Day Trader Behavior and Performance: Evidence from the Taiwan Futures Market

Transcription

1 Day Trader Behavior and Performance: Evidence from the Taiwan Futures Market Teng Yuan Cheng*, Hung Chih Li, Nan-Ting Chou, Kerry A. Watkins ABSTRACT Using data from the Taiwan futures market, a closer examination is given to day traders behavior and performance. Day traders performances are risk-adjusted and analyzed to identify behavioral biases and the resulting impact on performance. Upon examination, there is no evidence found that trading too much is detrimental to investment performance. Sophisticated investors are more aware of the danger of behavioral biases and are as a result less prone to the disposition effect. Contrary to expectations, day traders in my study sample are shown to be non-loss averse. There is no evidence found that supports that more successful traders follow a momentum strategy. Keywords: Day trader, Disposition effect, Futures market, Loss aversion behavior, Momentum strategies, Sophistication. * Correspondence author, Teng Yuan Cheng, School of Finance, Nanjing Audit University, Nanjing, China, tybrian@gmail.com; Hungchih Li, Graduate Institute of Finance, National Cheng Kung University, Tainan, Taiwan, hcli@mail.ncku.edu.tw; Nan-Ting Chou, Department of Economics, University of Louisville, USA, ntchou01@louisville.edu; Kerry A. Watkins, Graduate Institute of Finance, National Cheng Kung University, Tainan, Taiwan. 0

2 Day Trader Behavior and Performance: Evidence from the Taiwan Futures Market ABSTRACT Using data from the Taiwan futures market, a closer examination is given to day traders behavior and performance. Day traders performances are risk-adjusted and analyzed to identify behavioral biases and the resulting impact on performance. Upon examination, there is no evidence found that trading too much is detrimental to investment performance. Sophisticated investors are more aware of the danger of behavioral biases and are as a result less prone to the disposition effect. Contrary to expectations, day traders in my study sample are shown to be non-loss averse. There is no evidence found that supports that more successful traders follow a momentum strategy. Keywords: Day trader, Disposition effect, Futures market, Loss aversion behavior, Momentum strategies, Sophistication. 1

3 1. INTRODUCTION Since the late 1990 s, the growth in popularity of futures trading has increased dramatically. This is largely due to the perception that it offers a relatively easy way for minimum initial capital to earn a great deal of money. Advocates maintain that newer sophisticated software and easier access to real-time market information make it possible for individual investors to successfully compete with professional investors. Although this hope of easy money is widely held, much academic evidence exists that calls into question the profitability of traders. It is often assumed that people will always behave in a rational manner, but the truth is they often do not. Irrational behavior is widely known to cause an investor s financial performance to suffer. This paper will focus on day trader behavior and performance in the Taiwan futures market. By examining the transactions at an individual level, patterns of behavior can be identified. After determining the existence of any behavioral biases, these biases will then be more closely examined to determine their impact on day trader performance. One goal of this paper is to better understand what patterns of behavior produce any given performance. Barber and Odean (2000) find that a investor who is overconfident in their ability to choose winning investments will overtrade. This is done to their detriment since as a whole, they significantly underperform the market. By attaching too much relative importance on the interpretation of market information, Jordan and Diltz (2003) find that only one day trader in five is more than marginally profitable and at least 64% lose money. In addition, Barber et al. (2009) look at Taiwanese investors in the equity market, and conclude that individual investor trading results in systematic and economically large losses. They find that almost all of the individual trading losses can be traced to aggressive trading behavior. Though institutions tend not to engage in this behavior and in fact typically get a boost, the individual investor loses about 2.8% of total personal 2

4 income. While it is understood that professional traders trade more than individuals, it appears that no research looks at too-frequent trading among professional day traders in the futures market. Therefore, the first issue is to examine whether retail traders tend to trade too much. The key to ascertaining if the behavior is detrimental is to determine the true impact of frequent trading on profitability. Another behavioral bias investors often display is a financial adaptation of the prospect theory known as the disposition effect. Investors are sometimes motivated by factors that make them more unwilling to recognize losses rather than to recognize gains resulting from the sale of assets. Odean (1998) revealed that several explanations that seemed rational, such as the desire to rebalance portfolios or avoid higher trading costs, do not always reflect the data. Investors were reluctant to realize their losses. Frino et al (2004) analyzed trader behavior and found evidence of a disposition effect in the futures market at the individual level for day traders that are both on-floor professional traders (locals) and a matched sample of non-local traders. Locke and Mann (2005) affirmed that day traders reveal a disposition effect, though they do not find any expected associated costs. Day traders often practice trading discipline by setting into place rules to follow for exiting at certain times or prices. Their successful performance is based on behavior that is rational and disciplined. Behaving in a rational and disciplined manner can be attributed to the sophistication of the investor. Sophistication applied here can be defined as demographic and socioeconomic characteristics that point to higher levels of knowledge regarding investment products. Feng and Seasholes (2005) find that the higher the investor sophistication, the lower the level of disposition effect. Furthermore, Dhar and Zhu (2006) agree a sophisticated investor will have less of a disposition bias, and Grinblatt and Keloharju (2000) demonstrate that performance appears to be strongly associated with the investor s level of sophistication. 3

