the trading behavior on ex-dividend day: a study on french...
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DOI: 10.7763/IPEDR. 2014. V69. 8
The Trading Behavior on Ex-Dividend Day: A Study on French Stock
Market
Hung T. Nguyen1
, Hang V. D. Pham2, and Hung Nguyen
3
1, 3 College of Business, Massey University, New Zealand
2 Sobey School of Business, Saint Mary’s University, Canada
Abstract. This paper studies trading behavior around ex-dividend days on French stock market in the sample
period from January 1, 2012 to December 31, 2012. We find that on average, abnormal turnover significantly
decreases before the ex-dividend day; whereas, abnormal return follows a volatile trend around event dates. We
record a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of abnormal return
(from 0.89% to -0.01%) on the first day after the event date. In addition, we investigate the effect of tax
heterogeneity to trading volume and find strong evidences to reject the increase in trading volume when tax
heterogeneity among investors exits.
Keywords: Ex-Dividend Day, Abnormal Turnover, Trading Behavior.
1. Introduction
For years, trading behavior around ex-dividend days remains a controversial issue for both academics
and practitioners. Financial theory provides ambiguous forecasts about stock price and trading volume
around ex-dividend day. Several studies point out that, on ex-dividend days when stock is traded without
dividend, on average, stock price drops by less than the value of dividend due to the effect of income tax
(Michaely and Vila, 1996; Murray Frank and Ravi Jagannathan, 1988; Booth and Johnston, 1984; Rakesh
Bali and Gailen L. Hite, 1998). From different approaches, a number of researches document a mixed result
of trading volume around ex-dividend days: a decrease of trading volume before scheduled announcement
and an increase before unscheduled announcement (Joon Chae, 2005; Fabiano, 2008). Currently, several
studies examine trading behaviors around ex-dividend days by considering the effect of the tax heterogeneity
and transaction cost (Michaely and Vila, 1995; Michaely and Murgia, 1995). However, the puzzle is not
solved yet and investor demands more empirical researches to unmask the stock behaviors around ex-
dividend day.
In this paper, we study the trading behavior around ex-dividend day on French stock market by applying
the method suggested by Joon Chae (2005) with the main interest on abnormal turnover and abnormal return.
Our objective is to seek a persuasive answer for the question of how stock returns and trading volumes
performing around ex-dividend days. In addition, the relationship between information asymmetry and
trading behavior around ex-dividend date is of our most concern. Furthermore, we want to examine how tax
heterogeneity theory affects trading behavior around ex-dividend day on French stock market. As previous
studies employ either abnormal turnover or abnormal return to investigate the stock behaviors around ex-
days, our research will contribute to existing literatures by suggesting a new measure of trading behavior that
takes into account both abnormal turnover and abnormal return. We provide more insights on current
findings about the stock behavior in French stock market with an empirical approach. Finally, we address the
problems of previous studies when dealing with time-series data by conducting robustness tests.
Corresponding author. Tel.: + 64.223.893.900.
E-mail address: [email protected]. 45
With such objectives, we test a sample period from January 1, 2012 to December 31, 2012 in daily basic
with data extracted from Datastream. Following Joon Chae (2005), we conduct an event study over 949 stocks
existing in French stock market. In this study, two separate methods, a market model and a constant mean
model, are employed to calculate abnormal return and abnormal turnover. The estimation window (nest) is
200 trading days and the event window is 5 days before and after event date (nwindow=10). We find that
abnormal return changes by 21.25% from t=-1 to t=0 with highly significant t-stat of 8.3. However, a
remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of abnormal return (from
0.89% to -0.01%) from event day to day t=1 with t-stat of -0.88 and -0.049 respectively, is recorded. In
general, the study finds a significant decrease in the level of abnormal turnover in the period before the ex-
dividend day.
We address the robustness of these findings in several approaches. We first consider larger estimation
window (nest=250) and keep event window unchanged (nwindow=10). We then estimate the abnormal
return and abnormal turnover by using larger event window (nwindow=20) and keep estimation window
constant (nest=200). Finally, we consider median abnormal turnover and return for comprehensively
analyzing the trading behaviors around ex-dividend days. For further analyzing investor ‘s behavior and to
answer the question why trading volume decrease prior to the ex-dividend date, we run the regression of abnormal
trading volume and turnover on information asymmetry proxies and control variables to see whether information
asymmetry actually reduce the motivation of trading. The dependent variable is defined as the cumulative
abnormal trading volume over the period of t=-10 and t=-2. We find that size does positively correlate with
trading volume, but that alone is not sufficient to fully explain abnormal turnover. In addition, there is almost no
relationship between market-to-book ratio and trading volume as well as abnormal return. Further, we use dummy
variables to take into account the nature of industries and find that ex-dividend event has a relatively low impact
to raw material industry.
