-adjusted return
performance of the portfolios formed by applying our
investment strategy. We attempted to find any significant
superior or inferior performance in the context of the CAPM,
by regressing monthly portfolio returns in excess of one-year
term deposit returns, on market excess returns (the RmRf
variable measured by Fama-French), whereby the intercept
return in this regression form is interpreted as the traditional
Jensen’s alpha. The results are showed in Table 8. We also
measured alpha after controlling (1) RmRf, SMB and HML
in the three-factor Fama-French model; and (2) RmRf, SMB,
HML, and UMD in the four-factor Fama-French model. The
results are presented in Table 9 and Table 10 respectively.
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
Table 8: CAPM Risk-Adjusted Portfolio Performance, 2002-2009 (Adjusted Investment Strategy)
CAPM
D10-D1
Alpha
RmRf
Turnover Sorted, Sub1
-0.034
1-Month Holding Period
1.027
2.878
-0.515
(t-stat)
0.152
3-Month Holding Period
1.755
2.066
2.931
(t-stat)
0.022
6-Month Holding Period
4.988
5.761
0.832
(t-stat)
-0.003
12-Month Holding Period
6.208
7.025
-0.197
(t-stat)
Trading-Volume Sorted, Sub2
-0.907
-0.135
1-Month Holding Period
-0.713
-1.177
(t-stat)
-1.335
-0.187
3-Month Holding Period
-0.694
-3.232
(t-stat)
-0.109
6-Month Holding Period
-3.994
-2.099
-3.801
(t-stat)
-0.045
12-Month Holding Period
-7.275
-3.053
-2.534
(t-stat)
Table 9: Fama-French Three Factor Risk-Adjusted Portfolio Performance, 2002-2009 (Adjusted Investment Strategy)
Fama-French Three Factor
D10-D1
Alpha
RmRf
SMB
HML
Turnover Sorted, Sub1
-0.031
-0.075
-0.016
1-Month Holding Period
0.946
2.400
-0.463
-0.496
-0.102
(t-stat)
0.136
-0.161
0.225
3-Month Holding Period
2.005
2.248
2.650
-1.406
1.862
(t-stat)
0.018
0.003
0.055
6-Month Holding Period
5.327
5.624
0.647
0.043
0.854
(t-stat)
-0.006
0.013
0.044
12-Month Holding Period
6.863
7.385
-0.471
0.426
1.402
(t-stat)
Trading-Volume Sorted, Sub2
0.871
-0.223
-0.626
0.419
1-Month Holding Period
0.527
-1.869
-2.084
1.429
(t-stat)
-0.543
-0.209
-0.140
0.054
3-Month Holding Period
-0.206
-3.280
-0.873
0.345
(t-stat)
-0.093
0.103
-0.040
6-Month Holding Period
-5.169
-2.012
-3.002
1.319
-0.529
(t-stat)
-0.047
-0.022
0.039
12-Month Holding Period
-5.528
-1.683
-2.383
-0.437
0.812
(t-stat)
Table 10: Fama-French Four Factor Risk-Adjusted Portfolio Performance, 2002-2009 (Adjusted Investment Strategy)
Fama-French Four Factor
D10-D1
Alpha
RmRf
SMB
HML
UMD
Turnover Sorted, Sub1
-0.043
-0.207
0.091
-0.314
1-Month Holding Period
1.047
2.587
-0.629
-1.057
0.481
-1.059
(t-stat)
0.148
-0.024
0.113
0.325
3-Month Holding Period
1.690
1.876
2.904
-0.165
0.809
1.480
(t-stat)
0.021
0.043
0.022
0.096
6-Month Holding Period
5.141
5.243
0.765
0.544
0.286
0.803
(t-stat)
-0.007
0.003
0.052
-0.023
12-Month Holding Period
6.952
7.169
-0.521
0.078
1.375
-0.388
(t-stat)
Trading-Volume Sorted, Sub2
1.163
-0.236
-0.586
0.511
0.653
1-Month Holding Period
0.727
-2.049
-2.018
1.779
1.781
(t-stat)
-0.596
-0.208
-0.142
0.048
-0.040
3-Month Holding Period
-0.221
-3.205
-0.870
0.299
-0.192
(t-stat)
-0.090
0.094
-0.060
-0.142
6-Month Holding Period
-5.549
-2.193
-2.965
1.228
-0.795
-1.467
(t-stat)
-5.511
-0.047
-0.022
0.040
0.003
12-Month Holding Period
-1.639
-2.339
-0.424
0.793
0.048
(t-stat)
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© 2015 | Published by The Standard International Journals (The SIJ)
R2
0.008
0.207
0.021
0.001
0.044
0.258
0.325
0.176
R2
0.021
0.293
0.052
0.106
0.173
0.280
0.368
0.196
R2
0.056
0.341
0.072
0.111
0.281
0.415
0.196
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
The three tables above show consistent results that by
applying our investment strategy, we can generally obtain
significantly positive Jensen’s alpha under CAPM model,
three factor and four factor Fama-French model. According
to the results, during the bear market period, we can obtain
significantly positive Jensen’s alpha if we long large turnover
stocks and short small turnover stocks. No matter how long
we hold the portfolio, short-term (1-month) to long-term (12month), we can always get significant abnormal return. For
example, by applying our investment strategy, after adjusted
by the Fama-French four factors, we can averagely obtain
1.05 percent returns holding one month, 1.69 percent returns
holding three months, 5.14 percent returns holding six
months and 6.95 percent returns holding twelve months.
