Liquidity, liquidity risk and stock returns the evidence of viet nam

Liquidity, liquidity risk and Stock Returns – Evidence from Vietnam

Xuan Vinh Vo & Hong Thu Bui

Abstract
The question of whether liquidity is priced is a subject for a huge volume of papers in the asset pricing literature.
The common results are a negative relationship between these two variables as investors demand for higher
returns to compensate for higher stock volatility. This paper investigates the relationship between liquidity and
stock return in Vietnam by employing an updated dataset of market and financial data of listed companies in
Ho Chi Minh City stock exchange ranging from 2007 to 2012. Our results are proving the reverse. In other
words, we document a reliable positive relationship between liquidity measures and stock returns and negative
relationship between illiquidity measures and stock returns. We also confirm the results by controlling for many
frequently used factors determining stock returns which are well documented in the literature. However, we do
not find evidence in supporting the relation between risk associated with fluctuation in liquidity and stock
returns.

Keywords: liquidity, stock return, Vietnam
GEL Classification Code: G11, G12, G14

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Liquidity, liquidity risk and Stock Returns – Evidence from Vietnam

1. Introduction
One of the very important primary functions of capital markets is the efficient pricing of asset. Sharpe (1964),
Lintner (1969) and Black (1972) develop the asset-pricing model and shape the way academics and practitioners
think about risk and average returns. However, many researchers find evidences that those returns on stocks
display only little relation to those of CAPM's projection. For example, Banz (1981) demonstrates that firms’
market equities could be another explanatory factor to explain returns in addition to the market’s return. When

examining the size-return relationship, the writer conjectures that most of the investors are not interested in
small firms’ stock due to insufficient information; therefore, it brings higher rewards to shareholders who
invested in. Another prominent critics of CAPM is Chan et al. (1991), in which they find evidence in Japan
stock market in support to the role of book-to-market equity in explaining the cross-section average returns.
Continuing this stream of theory, after investigating the roles of other factors in explaining average stock return,
Fama & French (1992) claim that besides the market’s return, size and book-to-market play strong roles and
diminish the impact of two other factors, leverage and E/P in explaining average return. Furthermore, Fama &
French (1993) propose another asset pricing model which augments the CAPM model with excess returns of
small caps over big caps and of value stocks over growth stocks.
Although being supported by more empirical evidence (Fama & French 1995, 1998), the economic
fundamentals of the additional factors to CAPM model are not reasonably clear. It is for that reason, several
studies do not reach the same agreement (Kim 1995; Kothari et al. 1995; Y. Peter Chung et al. 2006). While
Fama & French (1993) document that factor loadings explain stock return, Daniel & Titman (1997) advocate
that stock characteristics such as size and book-to-market, not factor loadings, explain stock returns when they
provide some significant in-sample evidence that stock returns do not co-move with the differences of returns
between neither small size and large size portfolios nor high and low book-to-market portfolios.
It is quite reasonable to expect that liquidity is an important variable for asset pricing. As defined by Amihud
and Mendelson (2008), liquidity is the capacity of the assets that can be traded quickly and at low cost. Amihud
& Mendelson (1986) is one of the very first papers that pioneer research on the relation between the liquidity
and stock return. Using the bid-ask spread as the proxy for measuring liquidity, they point out that investors
requires additional liquidity premium for holding illiquid stocks. Motivated by this observation, many
subsequent studies continue to focus on addressing return-liquidity linkage. Following the approach of Amihud
and Mendelson(1986) with updated data, Eleswarapu & Reinganum (1993) address the question of this relation
when they find that the liquidity premium is a seasonal effect as it is reliably positive during the month of
January. Nevertheless, Brennan & Subrahmanyam (1996) find strong evidence consistent with the notion of a
premium for illiquidity as their findings are stable over time and robust to controlling for Fama and French
(1993)’s risk factors.
In keeping with the literature, however, Petersen & Fialkowski (1994) and Brennan & Subrahmanyam (1996)
criticize that the quoted spread is a poor proxy for liquidity as smaller equity trades are often executed inside
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the quoted prices, while larger trades often face prices far inferior to those quoted. As such, many alternative
proxies for liquidity have been used for further exploring the association between asset returns and liquidity,
such as trading volume (Brennan et al. 1998), turnover ratio (Chan & Faff 2005; Datar et al. 1998), zero return
(Lesmond et al. 1999), price impact of trading (Breen et al. 2002), Amihud’s illiquidity ratio (Amihud 2002),
the Pastor & Stambaugh (2003) liquidity measure, and the Liu (2006) liquidity ratio. Most of these papers
support Amihud and Mendelson(1986)’s finding.
In recent study, many researchers have extended this notion by regarding liquidity as an aggregate risk factor
instead of as a characteristic of stock in earlier papers. For instance, Datar et al. (1998), using his proposing
liquidity proxy – turnover ratio in investigating the return-liquidity relation, posit that liquidity establishes its
roles in explaining asset returns, stronger than the size risk factor and conjecture that the size effect might
probably be a reflection of liquidity impact. This might partly be due to the fact that institutional investors has
boosted the demand for large and liquid stocks and thus diminished the relative performance of small stocks
(Gompers & Metrick 2001). Base on the preceding argument, Jacoby et al. (2000) re-construct the CAPM model
by re-measuring excess returns over one-period of both stock and market by taking liquidity costs, which are
effective spread, into account and prove that the measure of systematic risk must relate the changes in the
security’s spread.
Pastor & Stambaugh (2003) assess whether market-wide liquidity is priced and conclude that returns of stocks
with higher sensitivity to market liquidity exceeds those with lower sensitivity. Acharya & Pedersen (2005)
present the liquidity-adjusted CAPM model by adding three more risk factors related to liquidity risk,
commonality in liquidity of stock and market, return sensitivity to market liquidity and liquidity sensitivity to
market return. The authors report that their model explains excess returns better than the standard CAPM model
does. Liu (2006), introducing both a new proxy for liquidity and the liquidity-augmented CAPM model, states
that the model explains well the cross-sectional stock returns, particularly in case of the present of well-studied
market anomalies which both CAPM and Fama-French three-factor models fail to explain.
Questioning on whether the existing asset pricing models with well-documented factors such as market
premium, size, book-to-market, coskewness, and Pastor-Stambaugh’s factors capture the characteristic liquidity
effect, Nguyen et al. (2007) conduct both time-series and cross-section tests on the three-moment CAPM and

