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Successful quantitative investment usually relies on precise predictions ofthe future movement of the stock price. Recently, machine learning basedsolutions have shown their capacity to give more accurate stock prediction andbecome indispensable components in modern quantitative investment systems.However, the i.i.d. assumption behind existing methods is inconsistent with theexistence of diverse trading patterns in the stock market, which inevitablylimits their ability to achieve better stock prediction performance. In thispaper, we propose a novel architecture, Temporal Routing Adaptor (TRA), toempower existing stock prediction models with the ability to model multiplestock trading patterns. Essentially, TRA is a lightweight module that consistsof a set of independent predictors for learning multiple patterns as well as arouter to dispatch samples to different predictors. Nevertheless, the lack ofexplicit pattern identifiers makes it quite challenging to train an effectiveTRA-based model. To tackle this challenge, we further design a learningalgorithm based on Optimal Transport (OT) to obtain the optimal sample topredictor assignment and effectively optimize the router with such assignmentthrough an auxiliary loss term. Experiments on the real-world stock rankingtask show that compared to the state-of-the-art baselines, e.g., Attention LSTMand Transformer, the proposed method can improve information coefficient (IC)from 0.053 to 0.059 and 0.051 to 0.056 respectively. Our dataset and code usedin this work are publicly available:https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA.

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