A Study on Long-Horizon Stock Forecasting Failures With Deep Sequential Models

Abstract

Short-term daily financial prediction is possible due to the high correlation between two consecutive daily price movements. However, long-term financial prediction through deep architectures fails due to the Markovian nature of the underlying financial dataset, resulting in correlation decay or raising temporal independence. This research uses a novel deep architecture, the KAN-RNN-Wiener framework, that integrates Kolmogorov-Arnold Networks (KAN) with physicsinformed Wiener processes to model complex non-linear financial dependencies. Although the architecture achieves superior next-day predictive accuracy over baseline deep-KAN, LSTM, and GRU models, it encounters a systemic breakdown in recursive 15-day forecasting. This paper studies how this divergence in the long-term is driven by market statistical complexities, such as non-stationarity and correlation decay. Despite advances in the architectural design proposed in this work, our observations indicate that while advanced hybrid models excel at capturing localized volatility surfaces, it fails to overcome long-term correlation decay in high-entropy environments.