TRADING STRATEGY BASED ON INTRADAY ABNORMAL VOLUME …icmsit.ssru.ac.th/icmsit/fmsicmsit/...INTRADAY-ABNORMAL-VOLUM… · excess return on the same day. Thus, this paper further improved

ICMSIT 2017: 4th International Conference on Management Science, Innovation, and Technology 2017 Faculty of Management Science, Suan Sunandha Rajabhat University (http://www.icmsit.ssru.ac.th)

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TRADING STRATEGY BASED ON INTRADAY ABNORMAL VOLUME IN THE STOCK EXCHANGE OF THAILAND

Nathawuth Dejbordin1

Abstract

This paper proposes a trading strategy which trades based on the observation and prediction of

abnormal volume of stocks listed on the Stock Exchange of Thailand during July 2015 to June 2016. This paper

found that a positive excess returns follow an abnormal volume events defined by the abnormally-high

standardized volume and standardized directional volume. To confirm that such events are exploitable, a strategy

that trades on those events is tested and found that they generate positive alphas even after including

commission fees.

Previous work has shown that typically an abnormal volume events are accompanied by a substantial

excess return on the same day. Thus, this paper further improved the strategy by attempting to capture the

excess returns on the same day as abnormal volume events. A prediction algorithm is developed, which can

predict those events with a high precision and generates incremental excess returns. A portfolio simulation on

out-of-sample data shows that the performance slightly improves after a prediction algorithm augments the

strategy.

Keywords: Abnormal trading volume, intraday volume, Stock Exchange of Thailand, Trading strategy.

Introduction

Trading volume is one of the most common market data available in an equity market across the globe

after prices (such as open, high, low, and close price). Professional traders around the world use volume as one

of their trading tools to either screen out liquid stocks, determine the market participant’s interest in a particular

asset or even use it directly to forecast future stock price movement. In general, a rise in volume is believed to

confirm the price uptrend as a majority of the market participant is willing to buy in the hope that the momentum

1 Master student under Department of Banking and Finance, Faculty of Commerce and Accountancy, Chulalongkorn University, Email: [email protected]


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will continue. In opposition, a decline in volume is thought to hint a weakness of the trend and a reversal is

imminent as people are no longer confident in the direction and the trend-following behavior dissipates. This

belief has been proven to exist by many academic works, for instance, the very first empirical study done by Ying

(1966) which stated that there exist a positive correlation between absolute price change and volume of Standard

& Poor’s 500 Composite Index. Other similar studies by Miller (1977) and Karpoff (1987) confirms that this

behavior is consistent across other equity markets and multiple timeframes ranging from hours to weeks. It is

important to point out that this behavior is valid on average but not always the case because this inefficiency is

well known and the market participants will trade on this while at the same time introduce more noise on to the

volume-based signal. Trading solely on the price trend along with a rise and fall of the volume, therefore, does not

guarantee a good performance.

The core objective of this paper is to propose a trading strategy which trades on the liquid stocks listed

on SET100 (Thailand) constituent over the July 2015 to June 2016 period. The paper would examine alternative

methods to use trading volume to forecast future price change and gauge its profitability when applying into

trading. An additional contribution of this article is to propose a prediction model which can further augment the

strategy performance.

Literature review and hypothesis development

The relationship between abnormal volume and stock returns

Abnormal volume refers to a sudden large change in volume or more familiar name a volume-spike or

volume-shock. Pritamani and Singal (2001) found that stocks tend to show price continuation (trending) after a

good earnings announcement with a large price change and especially when accompanied by a large increase in

volume. On the other hand, Gervais, Kaniel, and Mingelgrin (2001) showed an empirical study result based on

weekly data that stocks tend to exhibit high-volume premium (positive excess returns) after it experiences an

abnormal volume regardless of earning announcement. This behavior is consistent with Huang and Heian (2010)

who showed that the premium after the abnormal volume is vigorous and persistent across stocks listed in New

York Stock Exchange (NYSE) and American Stock Exchange (AMEX). They further stated that most of the excess

returns reside within first four weeks after the abnormal volume events and decline as holding period increase.

