Paper: arXiv 2411.02558

Abstract

In the financial field, precise risk assessment tools are essential for decision-making. Recent studies have challenged the notion that traditional network loss functions like Mean Square Error (MSE) are adequate, especially under extreme risk conditions that can lead to significant losses during market upheavals. Transformers and Transformer-based models are now widely used in financial forecasting according to their outstanding performance in time-series-related predictions. However, these models typically lack sensitivity to extreme risks and often underestimate great financial losses. To address this problem, we introduce a novel loss function, the Loss-at-Risk, which incorporates Value at Risk (VaR) and Conditional Value at Risk (CVaR) into Transformer models. This integration allows Transformer models to recognize potential extreme losses and further improves their capability to handle high-stakes financial decisions. Moreover, we conduct a series of experiments with highly volatile financial datasets to demonstrate that our Loss-at-Risk function improves the Transformers’ risk prediction and management capabilities without compromising their decision-making accuracy or efficiency. The results demonstrate that integrating risk-aware metrics during training enhances the Transformers’ risk assessment capabilities while preserving their core strengths in decision-making and reasoning across diverse scenarios.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced mathematical formulations involving Value at Risk (VaR) and Conditional Value at Risk (CVaR) integrated into loss functions, demonstrating high complexity, and it conducts rigorous experiments on volatile financial datasets with specific backtesting results, showing strong empirical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve risk sensitivity of Transformer models for financial forecasting"] --> B["Methodology: Integrate Risk-Aware Loss Function<br>(Loss-at-Risk)"]
  B --> C["Inputs: Time-Series Financial Data<br>(Highly Volatile Datasets)"]
  C --> D["Computational Process: Train Transformer Model<br>Using Loss-at-Risk (VaR & CVaR)"]
  D --> E["Key Findings: Enhanced Risk Assessment<br>Precise Extreme Loss Prediction<br>Maintained Decision Accuracy"]
  
  style A fill:#f9f,stroke:#333,stroke-width:2px
  style E fill:#9f9,stroke:#333,stroke-width:2px

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2 of 150 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.