A Comprehensive Approach to Predicting Financial Market Trends Using Advanced Machine Learning Algorithms

Abstract

Abstract. Financial market movements show extreme complexity and variability making predictions about them difficult to achieve. A several machine learning algorithm test is conducted to evaluate their potential in stock market change forecasting. The analysis used data from 2010 to 2020 to test six machine learning methods including decision trees, random forests, support vector machines (SVMs), XGBoost, neural networks and long short-term memory (LSTM) networks. The models were evaluated using accuracy alongside precision, recall and F1 score and ROC-AUC measurements. Based on 79.4% F1 score, 78.3% recall, 80.5% precision alongside 82.4% accuracy the results indicate the superiority of LSTM over other models. The performance metrics show that SVMs achieved 75.1% accuracy yet random forests recorded 72.8% accuracy whereas XGBoost secured 78.6% accuracy. The accuracy of decision trees measured at 65.4% proved to be the lowest among all models. LSTM's predictions in financial markets show strong results which proves deep learning models can optimize trading and financial prediction accuracy