four-factor model based on Fama–French and Pastor–Stambaugh factors as well as the mix of these two models.
Their empirical work shows that all of these factors do not subsume the characteristic liquidity premium. In
other words, characteristic liquidity should play its significant role in explaining stock returns together with
other well-known risk factors. This view is shared by Keene & Peterson (2007), however, they add that there is
a need for finding more risk factors for capital asset pricing models due to nonzero intercepts.
In view of liquidity variability, recent studies have attempted to explore its linkage with asset returns. For
example, Chordia et al. (2001) report a negative association between liquidity volatility and cross-sectional
equity returns, in contrast of the intuition of risk-return relationship. These findings stand up in the face of
various controls for the size, book-to-market ratio, momentum, price level and dividend yield effects. However,
Pastor & Stambaugh (2003) and Acharya & Pedersen (2005) refute those of Chordia et al. (2001) while their
findings indicate that investors require higher expected future asset returns to compensate for the
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contemporaneous liquidity’s shocks. The latter is suppored by Chien & Lustig (2010) as they claim that liquidity
risks caused by different business cycles should be rewarded by higher expected return on stocks.
As mentioned by Lo and MacKinlay (1990), besides the US market, other markets need to be examined in
order to avoid the data-snooping problem. The majority of studies of liquidity and liquidity risk associated with
asset returns are in US market which is generally recognized as the most liquid market in the world with a small
impact of liquidity than those of other markets, especially the emerging markets. In recent years, some
investigations have been conducted in other equity markets to reinforce return-liquidity relation.
Jun et al. (2003) report the positive relation between market-wide liquidity and stock returns using data for
emerging equity markets. However, subsequent studies on the liquidity premium in each market differ from this
result. A well-known example of this is Loderer & Roth (2005) since their findings are in favor of the liquidity
premium theory in the Swiss Exchange. Chang et al. (2010) provides more significant evidence regarding to
the notion using data in Japanese stock market. Lam & Tam (2011) contend further that the liquidity augmented
Fama-French model explains well stock returns in the Hong Kong stock market. In a nutshell, these studies
support the notion that illiquidity yield higher stock returns.
The conflicting conclusion and division in the literature, particularly the effect of liquidity volatility in security’s
expected return, indicate the need for further work. This paper firstly contributes to the literature firstly in that

way. Secondly, most of the empirical research concerning the liquidity–stock return relationship has been
focused on US markets (Chang et al. 2010). There is not much work done employing emerging market data as
the literature investigating this nexus in emerging markets is still very light and this paper considering the case
in Vietnam is another contribution.
Our results differ from the large body of the existing literature. First, high level of liquidity, especially those
related to trading activities, indicates high returns on stock. This effect persists after controlling for the well
known market-wide determinants such as Fama-French three-factors, momentum factor and liquidity factor as
well as well-studied characteristics of stocks. The line of reasoning would lead us to believe that newly listed
big firms would possibly lead to abnormal trading volume and thus, cause a rise on these stock prices which are
supported by the investment preferences towards big firms’ stock (Merton 1987; Miller 1977). Second, when
seeking to establish a link between liquidity risk and asset returns, we do not find strong evidence in support
the notion that the higher return on stocks should be compensated for suffering high liquidity risk.
The remainder of the paper proceeds as follows. Section 2 describes our data, key variables and experimental
design. Section 3 examines the results of the investigation. The final section concludes and formulates practical
implications.

2. Data and variable description
Data are collected from different sources. Market data are provided by Ho Chi Minh City stock exchange.
Financial accounting data are collected and tabulated from financial reports of listed company. Our data sample
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includes of monthly returns and other firm attributes of non-financial firms listed on Ho Chi Minh stock
exchange. Our data range from 2009 to 2012.
We employ a number of liquidity and illiquidity measures for a thorough investigation. Our choice of control
variables was based on the existing literature (see, for example, (Chan et al. 1991; Chang et al. 2010; Chordia
et al. 2001)). For each stock, the following variables were calculated each month. Liquidity measures and
control variables are defined as follows:
RET

TURN

DVOL
ILLIQ
ZERO1

monthly excess returns risk adjusted using 5 factors, Fama-French three factors, a
momentum, and a liquidity factors, liquidity proxy used is the turnover ratio by Datar et al.
(1998).
natural logarithm of the average of the share turnover of the prior 3 months where the share
turnover is calculated as the number of shares traded divided by the number of share
outstanding. (Chang et al. 2010).
natural logarithm of the average of the VND trading volume over the prior 3 months
(Brennan et al. 1998).
Amidhud’s illiquidity measure based on the previous 3 months data.

