2012 Cambridge Business & Economics Conference
ISBN : 9780974211428
Distinguish Liquidity Trading From Noise Trading: Evidences from
Taiwan Stock Market
Wen-Chen Lo
Department of Finance, St. John’s University
Taiwan
wenchen@mail.sju.edu.tw
ABSTRACT
The trading activity can be classified in three categories: informed trading, liquidity
trading, and noise trading. We employ trading volume and net individual trading to
proxy for trading activities. The information content of trading activities and returns
can detect the liquidity trading and noise trading in the stock market. There are some
findings from our results. First, when analyzing high trading activities, trading
volume can reflect liquidity trading from speculative needs and net individual
trading can imply liquidity trading from hedging motives or self-attributed biases.
Besides, after considering unusual high trading activities and extreme prices, both
proxies can reflect noise trading in the stock market. The main contribution of this
study is that we construct indicators to reflect liquidity trading and noise trading.
Then investors can explore furthermore information to make decisions.
1. INTRODUCTION
Studies propose various types of trades and group investors for different trading
reasons (Grossman and Miller, 1988; De Long, Shleifer, Summers, and Waldmann,
1990; Campbell, Grossman, and Wang, 1993; Llorente, Michaely, Saar, and Wang,
2002; Chordia, Huh, and Subrahmayam, 2007; Bloomfield, O’Hara, and Saar, 2009).
Based upon their studies, we summarized the types and reasons of trades as
followings: informed, liquidity, and uninformed traders. The informed investors buy
or sell stocks according to new public information about stocks’ future payoff.
Liquidity investors rebalance portfolios and trade for their exogenous reasons, such
as risk-sharing, speculative, or liquidity needs. Uninformed investors trade for
irrationality. However, they are not always acting as irrational noise traders. Most of
time, uninformed investors are liquidity providers and make the transaction prices
efficient. They trade irrationally when the stocks’ prices are far from fundamental
value. Hence, their irrationality causes trading volume dramatically high.
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Although these studies mentioned above also find unusual high volume
accompanies with liquidity or noise trading, Bloomfield et al. (2009) suggest that
liquidity investors should be distinguished from noise traders. Liquidity investors
rebalance their portfolios because of their liquidity, risk-sharing, or speculative
motives. They trade for exogenous reasons, not for stocks’ fundamental information.
Hence, Liquidity investors inevitably trade irrationally sometimes, and at such a
time they are considered as noise traders. Unlike liquidity investors, uninformed
investors are trading as if they have private valuable information which they actually
do not have. Moreover, when liquidity trading happens, the stocks’ volume is getting
unusual high. When uninformed trading appears, the stocks show extreme prices and
high volume. Bloomfield et al. (2009) state that uninformed investors’ unwise
investing strategies keep stock prices away from true values. Besides, when stock
prices are extremely high or low, noise trading generated by uninformed investors
dramatically increase market volume.
Furthermore, studies also concern about the relationships among trading
activities, future returns, and volume. Llorente et al. (2002) present that hedging
trades accompanied with high volume make returns patterns reverse next periods.
Speculative trades accompanied with high volume tend to make returns continue
themselves. They find prices’ changes generated by hedging or speculative trading
must accompany abnormal volume. Chordia et al. (2007) propose that the liquidity
or noise trading from rebalancing needs is triggered by past returns. Besides, the
more extreme the return (either positive or negative) is, the higher the trading
activity is. Bloomfield et al. (2009) show when stock prices are extreme, noise
trading can dramatically make market volume increase.
In summary, those studies above suggest that liquidity trading resulting from
risk-sharing, speculative needs or other reasons accompanies with abnormal volume,
and noise trading can be observed when stock prices and volume go extremely. In
this study, we try to distinguish liquidity trading and noise trading by the
information content of unusual volume and unusual returns. First, we employ two
indictors, trading volume and net individual trading, to present investors’ trading
activities. Then, we analyze unusual trading activities and unusual returns to explore
liquidity trading and noise trading. Finally, we discover relation among noise trading,
liquidity trading, unusual trading activities, and unusual returns to help investors
make investment decisions.
The rest of this article is organized as follows. Section 2 discusses related
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literature about information content of volume and about proxies for individual
investor activities. Section 3 presents the data and proxies for trading activities.
Section 4 shows the methodology and results. We also provide the interpretations of
findings. Section 5 summarizes conclusions and offers the discussion about our
evidences.
2. TRADING VOLUME, INDIVIDUAL INVESTOR TRADING, AND
TRADING ACTIVITIES
2.1 Trading Volume and Trading Activities
Based upon the findings of articles we mentioned above (Llorente et al., 2002;
Chordia et al., 2007; Bloomfield et al., 2009), trading activities are related to trading
volume. Liquidity and speculative trading result in abnormal volume. Noise trading
dramatically increases the volume. Hence, trading volume is an intuition proxy for
trading activities. Besides, many studies propose that information content of trading
volume is more than liquidity and provides investors’ misperception. For example,
Lee and Swaminathan (2000) consider the change in volume can measure abnormal
trading activity and is not only a liquidity proxy. They state that trading volume
captures investors’ disagreement about a stock’s intrinsic value. High volume stock
results from great disagreement among investors about its intrinsic value. As studies
mentioned in section 1, unusual volume accompanies with liquidity or noise trading.
