KASPER: Kolmogorov Arnold Networks for Stock Prediction and Explainable Regimes

TL;DR

KASPER introduces a regime-aware, interpretable framework for stock prediction that combines regime detection, sparse spline modeling, and symbolic rule extraction, outperforming traditional deep learning models in financial forecasting.

Contribution

The paper presents KASPER, a novel approach integrating regime detection, sparse spline functions, and symbolic learning for transparent and adaptive stock prediction.

Findings

Achieves an R^2 score of 0.89 on real-world data

Attains a Sharpe Ratio of 12.02, indicating high risk-adjusted returns

Outperforms existing methods in accuracy and robustness

Abstract

Forecasting in financial markets remains a significant challenge due to their nonlinear and regime-dependent dynamics. Traditional deep learning models, such as long short-term memory networks and multilayer perceptrons, often struggle to generalize across shifting market conditions, highlighting the need for a more adaptive and interpretable approach. To address this, we introduce Kolmogorov-Arnold networks for stock prediction and explainable regimes (KASPER), a novel framework that integrates regime detection, sparse spline-based function modeling, and symbolic rule extraction. The framework identifies hidden market conditions using a Gumbel-Softmax-based mechanism, enabling regime-specific forecasting. For each regime, it employs Kolmogorov-Arnold networks with sparse spline activations to capture intricate price behaviors while maintaining robustness. Interpretability is achieved through symbolic learning based on Monte Carlo Shapley values, which extracts human-readable rules tailored to each regime. Applied to real-world financial time series from Yahoo Finance, the model achieves an $R^2$ score of 0.89, a Sharpe Ratio of 12.02, and a mean squared error as low as 0.0001, outperforming existing methods. This research establishes a new direction for regime-aware, transparent, and robust forecasting in financial markets.