5 In examining the behavior of day traders, Coval and Shumway (2005) have identified conditions where behavioral biases tend to appear. A trader that has experienced losses in the morning is significantly more likely to take an above-average risk than a trader who has earned a profit. Locke and Mann (2009) produce results that agree with the initial findings of Coval and Shumway (2005): traders that have accrued morning losses are more likely to execute a higher number of bad trades where traders buy high and sell low. However, they find no evidence that trading performance is negatively affected after examination of the day traders risk-adjusted performance. They may not be exhibiting loss aversion behavior as much as trying to meet daily income targets. A third issue is to explore whether day traders display the behavior of loss aversion Jegadeesh and Titman (2001) present evidence that behavior based on momentum strategies are legitimate and have continued to be profitable. Grinblatt and Keloharju (2000) examine the performance of investors that display this past-return-based behavior and find that investors that are more sophisticated follow a momentum strategy and have positive performance on average, while less sophisticated investors follow a contrarian strategy produce negative average performance. These findings again underscore the impact the level of sophistication has on the investor s performance. Therefore, the last issue is to investigate whether day traders can make decisions to buy and sell based on past returns. There has not been found any research that together focuses on these four patterns of behavior exhibited by day traders in the futures market. By analyzing transaction records of day traders from the Taiwan stock index futures (TXF) in the Taiwan Futures Exchange, this paper hopes to extend the literature concerning these behaviors and the impact on performance. The results are important in one sense because of the increasingly large number of people that are choosing to participate in day trading. The 4

6 question to be addressed is why some day traders are profitable and others are not. By looking at individual trader accounts in the Taiwan Futures Exchange, behavior and profitability of day traders can be linked, allowing us to better evaluate the impact of each behavior style on profitability, which can thus help investor to make more rational decision. To better define the impact of behaviors on day trader profitability, traders will be segregated into groups arranged from lowest to highest based on their performance. This performance is first adjusted to take into account the risks that were taken to achieve them. Upon analyzing the results, it is expected to find that groups of traders that have a higher average number of gained contracts will have higher profits than those with a lower average. Higher numbers of day traders prone to the disposition effect should decrease as profitability increases. The risk-adjusted performance in dollars is expected to be higher due to the institution of trader disciplines designed to protect from behavior biases. Additionally, it is anticipated that groups of traders with a lower average exposure will be more profitable than groups with a higher exposure average. Finally, the percent of traders following momentum strategies should increase as day trader performance improves. 2. LITERATURE REVIEW A prominent theory of decision-making called Prospect Theory was proposed by Kahneman and Tversky (1979). Years later, this theory was extended and further developed (Tversky & Khaneman, 1992). They propose that people do not consider final wealth levels, but instead evaluate risks undertaken in terms of gains and losses. These gains and losses are processed in a way that maximizes an S-shaped value function that is concave for gains and convex for losses. Since the function is steeper 5

7 for losses than for gains, this implies that people for the most part are risk averse. This helps to understand why if an investor is holding a stock that has risen in value since the time of purchase, he may think of the stock as trading at a gain. The reference point is assumed to be the purchase price. Upon examining some traders tendency to trade too much, Barber and Odean (2000) present evidence that excessive active trading can be a hazard to an investor s wealth. Frequent trading carries with it a significant performance penalty. If an investor exhibits overconfidence, this leads to excessive trading and increasingly active trading leads to lower after-trading-cost returns. Those that traded the most had the most anemic yearly returns of 11.4% while the market returned 17.9%. Jordan and Diltz (2003) find that about twice as many day traders lose money as make money, and only about 20% of day traders are more than marginally profitable. An overconfident day trader lured by the prospect of an easy way to make substantial gains may overestimate their ability to correctly interpret market information. In a 2009 article by Barber et al., upon analyzing trading activity on the Taiwan Stock Exchange, they further define the cost to investment portfolios by overtrading. They find that institutions tend not to participate in this behavior; however, individuals suffer a 3.8 annual performance penalty. Aggressive trading is linked to virtually all individual trading losses. Over the long term, this shortfall caused by trading too much will greatly reduce potential wealth. Since the disposition effect was labeled by Shefrin and Statman (1985), literature focusing on this behavior has been growing. While this behavior seems to be a fundamental feature in trading and contributes greatly towards our understanding of behavior finance, the reason for it remains unclear. For example, Odean (1998) looks at trading records and calculated a proportion of gains and losses realized. Study revealed that several explanations that seemed reasonable, such as rebalancing portfolios or 6

