Prospect theory for online financial trading

Abstract

Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to losses, known as the "reflection effect". People are much more sensitive to losses than to gains of the same magnitude, a phenomenon called "loss aversion". Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon. Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch and follow the trading activities of others, by predicting potential winners based on their historical trading behavior.

Figure 1. Performance comparison of different types of trades.

(a) Fraction of positive trades. Mirror trade has the highest fraction of positive trades. (b) Mean ROI. Mirror trade is the only trade type that has the positive . Here error bars mean the standard error of the mean (SEM). (c) Mean ROI of positive trades. Mirror trade has the lowest for positive trades. (d) Mean ROI of negative trades. Mirror trade has the highest negative for negative trades.

Figure 2. Duration distribution of different trade types.

For each trade type, we further distinguish negative and positive trades based on their net profit. The trades with zero net profit are negligible. The duration distributions of negative and positive trades are normalized according to their corresponding occurrence. (a) All trades. (b) Non-social trades. (c) Copy trades. (d) Mirror trades.

Figure 3. Disposition effect in different trade types.

Here, we bin the net profit of different trade types in logarithmic bins. (If , we bin it using .) For the trades contained in each bin, we draw the box-and-whisker plot for their duration (), representing the minimum, first quartile, median, third quartiles, and maximum of the data in the bin. (a) All trades. (b) Non-social trades. (c) Copy trades. (d) Mirror trades.

Figure 4. Characterizing winning and losing traders based on historical trading behavior.

(a, b) Distribution of risk-reward ratio (). and are the profit of winning positions and the loss of losing positions, respectively, of traders. (c, d) Distribution of win-loss waiting time ratio (). Here and are the average duration time of winning and losing positions, respectively, of traders. (e, f) Distribution of win-loss ROI ratio (). and are the ROI of winning and losing positions, respectively, of traders. (g, h) Distribution of winning percentage () of traders. and are the number of winning and losing positions, respectively, of traders.