V_ILLIQ

proportion of trading days in the past 3 months in which the return is zero (Lesmond et al.
1999).
proportion of trading days in the past 3 months in which the trading volume is positive and
the return is zero (Lesmond et al. 1999).
natural logarithm of the coefficient of variation of share turnover (Chang et al. 2010; Chordia
et al. 2001).
natural logarithm of the coefficient of variation of VND trading volume (Chang et al. 2010;
Chordia et al. 2001).
natural logarithm of the coefficient of variation of the illiquidity measure of Amihud (2002).

SIZE

natural logarithm of the market capitalization at the end of the second to the last month.

BM

natural logarithm of the most recent book value of common shares divided by the market
value in the second to the last month.
natural logarithm of the reciprocal of the closing price in the second to the last month (Chang
et al. 2010; Chordia et al. 2001).
standard deviation of regression residuals of the factor model based on data month t−24 to
month t−1.
amount of cash dividends for the last fiscal year over the closing price in the second to the
last month (Chang et al. 2010; Chordia et al. 2001).
net income before extraordinary items for the last fiscal year over the market capitalization
at the end of the second to the last month (Chang et al. 2010; Chordia et al. 2001).
sum of earnings and depreciation for the last fiscal year over the market capitalization at the
end of the second to the last month (Chang et al. 2010; Chordia et al. 2001).

ZERO2
V_TURN
V_DVOL

PRICE
IDIORISK
DYLD
ELYD
CFYLD

5

RET2_3
RET4_6
RET7_12

cumulative returns over the second through the third prior to the current month (Chang et al.
2010; Chordia et al. 2001; Jegadeesh & Titman 1993).
cumulative returns over the fourth through sixth prior to the current month (Chang et al.
2010; Chordia et al. 2001; Jegadeesh & Titman 1993).
cumulative returns over seventh through 12th months prior to the current month (Chang et
al. 2010; Chordia et al. 2001; Jegadeesh & Titman 1993).

Table 1 shows the summary statistics of data employed in this study.
[INSERT TABLE 1 ABOUT HERE]
Following Brennan et al. (1998) and (Chordia et al. 2001), RET is calculated as followings:
𝑅̃𝑗𝑡 = 𝐸(𝑅̃𝑗𝑡 ) + ∑𝐿𝑘=1 𝛽𝑗𝑘 𝑓̃𝑘𝑡 + 𝑒̃𝑗𝑡
where R̃ jt is the return on security j at time t, βjk is the factor loading of the security's return on factor k, f ̃kt is
the return on factor k at time t, and ẽjt is the error term. We begin by estimating each year the factor loadings,
βjk, for all securities that had at least 24 return observations over the prior 60 months. The factors we use to
measure the excess return are Fama & French (1993) factors, a momentum factors and a liquidity factor. The
momentum and liquidity factors are calculated as the same way of the Fama-French HML factor, which means
that these factors are the differences each month between the average of the returns on the high
momentum/liquidity portfolios and the average of the returns on the low momentum/liquidity portfolios. The
momentum is calculated as the raw return annually, while liquidity is measured as the turnover of every firm
through a period of one year. Meanwhile, the portfolio foundation procedure is repeated at the end of fiscal
years. Then, estimated risk-adjusted return on each of stock for each month of the following year is measured
as follow:
∗
𝑅̃𝑗𝑡
= 𝑅̃𝑗𝑡 − 𝑅𝐹𝑡 − ∑𝐿𝑘=1 𝛽̂𝑗𝑘 𝐹̃𝑘𝑡

∗
where 𝑅̃𝑗𝑡
is the risk-adjusted return, 𝑅𝐹𝑡 is the risk-free rate, and 𝐹̃𝑘𝑡 is the sum of the factor realization and its

associated risk premium. The next step is to test whether liquidity carry premium after controlling for other firm
characteristics:

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∗
𝑅̃𝑗𝑡
= 𝑐0 + 𝛾𝐿𝑗𝑡 + ∑𝑀
𝑘=1 𝑐𝑚 𝑍𝑚𝑗𝑡 + 𝜀̃𝑗𝑡

where 𝐿𝑗𝑡 is the liquidity of stock j at time t, 𝑍𝑚𝑗𝑡 is the characteristic m of stock j at time t, and 𝜀̃𝑗𝑡 is the error
term. Finally, we also test the liquidity roles in explaining security’s return after controlling for liquidity and
other firm characteristics:
∗
𝑅̃𝑗𝑡
= 𝑐0 + 𝛾𝐿𝑗𝑡 + 𝛿𝑉𝑗𝑡 + ∑𝑀
𝑘=1 𝑐𝑚 𝑍𝑚𝑗𝑡 + 𝜀̃𝑗𝑡

where 𝑉𝑗𝑡 is the liquidity variability of stock j at time t.

