KASPER用可解释的分段模型预测股市，效果优于传统方法。
KASPER: Kolmogorov Arnold Networks for Stock Prediction and Explainable Regimes
- 基于稀疏样条的柯尔莫哥洛夫-阿诺德网络捕捉复杂价格行为
- 在真实股市数据上实现0.89的R²和12.02的夏普比率
- 通过符号规则提取提供人类可读的市场状态解释
金融市场的非线性与状态依赖特性使其预测极具挑战。传统深度学习模型如LSTM和多层感知机在市场状态切换时难以泛化，亟需更自适应且可解释的方法。为此，我们提出用于股票预测与可解释状态识别的柯尔莫哥洛夫-阿诺德网络（KASPER），融合状态检测、基于稀疏样条的函数建模与符号规则提取。该框架采用基于Gumbel-Softmax的状态识别机制，实现分状态预测；每种状态下使用具有稀疏样条激活的柯尔莫哥洛夫-阿诺德网络，捕捉复杂价格动态并保持鲁棒性；通过蒙特卡洛谢帕利值驱动的符号学习，生成各状态下的可读规则。在Yahoo Finance的真实金融时间序列上，模型达到0.89的R²、12.02的夏普比率，均方误差低至0.0001，显著优于现有方法。本研究为金融市场的状态感知、透明与稳健预测开辟了新路径。
原文摘要 · Abstract (English)
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.
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