Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks

ArXiv ID: 2510.07444 “View on arXiv”

Authors: Albert Di Wang, Ye Du

Abstract

Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios.

Keywords: Peer-to-Peer Lending, Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Deep Neural Networks, Portfolio Diversification, Credit

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including VaR optimization, probability distributions, survival analysis, and neural network architectures (DSNN/DeNN) with detailed derivations, justifying a high math score. It demonstrates empirical rigor by using real-world Lending Club data, implementing deep learning models, conducting Monte-Carlo simulations, and reporting comparative performance against benchmarks, making it backtest-ready.

  flowchart TD
    A["Research Goal: Minimize VaR of Loan Portfolio using Deep Neural Networks"] --> B["Methodology: DeNN & DSNN Models"]
    B --> C["Data Input: Peer-to-Peer Lending Historical Data"]
    C --> D{"Computational Process: Predict Default Probability & Timing"}
    D --> E["Simulate Portfolio Distributions"]
    E --> F["Calculate VaR & CVaR"]
    F --> G["Key Findings: Models significantly reduce Portfolio VaR"]
    G --> H["Outcome: Low-freedom DeNN outperforms High-freedom DSNN"]