Overconfident Individual Day Traders: Evidence from Taiwan Futures Market

Transcription

1 Overconfident Individual Day Traders: Evidence from Taiwan Futures Market Wei-Yu Kuo a Tse-Chun Lin b a Department of International Business, National Chengchi University, Taiwan b School of Economics and Finance, University of Hong Kong, Hong Kong (This version January 16, 2012) Abstract We take advantage of a day-trading policy implementation in Taiwan futures market to investigate the performance of day traders. Since October 2007, investors can commit to be day traders by closing the day-trade position on the same day to enjoy 50% deduction for the initial margin. This ex ante nature provides us a laboratory to explore their trading behavior without potential biases from other trading motivations and disposition effect. The result shows that the 3,470 individual day traders on average make a significantly loss of 61.5 (26.7) thousand New Taiwan dollars after (before) transaction costs from October 2007 to September This implies that those day traders are not only overconfident in precision of information but also having biased interpretation of information. We also find that trading is only hazardous to the overconfident losers, but not to the winners. Last, the evidence suggests that more experienced individual investors exhibit more aggressive day trading behavior. But these experienced day traders do not learn their types or gain superior trading skills, which could mitigate the losses due to overconfidence. JEL Classification: G10, G12, G13, C51. Keywords: overconfidence, index futures, day traders, individual investors, learning We have benefited from the comments of Xiaohui Gao, Mark Grinblatt, Juhani Linnainmaa, Mark Seasholes, Kam-Ming Wan, Pai Xu, and participants at Paris December 2011 Finance Meeting, 19th SFM Conference, National Chengchi University and University of Hong Kong. Tse-Chun Lin gratefully acknowledges the research support from the Faculty of Business and Economics at the University of Hong Kong and the Research Grants Council of the Hong Kong SAR government. Any remaining errors are ours. Tel.: ; fax: address: wkuo@nccu.edu.tw. Tel.: ; fax: address: tsechunlin@hku.hk.

2 1. Introduction Day traders are both controversial and mysterious. Few studies have been devoted to them mainly due to the lack of their identity and trading history. Even with comprehensive investor account data, the day traders can only be identified ex post after tracing their trading record at daily frequency. However, as pointed out by Barber and Odean (2000), investors could trade for reasons like consumption needs, tax-motivation, liquidity, portfolio re-balancing, etc. Hence, ideally, we would like to have an ex ante way of identifying day traders who only trade for profits as it would provide us a more accurate documentation of their trading activities. This paper takes advantage of a day-trading policy implementation in Taiwan futures market, which provides us a clear-cut way to identify day traders ex ante, to study the performance of day traders. On October 8th 2007, Taiwan Futures Exchange (TAIFEX) implemented a new margin requirement policy that allows investors to indicate their orders as day-trade orders and deposit only halved required margin. When a day-trade order is executed successfully, the position has to be closed before the end of the day. Essentially, an investor commits to be literally a day trader ex ante. Unlike the existing literature, which relies on the executed transactions to define day traders, our day traders reveal themselves by submitting the day-trade orders. Hence, this study focuses on the day traders identified by the day-trade orders instead of investors who merely buy and sell the same security on the same day. 1 Our contribution is to take advantage of this new margin rule in the Taiwan futures market to address the following questions: Are the day traders overconfident in the precision of information? Do they have biased interpretation of information? Do they lose more money by trading more contracts? In other words, is trading hazardous to their wealth? Is there any difference in the relation between trading frequency and performance among winning and losing day traders? Finally, do the day traders past trading experience and performance affect their day trading behavior? The first two questions are investigated in in Odean (1999) for regular individual investors, 1 For the rest of the paper, the day trading and day traders in the Taiwan futures market are referred to the transactions executed by day-trade orders and the investors who submit day-trade orders respectively. 1

3 while the third is addressed in Barber and Odean (2000). However, for reasons to be articulated below, we believe that it is worth revisiting the questions with our day trader sample. The last two questions are an insightful extension that provides a more complete picture on the investment behaviors of day traders. Several features motivate us to answer these questions by studying the day traders in Taiwan futures market. First, the day traders have to close their positions before the end of the day, which leads to an additional liquidation risk. Intuitively, those who engage in day trading are supposed to be either the most informed or the most confident that they are the informed since they are willing to take the extra liquidation risk for reduced margin. This unique institutional setup provides us a sharper angle to revisit the question of investor overconfidence. Second, the maximum investment horizon for the day traders is just one day by default. Therefore, the realized end-of-day positions of our day traders are not related to the intra-day returns. This is an important feature since we can observe the day traders positions without impact from disposition effect. 2 Besides, we do not need to calculate average returns of several holding periods to accommodate variation of investment horizons among investors as in Odean (1999). Third, our day traders can easily capitalize their negative information by shorting since there are no short-sale constraints in the futures market. Jordan and Diltz (2003) point out that the conventional Wall Street wisdom holds that day traders are profitable when the overall market is running up. One explanation is the high cost of selling short stocks. However, the cost of shorting futures is identical to taking a long one. We are thus able to examine the profitability of day traders for both directional movements of the market. Finally, our day traders are not trading for consumption or liquidity needs as they cannot get proceeds from selling short. Instead, they have to deposit the margin. The reason to trade is also unlikely to be portfolio re-balancing or diversification since they have to close their positions within a day. Moreover, there is no capital gain tax in Taiwan. We can safely conclude that the 2 Barber and Odean (2000) use monthly holding positions and calculate monthly returns to avoid such bias. By doing so, they need to assume that trading takes place on the last day of the month and also ignore the intra-month trading. 2

