Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capacity to give more accurate stock prediction and become indispensable components in modern quantitative investment systems. However, the i.i.d. assumption behind existing methods is inconsistent with the existence of diverse trading patterns in the stock market, which inevitably limits their ability to achieve better stock prediction performance. In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA) 2021: Hengxu Lin, D. Zhou, Weiqing Liu, J. Bian https://arxiv.org/pdf/2106.12950v2.pdf