8 transaction costs, do not always reflect the data. The conclusion is that investors are reluctant to realize their losses. Frino et al. (2004) examined the disposition effect in the futures market setting since traders in this environment were unlikely to consider portfolio rebalancing, transaction costs and taxation to be motivators. They looked at samples of two different types of day traders: professional traders known as locals, and non-local traders. They found evidence of a disposition effect among both samples. Since prior research could offer no evidence of a rational explanation for the disposition effect, they compared the frequency of losing positions becoming profitable for locals and matched non-locals to ascertain whether evidence existed of rational informed trading as an explanation for the disposition effect. Their hypothesis that the disposition effect is reinforced by behavioral biases can not be rejected. However, what their research revealed was that among professional futures traders their evidence supported the idea that the inclination to ride losses is at least partly a product of rational informed trading. Locke and Mann (2005) add to the study of the disposition effect by looking at the disciplines of professional full-time traders. Knowing they are susceptible to behavior bias, professional traders often guard by having in place predetermined exit points, such as a particular time or price, to lessen the likelihood of potential behavioral costs. To link whether trading success is related to trading behavior, Locke and Mann (2005) use an indicator labeled risk-adjusted performance or RAP which is the average daily income divided by the value-at-risk (VaR). Using transactions-level data for professional futures traders, they find that full-time traders hold losing investments significantly longer than gaining ones, but do not find evidence of costs associated with this behavior. In addition, the authors discover that the aversion to realizing losses is not the only trading behavior driving the results. While offsetting losses quickly is likely 7

9 to make traders more successful in the future, speed in closing gains can also be used as a predictor of success. Using futures data from the Chicago Board of Trade, Coval and Shumway (2005) have presented strong evidence identifying loss aversion among day traders, the conditions in which the bias appears and the resulting consequences. If traders incur losses in the morning, they are more likely to assume greater afternoon risk than if they had incurred morning gains. When faced with losing money for the day, a losing trader is 15.5% more likely to take an above-average afternoon risk than is a winning trader. Data from the 1995 Chicago Mercantile Exchange was used by Locke and Mann (2009) to further examine loss aversion behavior among day traders and how the morning trading results impact the afternoon trading behavior of professional futures traders. They delineate that even though traders with morning losses do indeed increase afternoon trading and are more likely to execute a higher number of bad trades, it is perhaps not due to loss aversion behavior. Instead they assert that afternoon activity is consistent with the existence of daily income targets set by the trader. Day traders can exhibit past-return-based behavior to make future decisions to buy and sell. Jegadeesh and Titman (2001) present evidence that behavior based on momentum strategies of buying past winners and selling past losers are legitimate and have continued to be profitable. In addition, the return predictability has not been eliminated by investors altering their investment behavior. Grinnblatt and Keloharju (2000) analyze the degree in which past returns affect the inclination to buy and sell. They focus on momentum and contrarian traders. A correlation is found between those demonstrating momentum behavior and investor performance and both tend to be associated with the level of sophistication of the investor. Foreign investors tend to follow a momentum strategy and have positive 8

10 performance on average, while household investors lean towards a contrarian strategy that produces negative average performance. 3. RESEARCH DESIGN AND METHODOLOGY The data consists of the complete set of trades of the nearest-to-maturity TAIEX Futures (hereafter, TX, the tick symbol) from the Taiwan Futures Exchange (TAIFEX) from January 1, 2004 to December 31, Introduced in July 1998, TX is the first and most active index product traded in Taiwan, and accounts for close to 70% of the trading volume of the TAIFEX futures contracts. TX is based on the major stock index of the Taiwan Stock Exchange (TSE), the TSE Capitalization Weighted Stock Index (TAIEX), which includes all stocks traded on the TSE. By focusing on futures contracts as opposed to stocks, complicated issues such as frequency of trading, risk levels, as well as portfolio composition and rebalancing are avoided. Furthermore, the daily marking to market that leads futures traders to constantly evaluate their performance makes futures traders trading a better reflection of their profit motive and offers a clearer opportunity to examine any behavior biases. The absence of capital gain taxes in Taiwan eliminates tax-loss selling as a possible explanation of the disposition effect. There are a total of 90,895 accounts from which the sample is taken. The data includes information such as the time, date, price, number of contracts, and a buy-sell indicator of the transaction. Additionally, each record also includes a unique, Taiwan Futures Exchange-assigned account number that makes it possible to distinctively identify every trader and indicate whether the trader is an individual, institution, or proprietary trader. Trades by institutional and proprietary traders are excluded. Instead, the focus is on trades executed by individual traders. This is done for three reasons. First, 9