4 main purpose to submit day-trade orders is for leveraging up the position to maximize the trading profit. Together with previous points, Taiwan futures market provides an ideal environment to answer the questions raised above. By using the complete trading record in TAIFEX from October 8 of 2007 to September 30 of 2008, we find that the average net profit for a round-trip trade is 613 Taiwan dollar (one US dollar is roughly 32 TWD during our sample period) for domestic individual day traders. Aggregating to the investor level, the 3,470 individual day traders make a significantly loss of 61.5 thousand TWD after taking transaction costs into account. The result implies that the individual day traders are overconfident in the precision of information. Before transaction costs, the individual day traders still suffer an average of 26.7 thousand TWD losses. This indicates that those day traders are not only overconfident but also having biased information. The finding is consistent with Odean (1999) who studies individual traders in the US stock market. For institutional day traders, the result is similar but statistically insignificant due to a much smaller number of investors. We also find that the numbers of short position and long position are similar for the individual day traders when we differentiate the direction of the round-trip trades. The individual day traders are not as reluctant to short as US individual investors (see Boehmer and Kelley (2009)). Interestingly, the average return of short-initiated round-trip trades is higher than which of the long-initiated round-trip trades for individual investors, with a 160 TWD profit for short positions and a 676 TWD loss for long positions respectively. It implies that when individual investors are shorting, they are less overconfident than when they are taking long positions. In addition, the result shows that the relation between trading frequency and performance is U- shaped. By performing a quantile regression of profits on contract number for every 5 percentile, we find that for day traders below the 15th percentile in profit, the more they trade, the more losses they suffer. However, for day traders above the 60th percentile, the more they trade, they more profit they make. Collectively, the documented relation between profit and contract number corroborates that trading is only hazardous to the wealth of overconfident losers, but not to which 3

5 of the informed or skillful winners. This is consistent with Barber, Lee, Liu, and Odean (2011) who find cross-sectional differences in day trading skill in Taiwan stock market. Last, we find that the more experienced individual investors exhibit more aggressive day trading behavior. However, the profits earned in the past is ony weakly related to the day trading activity. The higher past profit alone does not contribute to higher day trading activity. The result also shows that the overconfident day traders do not learn their types from their past performance as suggested by Gervais and Odean (2001). The trading experience plays a limited role to help individual investors to earn profit by taking advantage of the extra leverage brought by the new day trading policy. The rest of the paper proceeds as follows. Section 2 provides a discussion of the literature most closely related to our work. Section 3 describes the data on the Taiwan futures market. Section 4 outlines our empirical approach, and presents our main results and additional confirmation exercises. Conclusions and summary of our findings are provided in Section Connection to the literature on day trader behavior and overconfidence bias This section provides a brief literature review, and aims to posit our empirical findings in the context of both day traders and their overconfidence bias Day traders Harris and Schultz (1998) use Nasdaq s Small Order Execution System to analyze the performance of individual day traders. The data contains around 10,000 round-trip trades over a three week period. Their individual day traders earn a small average profit, even trading with market makers. Jordan and Diltz (2003) examine the profitability of a sample of 324 US day traders. They show that about twice as many day traders lose money as make money. They also document that around 20 percent of sample day traders were more than marginally profitable. Garvey and 4

