An Empirical Analysis of Machine Learning and Deep Learning for Stock Market Forecasting

This chapter delves into the empirical analysis of machine learning (ML) and deep learning (DL) models for stock market forecasting, with a focus on predicting stock prices using historical data from Nvidia Corporation (NVDA) from January 1, 2019, to January 1, 2024. The study evaluates several models, including Random Forest, AdaBoost, and Long Short-Term Memory (LSTM), using a range of metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), R-squared, Sortino Ratio, Jensen’s Alpha, Maximum Drawdown (MDD), Directional Accuracy (DA), and Mean Directional Accuracy (MDA). The findings reveal that Random Forest outperformed other models in most metrics, while CatBoost showed the best Sortino Ratio and Directional Accuracy. LSTM demonstrated excellent results in Jensen’s Alpha and Maximum Drawdown, indicating its value in capturing unique financial dynamics. The chapter also discusses the system architecture, including data fetching, preprocessing, feature engineering, model training, and prediction. It highlights the use of the Prophet model for creating future datasets, which enhances the accuracy of regression models. The results are visualized using interactive line graphs, providing a comparative analysis of model performance against actual stock prices. The conclusion underscores the merits and demerits of the stock market, emphasizing the potential for high returns and liquidity, as well as the challenges posed by volatility and unpredictability. The chapter concludes with insights into the future of stock market forecasting, suggesting the need for more advanced trading strategies and improved risk management metrics.