کارشناسان آنلاین: تجزیه و تحلیل فعالیت های تجاری در یک اتاق گفتگوی عمومی اینترنت

We analyze the trading activity in an Internet chat room over a 4-year period. The data set contains nearly 9000 trades from 676 traders. We find these traders are more skilled than retail investors analyzed in other studies. 55 percent make profits after transaction costs, and they have statistically significant α s of 0.17 percent per day after controlling for the Fama–French factors and momentum. Traders hold their winners 25 percent longer than their losers. 42 percent trade both long and short, with equal success rates, and almost double the profit per trade when short. The estimates show a strong influence from other traders, with a buy (sell) order 40.7 percent more likely to be of the same sign if there has been a recent post. Traders improve their skill over time, earning an extra $189 per month for each year of trading experience. They also gain expertise in trading particular stocks. Traders who raise their Herfindahl index by 0.1 raise their profitability by $46 per trade.

The individual investor has been carefully scrutinized in the growing literature on behavioral finance. These studies typically document the underperformance of the do-it-yourself trader. Barber and Odean (2000) find, in a large sample of households from a major discount stock broker, annual average returns trail the market benchmarks by nearly 200 basis points. The most active quintile of traders has the lowest returns, underperforming the market by more than 700 basis points. Barber and Odean conclude that “trading is hazardous to your wealth.” Day traders, who, as the SEC defines, “rapidly buy and sell stocks throughout the day,” fare no better than retail investors. Barber et al. (2009) study a large sample of day traders in Taiwan and document that over 80 percent lose money. Jordan and Diltz (2003) found 73.4 percent of the 334 traders they studied in 1998 and 1999 at a national brokerage firm had negative net profits. The traders lost almost $8000 on average. Odean (1999) and Barber and Odean (2000) attribute poor performance to excessive trading. Overconfidence, Odean (1998) observes, leads investors to overestimate their own knowledge about a security. This leads to divergent views about fundamental values, that in turn motivates trading, despite the fact that trading lowers their expected utility. Graham et al. (2005) identify a competence effect which makes investors more willing to act upon their self-perceived skill. Competence, they find, leads to greater international diversification, but it also increases trading frequency. A tendency to sell winners quickly and hold onto losers, the disposition effect of Shefrin and Statman (1985), also leads to underperformance. This psychological bias appears in the traders studied by Odean (1999) and Grinblatt and Keloharju (2001). Genesove and Mayer (2001) document similar loss aversion in the housing market. Other studies have attributed underperformance to poor stock selection. Goetzmann and Kumar’s (2004) retail traders are underdiversified. Barber and Odean (2008) observe a tendency to buy attention grabbing stocks. Investors in Barber et al. (2006) overweight past returns, which they attribute to Kahneman and Tversky’s (1974)representativeness heuristic. Stock selection, Huberman, 2001 and Massa and Simonov, 2005, and Amadi (2004) have noted, is subject to familiarity bias, a tendency to pick the same stocks again and again. An excellent survey of this literature is by Barberis and Thaler (2003). A distinct feature of retail traders is their unwillingness to take short positions. Angel et al. (2003) found that only 1 in 42 trades on NASDAQ is a short sale. In Barber and Odean (2008) only 0.29 percent of the more than 66,000 traders in the room take short positions. We will break out many of our results into short and long trades. There is also evidence that traders of all types can learn over time and improve their performance. Barber et al. (2009) identify a select group of approximately 1300 traders who consistently earn profits. Coval et al. (2005) find that the top 10 percent of investors make persistent abnormal profits. Niccolosi et al. (forthcoming) observe that individual investors learn about their trading skill and increase their trades and profits in subsequent periods. Kaniel et al. (2008) also show that, in the aggregate, individual investors may be smart money: excess returns are positive (negative) in the month after intense buying (selling) by individuals. This paper studies a group of active traders who voluntarily post their trades in real time into a public Internet chat room called Activetrader. We rely on a previously unexplored data set of chat room logs compiled by the first author over a 4-year period. We analyze the trading activity in four 1-month snapshots from 2000 to 2003. The authors surveyed the chat room