6 Murphy (2005) report consistent intraday trading profits by 15 proprietary stock traders through 96 thousand trades in three months in Garvey and Murphy (2004) use the same data and find that those traders earned more than $1.4 million in intraday trading profits. Instead of studying a small number of US day traders, the two following papers examine a large sample of day traders by taking advantage of the data availability from two international markets. Linnainmaa (2005) studies a Finish data set, which contains 22,529 day traders and spans from January 1995 to November He shows that individual day traders are reluctant to close losing day trades, which consequently hurts their performance of portfolios up to -6% in three months after a holdings change. Barber, Lee, Liu, and Odean (2011) document large variation in the gross and net returns earned by day traders in Taiwan stock exchange from 1992 to They find that the spread in returns between top-ranked and bottom-ranked day traders exceeds 60 basis point per day. All the studies aforementioned focus on the day traders in the stock market and have a mixed evidence on the profitability. Hence, we aim to complement the literature by providing new evidence on the performance of day traders in Taiwan futures market. As far as we know, we are the first paper exploring the day trading behaviors in the futures market. Besides, as expressed in the previous section, our day traders are not defined ex post by their round-trip trades on the same day. Our day traders instead commit to close their positions when they initiate them. The estimation of their performance is thus free from noises due to other trading needs or behavioral biases. Therefore, our paper also adds to the existing literature by providing this new perspective Investor overconfidence There is a plethora of literature on investor overconfidence. We only briefly discuss the most relevant ones. Odean (1999) and Barber and Odean (1999) use discount brokerage accounts to demonstrate that overall trading volume in equity markets is excessive. They show that investors are not only overconfident but also biased in the interpretation of information. Barber and Odean (2000) show that individual investors who hold common stocks directly pay a tremendous per- 5

7 formance penalty for active trading. Those that trade most earn an annual return of 11.4 percent, while the market returns 17.9 percent during 1991 to They also attribute their findings to the investor overconfidence. Barber and Odean (2001) further explore overconfidence among genders and find that men trade 45 percent more than women. The result shows that trading reduces men s net returns by 2.65% a year, compared with 1.72% for women. Barber, Lee, Liu, and Odean (2009) analyze a complete trading history of all stock investors in Taiwan from 1995 to 1999 and find that individual investor trading results in systematic and economically large losses. They argue that the investor overconfidence is one of the major cause of the losses. Unlike the previous literature, our paper analyzes a specific group of investors i.e., the index day traders. Our day traders have to close their positions before the end of the day ex ante in exchange for the halved margin deposit. The commitment to the liquidation constraint intuitively make the day traders the ideal candidates for studying the overconfident behaviors since they have the strongest belief to be the informed. 3. Data description 3.1. The Taiwan futures market The futures contracts of Taiwan Futures Exchange (TAIFEX) are traded by the Electronic Trading System (ETS) from 8:45 AM to 1:45 PM. The matching rules are price priority and time priority. The submitted orders are matched on a continuous basis. The four major contracts traded in the TAIFEX are the Taiwan Stock Exchange Index Futures (hereafter TXF), which is based on all listed stocks on the Taiwan Stock Exchange, the mini-taiwan Stock Exchange Index Futures (hereafter MXF), whose payoff is only one-quarter of the TXF, the Electronic Sector Futures (hereafter EXF), which is based on the Taiwan Stock Exchange Electronic Sector Index, and the Finance Sector Futures (hereafter FXF), which is based on the Taiwan Stock Exchange Financial Sector Index. 6

8 Our data contains all the trades from October 8 of 2007 to September 30 of In each transaction, we observe the investor s account number, her type (individual or institutional), contract type, and other relevant information. Table 1 shows descriptive statistics of all the transactions in our sample period. The first data trait worth emphasizing is that futures market trading is dominated by individual investors. The ratio of transactions by individual investors over total transactions is 71%, which is higher than the one of the Taiwan stock market (63% for 2008 and 72% for 2009). Majority of the transactions are based on the Taiwan Stock Exchange Index, with 55% from TXF and 34% from MXF. The Electronic and Financial Sector Index Futures account for 5.7% and 5.6% respectively, while all other futures account for less than 1.4% The day trading rules in TAIFEX On October 8th 2007, TAIFEX implemented a new margin requirement policy to increase the trading volume of the index futures. For the major four index futures, i.e., TXF, MXF, EXF, and FXF, the margin requirement is reduced by 50% if an investor indicates her order as a day-trade order. When a day-trade order is successfully executed, the position has to be closed by the investor before 1:30 PM, 15 minutes before the market close. Otherwise, the position will be forced to close by a market order or a limit order that is five ticks within the latest trade. Essentially, through submitting a day-trade order, an investor commits to be a literally day trader. In other words, the maximum duration of an index futures position that is initiated by a day-trade order is less than five hours. In the U.S., the pattern day traders are defined as those investors who trade the same stock four or more times in five business days. 3 In this study, index futures investors in Taiwan are defined as day traders if their positions are established through day-trade orders. Our definition is thus different from which in the U.S. as we have a clear flag to classify the day traders ex ante. 3 Under the rules of NYSE and the Financial Industry Regulatory Authority (FINRA), investors who are deemed pattern day traders must have at least $25,000 in their accounts. See for more details. 7