11 individual traders are most inclined to trade for themselves. The motive for any trade is not tainted by the inclusion of factors such as an agency relationship or hedging concern. Rather, pressured purely by the need to accumulate wealth in order to survive, they make ideal subjects for the analysis of profitability and behavior biases. Second, many institutions employ multiple traders who work in rotating shifts. When this occurs, it causes trades by institutions to be a reflection of the behavior of more than a single individual, thus distorting the analysis of individual behavior biases. Lastly, since the majority of traders in the TAIFEX are executed by individuals, it seems most contributory to focus attention on them and make their trades the main topic of study. The research design framework is to test the existence of four trading behaviors among day traders in the futures market: trading too much, disposition effect, loss aversion and momentum trading. The aim is to identify the existence and measure the prevalence of each behavior. Then the goal is to determine the impact of each behavior on a day trader s bottom-line profitability. Upon examining evidence of irrational behavior among investors, Locke and Mann (2005) looked at professional traders who act in a manner consistent with the disposition effect, but are in fact employing trading strategies designed to minimize potential behavioral influences. These are commonly referred to as trading disciplines. They found that while successful full-time traders exhibit more of a disposition effect, there is no evidence of costs associated with acting in this manner. Their contribution was to present evidence that employing effective trading disciplines is of higher importance than whether one is exhibiting a behavioral bias. Moreover, measures of relative trading discipline can be a more accurate indicator of future trading success. Locke and Mann (2005) asserted that there ought to exist a direct relation between trading success and trading revenue, and formulated a measure of success known as risk-adjusted performance (RAP). The RAP is designed to measure how a trader s 10

12 daily return relates to the amount of money required to cover potential losses that could occur during the trading period. Average _ Profit RAP VaR The RAP is the average daily income divided by the value-at-risk (VaR). The VaR measure is defined as the 95th percentile daily maximum exposure for the trader. As an example, assuming that a day trader trades for 100 days, the trader s fifth largest potential loss over the 100 days is used as the VaR. Traders who are successful in generating income, but do so by exposing themselves to higher levels of risk will be given a lower RAP measure. Its application is useful because it can be a good indicator of success that takes into account the risks taken to achieve it. A higher RAP indicates the day trader is more profitable than one with a lower RAP. Barber and Odean (2000) quoted Benjamin Graham when he said, The investor s chief problem and even his worst enemy is likely to be himself. They show that individual active-trading investors in common stocks undermine their own performance and incur a large performance penalty by trading too frequently. Trading too much is a hazard to profitability. While this was shown to apply to traders in common stocks, no such research has been found that tests whether too-frequent trading is also detrimental to day traders in the futures market. Professional day traders by definition could be said to engage in trading that is frequent. The question is whether or not professional day traders can also trade too much for their own good. There appears to be no research that directly addresses this concern. To ascertain if day traders trading frequency is proving to be counterproductive, the following simple formula will be used: Number _ of _ Gained _ Contracts Percentage _ of _ Gained _ Contracts Number _ of _ Total _ Contracts 11

13 By taking the number of contracts where gains were realized, having already deducted transaction fees, and dividing it into the number of total contracts, the result will be a proportion showing the percentage of contracts where true gains were realized. If the percentage of gained contracts is high, the majority of trades are providing profit. Calculating the percentage of gained contracts is instrumental in providing insight into whether frequent trading is a detrimental behavior since it identifies the proportion of gains irrespective of how many orders were placed. The bottom line is that traders may frequently trade, but if they do so profitably, it cannot be said that frequent trading is an adverse behavior. Similarly, the same formula is used to get a percentage of contracts that lost money: Percentage _ of _ Losing _ Contracts Number _ of _ Losing _ Contracts Number _ of _ Total _ Contracts A high proportion suggests that though the day trader is active in trading, the activity is not delivering profitable performance. Next, after calculating the two proportions, the percentage of losing contracts is subtracted from the percentage of gained contracts. This provides insight as to the trading frequency s impact on day trader profitability. If the difference between the percent of gained contracts and losing contracts is significantly greater than zero, then it is expected that frequent trading of retail trader is not detrimental to profitability. To determine the presence of a disposition effect, gains and losses by each trader are tracked, calculated and then analyzed to see how profitability is affected by the risktaking behavior. To achieve this, a sequence of trades for each trader is constructed for each contract by tracing executed trades back to the first trade in the series. Since this requires searching for trades executed as far as one year before January 2004, the examination extends beyond the one-year sample period. Once the first trade is located, each subsequent trade is tracked and marked to market after each trade. All the 12

