[2N1-IS-2a] Machine learning (1/5)

[2N1-IS-2a-03] STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets based on Residual Blocks or Transformers

〇Masanori HIRANO¹, Kiyoshi Izumi¹, Hiroki Sakaji¹ (1. The University of Tokyo)

Keywords:Financial Market, Machine learning, Trader behavior

This paper proposes a new model to reverse engineer and predict traders' behaviors for financial market. In this model, we used an architecture based on the transformer and residual block, and a loss function based on Kullback-Leibler divergence. In addition, we established a new evaluation metric, and consequently, succeeded in constructing a model that outperforms conventional methods and has an efficient architecture. In the future, we will build a model with higher performance and versatility. Moreover, we will introduce this model to financial simulations.