9 4. The performance of the day traders in Taiwan futures market This section addresses our main research questions related to the index futures day traders: Are the day traders overconfident in the precision of information? Do they have biased interpretation of information? Do they lose more money by trading more contracts? In other words, is trading hazardous to their wealth? Is there any difference in the relation between trading frequency and performance among winning and losing day traders? Finally, do the day traders past trading experience and performance affect their day trading behavior? We provide evidence on these questions in turn. Our results are important as they shed light on the possible behavioral biases for the most aggressive individual traders in the market The definition of day traders and round-trip trade To analyze the performance of day traders, we first have to filter transactions through day-trade orders out from those through normal orders. We thus exclude transactions which are not initiated by the day-trade orders. Second, we need to translate the raw transactions into completed roundtrip trades. Essentially, a round-trip trade is when an initiated position, long or short, is covered. After the filtration, the profit of a round-trip trade can be easily calculated when an investor s net position for an index futures is back to zero again. Our way of calculating the payoff of a round-trip trade is the same as Jordan and Diltz (2003) and Feng and Seasholes (2005). Finally, we only include investors who conduct more than 5 day trades during our sample period in the analysis. The resulting descriptive statistics of the raw transactions by the day traders are in Table 2. The transactions from the domestic individual investors and institutional day traders are reported separately. In total, we have 1.42 million contracts traded by the individual day traders while only 44 thousand contracts traded by the institutional day traders. For domestic individual investors, 8

10 each transaction contains roughly 1.5 contracts. 4 The ratio is much higher for the institutional day traders. Except for the first month, we have more than one thousand individual day traders in each month. 5 The number stabilizes around 15 hundred per month for the later half of the sample period. There are only few institutional day traders conducting day trading with average number of 14 per month. Although the institutional day traders only account only 3% of the day-trade contracts, it is worth exerting some effort to study whether their performance differs from which of the individual day traders The poor performance of the aggregate round-trip day trades Before we dig into the performance of the day traders, looking at the performance of the aggregate round-trip trades first would offer us a general picture. Table 3 Panel A reports the profit of all the round-trip trades by individual and institutional day traders. There are 348,000 round-trip trades by the individual investors. On average, one round-trip day trade suffers a loss of 266 TWD. After trading fee and transaction tax, the net profit is 613 TWD. 6 The profit for institutional day traders is also negative though insignificant due to a small number of observations. The positive medians of investors round-trip trades by individual and institutional day traders suggest a skewed distribution the round-trip trade profit. Panel B and C report the profits of long-initiated and short-initiated round-trip trades. The numbers of long position and short position are similar for the individual day traders. It seems that the individual day traders are not reluctant to short as we anticipated. Several reasons might explain the phenomenon. First, the cost of initiating a long or short position is symmetric in the index future market. Second, the daily downside risk is caped at 7% due to price limit in Taiwan 4 There is barely any foreign individual day trading in TAIFEX. 5 The identify of day traders in each month could be largely overlapped over months. In total, we have 3,470 individual day traders. 6 The transaction tax of Taiwan futures market is 0.8 basis point for initiating or closing one contract. The trading fee varies among different types of investors and across different brokerage firms. The comprehensive trading fee data is unfortunately unavailable. However, the average trading fee for initiating or closing a contract is around 37.5 TWD according to the press release of the Ministry of Finance on 4th of October We thus apply the fee to all the transactions we analyze. 9

11 stock market. Third, the margin call also limits the exposure of the downside risk. Turning to the performance, we see an intriguingly opposite pattern across the investor types for the long and short positions. Individual day traders perform worse when they take long positions. The gross and net profits are 676 and 1,014 TWD respectively. With short positions, their average payoff is 160 TWD and 196 TWD after transaction costs. This suggests that when individual day traders are shorting, they are more cautious and less overconfident than when they are taking long positions in general. For institutional day traders, on the contrary, the average payoff for long-initiated round-trip trade is much higher than which of the short-initiated, with a difference of 12,274 (12,339) TWD including (excluding) transaction costs. With more transactions in each round-trip trade as shown in Table 2, the magnitude of gains and losses is larger for institutional day traders. Overall, the crucial insight to be garnered from Table 3 is the poor average performance of round-trip trades. It bodes the unpromising performance of the day traders that we analyze in the next section The overconfident individual day traders With the payoff of every round-trip day trade, we can aggregate trades at investor level over our sample period to gauge the profitability. Following Jordan and Diltz (2003), Table 4 reports the distribution of the gross and net profits for day traders with 50 thousand TWD in each bin. Like previous section, we also present the performance when long-initiated and short-initiated day trades are aggregated separately. In total, we have 3,470 individual day traders who submit daytrade orders at least on five trading days during our sample period. Similar to previous section, the numbers of day traders taking long and short positions are similar. This implies that most of the day traders trade in both directions. If day traders are rational, they are supposed to be the most informed or with best information processing technology since they are willing to bear the risk of forced liquidation at the end of the day. They believe that the benefit of extra leverage, with 50% reduction of initial margin by submitting day-trade order, outweighs the risk of premature liquidation. Therefore, following 10

