Trading with the Devil: Risk and Return in Foundation Model Strategies

TL;DR

This paper extends the CAPM to separate systematic and idiosyncratic risks in foundation-model-based trading strategies, using uncertainty quantification to improve risk assessment and strategy evaluation.

Contribution

It introduces a novel risk decomposition framework for foundation models in finance, linking systematic risk to epistemic uncertainty and idiosyncratic risk to aleatory uncertainty, with practical measurement methods.

Findings

Disentangling risks reveals performance limits of foundation-model strategies.

Uncertainty-based risk measures improve transparency of trading strategies.

Insights into model degradation and targeted refinements for financial models.

Abstract

Foundation models - already transformative in domains such as natural language processing - are now starting to emerge for time-series tasks in finance. While these pretrained architectures promise versatile predictive signals, little is known about how they shape the risk profiles of the trading strategies built atop them, leaving practitioners reluctant to commit serious capital. In this paper, we propose an extension to the Capital Asset Pricing Model (CAPM) that disentangles the systematic risk introduced by a shared foundation model - potentially capable of generating alpha if the underlying model is genuinely predictive - from the idiosyncratic risk attributable to custom fine-tuning, which typically accrues no systematic premium. To enable a practical estimation of these separate risks, we align this decomposition with the concepts of uncertainty disentanglement, casting systematic risk as epistemic uncertainty (rooted in the pretrained model) and idiosyncratic risk as aleatory uncertainty (introduced during custom adaptations). Under the Aleatory Collapse Assumption, we illustrate how Monte Carlo dropout - among other methods in the uncertainty-quantization toolkit - can directly measure the epistemic risk, thereby mapping trading strategies to a more transparent risk-return plane. Our experiments show that isolating these distinct risk factors yields deeper insights into the performance limits of foundation-model-based strategies, their model degradation over time, and potential avenues for targeted refinements. Taken together, our results highlight both the promise and the pitfalls of deploying large pretrained models in competitive financial markets.