The rest of the paper is structured as follow. Section II discusses the literature review. Section III reports
the methodology and data description. Section IV discusses the results of empirical analysis by providing
summary statistic and plot of cumulative abnormal return and turnover. Section V examines robustness of
the findings in several approaches and reports the regression analysis. Section VI provides concluding
remarks and reports research limitations.
2. Literature Review
Trading behavior has been a subject of intensive studies from the very beginning of financial market.
Campell and Beranek (1995), by examining stocks listed on New York Stock Exchange (NYSE), conclude
that on average, the stock price drops about 90 percent of the amount of the dividend (Campell and Beranek,
1995). Michealy and Vila (1995), however, argue that “even in the market without transaction cost, the price
drop on ex-dividend day need not to be equal with dividend amount” after studying the same market.
Michealy and Vila (1995) explain the higher market trading volume around ex-day is a function of tax
heterogeneity among traders. Particularly, traders with different tax rates of dividend and capital gain will
have motivation to trade with each other around the ex-dividend day, thus stimulate trading volume
(Michealy and Vila (1995). This argument is also supported by a study of Koski and Scruggs (1998) that
documents some abnormal trading volume consistent with corporate dividend-capture trading.
In this paper, we apply the previous findings and suggest a new measure to investigate the stock behavior
around ex-dividend day in French stock market where tax difference between dividend (40%) and capital
gain (34%) is noticeable. If tax heterogeneity thesis from Michaely and Vila (1995) can explain French stock
market behavior among ex-dividend date, we do expect an increase in trading volume around event date
46
since there is a tax advantage between dividend income (40%) and capital gain income (34%), different
investors are motivated to trade with each other’s (Michealy and Vila, 1995). We examine the following
hypotheses:
Hypothesis 1: Around the ex-dividend day, trading volume should increase only if there is effect of tax
heterogeneity among investors.
On the other hand, Michealy and Murgia (1995) examine stocks in Milan stock market and conclude that
the ex-dividend day price declines and abnormal volume increase in relation to the event date cannot be
explained by the relative after-tax valuation of dividends and capital gains alone. As shown in Black (1986)
and Wang (1994), uninformed investors will trade less in the financial market if there is a high chance of
dealing with informed counterparty. It is reasonable to infer that trading volume will decrease prior to the
announcement date. A necessary condition for this prediction to hold is that uninformed investor must
recognize a high level of information asymmetry and attempts to trade by the informed trader. Therefore,
only before scheduled announcement, such as ex-dividend day, can uninformed investor realize their
weakness and avoid unnecessary trading. In response, total trading volume before ex-dividend day should
decrease. The decision of giving up trading depends heavily on the how much uninformed investor forecast
about information asymmetry. In other word, the trading volume before ex-dividend day should be inversely
correlated with the level of information asymmetry. We, therefore, come up with the decision on testing the
correlation between trading volume and commonly used proxies for information asymmetry, including
company size, market-to-book value and industry dummies.
Hypothesis 2: Trading volume before ex-dividend day is negatively correlated with level of ex ante
information asymmetry.
3. Methodology
We test a sample period from January 1, 2012 to December 31, 2012 with data extracted from Datastream,
including daily stock return, daily local market index, daily trading volume, daily number of outstanding share
and ex-dividend days. Following the method suggested by Joon Chae (2005), we conduct an event study over
949 stocks existing in French stock market. First of all, we used return index (RI) and market index (LI) to
calculate individual stock return index and market return. We then apply the market model to calculate
abnormal returns. When it comes to abnormal turnover, we apply the similar procedure with two adjustments.
First, for data inputs, we use trading volume (VO), number of share outstanding (NOSH) to calculate market
turnover. Following Joon Chae (2005), I chose to apply logarithms for turnover to reduce outliers close to
normal distribution. Second, to estimate abnormal turnover, we apply a constant mean model instead of market
model as it often yields similar results to more complicated model while the drop in variance of abnormal
turnover is negligible (Stephen J. Brown and Jerold B. Warner, 1985).