Also, Bajo (2010), who expand this topic further by decreasing the timeframe down to daily showed that a positive


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excess returns persist after abnormal volume events and there are no price-reversal over the following month. He

also suggests that this behavior arises from the exploitation of an undisclosed information with a mixture of mostly

positive and few negative private information (based on the excess returns following it). This paper examines the

excess returns following the abnormal volume events defined by abnormally-high standardized volume (V-event)

in Thai market in an attempt to extend previous literature.

H1: The excess returns after abnormal volume events defined by abnormally-high standardized volume is

positive.

Price adjustment under information asymmetry

Information asymmetry refers to a scenario where not every market participants possess the same

information which gives rise to an informed and uninformed trader. Theoretical work by Glosten and Milgrom

(1985) shows that given this condition the price would adjust to its fair value through a sequence of same-side

trades by an informed trader. Another theoretical work by Kyle (1985) suggests that an informed trader and

market maker buy and sell strategically against each other to maximize their profit and thus slow down the price

adjustment process. The reason is that informed trader must remain discrete to prevent bid-ask spread widening

but at the same time must be aggressive enough to realize profit using their inside knowledge. This idea is

consistent with a theoretical work by Easley and O'Hara (1992) which states that the market maker can reveal the

information held by the informed trader by observing the buy and sell trading behavior. This idea later supported

by an empirical study by Louhichi (2012) who points out that the asymmetry between buy and sell volume is more

informative in predicting stock returns than normal volume.

With these concepts, it seems plausible that the asymmetry between buy and sell volume could be a

proxy for the information held by an informed investor. In other words, an extreme buy over sell (sell over buy)

volume may reflect the positive (negative) information of the informed investor. This paper inspects the excess

returns following the abnormal volume events defined by abnormally-high standardized volume and standardized

directional volume (VD-event) in Thai market to test this theory.

H2: The excess returns after abnormal volume events defined by abnormally-high standardized volume and

standardized directional volume is positive.


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According to Bajo (2010), these abnormal volume events also tend to show an enormous excess return

on the event day (same day returns). However, this phenomenon cannot be exploited unless these abnormal

volume events could be anticipated before the end of the day. An intraday prediction algorithm for abnormal

volume events is needed to capture these same-day excess returns.

End-of-day volume forecast model

Various literatures have proposed many end-of-day volume prediction algorithms in the form of complex

time-series model that predicts using intraday volume. (Chen, Chen, Ardell, & Lin, 2011; Yan & Li, 2012; Satish,

Saxena, & Palmer, 2014) However, the aim for these algorithms is to reduce tracking error which does not suit our

need as the goal is to anticipate only the abnormal volume events. Hence, this paper proposes a prediction

algorithm based on intraday volume that forecasts the abnormal volume events defined by abnormally-high

standardized volume and standardized directional volume (VD-event).

H3: The abnormal volume events can be predicted by an algorithm and exploited to generate positive excess

returns.

Portfolio simulations on out-of-sample data are also performed to illustrate the improvement in term of

portfolio performance for the two definitions of abnormal volume events (V-event and VD-event) as well as after

augmenting it with a prediction algorithm. Commissions are also factored in to obtain results that are better

indicative of the trading strategies’ performance in live trading.

H4: There exists an implementable trading strategy based on abnormal volume events, which generates positive

alphas and positive information ratio.


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Methodology

Data sample and scope of studies

The required data to investigate the behavior of the excess return around abnormal volume event is

adjusted price (as total return index) and volume. This work also explores the asymmetry between buy and sell

volume as well as prediction of the abnormal volume event. Thus, a high-frequency tick data, containing the

timestamp, trade flag (auto matching, big lot, etc.), best bid/ask, matching price and volume, and the trade side

(deduce from up/down tick), is needed. A tick data is collected from Thomson Reuter’s database for the stocks1

that are members of the SET100 index (Thailand) during April 2015 to June 2016. The tick data is processed to

create the directional volume variable by taking the difference between buy and sell volume of the auto-matching2

deals, and both directional volume and matching volume are consolidated into every 5-minutes intraday interval.