participants, and this paper helps clarify the portrait of the individual trader provided by Vissing-Jorgensen (2003) and Lo et al. (2006). Our traders have a median trading experience of 5 years, holding periods less than a day, and trade primarily using technical analysis. The average portfolio size is $198,000. The data set has 676 traders and contains information on almost 9000 trades. This is one of the largest panels of U.S. day traders to be analyzed in the literature. It also covers the neglected semi-professional traders identified by Goldberg and Lupercio (2003). They estimate that this group of approximately 50,000 traders makes between 25 and 50 trades per day and is responsible for nearly a third of daily trading volume during our sample period. Lastly, no other data set allows us to observe the impact of real time interaction among the chat room members. The paper analyzes nine hypotheses. (1) Do the traders trade profitably? (2) Are their returns due to alpha? (3) Are they subject to the disposition effect? Is their stock selection influenced by (4) the representative heuristic; (5) familiarity bias; (6) the trades of other traders; (7) a tendency to avoid short positions? We then analyze two dimensions of the evolution of skills our traders appear to possess: (8) Do traders become more profitable over time? (9) Do they develop stock specific trading skills? We find that our traders resemble, in some aspects, the more unsophisticated retail investors. They trade frequently. The most active quintile makes 26 trades per day. They exhibit the representativeness heuristic and familiarity bias, concentrating their trading in a small number of high volatility and volume NASDAQ stocks. Their stock picks are 41 percent more likely to follow the direction of a recent trade post. For our skilled traders, many of these psychological biases do not impact their profitability. The majority of them trade profitably, after transactions costs, in each month. Contrary to the overtrading results, the traders who trade more frequently make more money, earning $153 per trade. Adjusting for the Fama–French factors and momentum, the traders have statistically significant α s of 0.17 percent per day. They stick with their favorite stocks throughout the trading month, independent of past returns and volatility. In other respects, our chat room traders are quite different from the retail traders in many other studies. Our traders do not exhibit the disposition effect, holding their winners 25 percent longer than their losers. 42 percent of the traders take short positions, and their trading is more profitable short than long. Traders who trade both short and long have a 10 percent higher chance of trading profitably. We also find evidence of learning along two dimensions: experience and stock specific skill. Trading profits from the previous year for an individual trader strongly predict trading profits in the next year; 38 percent of profits persist in the next year. Traders benefit from experience, each year in the trading room adding $189 to their monthly trading profits. Highly concentrated portfolios have the highest profitability. Raising the trader’s Herfindahl index by 0.1 raises their profit per trade by $46. The paper is organized as follows. The second section describes the chat room and illustrates the kind of information that we have logged. The third section describes the results of a survey of chat room participants. The fourth section focuses on profitability. We study stock selection in the fifth section. Skill evolution and survivorship is analyzed in the sixth section. A final section concludes.

Our group of skilled traders has ignored many of the lessons from their finance classes. They trade very frequently; they focus on the same stocks regardless of market conditions. They make no attempt to diversify. In spite of all these errors, nearly 55 percent earn profits after transactions costs. Trading earns them money, and not surprisingly, they trade often. They are more sophisticated than simple momentum investors. The momentum factor accounts for little of their daily returns. Together with the other Fama–French factors, we estimate a statistically significant α of 0.17 percent per day. Further evidence of their skill can be seen in their ability to earn profits both long and short. Their knowledge also appears to grow and adapt to market conditions. Traders realize losses quickly and hold their winners 25 percent longer. Traders maintain 38 percent of their profits from 1 year to the next. Each year of experience adds to their profits. Concentrating on a small group of stocks enhances their profitability. Goldberg (2006) estimates that, even as day trading ranks have thinned, 27 percent of daily volume on the NYSE and NASDAQ comes from semi-professional traders. We hope that this paper has helped to shed some light on this small but important group.