Comprehensive Analysis of Machine and Deep Learning Models for Stock Market Prediction

Abstract

Stock market prediction is a core task in financial engineering that requires sophisticated methods to extract subtle market and volatility trends. The increasing complexity of the stock market has led to the integration of advanced machine learning (ML) and deep learning (DL) techniques to improve accuracy beyond traditional statistical methods. This research provides a taxonomy of stock market prediction methods and reviews key regression-based models, including linear regression and advanced neural networks like recurrent neural networks (RNNs), long short-term memory (LSTM), and hybrid (CNN-LSTM) models. The study deploys and evaluates three specific models: Linear Regression, RNNs, and LSTMs. The models were trained and tested using modern data preprocessing procedures, including Z-score normalization and temporal sequencing. The findings show that the Linear Regression (LR) model performed better, with a Root Mean Square Error (RMSE) of 0.334 during training and 0.304 during testing, and a Mean Absolute Error (MAE) of 0.203 and 0.207, respectively. This contrasted with the deep learning models, which had higher error rates. The LSTM achieved a training RMSE of 0.355, while the RNN model had a training RMSE of 0.383. These results provide empirical evidence that increased model complexity does not necessarily translate into better forecasting accuracy in financial applications, and that model selection is both context-sensitive and data-driven. The findings mentioned the challenge of nonstationarity in stock market data and the need to periodically retrain models on recent data.