This process creates a total of 55 intervals that start from the market opening auction and end at the market

closing auction (roughly from 10.00 to 16:30 with an afternoon break in between from 12:30 to 14:30).

In addition to tick data, a daily data of SET100 index and all firms listed on SET are acquire over a similar

period to construct a factor portfolios which are used to examine the portfolio factor-adjusted performance and

the event’s excess returns as well as to double check the quality of the tick data. The data obtained are open and

close price (as total return index), trading volume, market capitalization, and price-to-book ratio. The daily total

return of Thai Short-term Government Bond Index obtained from The Thai Bond Market Association (Thai BMA)

database is used as a risk-free rate.

Definition of abnormal volume events This paper examines two definitions of abnormal volume events: one defined by abnormally-high standardized volume (V-event) and one defined by abnormally-high standardized volume and standardized directional volume (VD-event).

The criterion to identify V-event, which inspired by Bajo (2010), is designed to detect an extreme

deviation of trading volume from its normal level. It is done by converting the daily volume into z-score (Vi,t), which compares with its 66 most recent daily observation including the current day

1 Use the actual historical constituent to prevent survivorship bias. However, this paper remove U City PCL (U) from the list as the price is too low such that one up/down tick tend to hit the ceiling/floor price. 2 Auto matching deals refers to a trade in the main trading board which has an up/down tick.


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(roughly three months) and look for the occurrence of large value. Thus the V-event occurs for the stock i on day t when

Vi,t > c1,

where Vi,t = logvi,t − μi,t

σi,t and c1 is a threshold parameter

logvi,t is the natural logarithm of (1 + daily volume of stock i on day t)

μi,t and σi,t are the mean and standard deviation of the 66 most recent

observation on logvi,t including the current day

To define VD-event, another criterion which checked for an extreme deviation of the asymmetry between buy and sell volume from its normal level is needed. Similarly, this is done by converting the directional volume

(the differences in daily buy and sell volume) into z-score (Di,t), which compares using the same look back

period as that of Vi,t, and look for the occurrence of large value. The VD-event is said to occur for the stock i on the day t if

Vi,t > c1 and Di,t > c2,

where Di,t = di,t − θi,t

ηi,t and c2 is a threshold parameter

di,t is the difference between daily buy and sell volume for stock i on day t

θi,t and ηi,t are the mean and standard deviation of the 66 most recent observation

on di,t including the current day

Note that all repeated events during the subsequent 22-day period are discarded to make the abnormal volume event unique. Prediction algorithm for VD-event The prediction algorithm extends the criteria for VD-event from daily data to intraday data. The calculations of z-score are adjusted by replacing the current day volume with a cumulative intraday volume1

1 The 65 daily volumes prior to current day remain the same.


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(Vi,tn and Di,t

n ), and modify the threshold to be time interval-dependent resulting in a new intraday criteria. Hence, the prediction of VD-event is made for the stock i on day t after the nth 5-minute intraday interval when

Vi,tn > b1 − (

b1 − c1

54) (n − 1) and Di,t

n > b2 − (b2 − c2

53) (n − 1),

where Vi,tn =

logvi,tn − μi,t

n

σi,tn and Di,t

n = di,t

n − θi,tn

ηi,tn with b1, b2, c1, c2 as a parameters

and n as an integer correspond to the position of 5-minute intraday interval1

logvi,tn is the natural logarithm of

(1 + cumulative intraday volume up to nth interval of stock i on day t)

μi,tn and σi,t

n are the mean and standard deviation of 66 most recent observation

on logvi,t including the logvi,tn on current day

di,tn is the differences between cumulative intraday buy and sell volume

up to nth interval of stock i on day t

θi,tn and ηi,t

n are the mean and standard deviation of 66 most recent observation

on di,t including the di,tn on current day

Event study analysis

1 Trading session for the day is split into 55 equal interval with duration of 5-minute each with the last interval as the closing auction therefore the value of n

ranges from 1 to 54 (at n = 55 the prediction is no longer needed).