14 necessary statistics such as open interests (OIs), weighted average costs, and realized and unrealized gains or losses are calculated and updated until the contract matures. This continuous updating after each trade generates a running tally of OIs and both realized and unrealized gains or losses. Following the methodology of Cheng et al. (2009) and examining how the unrealized gains or losses affect the decision to offset the positions traders have accumulated makes it possible to investigate the disposition effect. The offsetting trade made by a trader results in either a realized gain or a loss. It is possible to calculate the proportion of offsets that results in a realized gain, called proportion of positive offset (PPO): PPO t number _ of _ contracts _ offsett open_ int rests t 1 If by using the same formula the proportion of offsets leads to a realized loss, this is called a proportion of negative offset (PNO): number _ of _ contracts_ offsett PNO t open _ int erests t 1 By subtracting the two proportions ( PPOt PNO t ), it is then possible to identify the existence of a disposition effect if a higher proportion of offsets result in gains rather than in losses (PPOt PNOt > 0). If traders are more inclined to offset a position resulting in a realized gain rather than a realized loss, the result will be significantly greater than 0. Significance is determined by a t-test. In addition, by examining PPO and PNO at an individual level, further insight is possible as to how the disposition effect varies among traders. Since each trader is tracked from the first trade of each contract until the last contract expires, it is possible to calculate a PPO or PNO whenever an offsetting trade 13

15 takes place. This allows for a higher level of detail and distinctiveness, which is not available under the aggregate trader approach used in previous studies. Furthermore, employing this methodology makes it possible for the inclusion of all traders, whether they accumulate positions or not. This proves be an important consideration since individual traders in Taiwan tend to trade in and out of positions very quickly, though still many do not. This methodology preserves trader heterogeneity and allows further diversity exploration. In addition, traders who are profitable are examined separately from traders who are unprofitable to look into the linkage between the disposition effect and its effect on trader profitability. Upon examining transactions-level data for professional futures traders, Coval and Shumway (2005) find evidence that day traders are loss averse. If day traders incur losses in the morning, they take above-average risks in the afternoon in efforts to recover, though trading performance suffers. They determine this loss-related irrational behavior exists by looking at the effect of accumulated profitability on subsequent behavior. Locke and Mann (2009) also use this methodology of cumulative profitability and agree that risk taking increases among traders with morning losses. However, they come to a different conclusion. They say professional traders increase work effort to reach trader daily income targets. To examine day traders behavior, both Coval and Shumway (2005) and Locke and Mann (2009) divide each trading day into two intervals of equal duration called morning and afternoon. It is assumed that traders calculate their income at the close of the morning session and add the mark-to-market value of existing positions using prices at the morning close. This allows the separation of trader days into those whose morning income is positive or negative. The afternoon sessions are also determined and the morning and afternoon sessions are compared. If the afternoon session is 14

16 significantly higher, then the trader is loss averse and willing to take risks in an attempt to recover morning losses. Noteworthy is how Coval and Shumway (2005) and Locke and Mann (2009) both use an accumulated value of profits or losses in the morning, and then compare the result with the actions day traders make in the afternoon. This methodology will be applied to the data, but with a change in how the benchmark is obtained. Whereas they use the trading midday break as a cutoff to get the morning accumulated profits or losses, instead the largest realized loss (LRL) for each trader will be identified. Once the LRL is known, a look will be taken at the loss exposure of each trader one hour before and one hour after the LRL. If the exposure amount one hour before the LRL is significantly greater than the exposure amount one hour after (exposure_after < exposure_before), then the day trader is considered to be loss averse and willing to take a greater risk that day in order to recover their losses. To ascertain whether day traders employ momentum strategies, linear regression analysis is utilized to find the relationship between two variables. In particular, a closer look is given to how trader positions covary with readily available market information. The determinant of trading decisions by day traders is tested by estimating the following equation: NP t R m,t 1 where NP represents the change in the net position at time t. Here a net position is defined as the long position (given a positive sign) minus the short position (given a negative sign) which results in the net transaction quantity in units of contracts. R m,t 1 denotes the market return at time t-1. Both one and five minute intervals have been chosen for the regression. The one minute interval represents one minute earlier and the 15

