  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 risk-averse with respect to gains and risk-seeking with
respect to losses, 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 them 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 loss aversion phenomenon, an
essence 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 effect of loss aversion. Moreover, we introduce three
risk-adjusted metrics inspired by prospect theory 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 statistically based on their historical trading behavior
rather than their trading performance at any given point in time.