Hence, it is very suitable to uncover the implication of unusual trading volume about
liquidity trading or noise trading.
Moreover, some studies discuss trading volume can reflect investors’ biased
self-attribution or overconfidence (Daniel, Hirshleifer, and Subrahmanyam, 1998;
Gevrais and Odean, 2001; Statman, Thorley, and Vorkink, 2006; Baker and Stein,
2004) They state that investors’ overconfidence and biased self-attribution affect
trading activities. After investors gain from the stock market, they become more
confident about their investing skills and thus trade aggressively. Hence, the
self-serving attribution bias triggers investors’ overconfidence and leads to great
trading volume.
In addition, Baker and Stein (2004) build a model and propose that high trading
volume shows irrational investors dominating the market. Irrational investors are
overconfident with their valuation skill and they generate sentiment shocks. If their
sentiment is positive, they are active in the market and volume increases. If
sentiment is negative, based upon the short-sales constraint, they will be out of the
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market and volume decrease. Hence, high trading volume can reflect the existence
of irrational investors.
In conclusion, trading volume is not only common market statistics and
liquidity indicator. According to academic studies, the information content of
volume providing is more than normal trading activity and full of implication of
irrational investor sentiment. Therefore, we take trading volume as one proxy of
trading activities.
2.2 Individual Investor Trading and Irrational Trading Activities
Some indices for individuals’ sentiment constructed by scholars can help us
understand individuals’ trading activities and irrational behavior. For example, the
fluctuations in the discount on closed-end funds, the ratio of odd-lot sales to
purchase, and the net mutual fund redemptions can be driven by changes in
individual investor sentiment (Zweig, 1973; Lee, Shleifer, and Thaler, 1991; Neal
and Wheatley, 1998). In addition, Lashgari (2000) uses TED spread and Barron’s
yield spread ratio as two measures of confidence indices. Lee, Jiang, and Indro
(2002) use the sentiment index provided by Investors Intelligence as a proxy for
investor sentiment. Kumar and Persaud (2002) develop a Risk Appetite Index (RAI)
to discover the investors’ risk appetite shifting in the currency market.
Bandopadhyaya and Jones (2005) employ RAI developed as an Equity Market
Sentiment Index (EMSI) to specify investor sentiment of Massachusetts Bloomberg
Index.
Furthermore, Brown and Cliff (2005) examine several proxies for investor
sentiment including closed-end fund discounts, the net flow of funds into mutual
funds, the percentage of mutual fund assets held as cash, and the number of IPOs
during the month, the first-day return on IPOs during the month, and the bull-bear
spread. Baker and Wurgler (2006) also find several indicators measuring investor
sentiment, such as the close-end fund discount, NYSE share turnover, the number
and average first-day returns on IPOs, the equity shares in new issues, and the
dividend premium. They show that investor sentiment have significant influence on
the cross-section of stock returns. There are some studies connecting some
information to reflect investor sentiment. For example, Tetlock (2007) examine the
interactions media content and the stock market by constructing a measure of media
content corresponding to negative investor sentiment. Edmans, García, and Norli
(2007) investigate some important sports games and find a significant loss effect of
games on stock market.
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Furthermore, Kaniel, Saar, and Titman (2008) develop a measure of net
individual trading to understand the information of net buying and selling trades by
individuals. More importantly, such a measure of individual investor trading can
reflect the individuals’ sentiment and may have more direct relations with noise
trading. Many financial economists and market participants think that individuals
and institutions are two different types of investors. That is, institutions are grouped
into informed investors and individuals are often viewed as uninformed traders who
usually have biased self-attribution or overconfidence in investment decision
marking. Hence, it is very intuitional to analyze the information of individuals’
trading activities when we want to understand noise trading.
Individuals tend to buy stocks after prices decrease, but sell them after prices
go up. Kaniel et al. (2008) find after a period of positive excess returns, individual
investors sell stock intensely, and then stocks show significant negative excess
returns. On the other hand, after a period of negative excess returns, individual
investors will keep buying stock for a while and then they gain positive excess
returns. Kaniel et al. (2008) explain two reasons for such return reversals. One is
that individual investors are liquidity providers to institutions. The other is due to
investors’ overreaction. Besides, Kaniel et al. (2008) conclude the predicting ability
of net individual trading is not subsumed by trading volume. They also state that net
individual trading and trading volume seem to contain different information.
Hence, we consider net individual trading as the other proxy of trading
activities. Then, we explore further information about liquidity trading and noise
trading in the stock market from net individual trading.
