Forecasting S&P 500 Using LSTM Models (2501.17366v1)

Abstract: With the volatile and complex nature of financial data influenced by external factors, forecasting the stock market is challenging. Traditional models such as ARIMA and GARCH perform well with linear data but struggle with non-linear dependencies. Machine learning and deep learning models, particularly Long Short-Term Memory (LSTM) networks, address these challenges by capturing intricate patterns and long-term dependencies. This report compares ARIMA and LSTM models in predicting the S&P 500 index, a major financial benchmark. Using historical price data and technical indicators, we evaluated these models using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The ARIMA model showed reasonable performance with an MAE of 462.1, RMSE of 614, and 89.8 percent accuracy, effectively capturing short-term trends but limited by its linear assumptions. The LSTM model, leveraging sequential processing capabilities, outperformed ARIMA with an MAE of 369.32, RMSE of 412.84, and 92.46 percent accuracy, capturing both short- and long-term dependencies. Notably, the LSTM model without additional features performed best, achieving an MAE of 175.9, RMSE of 207.34, and 96.41 percent accuracy, showcasing its ability to handle market data efficiently. Accurately predicting stock movements is crucial for investment strategies, risk assessments, and market stability. Our findings confirm the potential of deep learning models in handling volatile financial data compared to traditional ones. The results highlight the effectiveness of LSTM and suggest avenues for further improvements. This study provides insights into financial forecasting, offering a comparative analysis of ARIMA and LSTM while outlining their strengths and limitations.

• The paper demonstrates LSTM's superior performance over ARIMA, achieving up to 96.41% accuracy in S&P 500 forecasting.
• It employs a rigorous decade-long dataset with technical indicators and advanced preprocessing to capture complex market trends.
• The study raises methodological insights on model overfitting when using additional features, suggesting avenues for hybrid approaches.

Evaluation of LSTM and ARIMA Models for Forecasting the S&P 500 Index

In the presented paper, Pilla and Mekonen rigorously examine the applicability of Long Short-Term Memory (LSTM) networks to the task of forecasting the S&P 500 index, a complex and seminal challenge within financial time series analysis. They juxtapose the performance of LSTM networks against that of traditional Autoregressive Integrated Moving Average (ARIMA) models.

Summary of Methods

The paper spans over a decade-long dataset from October 2013 to September 2024, inclusive of daily values for the S&P 500 index and a comprehensive array of technical indicators and macroeconomic factors. The LSTM model, characterized by its memory cells and gate structures, is designed to exploit both short- and long-term dependencies inherent in the temporal domain. It was analyzed in two configurations: with and without exogenous features. Conversely, ARIMA, a renowned statistical model well-suited for linear components of time series data, is deployed with optimal parameters selected via Auto-ARIMA.

Prior to modeling, the dataset underwent rigorous preprocessing, including data correlation-based feature selection and normalization. ARIMA utilized historical price data exclusively, whilst the LSTM models incorporated a broader set of features encompassing moving averages, volatility indices, and economic indicators, albeit with variability in model configurations.

Key Findings

The empirical findings underscore the superior performance of LSTM networks over the ARIMA model. The LSTM model achieved an impressive accuracy of up to 96.41% when deployed without additional features, significantly overshadowing the 89.8% accuracy yielded by ARIMA. Furthermore, the LSTM models demonstrated commendably lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), a testament to their proficiency in capturing complex and nonlinear associations in financial datasets over extended periods.

The disparity in accuracy between the two LSTM configurations—favoring the feature-sparse model—raises methodological questions regarding overfitting and the influence of feature noise in forecast predictions. Such findings align with broader literature, where deep learning's efficacy in financial forecasting underscores its potential over traditional models constrained by stationary and linear assumptions.

Implications and Future Directions

This paper reinforces the growing epistemic convergence around deep learning’s advantageous role in financial forecasting. The improved prediction accuracy of LSTM networks suggests the promise of integrating such advanced deep learning models for financial decision-making processes, risk management, and policy formulation. Looking forward, research avenues could explore hybrid models combining LSTM strengths with those of econometric models like GARCH to encompass volatility insights. Moreover, expanding model evaluations across diverse stock indices or integrating innovations such as Bidirectional LSTM or Attention mechanisms could provide further insights into model robustness and generalizability.

Overall, Pilla and Mekonen effectively elucidate the critical advantages embodied by LSTM networks. Their paper underscores the paradigm shift within financial analytics, marking a transition from linear statistical to dynamic and adaptable deep learning frameworks, thereby enriching our methodological arsenal in handling intricate time series datasets.