Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions

TL;DR

This paper introduces a novel Loss-at-Risk function for Transformer models that incorporates VaR and CVaR, significantly improving their ability to assess and manage extreme financial risks in volatile markets.

Contribution

The paper proposes a new loss function, Loss-at-Risk, that enhances Transformer models' sensitivity to extreme risks in financial forecasting tasks.

Findings

Loss-at-Risk improves risk prediction accuracy.

Transformers better recognize potential extreme losses.

Risk-aware training preserves decision-making performance.

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.