12 Odean (1999), our first null hypothesis is that the net profit of day traders should be positive. However, the results in Table 4 clearly reject the null hypothesis. For all individual day trades, on average, they make a significantly negative profit of 61.5 thousand TWD after transaction costs. While the finding based on the sample average might be driven by outliers, the significantly negative median alleviates the concern. Besides, only less than 20% of the individual day traders make positive net profit. With no other reasons to trade like the tax filing or liquidity demand, our evidence strongly corroborates that the individual day traders are overconfident. The second question to ask is that whether the day traders are systematically misinterpreting information available to them. In other words, we would like to study whether trading decision based on the very information is wrong. Therefore, the second null hypothesis is that on average the day traders should have positive gross profits. Again, the first column in Table 4 shows that the hypothesis is rejected. The day traders suffer an average of 26.7 thousand TWD losses before transaction costs. The magnitude is smaller for median but still significant. The ratio of day traders making positive gross profit is only around 29%. Overall, the evidence indicates that the day traders have biased information which leads to negative gross profits. Meanwhile, consistent with the previous section, we also find that the long-initiated trades perform worse than short-initiated trades. The day traders in our sample voluntarily restrict the freedom of liquidation timing to the day of transaction. The information story cannot reconcile the ex ante nature of day trading and the poor average performance we document ex post. Therefore, a conclusion to be drawn from Table 4 is that the individual day traders are both overconfident and also biased in their information. The result is compatible with the findings in Odean (1999) and Barber, Lee, Liu, and Odean (2009) for regular individual equity traders The overconfident institutional day traders Now we turn to the performance of the institutional day traders. Table 5 shows that the overall performance for the 42 traders are also poor. The average net profit of is 89.1 thousand TWD 11

13 while the average gross profit is 4.2 thousand TWD. The larger difference between the profit and net profit, compared with individual day traders, is due to the higher number of transactions per institutional investor. However, due to the small number of observations, both profit and net profit are not statistically significant. When focus on the direction of trades separately, the institutional day traders perform better when taking long positions as we found in the previous section. However, probing further into the distribution, we see that the performance varies widely. The average performance is mainly driven by few outliers. Besides the small sample, the wide variation of payoff also contributes to the low statistical power Trading is only hazardous to the overconfident day traders Having established that the individual day traders are overconfident in Section 4.3, we would like to further ask whether those day traders lose more by trading more as documented in Barber and Odean (2000) for US equity stock traders. In our context, essentially, we are interested in whether day traders suffer more losses by trading more futures contracts. 7 Table 6 Panel A is analogous to Figure 1 in Barber and Odean (2000). We sort the total contracts traded into quintile and report the averages within each quintile for profit, profit per contract, net profit, net profit per contract, and number of round-trip trades. The losses are increasing with the number of contract traded for the first 4 quintile, from 16.4 thousand TWD to 62.6 thousand TWD. For the most frequent trading quintile, those day traders manage to make a total profit of 31.2 thousand TWD on average. However, the gain for the most aggressive day traders is all consumed by the transaction costs. The negative net profits are monotonically increasing from 18.3 thousand TWD to thousand TWD. The takeaway here is similar to the Barber and Odean (2000) that trading is hazardous to day traders wealth. The relation between number of contracts traded and the performance is quite different when we focus on the profit per contract. 8 The magnitude of negative profit per contact, both before 7 Due to the small number of the institutional day traders, the rest of the study will only focus on the individual day traders. 8 If we assume that the margin deposited for each trade remains at the same level for all time, the profit per contract 12

14 or after the transaction costs, reduces as the number of contracts traded increases. Overall, two core findings to be learned here. First, the day traders make less loss per contract when they trade more. Second, the day traders belonging to most frequent trading quintile could make more money by trading more. To further explore the relation, we thus sort day traders into quintile by the total profit or average profit instead of the contract number. Table 6 Panel B and Panel C report the results respectively. Panel B shows that only the fifth quintile day traders earn significant amount of profit. The average gross profit within the quintile is 267 thousand TWD, compared with a miserable thousand TWD for the first quintile. After the transaction costs, the fifth quintile day traders still make a thousand TWD profit. This is consistent with Barber, Lee, Liu, and Odean (2011) who find some day traders in Taiwan equity market have superior trading skills. However, the transaction costs roughly account for 30% of the grass profit. The profit per contract monotonically increases from a negative 504 TWD to 308 TWD while net profit per contract increases from a negative 593 TWD to 218 TWD. The similar pattern could be found in the Panel C as well. One of the most interesting findings for the paper is the pattern of contract number in Panel B and C. We use Figure 1 and Figure 2 to convey the most essential point. When sorted by total profit, we see an U-shape of the contract number traded. This implies that the most winners and and losers both trade a lot. Trading is hazardous to the wealth od losers but not to that of the winners. Mechanically, investors need to trades more to incur large gains or losses. However, the U-shape is not due to this simple mechanism. When we sort the day traders by the average profit per contract, Figure 2 shows that the U-shape largely preserves. To formally test the relation between profit and number of contracts traded among winners and losers, we adopt the quantile regression technique. The advantages of using quantile regression are twofold. First, the test is less affected by the outliers. Second, any quantile can be estimated with higher statistical power compared with running least square regressions within subgroups. 9 Empirically, we run 19 different quantile regressions of the total profit or total net profit on a serves as a reasonable proxy for the return. 9 Koenker (2005) provides a detail instruction of quantile regression. 13