Based upon studies and discussions above, we summarized the relationships
among high volume, extreme prices, types of trades, and return patterns in Table 1.
<Insert Table 1>
As Bloomfield et al. (2009) mentioned that noise traders are not always acting
as irrational investors. Most of time, they are liquidity providers. Noise traders do
not make investment decisions as informed traders based upon fundamental
information of stocks and they trade for exogenous reasons. Some studies consider
the information content of high volume is more than high liquidity, it can signal the
types of liquidity trading resulting from risk-sharing or speculative needs
(Bloomfield et al., 2009). Moreover, many researches propose that high volume can
show irrational investors dominating the market and imply investors are
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overconfident with their valuation skill (Daniel et al.,1998; Gevrais and Odean, 2001;
Statman et al.,2006; Baker and Stein, 2004).
Furthermore, Bloomfield et al. (2009) also state when the stocks’ prices are
extreme, the noise trading dramatically increase volume. De Long, Shleifer,
Summers, and Waldmann (1990) construct a model showing noise traders diverge
significantly prices from fundamental values, and hence asset returns exhibit the
mean reversion in the long run.
Based upon the findings, at first, we discover that information of high volume
to distinguish liquidity trading resulting from exogenous reasons, for example,
hedging, speculative, or other self-attributed biases. Such trades can be detected
from high volume and return patterns. Returns will show reversal patterns for
hedging trades, but continued patterns for speculative needs. For biased
self-attribution reasons, such as overconfidence, returns will also show reversal
pattern.
Then, we explore furthermore that information content of high volume and
extreme prices about noise trading. The noise trading pulls the stocks’ prices away
from fundamental values, then informed investors make contrarian investment
strategy against noise traders, and finally the returns will be mean-reverting.
Even there are many studies talking about types of trading, abnormal volume
and future return, and dynamic volume-return relations, few studies consider
information content of abnormal volume and extreme prices to detect the liquidity
and noise trading in the stock market. The objective of this study wants to employ
the proxies of trading activities to detect the presences of liquidity and noise trading.
Then, investors can make further investment decisions.
3. THE DATA AND PROXIES FOR TRADING ACTIVITIES
In this section, we will illustrate the variables to proxy for trading activities and the
definitions of unusual trading activities and unusual returns.
3.1 Data and Sample Description
The sample period of our study is from January 1 in 2000 to December 31 in
2008. The market index we investigate is TAIEX (Taiwan Stock Exchange
Capitalization Weighted Stock Index). TAIEX is the value-weighted index including
all listed stocks. We analyze that the whole market data to understand the trading
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activities. Statman et al. (2006) propose that if investors are overconfident with their
abilities of investment, they are likely to show their beliefs about stocks in general
rather than about specific securities. Bloomfield et al. (2009) report when
uninformed investors behave as irrational traders, they can increase market volume.
As a result, we employ aggregated market data to construct the measure to proxy for
trading activities. Then, we explore further information about liquidity trading and
noise trading in the stock market from proxies. Moreover, the data frequency we
analyze is daily. All data for the stock returns, trading volume, net individual trading,
and dollar volume are from the Taiwan Economic Journal (TEJ) data bank.
3.2 Variables for Trading Activities
As we mentioned above, we consider trading volume and net individual trading
as two different measures of trading activities. First, we take turnover to be a
measure of trading volume. Many studies use turnover to be a measure of trading
volume (Campbell et al., 1993; Lo and Wang, 2000; Gervais et al. 2001, Llorente et
al., 2002; Baker and Wurgler, 2006; Chordia et al., 2007). However, the time series
of turnover is nonstationary. By following the procedures proposed by academic
studies, nonstationary turnover series can be made to be stationary detrended
turnover series (Campbell et al., 1993; Lo and Wang, 2000; Llorente et al., 2002;
Baker and Wurgler, 2006; Chuang, Ou-Yang, and Lo, 2009.). We take the natural log
of raw turnover ratio and then detrended by the one-year moving average. Besides,
to avoid the problem of zero volume when taking logs, we add a small constant to
raw turnover ratio. Henceforth we term detrended log turnover as TURN.
Then, we follow the study of Kaniel et al. (2008) and use the aggregated market
volume of buying and selling orders by individuals to create a measure of net
individual trading. In Taiwan stock market statistics, we can find information of net
institution investors’ buying. Therefore, we compute net individual trading by net
institution investors’ buying, and then standardize it by the average daily dollar
volume. Therefore, this measure can capture imbalances in individuals’ executed
orders in TAMIX and reflect net buying or net selling of individual trading in the
market as a whole. Henceforth we term net individual trading as NIT.
3.3 Unusual Trading Activities
The unusual trading activity contains more information of future price
movements. Investors consider trading volume as a signal to predict stock prices.
Besides, unusual high level of trading volume predicts the higher stock price coming
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soon, while unusual lower volume represents there is a forthcoming low price.
Gervais, Kaniel, and Mingelgrin (2001) state unusual large (small) trading activity
over periods tends to experience large (small) return over subsequent periods.