3. Results and Discussion of Results
Table 2 reports the coefficients of correlation amongst variables employed in the study. There are some
particular notes here which are worthy to spell out regarding the correlation amongst liquidity variables. Firstly,
the relationship between the two liquidity variables TURN and DVOL is quite high (0.7584) and this reflects
the ability of substitution between these two liquidity variables. Secondly, the correlation between liquidity and

illiquidity measures are negative as TURN and DVOL are negatively correlated with ILLIQ, ZERO1 and
ZERO2. Thirdly, the correlation between Zero1 and Zero2 is quite high (0.7324). The relationship between
Zero measures and liquidity measures are negative.
Moreover, the correlation coefficients amongst adjusted stock return and liquidity/illiquidity measures provide
some preliminary indications regarding the relationship between stock returns and liquidity. Stock returns are
positively correlated with the two liquidity measures, TURN (0.0896) and DVOL (0.0857) and negatively
correlated with the illiquidity measures, ILLIQ (-0.0315), ZERO1 (-0.0748) and ZERO2(-0.0719).
[INSERT TABLE 2 ABOUT HERE]
Table 2 panel B also shows the correlation coefficients of stock returns and other market and firm characteristics
factors. Stock returns are negatively correlated with BM, PRICE, while positively correlated with SIZE,
IDIORISK, DYLD. This observation indicates that the firm with higher return tends to be large, valued, highly
priced, risky and paying more dividends. As also can be seen in table 2 panel B, DVOL has a positive association
with SIZE and negative correlation BM and PRICE, implying that high liquidity level happens in large, valued
and highly priced firms. The statistics for ZERO1 and ZERO2 show the similar result with that of DVOL.
Table 3 shows the results of regressions of risk adjusted returns on each of the five alternative measures of
liquidity level (TURN, DVOL, ILLIQ, ZERO1, ZERO2) controlling for a wide range of firm specific attributes
(SIZE, BM, PRICE, IDIORISK, DYLD, EYLD, CFYLD, RET2_3, RET4_6, RET7-12). In contrast to the
majority of the literature, our results show that all of the coefficients of liquidity measures are significantly
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affect cross-sectional returns of stocks. The coefficients of TURN and DVOL are positive while the other three
coefficients are negative. In other words, the evidence we present is perfectly inconsistent with the hypothesis
that there is a premium for illiquidity.
[INSERT TABLE 3 ABOUT HERE]

In order to confirm the robustness of the results, we re-run the regressions in each year in our sample to see
whether our results are similar over the year in our analysis. We find that in 2009 and 2010, the return-liquidity
relations are significant only for liquidity measures (TURN, DVOL), while three illiquidity measures (ILLIQ,
ZERO1, ZERO2) do not show significant linkage with stock returns. For the year of 2011, the relations between

stock return and all of five liquidity/illiquidity proxies are not significant. The contrasting view is demonstrated
in 2012 figure which shows that all of the liquidity measures have significant coefficient estimates in explaining
returns on stocks.
In terms of liquidity variability effect on stock returns, as illustrated in table 8, we do not find it significant as
the evidence from a large body of the literature.

4. Conclusion
The papers investigate the relationship between liquidity and stock returns in Vietnam. Most of the papers in
the literature report a negative relationship between these two variables. However, we prove the reverse
relationship in Vietnamese stock markets. As such, our findings cast some doubt in the positive return-illiquidity
relation in emerging stock markets since the investors possibly recognize that the illiquid firms are not probably
the good performers.
The first explanation for this might be the characteristics of Vietnam’s market where participants are small
investors are trading more frequently. The dominance of these investors means that their preferences towards
blue-chip stocks lead to boost the demand for large and liquid stocks, and thus, bring the higher returns on these
stocks. And further potential reason attributed to individual investors is that portfolio’s decisions of these
investors are driven by the high level of trading activities. In other words, our findings is supportive in the view
stated by Gervais et al. (2001) that high trading volume over a period of time could make the stock visible to
investors, help stimulate the demand of the shares and push up stock price. Second, the rationale behind this
finding can be supported on the basis that Vietnamese stock market is the really new market that was introduced
in 2000. Therefore, one important characteristic of the market is that there are newly stocks listed every year.
In this situation, one of the realities is that the big firms come to the market and make it visible and tradable to
investors, creating races in purchasing and causing a rise on these stocks (Merton 1987; Miller 1977). Another
problem in Vietnamese stock market could be the information inefficiency. As a consequence, institutional
investors do not probably find reliable data for their analysis and investment decision making.

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Although the main objective of this paper is not to come up with the roles of trade shocks on asset returns, the

findings have given us the prediction of this nexus. And this finding is consistent with intuition that in emerging
markets, which appear vulnerable to stock price manipulation, most of individual investors speculate and follow
the trading strategies of institutional and foreign investors.
In view of the impact of liquidity risk on equity returns, we do not find this risk-reward relationship in
Vietnamese stock market.

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Table 1: Summary statistics
Obs.

Mean

Median

RET

7653

-0.007

TURN

7653

-2.376

DVOL

7653

20.146

ILLIQ

7653

0.000

ZERO1

7653

ZERO2

7653

V_TURN

-0.015

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

Jarque-Bera

Prob.

