Paper: arXiv 2510.17165

Authors: Jinrui Zhang

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel CAPM extension with heavy formalization (mean-variance optimization, epistemic/aleatory uncertainty disentanglement, Monte Carlo dropout) and sophisticated theoretical derivations, warranting a high math score. While it includes empirical experiments on real assets and backtesting frameworks for risk decomposition, the described implementation is more conceptual and tool-agnostic than fully code- or data-heavy, justifying a moderate-high rigor score.

Research Flowchart

  flowchart TD
  RQ["Research Goal: Disentangle Systematic vs. Idiosyncratic Risk<br/>in Foundation Model Trading Strategies"]
  
  Inputs["Data/Inputs: Equity Market Data<br/>Foundation Model Embeddings"]
  
  Method["Methodology: Extension of CAPM<br/>+ Uncertainty Disentanglement"]
  
  Comp["Computational Process:<br/>Monte Carlo Dropout for<br/>Epistemic vs. Aleatory Risk Estimation"]
  
  Out["Key Findings:<br/>Transparent Risk-Return Profiles<br/>Identified Model Degradation<br/>Refinement Avenues"]

  RQ --> Inputs
  Inputs --> Method
  Method --> Comp
  Comp --> Out

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1 of 67 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.