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The excess (abnormal) returns1 around the abnormal volume events examined according to the standard

event study methodology. The market adjusted and market and risk adjusted (CAPM) returns are estimated for a

28 days window [-5,+ 22] around the abnormal volume events. Both alpha and beta value for CAPM are

determined by a linear regression of daily returns on 50 days window [-55,-6] before the event. Therefore, the 22-

day cumulative average abnormal returns or CAAR [1,22] analyzed the excess returns following the abnormal

volume events and the same-day average abnormal returns or AAR[0] explained the excess returns on the

abnormal volume events. Similarly, this paper employs an intraday event study to examine the excess returns of

the prediction of an abnormal volume event and is shown by the cumulative abnormal returns. Thus, the

incremental exploitable excess returns that follow the prediction of abnormal volume event is calculated as CAAR

[After prediction till next day open].

Out-of-sample portfolio simulation

This paper formulates a trading strategy to show that abnormal volume events are exploitable. When a

stock experiences an abnormal volume event, it is added to the portfolio using the opening price of the next day

and held for 22 trading days. The rebalance is done daily at the opening call auction to maintain an equally

weighted portfolio with a 15% limit on the maximum weight of any stock. Additional rebalance2 is done after a

prediction has been made as new stock is added into the portfolio to achieve equal weight. The data is split into

two equal portions with the first half as a training session and the later as a testing session to check for robustness

of the strategy. The optimization is done on training session which searches for a parameter that gives a highest

in-sample portfolio performance (information ratio) and also shows a statistically positive excess returns. The out-

of-sample portfolio is simulated with the obtained parameter using the data in testing session, and its

performance is gauged by the information ratio3 and 4-factor alpha (Carhart, 1997).

1 All returns on day t are calculated as log (closet /closet-1) except at day 0 (event day) and day 1 that calculated as log (openday1 /closeday-1) and

log(closeday1 /openday1), respectively, to reflect the appropriate realizable excess returns. 2 Incur extra bid-ask spread round-trip cost from buying at ask price and selling at bid price.

3 Measure the portfolio’s ability to outperform benchmark (SET100 index) adjusted by the volatility of the returns in excess of benchmark


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Result and discussion

First, this paper presents the results of event study analyses (hypothesis 1 and 2). H1 stated that the

excess return after abnormal volume events defined by abnormally-high standardized volume is positive. As

shown in Panel A of Table 1, the CAAR [1,22] for V-event is positive (and significant by both statistical tests),

reaching as high as 1.516% market adjusted and 2.338% market and risk adjusted returns on average for some

threshold values. This evidence confirms the existence of the relationship between abnormal volume events and

the subsequent excess returns for the stocks listed on SET100 (Thailand) constituent during July 2015 to June

2016 as reported in other markets (Gervais et al., 2001; Huang & Heian, 2010; Bajo, 2010). The excess returns

slowly accumulate and reaches the maximum value at the end of 22-day (roughly 1-month or 4-week). The

insignificance of CAAR [-5,-1] also reinforces that this following excess returns is probably influenced by the V-

event and not from a price continuation before the event. By considering the VD-event instead, the number of

events reduces while the overall excess returns increase. The evidence suggests that an excess return after

abnormal volume events defined by abnormally-high standardized volume and standardized directional volume is

positive (hypothesis 2). As shown in Panel B of Table 1, the CAAR [1,22] for VD-event is positive (and significant

by both statistical tests), reaching up to 2.060% market adjusted and 3.089% market and risk adjusted returns on

average, higher than the CAARs of V-event. The definition of VD-event excludes many V-events that are followed

by negative excess returns, suggesting that directional volume does carry additional information. Similarly, the

weak significance of CAAR [-5,-1] supports that the VD-event mostly influences the following excess returns and

not due to a price continuation from the earlier period.