0.728

-0.578

0.100

0.940

7.448

7436.401

0.000

-2.300

2.262

-10.080

1.587

-0.431

3.306

267.3752

0.000

20.240

25.184

11.660

2.003

-0.251

2.684

112.2823

0.000

0.000

0.002

0.000

0.000

4.125

25.725

186374.5

0.000

0.216

0.197

0.879

0.000

0.121

1.059

4.767

2427.449

0.000

0.185

0.172

0.708

0.000

0.096

0.803

3.896

1079.015

0.000

7653

-1.018

-0.918

0.332

-5.514

0.650

-1.022

5.150

2806.659

0.000

V_DVOL

7653

-0.935

-0.839

0.328

-5.713

0.626

-1.127

5.587

3753.29

0.000

V_ILLIQ

7653

-1.510

-1.431

0.344

-6.674

0.921

-0.618

3.733

659.0081

0.000

SIZE

7653

26.697

26.494

31.871

23.518

1.370

0.802

3.626

945.2665

0.000

BM

7653

0.091

0.128

2.225

-2.539

0.691

-0.321

3.094

133.8942

0.000

PRICE

7653

-9.479

-9.457

-7.550

-11.367

0.684

-0.077

2.476

95.09211

0.000

IDIORISK

7653

0.093

0.087

0.330

0.028

0.030

1.984

12.194

31975.6

0.000

DYLD

7653

0.099

0.076

1.154

0.000

0.100

2.077

12.326

33237.46

0.000

EYLD

7653

0.155

0.143

5.659

-1.874

0.227

5.554

126.653

4914936

0.000

CFYLD

7653

0.218

0.184

5.356

-0.941

0.227

5.446

96.937

2851611

0.000

RET2_3

7653

-0.005

-0.037

2.225

-0.738

0.228

1.815

10.720

23206.55

0.000

RET4_6

7653

0.006

-0.049

3.242

-0.755

0.313

2.321

14.628

49990.15

0.000

7653
-0.016
-0.125
6.325
-0.849
0.510
2.998
18.921
92288.63
0.000
RET7_12
Note: RET = monthly excess returns risk adjusted using 5 factors (Fama-French three factors, a momentum, and a liquidity factors); TURN = natural logarithm of the average
of the share turnover of the prior 3 months; DVOL = natural logarithm of the average of the VND trading volume over the prior 3 months; ILLIQ = Amidhud’s illiquidity
measure based on the previous 3 months data; ZERO1 = proportion of trading days in the past 3 months in which the return is zero; ZERO2 = proportion of trading days in
the past 3 months in which the trading volume is positive and the return is zero; V_TURN = natural logarithm of the coefficients of variation of share turnover based on the
past 3-month data; V_DVOL = natural logarithm of the coefficients of variation of VND trading volume based on the past 3-month data; V_ILLIQ = natural logarithm of the
coefficients of variation of Amihud’s illiquidity measure based on the past 3-month data; SIZE = natural logarithm of the market capitalization at the end of the second to the
last month; BM = natural logarithm of the most recent book value of common shares divided by the market value in the second to the last month; PRICE = natural logarithm
of the reciprocal of the closing price in the second to the last month; IDIORISK = standard deviation of regression residuals of the factor model based on data month t−24 to
month t−1; DYLD = amount of cash dividends for the last fiscal year over the closing price in the second to the last month; EYLD = net income before extraordinary items
for the last fiscal year over the market capitalization at the end of the second to the last month; CFYLD = sum of earnings and depreciation for the last fiscal year over the
market capitalization at the end of the second to the last month; RET2_3, RET4_6, and RET7_12 = cumulative returns over the second through the third, fourth through sixth,
and seventh through 12th months prior to the current month, respectively;

Table 2: Correlation matrix
Panel A: Part I
RET

TURN

DVOL

ILLIQ

ZERO1

TURN

0.0896

DVOL

0.0857

0.7584

ILLIQ

-0.0315

-0.5787

-0.5921

ZERO1

-0.0748

-0.6135

-0.6239

0.3838

ZERO2

-0.0719

-0.2962

-0.3201

-0.0932

0.7324

V_TURN

-0.0294

-0.1516

-0.2205

0.2110

0.1526

10

ZERO2

-0.0017

V_TURN

V_DVOL


V_DVOL

-0.0068

-0.0660

-0.1583

0.1786

0.0863

-0.0554

0.9390

V_ILLIQ

-0.0111

-0.0631

-0.1666

-0.0172

0.0530

0.0617

0.0838

0.0926

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

Panel B: Part II

0.0186

-0.0271

-0.0210

0.0899

0.0425

0.0534

0.0327

0.0836

0.0843

-0.0216

TURN

-0.0578

0.0002

0.0494

0.1418

-0.0078

-0.0303

-0.0508

0.2319

0.1499

0.1227

DVOL

0.5921

-0.3966

-0.3584

0.0683

-0.1647

-0.0659

-0.1722

0.2411

0.1847

0.2139

ILLIQ

-0.1972

0.0797

0.0114

0.0586

-0.0035

0.0250

0.0294

-0.0622

-0.0292

-0.0564

ZERO1

-0.1970

0.2190

0.1539

-0.1979

0.0645

0.0408

0.1248

-0.1954

-0.1320

-0.0799

ZERO2

-0.1193

0.2374

0.2350

-0.2678

0.0955

0.0197

0.1159

-0.2218

-0.1579

-0.0728

V_TURN

-0.1622

0.0907

0.0533

0.0354

0.0439

0.0120

0.0408

-0.0705

-0.0749

-0.0081

V_DVOL

-0.1739

0.0967

0.0844

0.0714

0.0514

0.0015

0.0345

-0.0235

-0.0535

-0.0210

V_ILLIQ

-0.1749

0.0517

0.0390

0.0050

0.0344

-0.0172

-0.0026

0.0567

0.0479

-0.0396

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET

Panel C: Part III

BM

-0.6676

PRICE

-0.6006

0.7092

IDIORISK

-0.0444

-0.1547

-0.0318

DYLD

-0.2441

0.2161

0.2559

-0.0234

EYLD

-0.1086

0.1326

-0.1581

-0.0833

0.2164

CFYLD

-0.2527

0.3591

0.1085

-0.1061

0.2592

0.8111

RET2_3

0.1060

-0.2436

-0.2068

0.0842

-0.0575

-0.0312

-0.0778

RET4_6

0.1215

-0.2685

-0.2364

0.1194

-0.0978

-0.0304

-0.0854

0.0591

0.1760
-0.3241
-0.3202
0.0760
-0.1652
0.0234
-0.0600
-0.0130
-0.0019
RET7_12
Note: RET = monthly excess returns risk adjusted using 5 factors (Fama-French three factors, a momentum, and a liquidity factors); TURN = natural logarithm of the average
of the share turnover of the prior 3 months; DVOL = natural logarithm of the average of the VND trading volume over the prior 3 months; ILLIQ = Amidhud’s illiquidity
measure based on the previous 3 months data; ZERO1 = proportion of trading days in the past 3 months in which the return is zero; ZERO2 = proportion of trading days in

