Integrating Behavioral Finance Factors with Temporal Convolutional Networks for Enhanced Cryptocurrency Return Predictions

Abstract

The rapid growth and significant fluctuations of the cryptocurrency market have increasingly attracted investors to add digital currencies in their portfolios. Compared to traditional financial markets, the cryptocurrency market exhibits more pronounced characteristics of behavioral finance. Investors demonstrate irrational behavior in trading processes, exhibiting clear cognitive biases, such as the endowment effect and the ostrich effect. This paper initially undertakes an analytical dissection and synthesis of various archetypal irrational behaviors, then we selected technical indicators that reflect these irrational behaviors. After the process of feature engineering, the study employs TCN-MLP model to predict the thirty-minute returns of ETH. This paper presents a comprehensive cryptocurrency returns prediction process, addressing the weakness of loosely connected theory in previous research.