Since the abnormal returns and abnormal turnovers for investigated period are calculated, we compute
three indicators including average abnormal return (AAR), cumulative abnormal return (CAR) and
cumulative average abnormal return (CAAR) and test these indicators in investigated period. The
corresponding indicators regarding turnovers are AAT, CAT and CAAT. For each event date, we examine
the four following periods: (-10,-2), (-1,1), (-1,0) and (2,10). The following section will provide the results of
estimated parameters and further approaches to test trading behavior around event dates, including random
day abnormal return and turnover, robustness test and regression analysis.
4. Empirical Analysis
4.1. Summary statistic
47
Table I reports summary statistics of daily return and turnover from stock in French stock market for 1
year (250 trading days). The summary statistics are the averages of estimates for each firm, including mean,
standard deviation, skewness, and kurtosis. We calculate the daily turnover by dividing daily trading volume
over corresponding outstanding shares.
Table I: Summary Statistic
Period Mean SD Skewness Kurtosis No. of Firms
Daily Return (%)
20120101-
20121231 0.0037 0.0041 -0.4746 1.6854 949
Daily Turnover (%)
20120101-
20121231 0.0005 0.00046 1.4063 3.4350 949
Log Daily Turnover
20120101-
20121231 -8.3833 0.1942 -0.0736 -0.0959 949
We reports summary statistic on daily return, turnover and log turnover over 949 stocks in French market
within one year from Jan 1 2012 to Dec 31, 2012. The literature of trading activity measure on financial
market, as summarized by Lo and Wang (2000), is vast and extensive. Previous studies employ a number of
methods to measure trading behaviors, including the aggregate turnover (Campell, Grossman, and Wang
1993; LeBaron, 1992), an individual share volume (Andersen, 1996), the number of trading days per year
(James and Edmister, 1983) and total number of trade (Conrad, Hameed, and Niden, 1994). In this paper, we
use log turnover instead of raw turnover (trading volume divided by outstanding shares) to reduce the risk of
fat tail and extreme positive skewness. The log turnover also helps reduce the outliners and thus, the results
and findings are more reliable. When it comes to stock return, the market model is employed to calculate
stocks’ return:
The cross-sectional skewness and kurtosis of daily return (-0.4746 and 1.6854, respectively) is relatively
smaller than those derived from trading volume turnover (1.4063 and 3.4350, respectively). We apply the
logarithm function of turnover proposed by Ajinkya and Jain (1989) to reduce problem of fat tails and other
possible biases of non-normal distribution. As a results, the sknewness and kurtosis of volume turnover
decrease to -0.0736 and -0.0959 respectively, much closer to normal distribution. In this paper unless further
notice noted, any reference to trading volume, volume or turnover will refer to log turnover as defined in
this equation:
( ) (
)
(2)
where
( ∑
)
48
(1)
We apply the constant mean model suggested by Brown and Warner (1985) in equation (2) to calculate
abnormal turnover which is slightly different to the approach suggested by Joon Chae (2005). We run the
regression of logarithm turnover of estimation period to achieve the coefficients. Measuring trading volume
near announcement by this method would provide more accurate prediction on expected abnormal turnover
rather than taking the difference between log turnover during the test period and the estimation period.
4.2. Daily abnormal return and turnover around ex-dividend date Table II reports the daily abnormal return and turnover around ex-dividend date from existing stocks in
French financial market between Jan 1, 2012 and December 3, 2012. The abnormal turnover is reported as
the difference between log turnover and constant model coefficient from t=-200 to t=-11, where turnover is
trading volume divided by shares outstanding. The t-statistics are given to the right of their corresponding
figures. Average ( ) is the average abnormal return and turnover from .
