An Investigation of Predictability of Traders' Profitability Using Deep Learning

Abstract

Trading in the financial market is a daunting task in spite of the attracting increase of the daily turnover of the Forex financial market from 6.5 trillion USD in 2022 to approximately 7.5 trillion USD in 2024. About 80% of retail investors lose money. However, to minimize the risk of losses, investors explore the possibility of profitable trading by resorting to social trading. In social trading of the financial market, the performance statistics and performance charts of traders with diverse trading strategies, methods and characteristics are showcased by the financial market brokers to enable investors decide on which trader’s signal to adopt or copy for profitable investment. However, investors are often faced with the problem of choosing a set of profitable traders among thousands with different past hypothetical results, in spite of the provision of traders’ performance ranking, made available by the brokers. The investors have serious concern on the stability, sustainability and predictability of a trader’s future performance which will eventually determine the investors profit or loss if the trader’s signals are copied or followed. This paper applies three deep learning models: the multilayer perceptron, recurrent neural network and long short term memory for the prediction of traders’ profitability to provide the best model for investment in the financial market, and reports the experience. The results of the study show that recurrent neural network performs best, followed by long short term memory while multilayer perceptron yields the least results for the prediction. These three models yield a mean squared error of 0.5836, 0.7075 and 0.9285 respectively in a test scenario for a trader.