17 five minute interval represents five minutes earlier. If the is significantly positive, then it constitutes evidence of momentum strategies being used. A significantly negative will show traders who are contrarians. 4. RESEARCH RESULTS In 2004, there were 90,895 accounts that traded in the Taiwan Futures Exchange with the number of trades totaling 17,983,478. The sample used in this study consisted of 105 accounts which is 0.12% of the total number of accounts. The trading volume of the sample totaled 783,872 trades, or 4.14% of the total trading volume. During the sample period, the average number of trades placed was 198 per account. It is 190 trades per account if the sample is subtracted from the total number of accounts. The sample average was 7,084. The accounts in the sample are hereafter categorized and often referred to as day traders. To be classified as a day trader, three criteria must be met. Included first were accounts where no overnight open interests were held during the sample period. Second, an account was included if it had overnight interests, but the ratio of days with open interests to the total number of trading days is 10 percent or less. Third, each account must have accumulated at least 90 trading days within the sample period. All accounts sustained a net loss of -28,564,603 ticks (1 tick = $200 NT), while day traders earned a net profit of 1,147,835 ticks. This means that each trader in 2004 incurred a 314 tick loss ($62,800 NT) on average, while the average day trader made a 10,932 ($2,186,400 NT) gain. The sample s profit earned by profitable accounts was 1.55% of the total profit, and the sample s loss experienced by unprofitable accounts was.06% of the losses sustained by all unprofitable accounts. Considering that day traders make up 0.12% of the total number of traders, this reveals that among profitable 16

18 day traders, their profits are higher on average. Among those day traders who are not profitable, losses suffered are less than the average trader. In looking at the sample period for one year, the maximum number of days of trading days for all accounts appears to be high. This is because the sample period actually begins prior to January 1, Since measurements are taken when contracts expire, some of the contracts begin prior to 2004, therefore adding to the total number of trading days possible. TABLE 1 DESCRIPTIVE STATISTICS All Accounts Sample Percent of Total Number of Traders 90, % Total Trading Volume 17,983, , % Profitable Traders' Total Profit 78,372,351 1,215, % Unprofitable Traders' Total Loss -106,936,954-67, % Total Net Profit/Loss -28,564,603 1,147, Average Profit/Loss , Maximum Profit 7,365, , Minimum Profit -3,326,170-12, Average Trading Volume 198 7, Maximum Trading Volume 576,060 73, Minimum Trading Volume Max Trading Days Min Trading Days Note: All trading volumes are given in number of contracts. All profits and losses are given in ticks. 1 tick = $200 NT. 190 is the average number of trades for accounts not in the sample. Table 2 reports the distributional statistics for RAP rankings. The average RAP for all day traders was.72, and all of the findings are highly statistically significant. The lowest quintile was the only group whose average profit was negative at -$1,602 NT dollars. The total average profit for the sample was $23,778 with a 95% potential loss (VaR) of $51,

19 The purpose of the RAP is to factor in the risks taken to achieve profits. It is obtained by dividing the average profit by the VaR. The RAP shows the relationship between income and potential loss. Table 2 indicates that for a given level of income, traders with lower RAPs expose themselves to much more risk. There is, however, a systematic increase of average profit. As RAP goes up, so does average profit, though the 95% potential loss may fluctuate. These results give support to the findings of Locke and Mann (2005) that suggest there is a link between trading discipline and success. Day traders that risked more for a given level of income experienced lower profits. Table 2 Risk-Adjusted Performance Mean Daily Income for Median Trader (NT$) 95% Potential Loss for Median Trader (NT$) RAP for Median Trader within the Quintile p-value (RAP) Lowest Quintile RAP -$553 $19, Below Median RAP $714 $15, Median RAP $2,766 $19, Above Median RAP $7,104 $20, Highest Quintile RAP $44,046 $16, Total $1,867 $16, The 105 day traders are divided into five groups of 21 arranged by RAP from lowest to highest. The loss percentage is subtracted from the gained contracts giving the mean difference. The three lowest quintiles have a mean difference that is not significantly greater than zero, and therefore it is expected that the groups are not trading profitably. However, since the Above Median RAP and the Highest Quintile RAP mean differences are significantly greater than zero, it is expected that these groups as a whole are trading profitably. In a 2009 article by Barber, Odean et al., the authors assert that in aggregate, the individuals portfolio incurs an annual performance penalty of 3.8 percent due to aggressive trading. The results in Table 3 shed more light on whether or 18

