The final concern is which traders can better learn from experience. We test this by dividing traders into subsamples based on various characteristics that show their financial sophistication. In particular, we sort traders into quartiles by their wealth and the ratio of long positions. We use the subsamples to run the semiparametric regression model (1). We observe the difference in shapes across the figures to test whether learning across groups takes place at different rates.
Similar to Feng and Seasholes (2005), we measure wealth by calculating the average number of new contracts per month. We classify traders as wealthy if they are in the top quartile and poor if they are in the bottom quartile. Choe and Eom (2009) show that less sophisticated traders are more likely to suffer from the disposition effect, and this effect is significantly stronger in long positions than in short positions. Therefore, we also use the ratio of long positions taken by the traders as a measure of sophistication.
No prior trades Profitable traders Unprofitable traders Panel A: Percentage of trading volume
Full sample 11.234 30.461 58.305
2005 10.643 28.700 60.657
2006 11.094 32.786 56.121
2007 9.202 34.066 56.732
2008 12.612 27.958 59.430
Panel B: Percentage of Traders
Full sample 21.004 17.631 61.365
2005 20.058 19.013 60.930
2006 19.841 18.847 61.311
2007 20.170 19.454 60.377
2008 23.551 13.846 62.603
Figure 7 reports the relation between the cumulative number of trades and profits per contract for various levels of trader sophistication. Panel A shows that wealthy traders display a U-shape pattern for the performance-experience nexus, but poor traders display an inverse U-shape. These shapes indicate that wealthy traders are more likely to learn from trading in the long run than poor traders.
Panel B illustrates that traders with higher proportions of long contracts do not learn from trading in the long run. However, traders with lower long contract ratios have positive profits if their number of trades is large enough, although their initial performance is unstable.
Figure 8 reports the relation between months in the market and profits per wealthy and poor traders have negative returns in the first year they are in the futures market. Subsequently, they earn positive returns, but the positive returns do not persist forever. Wealthy traders have a longer run (until 40 months) and poor traders have a shorter run (until 22 months) of positive returns. This difference reflects that the number of months in the market might not be a good way to learn. In comparison, wealthy traders can learn more by observing prices and quantities. Panel B illustrates that traders with higher proportions of long contracts do not learn from observing prices and quantities in the long run.
Traders with a lower ratio of long contracts also show unstable performance when experience is measured by the number of months. Their performance initially improves, but deteriorates afterwards. However, they earn positive profits if they are in the market for as long as four years. This finding indicates that learning from trading is irritatingly slow. In sum, our results provide evidence that learning-from-doing mainly takes place among sophisticated traders like wealthy people and those who do not frequently put on long orders.
5. Conclusion
There are two specific ways in which traders can rationally learn. First, traders can improve their ability through trading (learning-by-doing); second, traders can realize that their inherent ability is inferior and decide to cease trading (learning-about-ability). This paper empirically investigates these two types of learning using individual traders’ complete records of TAIEX futures contracts.
Panel A: Wealth (wealthy for the left figure, and not wealthy for the right figure)
Panel B: Long contract ratios (high for the left figure, and low for the right figure)
Figure 7
Cumulative numbers of trades and profits per contract: By investor sophistication
This figure plots the estimated functional form of f (Exp) in the model:
ε δ
δ β δ
β it it it it it
t
i f Exp R StdR Vol
R, = 0+ 1 ( , )+ 1 , −1+ 2 , −1+ 3 , −1+ , , where 𝑅𝑅𝑖𝑖,𝑡𝑡 is dollar profits per contract for investor i in month t; 𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 is the log cumulative numbers of trades for investor i in month t. In the specification, controlling variables include average dollar profits per contract over the prior month (Ri,t−1), standard deviation of dollar profits over the prior month (StdRi,t−1), as well as log trading volume over the prior month (Voli,t−1). The model is estimated using Yatchew’s (1998) differencing method.
Panel A: Wealth (wealthy for the left figure, and not wealthy for the right figure)
Panel B: Long contract ratios (high for the left figure, and low for the right figure)
Figure 8
Months in the market and profits per contract: By investor sophistication This figure plots the estimated functional form of f (Exp) in the model:
ε δ
δ β δ
β it it it it it
t
i f Exp R StdR Vol
R, = 0+ 1 ( , )+ 1 , −1+ 2 , −1+ 3 , −1+ , , where 𝑅𝑅𝑖𝑖,𝑡𝑡 is dollar profits per contract for investor i in month t; 𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 is the number of months in the market for investor i in month t. In the specification, controlling variables include average dollar profits per contract over the prior month (Ri,t−1), standard deviation of dollar profits over the prior month (StdRi,t−1), as well as log trading volume over the prior month (Voli,t−1). The model is estimated using Yatchew’s (1998) differencing method.
We find that the aggregate performance of individual traders is negative - that is, the majority of traders lose money; profitable traders do not exhibit persistence in performance; and traders’ performance does not consistently improve with experience. The above evidence provides strong evidence that, on average, TAIEX futures traders cannot learn by doing. On the other hand, although unprofitable traders represent the major proportion of the trading and the population, a large proportion of losing traders quickly learn about their inability and cease trading. This learning explains the high turnover in the trading population of the TAIEX futures market.
These results indicate that people have limited rationality, and a substantial part of learning occurs when traders stop trading after learning about their poor inherent ability. Our results only partially support the learning model of Mahani and Bernhardt (2007). Their model contends that losing traders learn quickly and leave the market, and good performers expand their trading and have persistent performance. Nevertheless, our results are consistent with Seru, Shumway, and Stoffman (2009) – namely, that a substantial part of learning by trading is explained by learning-about-ability.
Learning is a dynamic non-linear process. At the initial stage, those who earn profits regard themselves as skilled and expand their trading activities, which in turn reduces their profits. We show that their positive profits are not persistent until they accumulate abundant trading experience, such as more than 20,000 round-trip transactions. This evidence indicates that experience is a double-edged sword. While traders learn their ability from experience, those who survive in the TAIFEX market reinforce their overconfidence through self-attribution. However, as unprofitable traders accumulate plentiful trading experience, they discount their trading ability and reduce their trading intensities.
Finally, one question is why some losing traders remain in the market. The persistent and poor performance for those who continue to trade indicates that behavioral biases play an important role in trading. For example, they might trade, partly because they are overoptimistic about the prospect of their success, or obtain non-financial utility from gambling. In addition, due to the self-attribution bias, they attribute successes to their abilities and failures to bad luck. Hindsight bias also induces them to idealize their memory of what they believed or forecasted in the past. Confirmation bias, the tendency to search out
evidence consistent with one’s prior beliefs and ignore conflicting data, also contributes to limited rationality. These behavioral biases provide explanations as to why some losing traders cannot learn quickly about their abilities and leave the market.
The implication for a policy maker is that allowing unskilled traders to learn their inferior abilities without incurring considerable costs is more valuable than encouraging new entrants to become active traders. In such cases, the policy maker could devise a screening mechanism or test to reveal a person’s inherent trading ability.
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