Paper: arXiv 2412.03618

Authors: Fu Lei, Ge Shi

Abstract

In today’s complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of risk prediction. Through empirical analysis of data from multiple asset classes such as stocks, bonds, commodities, and foreign exchange, the results show that the proposed CNN model significantly outperforms traditional models in terms of prediction accuracy and robustness, especially under extreme market conditions. This research provides a new method for financial risk management, with important theoretical significance and practical value.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces an innovative approach by encoding multi-asset time series into 2D images for CNN feature extraction, bridging computer vision and portfolio risk modeling. While mathematically light on formal proofs, its empirical evaluation across diverse asset classes under extreme conditions demonstrates strong practical utility.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Multi-Asset Portfolio Risk via CNN] --> B[Data Inputs: Time Series of Stocks, Bonds, Commodities & Forex]
    B --> C[Image Processing: Convert Financial Time Series to 2D Images]
    C --> D[CNN Modeling: Feature Extraction & Pattern Recognition]
    D --> E[Empirical Analysis: Model Evaluation & Benchmark Comparison]
    E --> F[Outcomes: Superior Accuracy & Robustness, Especially in Extreme Markets]