Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals

TL;DR

This paper compares deep learning and traditional statistical models for stock price prediction across different time horizons, demonstrating that deep learning models like LSTM outperform traditional methods in accuracy but at higher computational costs.

Contribution

It provides a comprehensive comparison of deep learning and traditional models for stock prediction over short, medium, and long-term horizons using Nigerian stock data.

Findings

Deep learning models, especially LSTM, outperform traditional methods in accuracy.

Deep learning models require more computational resources and are less interpretable.

Traditional models are simpler but less accurate for complex data patterns.

Abstract

A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.