A portfolio simulation on out-of-sample data is done to reflect the performance of the strategy in live

trading. As shown in Panel A in Table 2, trading based on the observation of V-event generates a positive

information ratio and significant alpha at 5% level. However, the information ratio decrease substantially after a

commission fee of 0.15% is taken into consideration. The high turnover rate caused this massive reduction in

performance by amplifying the total transaction cost. The factor analysis also reveals that this strategy is a mix

between market and momentum trading style (but not fundamental as in value and size) which closely resemble a

trend following approach. By trading based on the observation of VD-event, the portfolio’s information ratio

improves drastically and shows a significant alpha at 1% level despite the commission fee as shown in Panel B in


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Table 2. This evidence reinforces the event study results in the sense that trading on the observation of VD-event

outperforms V-event as it possesses higher average excess returns while still retaining a sufficient number of

events.

The predictability of the VD-event is examined to push the strategy further. As mentioned by Bajo (2010),

this paper checks on the magnitude of the excess returns that belong to the day that the abnormal volume events

occur. According to Panel B in Table 1, the excess returns on the event day or AAR[0] is significant and is much

larger than the CAAR[1,22]. This result suggests that predicting the VD-event before it is revealed at the market

close could lead to an additional exploitable excess returns and thus advance the strategy. H3 stated that

abnormal volume events can be predicted by an algorithm and exploited to generate positive excess returns. As

shown in Panel C Table1, the intraday criteria can predict the VD-event at a very high precision of 94% which

result in significant incremental average excess returns of 0.986% at 1% level. This result also suggests that the

prediction algorithm prioritizes on getting the least false positive (high precision) rather than the coverage of all

events (high recall). This predictive performance achieved by delaying the prediction timing because the

uncertainty of the daily data decreases as information accumulates throughout the day.The out-of-sample

performance of the prediction-enhanced portfolio shown in Panel C in Table 2 as a result of final augmentation.

As expected from the intraday event study results, the performance improve as a consequence of this

development, reaching an information ratio of 1.205 with a significant daily 4-factor alpha of 0.185% (after

commission) at 1% level. This evidence directly answered the hypothesis H4 which stated that there exist an

implementable trading strategy based on abnormal volume events, which generates positive alphas and positive

information ratio.

Table 1: Excess returns around abnormal volume events Panel A: V-event (c1 = 2.225)

Market adjusted Market and risk adjusted Window CAAR (%) SD (%) T Test Sign Test CAAR (%) SD (%) T Test Sign Test N CAAR[-5,-1] -0.521 5.839 -1.43 -1.21 -0.288 5.691 -0.81 -0.18 258

AAR[0] 1.023 4.520 3.64*** 3.86*** 1.024 4.570 3.6*** 3.78*** 258 CAAR[1,22] 1.516 9.731 2.5** 2.79*** 2.338 11.508 3.26* 3.22*** 258 Panel B: VD-event (c1 = 2.225, c2 = 2.1)


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Market adjusted Market and risk adjusted Window CAAR (%) SD (%) T Test Sign Test CAAR (%) SD (%) T Test Sign Test N

CAAR[-5,-1] -0.765 5.551 -1.66* -1.03 -0.344 5.394 -0.77 0.25 145 AAR[0] 3.543 3.246 13.14*** 9.48*** 3.590 3.207 13.48*** 9.5*** 145

CAAR[1,22] 2.060 9.742 2.55** 2.45** 3.089 11.431 3.25*** 2.82*** 145 Panel C: Prediction of VD-event (c1 = 2.225, c2 = 2.6, b1 = 6.225, b2 = 1.6)

Predictions (93 correct predictions + 6 incorrect predictions) Window CAAR(%) SD(%) T Test Sign Test N

CAAR[After prediction till next day open] 0.986 1.703 5.76*** 5.24*** 99 Other statistics

Precision 94% Recall 70% Average prediction

timing 50.06th interval

(24.7 minutes before close) Notes: The threshold parameters associated with these definitions are shown within the table. The cumulative average abnormal returns (CAAR) are computed both with a market adjusted and a market and risk adjusted

(CAPM) approach except the intraday CAAR which calculated as raw returns. However, this intraday raw returns should not deviate significantly from its market adjusted value as on average the market intraday returns is

miniscule. The statistical significance is calculated using the parametric student’s T test, and non-parametric Wilcoxon singed rank test. ***, **, * indicate that the coefficients are significantly different from zero at 1%, 5%,

and 10% levels respectively.

