Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach

TL;DR

This paper introduces a deep learning framework that combines behavioral finance insights with multi-factor models to improve short-term stock trend prediction and trading performance.

Contribution

It develops a dual-task deep neural network that jointly predicts returns and directions, integrating behavioral factors for enhanced short-term forecasting.

Findings

Deep learning outperforms linear models in capturing factor interactions.

The dual-task MLP achieves higher predictive accuracy and economic relevance.

Empirical results show stable performance across multiple metrics.

Abstract

This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume patterns and behavioral finance insights. Empirical evaluation demonstrates that the dual-task MLP achieves superior and stable performance across both predictive accuracy and economic relevance, as measured by information coefficient (IC), information ratio (IR), and portfolio backtesting results. Comparative experiments further show that deep learning methods outperform linear baselines by effectively capturing structural interactions between factors. This work highlights the potential of structure-aware deep learning in enhancing multi-factor modeling and provides a practical framework for short-horizon quantitative investment strategies.