Robust returns ranking prediction and portfolio optimization for M6

Abstract

The M6 competition aims to address challenging problems in stock returns ranking prediction and portfolio optimization. To tackle the volatility and low signal-to-noise ratio in the stock market, our team designs the overall solution from the perspective of robustness. On the task of returns ranking prediction, we present the MultiTask Deep Neural Network with Denoising Autoencoder Enhancement (MT-DNN-DAE), which incorporates the self-supervised learning of DAE and jointly optimizes the multi-task loss. Additionally, we propose Robust Feature Selection (RFS) to identify features with a high signal-to-noise ratio for DAE’s representation learning and construct a separate branch for important ID feature to prevent its information loss. Results show our solution is capable of accurately predicting returns ranking while maintaining generalization. On the task of portfolio optimization, a Differential Evolution algorithm is also presented to optimize asset allocation and maximize returns under risk constraints, demonstrating improved performance over traditional techniques. Taken together, these methods led to a 4th place global ranking in the M6 competition.