Table 2: Portfolio performance on out-of-sample data (testing session) Panel A: V-event strategy

After commission Before commission Performance indicators Coeff. SE T Stat p-value Coeff. SE T Stat p-value

Information ratio 0.642 1.015

4-factor alpha (%) 0.102 0.053 1.421 0.039 0.125 0.053 1.871 0.011 Market beta 0.683 0.056 12.259 0.000 0.683 0.056 12.283 0.000 Value beta 0.087 0.104 0.837 0.404 0.083 0.103 0.799 0.426 Size beta 0.134 0.112 1.199 0.233 0.138 0.112 1.230 0.221

Momentum beta 0.477 0.093 5.110 0.000 0.475 0.093 5.093 0.000 Adjusted R2 0.666 0.666

Panel B: VD-event strategy After commission Before commission


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Performance indicator Coeff. SE T Stat p-value Coeff. SE T Stat p-value Information ratio 1.011 1.287



Panel C: Intraday VD-event anticipation strategy After commission Before commission

Performance Indicators Coeff. SE T Stat p-value Coeff. SE T Stat p-value Information ratio 1.205 1.513



Notes: The table details the out-of-sample performance of a portfolio that trades on the observation and prediction of abnormal volume event. The commission fee is set at 0.15% of traded value. All reported values based on daily frequency except information ratio which shown as a half

year value (121 days).

Conclusion

This research investigates the relationship between abnormal volume events and the associated excess

returns as well as proposes a robust trading strategy for the stock listed on SET100 index (Thailand). Consistent

with prior literature, the observation of stock’s abnormal volume events has a predictive power over its future

excess returns up to one month. Incorporating the asymmetry between buy and sell volume (VD-event) further

improves the excess returns. This finding agrees with both previous theoretical (Glosten & Milgrom, 1985; Kyle,

1985; Easley & O'Hara, 1992) and empirical (Louhichi, 2012) literature that an unbalanced trading sequence

possesses the secret information held by an informed trader and can be utilized to improve the prediction of the

futures stock returns. Our result suggests that directional volume contains incremental information after volume.


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It is possible to predict the arrival of the VD-event at a very high precision. The proposed algorithm,

which predicts based on the intraday data, can anticipate these events and generate significantly positive excess

returns. The algorithm signifies the importance of high-frequency data that, if handled correctly, it can further

improve the profitability of technical traders while introducing a slightly more risk. Also, this paper finds the

relationship between the abnormal volume events and the same-day excess returns to be consistent with earlier

findings.

The results of the portfolio simulation on out-of-sample data also agree with the event study findings. All

performance indicators improve after the definition of abnormal volume events is changed from V-event to VD-

event and reach their peak after combined with the prediction algorithm. It is worth to mention that the training

session is in a bear market while the testing session is in a bull market. This evidence suggests that the proposed

strategy is quite robust because it can outperform in both market conditions. A longer study period that includes

other market conditions would further help validate the strategy’s robustness. This paper also used one strong

assumption that the market has an infinite liquidity meaning that there is no market impact and price does not

move as marketable orders get executed. This effect is especially significant since the proposed strategy exhibit

a high trading activity (turnover) as can be seen through the impact of commission fee. A further testing on the

intraday VD-event anticipation strategy under real market liquidity is recommended to obtain results that are even

better indicative of the trading strategies’ performance in live trading.

References

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Chen, S., Chen, R., Ardell, G., & Lin, B. (2011). End-of-day stock trading volume prediction with a two-component hierarchical model. The journal of trading, 6(3), 61-68.

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Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The High-Volume Return Premium. The Journal of Finance, 56(3), 877-919. doi:10.1111/0022-1082.00349

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Satish, V., Saxena, A., & Palmer, M. (2014). Predicting intraday trading volume and volume percentages. The journal of trading, 9(3), 15-25.

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