Leveraging Machine Learning Algorithms for Stock Market Prediction: A Comparative Analysis of Approaches and Techniques

Stock market forecasting precision stands as an essential research point for financial analytics due to its ability to assist investors while minimizing financial risks. The research evaluates stock market trend prediction by implementing various machine learning and deep learning algorithms that analyze NASDAQ and NYSE alongside FTSE and Nikkei stock indices to identify their main targets. This study implements SVM, RF, NB, LSTM and ANN as prediction models. Traditional statistical methods receive enhancement for prediction accuracy by combining them with sentiment analysis and text mining systems according to the study. You will find these models' evaluation within the study's findings based on real-world data from Yahoo Finance which demonstrates their strengths and disadvantages. The research shows optimal stock market prediction outcomes result from integrating text mining with sentiment analysis through ML/DL methods but these methods encounter limitations due to feature selection problems along with data dependence and overfitting issues. This paper provides important findings about stock market prediction through computational intelligence techniques while recommending future research strategies for model development.