20 not the assertion that frequent trading is hazardous to a day trader s wealth is true. The top two quintiles of day traders traded at a profit no matter how aggressive or frequent trades were placed. Table 3 Results of Test for Trading Too Much Sampl e Size Average Average Trade Gained / Volume Lost (Contracts (NT) ) Gaine d Pct. Loss Pct. Mean Differenc e p-value Lowest Quintile RAP $1, Below Median RAP $1, Median RAP $10, Above Median RAP $29, a Highest Quintile RAP $78, a Note: a denotes 1% significance. The average number of trades for accounts not included in the sample is 190. Looking again at the day traders grouped into five quintiles and ranked by RAP, they are divided into five groups from least profitable to most profitable. The traders are then examined individually to ascertain which ones exhibit a disposition effect. After subtracting the proportion of negative offsets from the proportion of positive offsets, the mean difference is obtained. All groups have a negative mean difference though only the three highest quartiles are significant to the 10% level. This negative mean difference reveals that these groups are not inclined toward exhibiting the disposition effect. This avoidance of exhibiting the disposition effect appears to increase as day trader RAP increases. Feng and Seasholes (2005) found that investors who are sophisticated are less prone to the disposition effect than the average investor. It is assumed that investors who are sophisticated would be more aware of behavioral biases that affect bottom-line performance, and are therefore more likely to employ trading 19

21 strategies to keep from falling victim. The results presented in Table 4 seem to support Feng and Seasholes findings. Table 4 Results of Test for Disposition Effect Sample Size PPO PNO Mean Difference Average Gained / Lost p-value Lowest Quintile RAP $1, Below Median RAP $1, Median RAP $10, a Above Median RAP $29, b Highest Quintile RAP $78, a Note: a denotes 1% significance. b denotes 10% significance. PPO-PNO = Disposition Effect. For each day trader, the exposure amount one hour before the largest realized loss is subtracted from the exposure amount one hour after. Within each of the five groups arranged sequentially from lower to higher RAP, the average exposure amount is determined. No traders have significant positive means and can be shown to be loss averse. While a good portion of traders in the lower four RAP quintiles can be shown to be non-loss averse with at least marginal significance, the most robust results showing a high percentage of traders that are non-loss averse are in the fifth quintile. 74% of traders are shown to be non-loss averse. Coval and Shumway (2005) say that traders that are loss averse are eager to reverse their fortunes and as a result assume greater risk, purchase contrasts at higher prices and sell at lower prices. However, the results in Table 5 reveal that traders are shown to be largely non-loss averse. This is contrary to the findings of Coval and Shumway (2005) and Locke and Mann (2009). Locke and Mann (2009), however, also found no evidence that shows a consistent deterioration of trading performance subsequent to prior losses. They assert that day traders have daily income targets and exhibit increased work effort after a period of abnormal losses. 20

22 Table 5 Results of Test for Loss Aversion Sampl e Size Exposur e 1 Hour After LRL (Mean) Exposur e 1 Hour Before LRL (Mean) Exposure After - Exposure Before Loss Averse Mean Positiv e (Signif) Non-Loss Averse Mean Negative (Signif) Pct. Of Sampl e Signif. Lowest Quintile RAP 14 $496 $8,118 -$7, a 36% Below Median RAP 11 $5,266 $16,040 -$10, b 55% Median RAP 13 $62,014 $100,586 -$38, a 46% Above Median RAP 19 $17,632 $40,672 -$23, a 58% Highest Quintile RAP 19 $4,254 $13,014 -$8, % Total 76 $13,424 $28,768 -$15, % Note: Exposure amounts given in NTD. All significant to 5% level except where a denotes 1 trader at 10% significance included and b denotes 2 traders at 10% significance included. The 105 day traders are divided into five groups of 21 arranged by RAP from lowest to highest. From the regression equation, we know that if the coefficient is significantly positive, the day trader is following a momentum strategy. Contrarians will have a negative coefficient. It is expected that the findings will be similar to those given by Grinblatt and Keloharju (2000). They found that the sophistication of investors will drive performance and that sophisticated investors follow a momentum strategy. Two tests are given. The first is a test where the interval t-1 represents the market return one minute earlier. To add depth to the analysis, a second test is given where the interval t-1 is the market return five minutes earlier. Upon examination of the results in Table 6 where the interval is the market return one minute earlier, the results are curious. In the four lower RAP quintiles, the number of day traders with a significantly positive is either 15 or 19 traders out of 21 possible. However in the highest RAP quintile, there is a large shift where only 5 follow a momentum strategy and 14 are contrarians out of 21 possible. In the highest quintile RAP, the regression results do not give the expected results consistent with those of 21

