Optimization Analysis of Stock Price Prediction using Random Forest based Machine Learning Technique

Abstract

Stock price prediction has become an essential area of research due to its significance in financial planning, risk management, and investment decision-making. However, forecasting stock prices is highly challenging because financial markets are dynamic, nonlinear, and strongly influenced by external factors such as economic conditions, company performance, and investor sentiment. Machine learning techniques, particularly ensemble-based models, have shown remarkable potential for improving predictive accuracy. This study focuses on the use of the Random Forest (RF) algorithm for stock price prediction and examines how its performance can be enhanced through systematic optimization.

Random Forest is widely recognized for its robustness, ability to handle high-dimensional data, and capability to capture complex nonlinear relationships. Despite these strengths, its predictive efficiency largely depends on the appropriate selection of hyperparameters such as the number of trees, maximum depth, feature selection criteria, and sampling strategy. This research analyzes the impact of hyperparameter optimization using methods such as Grid Search, Random Search, and Bayesian optimization to identify the best-performing configuration of the RF model.

The results indicate that optimized Random Forest models significantly outperform their default counterparts, achieving lower prediction error and higher stability across various stock market datasets. Feature importance analysis further highlights the most influential technical indicators contributing to model performance. This study concludes that optimized RF-based models provide a reliable, scalable, and effective approach for stock price forecasting, offering valuable insights for investors and financial analysts.