the past 3 months in which the trading volume is positive and the return is zero; V_TURN = natural logarithm of the coefficients of variation of share turnover based on the
past 3-month data; V_DVOL = natural logarithm of the coefficients of variation of VND trading volume based on the past 3-month data; V_ILLIQ = natural logarithm of the
coefficients of variation of Amihud’s illiquidity measure based on the past 3-month data; SIZE = natural logarithm of the market capitalization at the end of the second to the
last month; BM = natural logarithm of the most recent book value of common shares divided by the market value in the second to the last month; PRICE = natural logarithm
of the reciprocal of the closing price in the second to the last month; IDIORISK = standard deviation of regression residuals of the factor model based on data month t−24 to
month t−1; DYLD = amount of cash dividends for the last fiscal year over the closing price in the second to the last month; EYLD = net income before extraordinary items
for the last fiscal year over the market capitalization at the end of the second to the last month; CFYLD = sum of earnings and depreciation for the last fiscal year over the
market capitalization at the end of the second to the last month; RET2_3, RET4_6, and RET7_12 = cumulative returns over the second through the third, fourth through sixth,
and seventh through 12th months prior to the current month, respectively;

11


Table 3: Stock returns and the liquidity level of stocks from 2009 to 2012

TURN

DVOL

ILLIQ

ZERO1

ZERO2

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.004***

0.004***

0.003

0.004

0.273***

0.043***

0.034***

-0.006

0.028***

0.023***

-0.005**

(5.01)

(3.50)

(0.90)

(1.30)

(6.88)

(3.47)

(3.58)

(-0.66)

(5.31)

(5.85)

(-2.06)

0.004***

0.000

0.002

0.004

0.274***

0.043***

0.034***

-0.007

0.028***

0.023***

-0.005**

(5.24)

(0.32)

(0.59)

(1.53)

(6.90)

(3.49)

(3.56)

(-0.69)

(5.33)

(5.88)

(-2.13)

-14.338**

0.004***

0.004

0.004

0.299***

0.041***

0.035***

-0.009

0.035***

0.026***

-0.003

(-2.05)

(2.99)

(1.22)

(1.58)

(7.54)

(3.35)

(3.74)

(-0.92)

(6.70)

(6.73)

(-1.31)

-0.034***

0.004***

0.004

0.005*

0.270***

0.043***

0.034***

-0.007

0.033***

0.025***

-0.003

(-3.47)

(3.09)

(1.30)

(1.69)

(6.73)

(3.50)

(3.62)

(-0.77)

(6.25)

(6.56)

(-1.28)

0.004***

0.004

0.006**

0.268***

0.044***

0.036***

-0.009

0.033***

0.025***

-0.003

-0.034***

(-2.70)
(3.66)
(1.32)
(2.12)
(6.58)
(3.60)
(3.80)
(-0.92)
(6.37)
(6.67)
(-1.09)
The sample period is from 2009 to 2012. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. DVOL is the natural logarithm of the average of the VND trading volume over the prior 3 months. ILLIQ is Amihud's illiquidity measure based on the previous 3-month data. ZERO1 is the
proportion of trading days in the past 3 months in which the return is zero. ZERO2 is the proportion of trading days in the past 3 months in which the trading volume is positive and the return is zero. SIZE is
the natural logarithm of the market capitalization at the end of the second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the
second to the last month. PRICE is the natural logarithm of the reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model
based on data from month t−24 to month t−1, following the approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last
month. EYLD is the net income before extraordinary items for the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for
the last fiscal year over the market capitalization at the end of the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third,
fourth through sixth, and seventh through 12th months prior to the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

12


Table 4: Stock returns and the liquidity level of stocks in 2009

TURN

DVOL

ILLIQ

ZERO1

ZERO2

0.007**
(2.15)
0.006**
(2.01)

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.004

-0.007

-0.001

0.380***

0.056*

0.095**

-0.072*

-0.007

0.018**

-0.028***

(0.98)

(-0.84)

(-0.07)

(3.04)

(1.74)

(2.32)

(-1.78)

(-0.56)

(1.99)

(-2.63)

-0.002

-0.009

0.002

0.381***

0.058*

0.096**

-0.077*

-0.005

0.019**

-0.027**

(-0.49)

(-1.04)

(0.21)

(3.05)

(1.81)

(2.35)

(-1.91)

(-0.44)

(2.12)

(-2.57)

-78.941

0.002

-0.004

-0.001

0.380***

0.060*

0.096**

-0.074*

0.003

0.025***

-0.023**

(-1.02)

(0.63)

(-0.55)

(-0.06)

(3.04)

(1.85)

(2.33)

(-1.83)

(0.30)

(2.99)

(-2.23)

0.004

0.003

-0.003

-0.001

0.383***

0.060*

0.092**

-0.071*

0.005

0.026***

-0.022**

(0.10)

(0.83)

(-0.44)

(-0.08)

(3.05)

(1.85)

(2.26)

(-1.75)

(0.44)

(3.11)

(-2.17)