23 Grinblatt and Keloharju (2000). Of the 105 traders in the sample, 95 traders, or ninety percent, can be said to be following either a momentum or contrarian strategy. Table 6 Results of Test for Momentum One Minute Interval Sample Size Coefficient Positive and Significant Momentum Coefficient Negative and Significant Contrarian Percent Traders Following Momentum Percent Traders Following Contrarian Pct. Signif. Lowest Quintile RAP % 5% 76% Below Median RAP % 10% 100% Median RAP % 0% 90% Above Median RAP % 24% 95% Highest Quintile RAP % 67% 90% Total % 21% 90% In Table 7 where a five minute interval is used, the regression test yields a lower percent of the sample that can be said to be following either a momentum or contrarian strategy. Seventy-one percent of the sample can be said to follow one of the two strategies. In the middle three quintiles, a larger number of traders follow a momentum strategy; however, within the lowest and highest RAP quintiles, larger numbers of traders follow a contrarian strategy. This test also yields results inconsistent with previous studies such as Grinblatt and Keloharju (2000). Table 7 Results of Test for Momentum Five Minute Interval Sample Size Coefficient Positive and Significant Momentum Coefficient Negative and Significant Contrarian Percent Traders Following Momentum Percent Traders Following Contrarian Pct. Signif. Lowest Quintile RAP % 43% 62% Below Median RAP % 19% 81% Median RAP % 14% 76% Above Median RAP % 24% 62% Highest Quintile RAP % 67% 76% Total % 33% 71% 22

24 5. CONCLUSION AND SUGGESTIONS Upon closer examination of day traders and their behavior, several insights can be made regarding their trading behaviors and the resulting impact on their profitability. Factoring in the risks taken for the revenue achieved gives a more accurate picture of trading success. As a group, day traders are profitable while the other traders as a group experience a loss. Among day traders, the top performers trade at a greater profitability even when adjusting for risk, and day traders in the highest quintile trade at the greatest profitability. Upon examination, no evidence found that trading too much is detrimental to investment performance. Frequent trading in the futures market appears to not have the damaging impact to profitability that other studies such as Barber and Odean (2000) have shown. Concerning the disposition effect, it was expected that sophisticated investors were more aware of the danger of behavioral biases and as a result are less prone to the disposition effect. The findings show this to be true. Since the sample consists of traders futures market who have traded frequently, a certain level of sophistication is assumed. Top performers exhibited the strongest disinclination toward the disposition effect. A sizable portion of day traders in the sample are not shown to be loss averse, and do not experience a significant deterioration in trading performance. This outcome does not meet the expected results. Coval and Shumway (2005) and Locke and Mann (2009) found that traders were loss averse, though they differed as to whether or not this behavior negatively affected trading performance. Lastly, when looking at the market return one minute earlier, there is a significant portion of the lower 80 percent of day traders judging by risk-adjusted performance that utilize a momentum strategy, but there is a shift when looking at the top 20 percent. They follow a contrarian strategy 23

25 which contradicts the expected results. Additionally, when using a five minute interval, results are similar except that in addition to the top 20 percent, a larger percentage of the bottom 20 percent also follows a contrarian strategy. It seems that when day traders employ discipline strategies, they tend to be more profitable. REFERENCES Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2009). Just how much do individual investors lose by trading? Review of Financial Studies, 22(2), Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance, 55(2), Cheng, T. Y., Lee, C. I., Li, H. C., & Lin, C. H. (2009). A direct test of the link between the disposition effect and probability in futures market. Paper presented at the meeting of the European Financial Management Association, Hamburg, Germany. Coval, J. D., & Shumway, T. (2005). Do behavioral biases affect prices? Journal of Finance, 60(1), Dhar, R., & Zhu, N. (2006). Up close and personal: Investor sophistication and the disposition effect. Management Science, 52(5), Feng, L., & Seasholes, M. (2005). Do investor sophistication and trading experience eliminate behavioral biases in financial markets? Review of Finance, 9, Frino, A., Johnstone, D., & Zheng, H. (2004). The propensity for local traders in futures markets to ride losses: Evidence of rational or irrational behavior? Journal of Banking and Finance, 28(2), Grinblatt, M., & Keloharju, M. (2000). The investment behavior and performance of various investor types: A study of Finland s unique data set. Journal of Financial Economics, 55(1), Jegadeesh, N., & Titman, S. (2001). Profitability of momentum strategies: An evaluation of alternative explanations. Journal of Finance, 56(2), Jordan D., & Diltz, J. (1985). The profitability of day traders. Financial Analysts Journal, 59(6), Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), Locke, P. & Mann, S.C. (2005). Professional trader discipline and trade disposition. Journal of Financial Economics, 76(2),

26 Locke, P. & Mann, S.C. (2009). Daily income target effects: Evidence from a large sample of professional commodities traders. Journal of Financial Markets, 12(4), Odean, T. (1998). Are investors reluctant to realize their losses? Journal of Finance, 53(5), Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long. Journal of Finance, 40(3), Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4),