Table II: Daily Abnormal Return and Turnover around Ex-Dividend Date
No. of Obs. Abnormal Return Abnormal Turnover
AAR t_AAR AAT t_AAT
-10 -0.0015 -1.4267
-0.1070 -2.9639
-9 0.0022 2.0988
-0.0599 -1.6597
-8 -0.0003 -0.3066
-0.0457 -1.2672
-7 -0.0016 -1.4521
-0.1235 -3.4214
-6 0.0013 1.1781
-0.0609 -1.6864
-5 -0.0007 -0.6229
-0.1777 -4.9238
-4 0.0006 0.5386
-0.1046 -2.8978
-3 -0.0004 -0.3905
-0.0637 -1.7649
-2 0.0014 1.3434
-0.0289 -0.8002
-1 0.0004 0.3521 0.0758 2.1011
0 0.0089 8.3117
0.0719 1.9919
1 -0.0001 -0.0493 -0.0320 -0.8868
2 -0.0006 -0.5404
-0.1238 -3.4304
3 -0.0023 -2.1665
-0.0899 -2.4893
4 0.0008 0.7801
0.0091 0.2521
5 -0.0007 -0.6579
-0.1411 -3.9078
6 0.0010 0.8989
-0.0967 -2.6800
7 -0.0018 -1.6398
-0.0962 -2.6641
8 0.0012 1.1083
-0.1026 -2.8409
9 0.0004 0.3975
-0.1715 -4.7506
10 -0.0020 -1.8861
-0.1524 -4.2225
Average (-10, -2) 0.0001 -0.0858
Average (2, 10) -0.0004
-0.1072
Average (-1,0) 0.0046
0.0739
Average (-1, 1) 0.0031 0.0386
To test the hypotheses, we use the variables described in the previous section to report cross-sectional
mean of abnormal return and turnover over different time-series around ex-dividend day, from (-10,-2) and
(2,10) to 1 day before and after ex-day performance to capture abnormalities in both return and trading
volume. One noticeable point is that abnormal return changes by 21.25% from t=-1 to t=0 with highly
significant t-stat of 8.3. However, we record a remarkable drop of abnormal turnover (from 7.1% to -3.2%)
and slight fall of abnormal return (from 0.89% to -0.01%) from event day to day t=1 with t-stat of -0.88 and -
0.049 respectively. On average, there is a significant decrease in the level of abnormal turnover prior to the
ex-dividend day. The negative 8.5% mean abnormal turnover in the period of t=-10 to t=-2 and negative 10.7%
in the period of t=2 to t=10 reject the hypothesis 1 of tax heterogeneity among investors. Following t=0, the
49
trading volume witnesses even a lager fall of average 10.7% from day 2 to day 10, and the stock return
decrease minor amount as Ill (-0.04%). This is probably due to the existence of short-term traders according
to the hypothesis proposed by Kalay (1982). Particularly, Kalay (1982) suggest that an investor would try to
buy stock before the ex-dividend date and sell it on the ex-day if the stock drops less than the dividend
payout. Karpoff and Walkling (1988) document similar findings in NYSE. Moreover, significant t-stat
around -2.9 means that the result is statistically significantly at 5%.
In addition to results in Table III, we provide four plotted graphs that summarize the cumulative abnormal
return (CAAR) and the cumulative abnormal turnover (CAAT) from day -10 to day 10 in Figure 1. These plots
show the cumulative return and turnover from t=-10 to +10 that excess over the benchmark. The benchmark
coefficient is determined from t=-200 to t=-11 days. For abnormal return, there is a significant difference prior to
and after the ex-dividend day. Between t=-10 and t=-1, the CAARs hover around 0% before quickly elevating
from 0.04% to 0.89% before the event date. Subsequently after the event date, the cumulative return follows a
downward trend before suffering from a loss of -0.002% at day 10. In contrast to CAAR, the cumulative abnormal
turnover, as expected, appears to be affected by the taxation heterogeneity and short-traders when it decreases for
5 consecutive days. It then recovers at the announcement day before dramatically decreasing from 7% to -15%.
To test for the precision of estimated abnormal return and turnover, we randomly choose event dates and
event stock and repeat the exact same process as for ex-dividend day. We record very near-zero abnormal
return and turnover over the benchmark. These flat lines confirm that the measurement is unbiased and
reliable.
Fig. 1: Cumulative abnormal return and turnover form t=-10 to t=10.
The benchmark abnormal return and coefficient turnover are derived from t=-200 to t=-11, where
turnover is daily volume divided by shares outstanding. These plots show cumulative abnormal return
(CAAR) and cumulative abnormal turnover (CAAT) from t=-10 to t=+10, that is the cumulative excess over
the benchmark. In the two last plots, we select random days as t=0 and the results illustrate that the measure
of CAAR and CAAT is unbiased.