0.003

-0.003

-0.001

0.383***

0.060*

0.092**

-0.071*

0.005

0.026***

-0.023**

0.002

(0.03)
(0.83)
(-0.44)
(-0.07)
(3.05)
(1.85)
(2.26)
(-1.75)
(0.42)
(3.11)
(-2.18)
The sample period is the year of 2009. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. DVOL is the natural logarithm of the average of the VND trading volume over the prior 3 months. ILLIQ is Amihud's illiquidity measure based on the previous 3-month data. ZERO1 is the
proportion of trading days in the past 3 months in which the return is zero. ZERO2 is the proportion of trading days in the past 3 months in which the trading volume is positive and the return is zero. SIZE is

the natural logarithm of the market capitalization at the end of the second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the
second to the last month. PRICE is the natural logarithm of the reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model
based on data from month t−24 to month t−1, following the approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last
month. EYLD is the net income before extraordinary items for the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for
the last fiscal year over the market capitalization at the end of the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third,
fourth through sixth, and seventh through 12th months prior to the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

13


Table 5: Stock returns and the liquidity level of stocks in 2010

TURN

DVOL

ILLIQ

ZERO1

ZERO2

0.005**
(2.30)
0.006***
(2.76)

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.004*

0.004

0.012*

0.230***

-0.019

0.038

-0.054

-0.022

-0.007

-0.004

(1.68)

(0.55)

(1.94)

(3.08)

(-0.34)

(0.89)

(-1.22)

(-1.47)

(-0.79)

(-1.18)

-0.001

0.002

0.013**

0.227***

-0.011

0.040

-0.060

-0.023

-0.007

-0.004

(-0.25)

(0.27)

(2.13)

(3.05)

(-0.20)

(0.94)

(-1.35)

(-1.56)

(-0.85)

(-1.35)

-17.721

0.004

0.006

0.014**

0.256***

-0.012

0.043

-0.062

-0.011

-0.002

-0.002

(-0.35)

(1.45)

(0.90)

(2.27)

(3.46)

(-0.22)

(1.01)

(-1.40)

(-0.79)

(-0.27)

(-0.64)

0.020

0.004

0.006

0.015**

0.267***

-0.013

0.044

-0.064

-0.010

-0.001

-0.002

(0.74)

(1.59)

(0.97)

(2.43)

(3.53)

(-0.23)

(1.02)

(-1.43)

(-0.69)

(-0.08)

(-0.48)

0.004

0.006

0.014**

0.261***

-0.012

0.043

-0.063

-0.010

-0.001

-0.002

0.011

(0.38)
(1.55)
(0.95)

(2.37)
(3.47)
(-0.23)
(1.01)
(-1.42)
(-0.73)
(-0.16)
(-0.55)
The sample period is the year of 2010. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. DVOL is the natural logarithm of the average of the VND trading volume over the prior 3 months. ILLIQ is Amihud's illiquidity measure based on the previous 3-month data. ZERO1 is the
proportion of trading days in the past 3 months in which the return is zero. ZERO2 is the proportion of trading days in the past 3 months in which the trading volume is positive and the return is zero. SIZE is
the natural logarithm of the market capitalization at the end of the second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the
second to the last month. PRICE is the natural logarithm of the reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model
based on data from month t−24 to month t−1, following the approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last
month. EYLD is the net income before extraordinary items for the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for
the last fiscal year over the market capitalization at the end of the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third,
fourth through sixth, and seventh through 12th months prior to the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

14


Table 6: Stock returns and the liquidity level of stocks in 2011

TURN

DVOL

ILLIQ

ZERO1

ZERO2

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.000

0.003

0.011

0.010*

0.260***

0.034

0.060***

-0.024

0.079***

0.075***

0.000

(-0.16)

(1.57)

(1.61)

(1.90)

(3.32)

(1.29)

(2.93)

(-1.40)

(5.16)

(5.33)

(-0.04)

0.000

0.004

0.011

0.010*

0.260***

0.034

0.060***

-0.024

0.079***

0.075***

0.000

(-0.19)

(1.29)

(1.61)

(1.90)

(3.33)

(1.29)

(2.94)

(-1.41)

(5.16)

(5.33)

(-0.04)

17.628

0.004*

0.011*

0.011**

0.261***

0.032

0.057***

-0.018

0.079***

0.074***

0.000

(1.46)

(1.89)

(1.71)

(2.11)

(3.36)

(1.20)

(2.80)

(-1.06)

(5.18)

(5.28)

(0.05)

0.011

0.004*

0.011*

0.010*

0.269***

0.034

0.061***

-0.025

0.078***

0.074***

0.000

(0.60)

(1.67)

(1.68)

(1.89)

(3.38)

(1.28)

(2.99)

(-1.44)

(5.04)

(5.18)

(-0.06)

0.003

0.010

0.010*

0.257***

0.034

0.059***

-0.024

0.079***

0.076***

-0.001

-0.001

(-0.07)
(1.57)
(1.63)
(1.89)
(3.24)
(1.30)
(2.93)
(-1.39)
(5.14)
(5.32)
(-0.06)
The sample period is the year of 2011. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. DVOL is the natural logarithm of the average of the VND trading volume over the prior 3 months. ILLIQ is Amihud's illiquidity measure based on the previous 3-month data. ZERO1 is the
proportion of trading days in the past 3 months in which the return is zero. ZERO2 is the proportion of trading days in the past 3 months in which the trading volume is positive and the return is zero. SIZE is
the natural logarithm of the market capitalization at the end of the second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the
second to the last month. PRICE is the natural logarithm of the reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model
based on data from month t−24 to month t−1, following the approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last

month. EYLD is the net income before extraordinary items for the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for
the last fiscal year over the market capitalization at the end of the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third,
fourth through sixth, and seventh through 12th months prior to the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

