KAN based Autoencoders for Factor Models

ArXiv ID: 2408.02694 “View on arXiv”

Authors: Unknown

Abstract

Inspired by recent advances in Kolmogorov-Arnold Networks (KANs), we introduce a novel approach to latent factor conditional asset pricing models. While previous machine learning applications in asset pricing have predominantly used Multilayer Perceptrons with ReLU activation functions to model latent factor exposures, our method introduces a KAN-based autoencoder which surpasses MLP models in both accuracy and interpretability. Our model offers enhanced flexibility in approximating exposures as nonlinear functions of asset characteristics, while simultaneously providing users with an intuitive framework for interpreting latent factors. Empirical backtesting demonstrates our model’s superior ability to explain cross-sectional risk exposures. Moreover, long-short portfolios constructed using our model’s predictions achieve higher Sharpe ratios, highlighting its practical value in investment management.

Keywords: Kolmogorov-Arnold Networks (KANs), Latent factor models, Autoencoder, Asset pricing, Cross-sectional risk, Equity

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including the Kolmogorov-Arnold theorem and spline parameterizations, indicating high complexity. It also demonstrates strong empirical rigor with a large dataset (CRSP data, 1957-2016), backtesting results, and reported Sharpe ratios, making it backtest-ready.

  flowchart TD
    A["Research Goal:<br>Improve Factor Model Accuracy &<br>Interpretability using Neural Networks"] --> B["Data: Asset Characteristics<br>Historical Returns"]
    
    B --> C["Methodology: KAN-based Autoencoder"]
    
    subgraph C [" "]
        C1["Encoder: Map Characteristics<br>to Latent Factors"]
        C2["Decoder: Map Factors<br>back to Returns"]
    end
    
    C1 --> D["Computational Process:<br>Nonlinear Approximation via<br>Kolmogorov-Arnold Networks"]
    C2 --> D
    
    D --> E["Key Findings & Outcomes"]
    
    subgraph E [" "]
        E1["Higher Accuracy:<br>Superior Cross-sectional Explanations"]
        E2["Better Interpretability:<br>Intuitive Latent Factor Framework"]
        E3["Practical Value:<br>Higher Sharpe Ratios<br>in Long-Short Portfolios"]
    end