Bloomfield et al. (2009) conclude uninformed traders act as noise traders when
prices go extremely and such behavior makes market volume increasing highly.
Based upon such findings above, high volume can signal liquidity and noise trading.
Therefore, we define unusual trading activity alone at first.
We follow the methodology of Gervais et al. (2001) to define the unusual
trading activity. We take 50 rolling trading days as trading intervals. In each interval,
the first 49 days are used to measure the trading volume of the 50th day is unusually
large or small. The top 10 percent of daily trading intervals during each trading
interval is defined as high-volume trading activity and the bottom 10 percent of
trading intervals during each trading interval is low-volume trading activity.
Moreover, we employ two proxies to measure the investor trading activities,
TURN and NIT. For both proxies, we follow the procedures above to define unusual
large and small trading activities. Hence, there are high-volume trading activities
and low-volume trading activities for TURN and NIT. When measuring trading
activities by TURN, high-volume are termed HTURN and low-volume are termed
LTURN. When measuring trading activities by NIT, high-volume are termed HNIT
and low-volume are termed LNIT.
3.4 Unusual Trading Activities and Unusual Returns
Next, we would like to explore further unusual trading activities and unusual
return relations by combining unusual return information with unusual trading
activities. First, we define unusual high returns and unusual low returns by the
methodology of high-volume and low-volume trading activities. In each 50-day
trading interval, the first 49 days are used to measure the return of the last day is
high or low. The top 10 percent in each trading interval is defined as high-return
(HReturn) and the bottom 10 percent denotes low-return (LReturn). Then, we
consider high-volume trading activities and unusual returns together,
high-volume-high-return and high-volume-low-return.
4. METHODOLOGY, ANALYSIS, AND EMPERICAL RESULTS
4.1 Summary Statistics
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We employ the daily data of Taiwan stock market to explore trading activities
of investors. We construct two indicators to uncover the trading activities of
investors. The first one is TURN, detrended log turnover, defined as log of raw
turnover and detrended by one-year moving average. The second is NIT, net
individual trading, which is computed by net individual buying dollar volume
divided by the past one-year moving average dollar volume.
Table 2 reports summary statistics of the data set. During the sample period, the
average stock return is negative. Negative mean of NIT reveals net selling dollar
volume performed by individuals on average during the sample period. Moreover,
the mean of TURN is around zero.
When measuring trading activities by NIT, HNIT is high-volume trading
activities and LNIT is low-volume trading activities. There are total 209 and 221
observations of HNIT and LNIT, respectively. Also, the positive mean of HNIT
implies high-volume trading activities are net buying activities by individuals and
negative average of LNIT shows net selling activities by individuals. These results
consist with the implications of NIT. Besides, when measuring trading activities by
TURN, HTURN is high-volume trading activities and LTURN is low-volume
trading activities. There are total 293 observations for HTURN and 332 observations
for LTURN in the whole sample period.
<Insert Table 2>
4.2 High-Volume Trading Activities
Table 3 shows the statistics of high-volume and low-volume trading activities.
We also report contemporary returns, the cumulated returns before as well as after 5,
10, 15, and 20 days when high-volume or low-volume trading activities occur.
Firstly, we take a look at TURN as the proxy of trading activities. When HTURN
appears, the contemporary returns and cumulated returns before as well as after 5,
10, 15, and 20 days are all positive. When LTURN appears, those returns are all
negative.
Moreover, when HTURN signals, the average cumulated returns are decreasing
from before 20-day to after 10-day. Then, they are slightly increasing. For LTURN,
the average contemporary return and the cumulated returns are all negative. The
average cumulated returns of LTURN are increasing from before 20-day to after
5-day. Then, they are decreasing. Besides, HTURN means high trading volume and
LTURN are low trading volume. Many academic studies report after high trading
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volume, investors earn lower future returns. While they earn higher future returns
after low trading volume (Lee and Swaminathan, 2000; Datar, Naik, and Radcliffe,
1998; Gervais and Odean, 2001). According to the results of Table 3, we find
positive returns after HTURN are lower than before, and negative returns after
LTURN are higher than before. Our results show consistent results with those
studies.
Furthermore, the return patterns of HTURN and LTURN are all continuous. As
mentioned in Section 2, high volume and continuous return pattern can signal
liquidity trading for speculative motives. Llorente et al (2002) state that in periods of
high volume, stocks of higher degree of speculative trading tend to exhibit positive
return autocorrelation. When investors trade for speculative reasons, they will chase
the trend. That is, they buy the stocks with good performances in recently past days
and sell stocks with bad performances.
We argue that HTURN signals speculative trading in the stock market, when
we employ TURN as a proxy of trading activities. In the bull market, there are lots
stocks with good profit and investors tend to earn positive returns. Based upon
speculative motives, investors observe the past positive returns and buy the stocks
with good performances. Then, trading volume increases due to such needs. Besides,
during the period of bull market, positive rewards will continue for a while. Hence,
after high volume happens, investors still gain positive returns even returns are
lower than before. On the other hands, during the bear market, most stocks show bad
profits. At first, investors sell stocks with bad performances. The negative returns
discourage investors and then loss aversion makes investors trade less.