15


Table 7: Stock returns and the liquidity level of stocks in 2012

TURN

DVOL

ILLIQ

ZERO1

ZERO2

0.005***
(4.00)
0.005***
(4.39)

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.005***

0.010*

-0.005

0.309***

0.011

0.020*

0.012

0.033***

0.010

0.007

(2.66)

(1.74)

(-1.11)

(4.56)

(0.61)

(1.67)

(0.93)

(3.61)

(1.39)

(1.03)

0.000

0.010*

-0.006

0.308***

0.011

0.019

0.012

0.033***

0.010

0.007

(0.07)

(1.68)

(-1.19)

(4.55)

(0.58)

(1.59)

(0.96)

(3.56)

(1.41)

(1.06)

-21.802**

0.005**

0.014**

-0.006

0.348***

0.012

0.024**

0.007

0.041***

0.012*

0.007

(-2.31)

(2.30)

(2.50)

(-1.18)

(5.08)

(0.64)

(2.07)

(0.53)

(4.54)

(1.77)

(0.99)

-0.067***

0.004*

0.012**

-0.004

0.284***

0.010

0.018

0.013

0.035***

0.011

0.010

(-4.62)

(1.90)

(2.02)

(-0.81)

(4.17)

(0.53)

(1.56)

(1.02)

(3.91)

(1.60)

(1.44)

0.006***

0.014**

0.000

0.249***

0.017

0.024**

0.008

0.038***

0.013*

0.010

-0.073***

(-3.46)
(3.28)
(2.52)
(0.04)
(3.51)
(0.96)

(2.02)
(0.68)
(4.27)
(1.84)
(1.41)
The sample period is the year of 2012. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. DVOL is the natural logarithm of the average of the VND trading volume over the prior 3 months. ILLIQ is Amihud's illiquidity measure based on the previous 3-month data. ZERO1 is the
proportion of trading days in the past 3 months in which the return is zero. ZERO2 is the proportion of trading days in the past 3 months in which the trading volume is positive and the return is zero. SIZE is
the natural logarithm of the market capitalization at the end of the second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the
second to the last month. PRICE is the natural logarithm of the reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model
based on data from month t−24 to month t−1, following the approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last
month. EYLD is the net income before extraordinary items for the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for
the last fiscal year over the market capitalization at the end of the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third,
fourth through sixth, and seventh through 12th months prior to the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

16


Table 8: Stock returns and the liquidity volatility of stock from 2009 to 2012

TURN

V-TURN

0.004***

-0.002

(4.80)

(-0.89)

DVOL

V-DVOL

0.004***

0.000

(5.39)

(-0.01)

ILLIQ

-15.641**

V-ILLIQ

-0.002

SIZE

BM

PRICE

IDIORISK

DYLD

EYLD

CFYLD

RET2_3

RET4_6

RET7_12

0.004***

0.003

0.003

0.275***

0.043***

0.034***

-0.006

0.028***

0.022***

-0.005**

(3.33)

(0.90)

(1.26)

(6.92)

(3.49)

(3.55)

(-0.64)

(5.28)

(5.80)

(-2.04)

0.000

0.002

0.004

0.273***

0.043***

0.034***

-0.006

0.028***

0.023***

-0.005**

(0.22)

(0.55)

(1.55)

(6.86)

(3.48)

(3.57)

(-0.68)

(5.28)

(5.84)

(-2.16)

0.003***

0.003

0.004

0.297***

0.042***

0.036***

-0.010

0.035***

0.026***

-0.003

(-2.23)
(-1.34)
(2.66)
(1.18) (1.49)
(7.51)
(3.39)
(3.79)

(-1.03)
(6.78)
(6.77)
(-1.35)
The sample period is from 2009 to 2012. The dependent variable is the excess returns risk-adjusted using 5 factors (Fama–French three factors, a momentum, and a liquidity factor). The explanatory variables
are as follows. TURN is the natural logarithm of the average of the share turnover of the prior 3 months, where the share turnover is calculated as the number of shares traded divided by the number of shares
outstanding. V-TURN is natural logarithm of the coefficients of variation of share turnover based on the past 3-month data. DVOL is the natural logarithm of the average of the VND trading volume over the
prior 3 months. V-DVOL is natural logarithm of the coefficients of variation of VND trading volume based on the past 3-month data. ILLIQ is Amihud's illiquidity measure based on the previous 3-month
data. V-ILLIQ is natural logarithm of the coefficients of variation of Amihud’s illiquidity measure based on the past 3-month data. SIZE is the natural logarithm of the market capitalization at the end of the
second to the last month. BM is the natural logarithm of the most recent book value of common shares divided by the market value in the second to the last month. PRICE is the natural logarithm of the
reciprocal of the closing price in the second to the last month. IDIORISK is the standard deviation of regression residuals of the factor model based on data from month t−24 to month t−1, following the
approach of Brennan et al. (1998). DYLD is the amount of cash dividends for the last fiscal year over the closing price in the second to the last month. EYLD is the net income before extraordinary items for
the last fiscal year over the market capitalization at the end of the second to the last month. CFYLD is the sum of earnings and depreciation for the last fiscal year over the market capitalization at the end of
the second to the last month. RET2_3, RET4_6, and RET7_12 equal the logarithms of the cumulative returns over the second through the third, fourth through sixth, and seventh through 12 th months prior to
the current month, respectively. ***, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

17


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