15 constant term and the contract number traded, starting the 5th percentile to 95th percentile. For brevity, we only show the coefficients in interest and the confidence intervals in Figure 3 and Figure 4 for the total profit and the total net profit respectively. Both figures concur with the U-shape pattern we found in Figure 1 and Figure 2. For day traders below the 15th percentile in profit or net profit, the more they trade, the more losses they suffer. However, for day traders above the 60th percentile, the more they trade, they more profit they can make. For example, the day traders in the 95th percentile (the most winning day traders) make extra 400 TWD gross profit by trading one more contact. On the contrary, the day traders in the 5th percentile (the most losing day traders) make extra 800 TWD gross loss by taking one more position. Collectively, the documented relation between profit and contract number does not fully corroborate that trading is hazardous to the investors wealth. Instead, trading is only hazardous to the overconfident losers wealth, but not to the informed or skillful winners Excessive day trading is related to past trading volume but not profit In this subsection, we try to answer whether the day trading activity and the profits are related to the investors past trading record. In other words, we would like to address the question that the overconfident day trading is due to learning as shown in Gervais and Odean (2001). We first identify 2,641 day traders out of 3,470 with trading record from January 2000 to September Panel A of Table 8 reports the performance of individual day traders in this subsample. Interestingly, the day traders with trading record perform worse than those without trading record. The average gross (net) profit is 38.4 ( 73.4) thousand TWD, which are both larger than those reported in Table 4. These experienced investors seem to gain no superior skills from their past trading. Next, we regress the number of round-trip day trades or total net profits of day traders on the number of contracts traded, number of trading days, and gross profits earned during January 2000 to September The number of contracts traded and the number of trading days serve as proxy for the trading experience while the gross profits help investors to know their type 14

16 in the context of Gervais and Odean (2001). The independent variables are standardized for the convenience of interpretation and the standard errors are adjusted for heteroscedasticity by Davidson and MacKinnon (1993). Table 7 shows that both past number of contracts traded and number of trading days are strongly correlated with the day-trade activity. One standard deviation increase in the number of contracts traded during January 2000 to September 2007 leads to 61 more round-trip day trades. Similarly, one standard deviation increase in the of number of trading days leads to 25 more round-trip day trades. The more experienced individual investors exhibit more aggressive day trading behavior. However, the day trading activity is only weakly related to the profits earned in the past. The higher past profit alone does not contribute to higher day trading activity. Finally, day trading profit is correlated with number of contracts traded. One standard deviation increase in the past number of contracts traded would lead to 270 thousand TWD day-trade profit. But the day-trade profit is unrelated to the past trading profits. The overconfident day traders do not to learn their types from their past performance. It could also be interpreted that they do not gain trading skills or better information processing technology. Overall, the evidence suggests that the trading experience plays a limited role to help individual investors to earn profits by taking advantage of the extra leverage brought by the new day trading policy The complete day-trader sample and the subsample convey qualitatively the same results The analysis in the previous sections requires at least five executed day trades for investors to be included in the sample. We relax the ad hoc threshold to examine whether our results are robust to other choices of threshold. We re-do the analysis in Table 4 but only report the main statistics for brevity. Panel B of Table 8 indicates that the performance of all individual day traders, who at least conduct one day trade, is as poor as that shown Table 4. On average, they make significantly losses of 48.8 thousand TWD after transaction costs. Before transaction costs, the day traders lose 26.7 thousand TWD on average. Panel C reports the results when we increase the threshold 15

