Advanced Stock Price Prediction with xLSTM-Based Models: Improving Long-Term Forecasting

Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they struggle with effectively modeling long-term dependencies. In this paper, we propose an advanced stock price prediction model based on the Extended Long Short-Term Memory (xLSTM) algorithm, designed to enhance predictive accuracy over both short and long-term periods. We conduct extensive experiments by building and evaluating models based on xLSTM and LSTM architectures for multiple stocks. Our results demonstrate that the xLSTM model consistently outperforms the LSTM model across all stocks and time horizons, with the performance gap widening as the prediction period extends. The observations underscore the superior capability of the xLSTM-based model to capture long-term patterns in financial data, offering a promising approach for more accurate and reliable stock price predictions.

I. Introduction

Predicting stock prices has long been a central focus in financial modeling due to its profound impact on investment strategies, risk management, and market efficiency. The complexity and volatility of financial markets, driven by both short-term fluctuations and long-term trends, make accurate stock price prediction a particularly challenging task [1]. The unpredictable nature of stock prices is further compounded by numerous factors, including economic indicators, market sentiment, and external events, all contributing to the intricate dynamics of financial markets [2]–[4].