Table 3 also lists the statistics of high-volume and low-volume by the other
indicator of trading activities, NIT. HNIT is high-volume trading activities and
means high net individual buying. Also, LNIT is low-volume trading activities and
represents low net individual buying. The average contemporary return when HNIT
appears is negative. It implies that individual investors gain negative return on
average when their trading activities are net buying. Besides, the average cumulated
returns before 5, 10, and 15 days are positive. When HNIT appears, the average
cumulated returns after 5, 10, 15, and 20 days are all negative. The return pattern is
return reversal.
Based upon the academic findings mentioned in Section 2, high volume and
reversal return pattern can signal liquidity trading for risk-sharing or other
exogenous reasons. Individual investors are encouraged by past positive returns.
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They feel confident with their investment valuations and they trade more. Hence,
their self-attribution biases make them get negative returns thereafter. Such return
reversal pattern is consistent with the overconfidence proposition (Daniel et al.,
1998; Gevrais and Odean, 2001; Statman et al., 2006; Baker and Stein, 2004). On
the other hand, the mean contemporary return when LNIT appear is positive. The
mean cumulated returns before and after 5, 10, 15, and 20 days are also all positive.
The results represent when individual investors show net selling activities, they gain
positive returns on average.
We construct the NIT indicator by the information of individuals’ trading
activities, and which can represent a measure of individual behavior. Hence, the NIT
seems to be an investor sentiment indicator and HNIT may reflect the liquidity
trades due to exogenous reasons. When HNIT happens, it implies that individual
investors buying activities are more and they are more optimistic about the market.
Therefore the stocks are overpriced and investors gain negative returns thereafter.
In this study we employ two measures of trading activities to detect different
types of trades in the stock market based upon results of the prior studies. Table 3
reports the return patterns after high volume with two different proxies of trading
activities. The return pattern after HNIT shows reversal and that after HTURN is
continuous. Hence, we can argue that HTURN signals the liquidity trading resulting
from speculative motives. HNIT represents the liquidity trading from risk-sharing or
other exogenous needs, for example irrational investor sentiment.
<Insert Table 2>
4.3 High-Volume Trading Activities and Unusual Returns
Next, we would like to explore further the information content of high-volume
trading activities and unusual returns. Based upon the academic studies mentioned in
Section 2, high volume, extreme prices, and reversal return pattern can signal noise
trades. Similarly, we define unusual high returns and unusual low returns by the
methodology of high-volume and low-volume trading activities. HReturn is
high-return which is on the top 10 percent in each trading interval. The bottom 10
percent in each trading interval is low-return denoted LReturn. Then, we combine
high volume with unusual returns into two combinations: high-volume-high-return
and high-volume-low-return. Moreover, we employ two proxies to measure the
investors’ trading activities, NIT and TURN. The summarized information of high
volume and unusual returns are presented in Table 4.
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By TURN as a proxy of trading activities, in the whole sample period, there are
64 HTURN-HReturn and 17 HTURN-LReturn, observations. In Table 3, we
summarize statistics as follows. Cumulated returns before as well as after 5, 10, 15,
and 20 days are all positive when HTURN-HReturn appears; cumulated returns are
all negative after HTURN-LReturn appears, but they are all positive before
HTURN-LReturn appears.
Compared with the results of Table 3, those of Table 4 reveal more findings.
First, the volume-return gives more information than volume itself. Table 3 shows
that the cumulated returns are all positive when HTURN happens. When we
consider returns as well as volume, Table 4 exhibits completely different results:
when HTURN-HReturn happens, the returns show continuous pattern. The average
cumulated returns are decreasing from before 20-day to after 10-day. Then, they are
slightly increasing. However, when HTURN-LReturn happens, the return pattern is
reversal.
Based upon the findings of Table 3, we argue that HTURN signals the liquidity
trading resulting from speculative motives. In Table 4, the results of
HTURN-HReturn still seem to support such argument. However, the return pattern
of HTURN-LReturn shows different dynamics. Besides, if we consider high volume
and extreme prices together, we can observe noise trading when the presences of
return reversals. The results of HTURN-LReturn consist with all criteria, but the
results of HTURN-HReturn do not. Therefore, we can argue that HTURN-LReturn
also signals the noise trading.
Table 4 also reports the volume- return information by employing the net
individual trading, NIT, to proxy investor trading activities. We summarize the
average value of HReturn and LReturn. The average returns of HReturn and
LReturn with HNIT are 2.42 and -2.47 percent, respectively. Moreover, we also
report the cumulated average returns before as well as after 5, 10, 15, and 20 days
when unusual volume-returns appear.