17 to ten day trades. The sample size reduces but it yields qualitatively similar results. The pattern of day traders losing more in long positions than in short postilions also preserves. To sum up, the documented overconfidence of the individual futures day traders in Taiwan appears to be structurally stable and is not an artifact of the sample. 5. Concluding remarks In this paper, we take advantage of a new day-trading policy implementation in Taiwan futures market to study the performance of day traders. The policy provides us a clear-cut way to identify day traders ex ante. Essentially, an investor commits to be a day trader by flagging her orders as day-trade orders. Those positions need to be closed on the same day. This setup provides us an ideal laboratory to address the following questions: Are the day traders overconfident in the precision of information? Do they have biased interpretation of information? Do they lose more money by trading more contracts? In other words, is trading hazardous to their wealth? Is there any difference in the relation between trading frequency and performance among winning and losing day traders? Finally, do the day traders past trading experience and performance affect their day trading behavior? Several features motivate us to answer these questions by studying the day traders in Taiwan futures market. First, the investors who voluntarily take liquidation risk are supposed to be either the most informed or the most confident that they are informed. Second, the maximum investment horizon for the investors is restricted to one day by default. The day traders positions can be observed without potential bias from disposition effect. Third, the futures day traders can easily capitalize their negative information by taking short position. Finally, they are unlikely to trade for consumption, liquidity, portfolio re-balancing, diversification, hedging, and tax-motivation. The results show that the 3,470 individual day traders make substantially negative gross profits and net profits. This implies that the individual day traders are not only overconfident in precision of information but also having biased interpretation of information. The result for institutional 16

18 day traders is similar but statistically insignificant due to a much smaller number of observations. We also find that the numbers of short position and long position are similar for the individual day traders. To our surprise, the short-initiated round-trip trades perform better than the longinitiated round-trip trades for individual investors. Beside, the result does not fully support that the investors lose more by trading more. Trading is only hazardous to the overconfident losers, but not to the winners. Last, we find that the more experienced day traders exhibit more aggressive day trading behavior. But the overconfident day traders do not learn their types from their past performance. The trading experience thus plays a limited role to help individual investors to earn profits by taking advantage of the extra leverage brought by the new day trading policy. 17

19 References Barber, B., Lee, Y.-T., Liu, Y.-J., Odean, T., Just how much do individual investors lose by trading?. Review of Financial Studies 22, Barber, B., Lee, Y.-T., Liu, Y.-J., Odean, T., The cross-section of speculator skill evidence from day trading. Unpublished working paper. University of California at Davis and Peking University and University of California, Berkeley. Barber, B., Odean, T., The courage of misguided convictions: The trading behavior of individual investors. Financial Analysts Journal pp Barber, B., Odean, T., Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics 116. Barber, B. M., Odean, T., Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance 55, Boehmer, E., Kelley, E., Institutional investors and the informational efficiency of prices. Review of Financial Studies 9, Davidson, R., MacKinnon, J. G., Estimation and Inference in Econometrics. Oxford University Press, New York. Feng, L., Seasholes, M., Do investor sophistication and trading experience eliminate behavioral biases in financial markets?. Review of Finance 9, Garvey, R., Murphy, A., Are professional traders too slow to realize their losses?. Financial Analysts Journal 60, Garvey, R., Murphy, A., Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team. Journal of Empirical Finance 12, Gervais, S., Odean, T., Learning to be overconfident. Review of Financial Studies 14, Harris, J., Schultz, P., The trading profits of soes bandits. Journal of Financial Economics 50,

20 Jordan, D., Diltz, D., The profitability of day traders. Financial Analysts Journal 59, Koenker, R., Quantile Regression. Cambridge University Press, Cambridge, UK. Linnainmaa, J., The individual day trader. Unpublished working paper. University of Chicago. Odean, T., Do investors trade too much?. American Economic Review 89,

21 Table 1 All Index Futures Descriptive Statistics Total Investor Type Product Type Transactions Individual Institutional TXF MXF EXF FXF Others 2007/10 1,870,994 1,381, ,718 1,078, , , , /11 2,470,271 1,779, ,011 1,454, , , , /12 2,716,747 1,804, ,128 1,525, , , , /01 4,176,970 2,544,218 1,632,752 2,235,007 1,361, , , /02 1,791,517 1,270, , , , , , /03 3,403,473 2,515, ,514 1,839,396 1,131, , , /04 2,886,335 2,174, ,466 1,510, , , , /05 2,864,748 2,095, ,056 1,522, , , , /06 2,970,428 2,125, ,641 1,607,198 1,027, , , /07 3,272,217 2,374, ,805 1,785,649 1,158, , , /08 3,349,750 2,552, ,447 1,798,758 1,203, , , /09 3,640,049 2,628,370 1,011,679 1,978,549 1,302, , , Total 35,413,499 25,247,038 10,166,461 19,261,552 11,779,063 1,987,741 1,912, ,978 Average 2,951,125 2,103, ,205 1,605, , , ,347 39,415 Ratio % % % % % 5.613% 5.400% 1.336% Notes. Table 1 reports the summary statistics of all trades in the Taiwan futures market from October 8th, 2007 to September 30th, The number of total transaction, the number of transactions by domestic individual and institutional day traders, and the number of transactions in the Taiwan Stock Exchange Index Futures (TXF), the mini-taiwan Stock Exchange Index Futures (MXF), the Electronic Sector Futures (EXF), the Finance Sector Futures (FXF), and the other types of futures are reported respectively. The number of transactions are reported on each month while the total and average over the months are also presented. 20