5. Robustness
In this section, we address the robustness of these findings in several approaches. We first consider
larger estimation Window (nest=250) and keep event window unchanged (nwindow=10). We then estimate
the abnormal return and abnormal turnover by using larger event window (nwindow=20). Finally, we
consider median abnormal turnover and return for comprehensively analyzing the trading behaviors around
ex-dividend days. In addition, we conduct a regression analysis for further analyzing trading behaviors
around ex-dividend day.
-10 -5 0 5 10
0.0
0.2
0.4
0.6
0.8
1.0
EventWindow
CA
AR
-10 -5 0 5 10
-4-2
02
4
EventWindow
CA
AR
_ran
dom
-10 -5 0 5 10
-4-2
02
4
EventWindow
CA
AT_
rand
om
-10 -5 0 5 10
-1.5
-1.0
-0.5
EventWindow
CA
AT
50
The following table reports alternative measures of abnormal log turnover. Panel A1 employs larger
estimation window of 250 days, Panel A2 tests larger even window of 20 days and Panel B uses the
difference between raw and median turnover. The row of (-10,-2) and (-20,-2) report the summary measure
of average daily abnormal trading volume in 9 days and 19 days respectively.
Table III (Panel A.1): Using Larger Estimation Window (nest=250; nwindow=10)
Abnormal Return Abnormal Turnover
No. of Obs. AAR t_AAR AAT t_AAT
(-10,-2) 0.0015 0.4869 -0.6360 -5.8180
(-1,0) 0.0020 1.3147 0.0470 0.9119
(-1,1) 0.0094 5.1265 0.1204 1.9084
(2,10) -0.0035 -1.0946 -1.1522 -10.5403
Table III (Panel A.2): Using Larger Event Window (nest=200; nwindow=20)
Abnormal Return Abnormal Turnover
No. of Obs. AAR t_AAR AAT t_AAT
(-20,-2) 0.0050 1.0806 -2.6371 -16.7442
(-1,0) 0.0093 6.1277 0.1959 3.8344
(-1,1) 0.0093 4.9987 0.1457 2.3275
(2,20) -0.0036 -0.7751 -2.8188 -17.8980
Table IV: Panel B: Using Median Abnormal Turnover and Return
Abnormal Return Abnormal Turnover
No. of Obs. AAR t_AAR AAT t_AAT
(-10,-2) 0.0016 -1.3453 -0.3433 -13.6698
(-1,0) 0.0016 -2.8208 0.1287 -6.6396
(-1,1) 0.0095 5.6849 0.2287 -6.3793
(2,10) -0.0013 -1.9528 -0.9220 -13.4963
Firstly, we increase the estimation window is from nest=200 days to nest=250 days and report the results
in Panel A of Table IV. The expanded evidence is much convincing than the figure in Table III. Particularly,
the aggregate abnormal turnover decrease more than 63% over 9 days compares to 8.5% in Table III. The t-
stats are highly significant around the ex-dividend day (-5.8 and -10.4 respectively). On the other hand, the
effect of increased estimate window on abnormal return is slightly stronger than figures in Table III. The
AAR (2,10) is negative of -0.35% instead of -0.04%. In Panel A.2, we extend the testing period for 10 days
more, turning the window to (-20,+20). The trends recorded are quite similar to results which we have
reported so far. The average trading volume in the period of (-20,2) is 263% lower than the benchmark with
significant t-start of -16.7; whereas, the slope of abnormal return 1 day prior to the event day is less steeper.
In brief, in either large estimate window or large event window, the results remain consistent. That is, there
is always a downward trend of abnormal return after the ex-dividend day regarding the existence of dividend
payout. Due to the limitation of this research, we do not focus on the reason of this phenomenon. Whether
the return amount decline in French stock market is positively correlated with the corresponding dividend
yield (Campbell and Beranek, 1955) or it is not (Michael and Vila, 1995)?. That is an open question left for
further researches. 51
In the second part of robustness, we examine the median of stock return in estimation period instead of
average market return to calculate abnormal return, and median of constant mean model instead of
coefficient in equation (2). In panel B, we use median raw turnover between t=-200 and t=-11 as the
benchmark. Once again, we observe the decrease of aggregate abnormal turnover 9 days around the event
date. There are slight upward trend of abnormal return from t=-10 to t=1, and then a downward slope as
similar as previous results.
