Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns

Paper: arXiv 2610.39205

Authors: Weijia Han, Lisha Qu

Abstract

Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversified portfolios. We study cryptocurrency returns, use equities as a secondary test, and compare the SAE with common benchmarks in a time-ordered evaluation. We find consistently lower volatility in SAE portfolios and link this result to lower correlations among the selected assets. However, the SAE does not outperform a simple contrarian strategy on risk-adjusted return, and its factor identities are not stable across training runs. Our contribution is to show that sparse coding can support portfolio diversification, while clearly separating this risk effect from claims about alpha or stable factor identities.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a solid empirical study using sparse autoencoders for portfolio diversification, clearly outlining its methodology and comparing against relevant benchmarks. While the mathematical complexity is moderate, the rigorous empirical evaluation across multiple asset classes and careful consideration of biases contribute significantly to its quality. The novelty lies in applying monosemanticity-style SAEs to financial factor discovery, specifically for risk reduction rather than alpha generation.

Research Flowchart

  flowchart TD
    A[Research Goal: Do SAEs produce stable financial factors & better-diversified portfolios?] --> B{Methodology: Compare SAE with common benchmarks};
    B --> C[Data: Cross-sectional Cryptocurrency Returns (Primary) & Equities (Secondary)];
    C --> D{Computational Process: Time-ordered evaluation of factor models & portfolio construction};
    D --> E[Outcome 1: Consistently lower volatility in SAE portfolios due to lower asset correlations];
    D --> F[Outcome 2: SAE does not outperform simple contrarian strategy on risk-adjusted return];
    D --> G[Outcome 3: SAE factor identities are not stable across training runs];
    E & F & G --> H[Conclusion: SAE supports portfolio diversification, but not alpha or stable factor identities];