22 Table 2 Day-trade Index Futures Descriptive Statistics Number of Contracts Number of Transactions Investor/Day Investor Date Individual Institutional Individual Institutional Individual Institutional Individual Institutional 2007/10 38,454 1,180 26, , /11 115,502 5,182 76,243 3,094 7, /12 99,043 3,602 69,780 2,516 7, /01 149, , , /02 67, , , /03 140,022 1,623 96,775 1,038 9, /04 117,438 1,102 82, , /05 114,628 6,378 80,227 3,222 9, /06 127,341 4,026 89,410 2,355 9, /07 142,063 1,610 98,211 1,258 8, /08 150,233 13, ,233 7,304 10, /09 160,646 4, ,746 3,733 9, Total 1,422,130 43, ,719 26,786 97,644 1,036 17, Average 118,511 3,664 82,143 2,232 8, , Max 160,646 13, ,746 7,304 10, , Min 38, , , Notes. Table 2 reports the summary statistics of the day trades for four Taiwan index futures, including TFX, MFX, EFX, and FXF, from October 8, 2007 to September 30, The number of contracts, number of transactions, number of investor/day, and the number of investors per month for domestic individual and institutional day traders are reported separately. The identify of day traders in each month could be largely overlapped over months. In total, we have 3,470 distinct individual day traders and 42 institutional day traders. 21

23 Table 3 Profit and Net Profit for Round-trip Trades Panel A: All round-trip trades Individual Institutional (thousands TWD) Profit Net Profit Profit Net Profit Mean *** *** p-value (0.000) (0.000) (0.994) (0.869) Median ***0.300 ***0.189 ***0.400 ***0.284 p-value (0.000) (0.000) (0.000) (0.000) Max 2,298 2,065 12,160 12,086 Min -5,180-5,235-10,464-10,653 number of obs 348, ,063 4,134 4,134 Panel B: Long-initiated round-trip trades Individual Institutional (thousands TWD) Profit Net Profit Profit Net Profit Mean *** *** p-value (0.000) (0.000) (0.374) (0.444) Median *** ***0.450 *0.294 p-value (0.000) (0.392) (0.000) (0.051) Max 1,908 1,799 12,160 12,086 Min -5,180-5,235-2,769-2,833 number of obs 177, ,415 1,862 1,862 Panel C: Short-initiated round-trip trades Individual Institutional (thousands TWD) Profit Net Profit Profit Net Profit Mean ***0.160 *** p-value (0.005) (0.001) (0.475) (0.409) Median ***0.350 ***0.193 ***0.400 ***0.248 p-value (0.000) (0.000) (0.000) (0.000) Max 2,298 2,065 10,385 10,314 Min -2,596-2,689-10,464-10,653 number of obs 170, ,648 2,272 2,272 Notes. Table 3 reports the profit and net profit of all, long-initiated, and short-initiated round-trip trades. Net profit is the gross profit minus the transaction fee and tax. The profit and net profit for domestic individual day traders and institutional day traders are reported separately. 22

24 Table 4 Distribution of Profit and Net Profit for Domestic Individual Investors All Trades Long-intiated Short-intiated Profit Net Profit Profit Net Profit Profit Net Profit (thousands TWD) below to to to to to to to to to to ,623 1,722 1,479 1,688 0 to , to to to to to to to to to above Mean * *** *** *** * p-value (0.071) (0.000) (0.000) (0.000) (0.281) (0.095) Median *** *** *** *** *** *** p-value (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Max 29,171 23,121 14,547 11,580 14,623 11,541 Min 20,991 23,384 19,201 20,982 2,993 3,351 Observations 3,470 3,470 3,443 3,443 3,326 3,326 Notes. Table 4 reports the total profit and net profit of all, long-initiated, and short-initiated trades of domestic individual investors. The profit and net profit are reported in the bin of 50 thousand TWD. 23

25 Table 5 Distribution of Profit and Net Profit for Institutional Day Traders All Trades Long-intiated Short-intiated Profit Net Profit Profit Net Profit Profit Net Profit (thousands TWD) below to to to to to to to to to to to to to to to to to to to to above Mean p-value (0.988) (0.785) (0.234) (0.265) (0.557) (0.513) Median ** * p-value (0.100) (0.041) (0.223) (0.054) (0.703) (0.504) Max 5,240 5,069 10,460 9,569 5,156 5,072 Min 9,962 11, ,422 21,525 Observations Notes. Table 5 reports the total profit and net profit of all, long-initiated, and short-initiated trades of institutional day traders. The profit and net profit are reported in the bin of 50 thousand TWD. 24