Table V: Regression Analysis
Panel A: Abnormal Return
Intercept Log Market
Cap
Market-to-
book value
Financial
service
Oil &
Mining Volatility R²
0.871** 0.013
0.011
(2.26) (0.17)
-0.724 0.022
81.423*** 0.062
(-1.57) (0.30) (5.99)
0.923***
-0.004
0.001
(4.74)
(-0.77)
-0.617*
-0.005
0.809*** 0.062
(-1.94)
(-0.99)
(6.05)
0.749***
3.476*** 1.364
0.025
(3.78)
(3.59) (0.96)
-0.7092**
3.124*** 1.262 77.550*** 0.080
(-2.247) (3.33) (0.92) (5.84)
*, **, *** indicate significance at 0.10, 0.05 and 0.01 level, respectively.
Panel B: Abnormal Turnover
Intercept
Log
Market
Cap
Market-
to-book
value
Financial
service
Oil &
Mining
Absolute Return [-10,-
2] R²
24.704 -0.011
0.002
(1.07) (0.25)
-30.354 -0.511
2026.8636*** 0.053
(-1.17) (-0.12) (4.23)
19.319
0.023
0.000
(1.616) (0.07)
-33.713**
-0.06182
2083.366*** 0.055
(-2.01)
(-0.18)
(4.38)
0.749***
3.476*** 1.364
0.025
(3.78) (3.59) (0.96)
-28.20
-0.723
-
2.749*** 2140.33*** 0.083
(-1.69) (1.28) (2.95) (4.57)
*, **, *** indicate significance at 0.10, 0.05 and 0.01 level, respectively.
Table V reports the results of regressing abnormal log turnover before the ex-dividend date on proxies of ex ante
information asymmetry. The coefficients are the time series averages of the coefficients from cross-sectional
regressions. The t-statistics are given below the corresponding coefficient in parentheses. The dependent variable is
defined as the difference between average log turnover from t=-10 to t=-2 and intercept coefficient from constant
model of log turnover from t= -200 to t= -11. means the average of the adjusted R-squares in each cross-sectional
regression.
Several previous studies conclude that greater information asymmetry leads to less trading ((Michaely
and Vila, 1995; Michaely and Murgia, 1995). For further analyzing investor behavior and to answer the
question why trading volume decrease prior to the ex-dividend date, we run the regression of abnormal
trading volume on information asymmetry proxies and control variable to see whether information
52
asymmetry actually reduce the motivation of trading. The dependent variables are defined as the cumulative
abnormal trading volume over the period of t=-10 and t=-2. We use Fama and Macbeth (1973) type
regression:
(3)
The notation is the average daily abnormal log turnover between t=-10 and t=-2 at quarter q for
company i; is a proxy for information asymmetry at quarter q for company i, including firm
size, market-to-book value and industry dummies; and is a control variable for risk factor. For robust
testing, we apply logarithm of market capitalization to bring down any outliners close to normally distributed.
The market-to-book ratio equals the ratio of market value of assets to book value of assets. Industry dummies
are financial service and oil & mining specification. Any firm that in financial service industry is defined as
1; otherwise they are defined as 0. Similarly, firms in oil & mining sector are defined as 1 and otherwise 0.
For abnormal return, we choose return volatility as a proxy for risk factor since previous empirical studies
prove that volatility increase around ex-dividend day (Donders and Vorst, 1996; Joon Chae, 2005). For
abnormal turnover, we choose the absolute value of cumulate abnormal return CAR [-10,-2] as a proxy for
control variable. All of proxies are widely used in financial analyzing and proved to have intuitive economic
relation with information asymmetry by finance literature (Joon chae, 2005).
Atiase (1985) study the relationship of firm size and private pre-disclosure information availability. The
empirical evidence shows that larger firms are more transparent, and have less information asymmetry before
scheduled announcement. Hypothesis 2 states that the larger ex ante information asymmetry, the less uninformed
investors are willing to trade. We, therefore, should expect a positive correlation between firm size and trading
volume prior to ex-dividend date. The market-to-book ratio is significantly positively related to the proportion
of a firm’s debt that is privately placed (Krishnaswami, Spindt, and Subramaniam, 1999). Given that most of
outsiders cannot have access to firm’s private information, a larger market-to-book ratio implies greater
information asymmetry. Hence, we should observe an inverse relation between the market-to-book ratio and
the trading volume. Because different nature of each companies’ business, ex-dividend date in some
industries release more meaningful information than others. For instance, the performances of oil & mining
firms rely heavily on the market price of raw crude, which all traders can obtain from publicly sources (Joon
Chae, 2005). As a consequence, a low dividend payout is expected when oil & mining firms witness a rough
year of continuously fluctuating raw ingredient’s price. We should observe a positive coefficient for the
dummy variables of specific industries such as oil and mining, financial services and etc.
