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

ArXiv ID: 2507.00332 “View on arXiv”

Authors: Ruisi Li, Xinhui Gu

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.

Keywords: LSTM, multi-factor investing, risk control, Value at Risk (VaR), Sharpe ratio, equities

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 7.2/10
  • Quadrant: Street Traders
  • Why: The paper applies a well-known deep learning architecture (LSTM) to a traditional finance problem (multi-factor investing), involving mathematical concepts like regression and time-series modeling; however, it provides empirical backtesting results with specific risk metrics (Max Drawdown, Sharpe, VaR), comparative tables, and discusses data preprocessing and application to portfolio optimization.
  flowchart TD
    A["Research Goal: Optimization of Multi-factor Investment Model<br>Driven by Deep Learning for Risk Control"] --> B["Data Preparation &amp; Processing"]
    B --> C["Key Methodology: LSTM &amp; Multi-factor Integration"]
    C --> D["Computational Process: Optimization of Factor<br>Selection &amp; Weight Determination"]
    D --> E["Risk Control Application:<br>VaR &amp; Max Drawdown"]
    E --> F["Outcome 1: Model Evaluation<br>Superior Sharp Ratio &amp; Robustness"]
    E --> G["Outcome 2: Portfolio Optimization<br>Enhanced Asset Allocation"]