When HNIT-HReturn appears, cumulated returns before 5, 10, 15, and 20 days
are all negative, but these are all positive after HNIT-HReturn. When HNIT-LReturn
occurs, cumulated returns before 5, 10, 15, and 20 days are from positive to negative,
but these are all negative after HNIT-LReturn. Compared with the results of Table 2,
the volume-return contains more information than volume itself. Table 2 reveals
positive past cumulated returns and negative future cumulated returns when HNIT
appears. When considering the volume-return together, those cumulated returns
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present different results. However, HNIIT in Table 3, HNIT-HReturn and
HNIT-LReturn in Table 4, the return patterns are all reversal.
When HNIT connects with information of HReturn and LReturn, the
volume-return combinations contain more information. Based upon the evidences in
Table3, we suggest that HNIT may signal the liquidity trading from risk-sharing or
other exogenous needs. Hence, we consider HNIT and extreme prices together and
may signal the noise trading. Bloomfield et al. (2009) state when stock prices are
extremely high or low, noise trading generated by uninformed investors dramatically
increases market volume. The results of HNIT-HReturn and HNIT-LReturn in Table
4 support arguments mentioned above. That is, when stocks’ prices go extremely
high (HReturn) or low (LReturn), noise trading resulting from irrational investors
makes market volume increasing dramatically (HNIT). Also, the return patterns of
HNIT-HReturn and those of HNIT-LReturn are consistent with the findings of De
Long et al. (1990). They are mean-reverting.
<Insert Table 4>
The evidences of Table 4 support our suggestions: HNIT alone can signal
liquidity trades resulting from other exogenous reasons. HNIT with extreme unusual
returns can signal noise trading. Then, we try to discover information content of
HNIT-HReturn and HNIT-LReturn during the stock market cycle. We may
differentiate the beginning and ending of the market top by considering
HNIT-HReturn and HNIT-LReturn. When HNIT-HReturn appears, which implies
that the bubble of the market just takes place. At that time, the market is full of noise
trading. Investors believe the prosperity will continue because of optimistic, news,
and analysts’ forecast. Hence, stocks’ prices are continuously going upwards. Hence,
the cumulated returns are positive after HNIT-HReturn. At such a time, investors can
gain positive returns because they bear noise trader risk created by themselves (De
Long et al., 2000). Before HNIT-HReturn, the negative cumulated returns could
result from investors’ overconfident and optimistic. Therefore, self-attribution biases
make investors gain negative returns on average.
However, the bubble can not last forever. The stock prices are far from their
fundamental prices and need downward correction. When HNIT-LReturn appears,
the market signals a coming crash. More and more investors sell stocks and then the
market crash happens. From Table 4, the cumulated returns are negative after
HNIT-LReturn. HNIT-LReturn signals the market will crash. Thus, investors gain
negative returns thereafter.
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<Insert Figure 1>
Figure 1 shows how HNIT-HReturn and HNIT-LReturn signal the beginning
and ending of the market bubble. From the information of HNIT-HReturn and
HNIT-LReturn, investors gain more information to predict whether the noise trading
arrives or not.
Then, we describe the relationship between HTURN-HReturn and
HTURN-LReturn in the stock market as in Figure 1. From Table 3, we find HTURN
alone can signal liquidity trading resulting from speculative reasons. In Table 4, we
find that HTURN-LReturn can also signal noise trading. As Bloomfield et al.(2009)
state that noise traders are not always acting as irrational investors. Most of time,
they are liquidity providers. HTURN can detect the liquidity trading when there is
the bull market. HTURN with extreme prices may also detect noise trading when the
stock market bubble appears. In Table 4, we find HTURN–LReturn also satisfies all
criteria for the presence of noise trading. Hence, the HTURN alone and HTURN
with LReturn can detect liquidity and noise trading, respectively. Similarly as
HNIT-LReturn, when HTURN-LReturn appears, the market signals a coming crash.
During the period of market top, there are full of noise trading. Even past cumulated
returns are still positive, the stocks prices have become overpriced and they need to
be corrected and downward to their intrinsic values. When HTURN-LReturn
appears, and which signals the bear market is coming. The stocks prices are going
down and then investors gain negative returns. In Figure 1, we also can find the
HTURN and HTURN-LReturn during the stock market cycle.
5. DISCUSSION AND CONCLUSIONS
From prior studies we know that investors trade for different reasons: new
fundamental information, risk-sharing needs, speculative motives, or irrationality.
This study tries to distinguish the liquidity trading from noise trading by information
content of unusual trading activities and unusual returns. Trading volume is a basic
market statistics to help investors make decisions. Investors consider trading volume
as a signal to predict stock prices. Unusual high level of trading volume may predict
that a higher stock price will come; while unusual low volume represents there is a
forthcoming low price. Hence, the trading volume, TURN, is an intuition proxy for
investors’ trading activities. The net individual trading, NIT, measures net buying
dollar volume by individuals and is also a suitable proxy for investors’ trading
activities. From our results, trading volume and net individual trading can both
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represent investors trading activities to reflect liquidity and noise trading.
