Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control

TL;DR

This paper introduces a deep learning-based multi-factor investment model using LSTM to improve risk control, adaptability, and portfolio performance across various market conditions.

Contribution

It develops a novel LSTM-driven optimization method for multi-factor investment models, enhancing risk management and asset allocation accuracy.

Findings

LSTM model outperforms benchmark in risk indicators

Model shows strong adaptability across market environments

Portfolio performance is significantly improved

Abstract

Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retracement, Sharp ratio and value at risk (VaR), and shows strong adaptability and robustness in different market environments. Furthermore, the model is applied to the actual portfolio to optimize the asset allocation, which significantly improves the performance of the portfolio, provides investors with more scientific and accurate investment decision-making basis, and effectively balances the benefits and risks.