Performance Analysis of Share Market by using ML Technique

Abstract

This research investigates the transformative impact of machine learning (ML) on stock market analysis, assessing its potential benefits and confronting associated challenges. The study employs various ML models, including regression, classification, clustering, and natural language processing, to analyze extensive datasets comprising historical stock prices, financial indicators, economic data, and sentiment from social media. Key findings reveal that ML models exhibit superior accuracy in forecasting market movements and stock prices compared to traditional methods. Automated anomaly detection algorithms demonstrate proficiency in identifying unusual market behaviour, offering timely warnings for potential market shifts and fraudulent activities. ML-powered risk assessment tools showcase the capacity to personalize investment strategies based on individual preferences, augmenting decision-making for investors. Despite challenges such as data quality, model selection, and ethical considerations, the research underscores the undeniable potential of ML in stock market analysis. Rigorous methodologies, including data preprocessing, feature engineering, and model evaluation, contribute to the robustness of the findings. Ethical considerations, including algorithmic biases and transparency are thoroughly explored to ensure responsible application in the financial domain.