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This paper compares various machine learning (ML), Prophet, and deep learning (DL) models’ performances in forecasting stock market trends using different datasets. The research uses the NVIDIA Corporation (NVDA) data from January 1, 2019, to January 1, 2024, showing forecasted trends and performance measures of each model. The Random Forest model performed best on important regression metrics: Mean Absolute Error (MAE = 0.31211), Mean Squared Error (MSE = 0.37742), Root Mean Squared Error (RMSE = 0.61434), R-squared (0.99986), and Mean Absolute Percentage Error (MAPE = 1.07827). At the same time, the LSTM model performed the best on risk-adjusted measures such as Sortino Ratio (6.26923), Jensen’s Alpha (1.51894), Maximum Drawdown (1.25614), and Mean Directional Accuracy (0.65088). The Decision Tree Regressor also exhibited high performance in Directional Accuracy (0.98857). The results emphasize the quality of ML and DL models for stock market forecasting to facilitate data-informed decisions by investors.

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

  • 会议论文

  • 发表日期：

    2026年

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  • N. J. Jesan,
  • R. Rahul Ganesh,
  • T. Gireesh Kumar,
  • I. Praveen

摘要

This paper compares various machine learning (ML), Prophet, and deep learning (DL) models’ performances in forecasting stock market trends using different datasets. The research uses the NVIDIA Corporation (NVDA) data from January 1, 2019, to January 1, 2024, showing forecasted trends and performance measures of each model. The Random Forest model performed best on important regression metrics: Mean Absolute Error (MAE = 0.31211), Mean Squared Error (MSE = 0.37742), Root Mean Squared Error (RMSE = 0.61434), R-squared (0.99986), and Mean Absolute Percentage Error (MAPE = 1.07827). At the same time, the LSTM model performed the best on risk-adjusted measures such as Sortino Ratio (6.26923), Jensen’s Alpha (1.51894), Maximum Drawdown (1.25614), and Mean Directional Accuracy (0.65088). The Decision Tree Regressor also exhibited high performance in Directional Accuracy (0.98857). The results emphasize the quality of ML and DL models for stock market forecasting to facilitate data-informed decisions by investors.