Besides, investors observe trading volume and stock returns together to gain
more information insight about stock prices. For example, investors tend to buy
when both the trading volume and stock prices steadily increase. Traders change
their mind and move to the selling side when they observe an increasing volume and
decreasing prices. That is, investors perceive trading volume and stock returns
together to make investment decisions. Hence, we combine unusual trading
activities with unusual returns to discover the dynamic volume-return relation and
future returns. The main findings are as follows.
First, HTURN can reflect liquidity trading for speculative needs. HNIT can
represent liquidity trading for hedging or other exogenous motives. HTURN
indicates high-volume trading activities. When investors trade for speculative
reasons, they will chase the trend. That is, they buy the stocks with good
performances in recently past days and sell stocks with bad performances. The
results consist with many studies about high volume and future returns(Lee and
Swaminathan, 2000; Datar, Naik, and Radcliffe, 1998; Gervais and Odean, 2001).
Besides, HNIT means net buying trading by individuals. When there is HNIT, it
implies that individual investors encouraged by past positive returns show more
buying activities, and they are more optimistic about the market. Moreover,
investors can hold stocks for a while when HTURN appears because HTURN
signals lots speculative trading in the market. They can sell stocks after HNIT,
because HNIT means investors are too optimistic and suggests negative returns
thereafter.
Second, the high-volume trading activities and unusual return combinations
give more information insight than volume alone. HNIT with unusual returns reflect
noise trading. We consider there is different information between HNIT-HReturn
and HNIT-LReturn. HNIT-HReturn signals the beginning of the market top. There is
lot of noise trading and at such a time irrational investors still gain positive returns,
because they take risk created by themselves. HNIT-LReturn signals market is on
the way of crash. Thus, investors gain negative returns thereafter. Moreover, like
HNIT-LReturn, HTURN-LReturn also can singal the market is going to crash. If
investors can detect the noise trading during the market bubble, they can make
investment decisions more conservatively.
In conclusion, this study employs two indicators to proxy for trading activities
to detect liquidity trading and noise trading. We find HTURN can present liquidity
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trading of speculative needs and HNIT can imply liquidity trading of risk-sharing or
other exogenous motives. HNIT with extreme prices and HTURN with LReturn can
signal noise trading. Also, investors can observe those indicators to understand
which stage of the stock market they may be located. Besides, investors make
investment decisions by such unusual trading activities and unusual return measures.
This study highlights issues of information content of unusual trading activities and
unusual returns. We hope such distinguished information by the measures of unusual
trading activities and unusual return is helpful when investors make decisions.
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Table 1: Summary information about volume, return patterns and types of trades
signals
return patterns
return reversal
types of trades
liquidity trading for
risk-sharing
literature
Llorente et al., 2002;
Chordia et al., 2007;
Bloomfield et al.,
high return continuous
volume
liquidity trading for
speculative
High
volume
+
return reversal
extreme
prices
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Cambridge, UK
1998; Gevrais and
Odean, 2001;
liquidity trading for other
return reversal
2009; Daniel et al.,
exogenous reasons
(overconfidence..ect.)
Statman et al., 2006;
Baker and Stein,
2004
Kaniel et al. 2008;
Bloomfield
noise trading
et
al.
(2009)
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Table 2: Descriptive statistics of variables
The sample period is from January 1 in 2000 to December 31 in 2008 with a total of 2253
daily observations. Return is the daily return on the Taiwan market index and presented in
natural logarithms. NIT is the net individual trading. TURN is detrended log turnover. We
take rolling 50 trading days to be an interval. In each interval, the first 49 days are used to
measure whether the trading activities during the last day is high or low. Top 10 percent of
trading activities during each trading interval is defined as high-volume and bottom 10
percent of trading activities during each trading interval is low-volume. HTURN and
LTURN are the high-volume and low-volume by measuring the proxy of trading activities
of TURN, respectively. HNIT and LNIT are high-volume and low-volume by measuring the
proxy of trading activities of NIT, respectively.
Variable
Number of
observations
Mean
Standard
Deviation.
