Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns

The noise and high randomness of the stock market are primary obstacles to profitability. These factors cause stock returns to consist of short-term predictable and stock-specific residual parts. Therefore, it is beneficial to separate and model the two items for forecasting returns in the randomness environment. On this basis, we propose a novel Temporal Return Separation and Restore Forecasting (TRSRF) method. TRSRF first extracts price- and relation-based features from historical stock inputs and uses them to decompose and restore predictable and residual elements of historical returns , respectively. Then it forecasts these components based on historical values. We conducted experiments on the Chinese small and mid-cap, blue-chip, and large-cap indexes over ten years to compare with seven proposed baselines. On the three Chinese indexes our method separately exceeds 6%, 16%, and 22% absolute annualized returns than our baseline. The ablation study also proves the necessity of return decomposition and inter-stock relation modeling. We find that modeling inter-stock relations on small-cap stocks works better than on large-cap stocks. Furthermore, we prove our method might be an implicit ensemble model of the predictable and residual parts, which is why it works stability under various market conditions.

Recommended citation: Yang J, Zhang M, Fang R, et al. Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns[J]. Applied Soft Computing, 2024, 165: 112116.
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