Table V reports the results of regressing abnormal log turnover before the ex-dividend date on proxies of
ex ante information asymmetry. The coefficients are the time series averages of the coefficients from cross-
sectional regressions. t-statistics are calculated with the standard errors of these time weighted series. The
dependent variable is defined as the difference between average log turnover from t=-10 to t=-2 and intercept
coefficient from constant model of log turnover from t=-200 to t=-11. As shown in Table V, the coefficient
of size factor has shown the same negative side as of trading volume (-0.511) with insignificant t-stat of -
0.12, which mean size does positively correlated with trading volume, but it is not fully able to explain
abnormal turnover. Meanwhile, the coefficient of market-to-book ratio, supposed to be positive, is slightly
negative (-0.0618). Since corresponding t-statistics are not significant even with the existence of control
variable, we cannot conclude any relationship between this indicator and trading volume as well as abnormal
return. As expected, the coefficient of raw material industries, such as oil and mining, in abnormal turnover
regression with control variable is -2.749 with significant t-stat of 2.95. Apparently, uninformed investors do
not worry much about their information disadvantage thanks to the availability of oil & crude pricing
information. Ex-dividend event has a relatively low impact on stock price of firms in these industries. On the
other hand, when it comes to financial services industry, the coefficients are significantly positive in
abnormal result regression but turn into negative in turnover section. Since the t-statistic for turnover section
is not significant in either 5% or 10%, we do not have the strong evidence for the effect of asymmetry
information on price change in financial sector around ex-dividend day.
6. Conclusion
53
In this paper, we study the trading behavior around ex-dividend day on French stock market by applying
the method suggested by Joon Chae (2005) with the main interest on abnormal turnover and return. Our
research contributes to existing literatures by suggesting a new measure of trading behavior that takes into
account both abnormal turnover and abnormal return. We test a sample period from January 1, 2012 to
December 31, 2012 with data extracted from Datastream. Following Joon Chae (2005), we calculate abnormal
return and abnormal turnover by employing two separate methods: a market model and a constant mean model.
We find that abnormal return changes by 21.25% from t=-1 to t=0 with highly significant t-stat of 8.3. We,
however, record a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of
abnormal return (from 0.89% to -0.01%) from event day to day t=1 with t-stat of -0.88 and -0.049 respectively.
In general, we find a significant decrease in the level of abnormal turnover prior to the ex-dividend day. We
address the robustness of these findings in several approaches. The results of robustness testing do support the
previous findings. For further analyzing investor behavior, we run the regression of abnormal trading volume
and abnormal turnover on information asymmetry proxies and control variables to see whether information
asymmetry actually reduces the motivation of trading. We find that size does positively correlated with trading
volume, but that alone is not sufficient to fully explain abnormal turnover. In addition, there is no relationship
between market-to-book ratio and trading volume as well as abnormal return. Further, we use dummy variables
to take into account the nature of industry and find that ex-dividend event has a relatively low impact on stock
price of firms in oil and raw material industry.
This research has some limitations. First, the investigated period in this paper is only 1 year and the
findings may be inconsistent if large sample period is tested. Second, in regression analysis, we do not
investigate the impact of bid-and-ask spread and dividend yield to trading behavior around ex-dividend day.
Since these two proxies are alternative measures of information asymmetry, we are interested in dig up
deeper these proxies in future research. Finally, we overcome the limitations of previous researches when
dealing with time-series data by conducting robustness test. To fully correct any biases when doing empirical
research with time-series data, we suggest future researches should combine robustness test and other
approaches for comprehensively analyzing the trading behavior around ex-dividend days.
7. Acknowledgements
We thank Nuttawat Visaltanachoti, Linh Nguyen, and workshop participants at Massey University, New Zealand and National Economics University of Vietnam for their generous comments. We thank Nam H. Vu, Huong D. Vu, Kien T. Tran and Lan Anh Nguyen for their research assistance.
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