Maximum
Median
Minimum
Return
2253
-0.0271
1.6283
6.1721
-0.0077
-6.9123
NIT
2253
-1.0008
0.4656
0.0225
-1.0742
-2.5397
TURN
2253
-0.0126
0.0800
0.5982
-0.0001
-1.6365
HNIT
209
0.0820
0.2617
0.5982
0.0877
-3.4173
LNIT
221
-0.1381
0.1173
0.1585
-0.1284
-1.6365
HTURN
293
-0.6628
0.2595
0.0224
-0.6859
-1.2370
LTURN
332
-1.4778
0.2647
-0.8786
-1.4588
-2.5396
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Table 3: Summary statistics of high-volume and low-volume trading activities
This table summaries statistics of high-volume and low-volume trading activities of two
proxies, the detrended log turnover (TURN) and the net individuals buying (NIT). We take
rolling 50 trading days to be an interval. In each interval, the first 49 days are used to
measure whether the trading activities during the last day is high or low. Top 10 percent of
trading activities during each trading interval is defined as high-volume and bottom 10
percent of trading activities during each trading interval is low-volume. HTURN and
LTURN are the high-volume and low-volume by measuring the proxy of trading activities
of TURN, respectively. HNIT and LNIT are high-volume and low-volume by measuring the
proxy of trading activities of NIT, respectively. We also report contemporary returns and the
cumulated returns before as well as after 5, 10, 15, and 20 days when high-volume or
low-volume trading activities occur. 1-day denotes the contemporary return when unusually
high or low trading activities happen. CR(-5), CR(-10), CR(-15), and CR(-20) represent the
cumulated returns before 5, 10, 15, and 20 days when unusually high or low trading
activities occur. CR(5), CR(10), CR(15), and CR(20) represent the cumulated returns after 5,
10, 15, and 20 days when unusually high or low trading activities occur. t means the
t-statistics.
LTURN
HTURN
Return
CR(-20)
CR(-15)
CR(-10)
CR(-5)
1-day
CR(5)
CR(10)
CR(15)
CR(20)
Observations
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Cambridge, UK
mean
t
HNIT
mean
t
mean
LNIT
t
mean
t
5.1480
11.48
-4.3150
-1.93
0.9143
1.71
1.2884
2.88
4.7432
13.05
-3.5260
-1.26
0.4004
0.96
1.7141
4.30
3.6454
11.74
-2.3562
-0.67
0.0817
0.23
1.8506
6.47
2.7730
12.71
-1.4765
-0.44
-0.8433
-3.381
1.9233
9.77
0.7271
8.30
-0.6010
-7.53
-1.3759 -13.63
1.4698
17.46
0.4863
2.44
-0.0987
-0.438
-0.2148
-0.77
0.2429
1.13
0.3934
1.33
-0.1950
-0.665
-0.3022
-0.83
0.2730
0.82
0.7948
2.03
-0.4595
-1.26
-0.4989
-1.15
1.0082
2.42
0.9004
2.02
-0.8310
-1.93
-0.4138
-0.80
1.0291
2.26
293
332
209
221
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Table 4: Summary statistics of high volume trading activities-unusual return
This table summaries statistics of high-volume and low-volume trading activities of two
proxies, the detrended log turnover (TURN) and the net individuals buying (NIT). HTURN
is the high-volume by measuring the proxy of trading activities of TURN. HNIT is
high-volume by measuring the proxy of trading activities of NIT. Therefore there are two
combinations: high-volume-high-return and high-volume-low-return. We also report
means of HReturn and LReturn. Besides, CR(-5), CR(-10), CR(-15), and CR(-20) are the
cumulated returns before 5, 10, 15, and 20 days when high volume trading
activities –unusual returns occur. CR(5), CR(10), CR(15), and CR(20) represent the
cumulated returns after 5, 10, 15, and 20 days when high volume trading activities –unusual
returns occur. t means the t-statistics.
HTURN
HReturn
HNIT
LReturn
Return
mean
CR(-20)
CR(-15)
CR(-10)
CR(-5)
2.8473
2.98
8.3526
5.44
2.7657
3.41
6.9322
1.7028
2.42
1.6126
HRetrun/LReturn
CR(5)
CR(10)
CR(15)
CR(20)
Observations
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mean
mean
t
-2.4706
-0.45
0.9982
1.55
5.80
-1.4859
-0.35
0.1996
0.36
4.7288
3.50
-3.6161
-1.63
-0.2774
-0.71
4.15
2.8158
3.52
-4.5877
-2.50
-1.0201
-3.78
2.4617
16.35
-1.7698
-7.83
2.4291
3.43
-2.4724 -21.87
0.7690
1.88
-0.8750
-1.18
5.0974
1.80
-0.3800
-1.16
0.4881
0.85
-0.6514
-0.62
5.4321
1.50
-0.3768
-0.81
1.3044
1.70
-3.0497
-1.78
4.3833
1.68
-0.9370
-1.50
1.3550
1.60
-1.2096
-0.76
6.9601
1.83
-0.5156
-0.76
17
t
mean
LReturn
t
64
t
HReturn
4
98
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Prices and volume going extremely;
a lot of noising trading
Signal:HNIT+LReturn
HTURN-LReturn
Signal:HNIT+HReturn
TOP
(Future Cumulated Return decreasing )
Bear Market
(Prices decreasing and volume shrinking)
(Future Cumulated Return increasing )
Signal:HTURN
HTURN+HReturn
Lots of informed traders
Bull Market
and liquidity traders
(Prices and volume increasing)
trading for rebalancing needs
Trough
Figure 1: HNIT-HReturn, HNIT-LReturn, and HTURN-LReturn signal the
beginning and ending of market bubbles
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