Those figures are all significant. While during the bull market
period, we can obtain positive Jensen’s alpha if we long small
trading volume stocks and short large trading volume stocks,
especially when we hold the portfolio for a long time (6month or 12-month) we can get significant abnormal return.
For example, by applying our investment strategy, after
adjusted by the Fama-French four factors, we can averagely
obtain significant 5.55 percent returns holding six months.
The returns in the three tables are not annualized.
These analyses above illustrate that our adjusted
investment strategy really works in China stock market.
Following our investment strategy will bring investors
significant excess returns.
(D10) and the lowest-decile portfolio (D1) of Hushen 300
stocks measured by (1) total trading volume of the past 3
months measured on a rolling 3-month basis (Dvol3mo) and
(2) average turnover ratio of the past 3 months measured on a
rolling 3-month basis (Turno). RmRf, SMB, HML, and UMD
are the four Fama-French factors included. Correlations are
measured between the trading-volume factor monthly returns
and the Fama-French factor monthly returns.
4.3. Trading-Volume Factor as a Pricing Factor in China
Market
UMD
Since we have proved that investment strategy formed
according to trading-volume variables (trading volume and
turnover) is effective, we further investigated whether
trading-volume factors can be formed and added into the
pricing model. Since we form our investment strategy using
different trading-volume variables in the two sub time
periods, we try to add different trading-volume variables as
factors accordingly into the pricing model.
We started by examining the return difference between
the highest and lowest trading-volume and turnover portfolios
(D10 and D1, respectively) and formed trading-volume
factors based on the Turno (for subgroup 1) and Dvol3mo
(for subgroup 2) variables in the spirit of the SMB and HML
factors. Table 11 and Table 12 report the portfolios’
properties and their correlations with the four Fama-French
factors. For the Turno variable, the average monthly mean
(median) return difference is 1.08 percent (0.95 percent),
which implies that stocks with bigger turnover perform better
than those with smaller turnover for our sample and sub time
period 1 (bear market period). For the Dvol3mo variable, the
average monthly mean (median) return difference is -1.67
percent (-0.69 percent), which implies that less heavily traded
stocks perform better than more heavily traded stocks for our
sample and sub time period 2 (bull market period).
In the two tables below, the trading-volume factor
measures are calculated monthly as the difference between
the equal-weighted return on both the highest-decile portfolio
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Table 11: Trading-Volume Factor Measures, Sub 1
TradingVolume
Fama-French Factors
Factors
Turno
RmRf
SMB
HML
Summary Statistics
1.08
-1.49
-0.88
-1.11
Mean
0.95
-1.54
-0.89
-0.90
Median
-2.88
-10.80
-4.87
-10.02
Minimum
6.39
9.25
5.23
5.10
Maximum
2.01
5.34
2.72
2.61
Std.dev.
Correlations
-0.09
RmRf
-0.11
SMB
-0.09
HML
-0.10
UMD
Table 12: Trading-Volume Factor Measures, Sub 2
TradingVolume
Fama-French Factors
Factors
Dvol3mo
RmRf
SMB
HML
Summary Statistics
-1.67
5.62
-0.40
-3.67
Mean
-0.69
4.45
-0.09
-3.39
Median
-27.11
-17.04 -10.07
-15.03
Minimum
7.13
27.70
12.69
5.55
Maximum
6.24
9.69
4.94
4.77
Std.dev.
Correlations
-0.21
RmRf
-0.17
SMB
0.05
HML
0.27
UMD
UMD
0.37
0.38
-3.03
3.90
1.59
0.21
0.25
-7.06
7.20
2.98
We then compared the correlations between these
trading-volume factor monthly series and those of FamaFrench. Our results showed in Table 11 and Table 12 suggest
that the trading-volume measures don’t have strong linear
relationships with Fama-French factors. This implies that
maybe trading-volume factors can explain some part of the
pricing that the four Fama-French factors can’t explain.
Therefore we use the following regression equation to test the
trading-volume factors, Turno and Dvol3mo respectively,
according to the time period.
(1)
The dependent variable is average monthly portfolio
returns of Hushen 300 stocks, and the independent variables
are the four Fama-French factors added with the tradingvolume (turnover) factor. The regression results are showed
in Table 13, and we only present betas of the trading-volume
(turnover) factor.
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 3, No. 7, August 2015
Table 13: Trading-Volume Factor as a Pricing Factor
Turnover-Bear
Trading VolumeMarket
Bull Market
Trading
Volume
0.126
-0.250
Coefficient
1.441
-3.374
(t-stat)
0.160
0.002
P-value
0.976
0.963
R2
According to Table 13, after adding the trading-volume
(turnover) factors into the pricing model, the explanation
strengths of the models sharply increase, with both R square
more than 95%. Also the beta of turnover is positive and
close to be significant in the regression for bear market
period, and the beta of trading volume is significantly
negative in the regression for bull market period. This means
that the two variables really make contributions to the pricing
model. The conclusion is that in bear market period, stocks
with bigger turnover have larger returns; while in bull market
period, stocks with smaller trading volume have larger
returns. Therefore we suggest that trading-volume is probably
a pricing factor in China market.
Regarding to the reason why trading-volume factor
performs as the way we have found, we suggest the
following. Firstly, in bear market, the turnover rates of all
stocks would generally shrink to quite small numbers. If
some stocks are heavily traded, they must be very different,
in fact, better or have bigger returns than the majority. The
phenomenon that they have bigger turnover rates should
contain some useful information. Therefore we should long
stocks with bigger turnover and short those with smaller
turnover in bear market period. Secondly, in bull market, the
stock market generally is performing well and investors may
behave over-confidently. Therefore those stocks with very
large trading volume may not be so good as they seem to be.
Actually they may be quite bad compared with the market
and other stocks. Therefore we should long stocks with
smaller trading volume and short those with larger trading
volume in bull market period.
V.
CONCLUSION AND FUTURE WORK
The major findings and contributions of our study are as
follows: Firstly, we find that the relationship between the
trading-volume factor and abnormal strock returns is
significantly positive. Second, we propose an investment
strategy for investors in China stock market, which is a
market sensitive investment strategy focusing on big stock:
(a) During the bull market period, long small trading volume
stocks and short large trading volume stocks; (b) During the
bear market period, long large turnover stocks and short small
turnover stocks. Third, according to the results of our paper,
“trading-volume” factor helps to price the security in China
market.
However, there should also be some risk considerations
when applying our proposed investment strategy in China
stock market, since there exist two limitations in our paper.
Firstly, our testing method of our strategy is a post-event
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study. Therefore there would be a problem that almost
nobody is able to accurately forecast or identify whether
current market is a bear market or a bull market. This may
make our strategy a little hard to employ in practical world.
However, we believe that many investors have some basic
sense of the nature of the market when the market condition
has lasted for a certain period and will probably last for
another sustainable time. Secondly, our investment strategy is
adjusted each month and we don’t take trading fees into
consideration. However, since we could obtain really
considerable positive abnormal return, we believe the trading
fee will not become a big problem.
In the future study, we may address the limitations and
extend the research scope of this paper. First, we could apply
the investment strategy proposed in this paper to different
stock markets besides China, and do comparison studies to
see if the strategy would also be competitive in other markets.
For example, different stock markets could be divided into
groups such as mature stock markets and immature stock
market. Second, we could also extend this research in terms
of time. For instance, we could get data of China stock
market through a longer time period, and try to divide those
data into segments based on different standards other than
bear market vs. bull market. By doing so, we may be able to
find out other potential investment strategies which are
compelling for investors. Third, in the future study, we could
take more factors into consideration, such as trading fees,
investors’ expectations as individuals or as groups. By taking
in other considerations into account, the results of our
analyses would provide more perspectives.
ACKNOWLEDGMENT
This research is supported by Science Foundation of China
University of Petroleum, Beijing (No. 2462014YJRC032).
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Xuan Zhang. Xuan Zhang is currently an
Assistant Professor in School of Business
Administration,
China
University
of
Petroleum, Beijing. She earned her Ph.D.
Degree
from
Guanghua
School
of
Management, Peking University. Her research
interests include consumer behavior, branding,
firm performance, crisis management, and
pricing strategy. Her research papers have
been published on Journal of International Marketing, Advances in
Psychological Science (in Chinese), Journal of Marketing Science
(in Chinese), Journal of Marketing Science (in Chinese), and
Economic Science (in Chinese). She also has attended international
academic conferences to present her research progress such as
INFORMS Marketing Science Conference and Annual Conference
of China Marketing Science.
[17]
Xin Cheng. Xin Cheng is currently a Consultant at Bain &
Company in China. He earned his Master Degree from Guanghua
School of Management, Peking University. His research interests
include business strategy, consumer behavior, branding, and firm
performance.
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122