AI Driven Investment Strategies for Enhancing Stock Market Forecasting with Machine Learning Models

Abstract

The swiftly chan ging stock market has generated interest in utilising Artificial Intelligence (AI) and Machine Learning (ML) for future predictions, as they may improve forecasting and decision-making abilities. Stock markets exhibit volatility and complexity, requiring efficient methods for prediction and decision-making. Traditional methods frequently inadequately represent complex market dynamics, leading to the rising significance of AI and ML. The main objective is to assess the precision of AI-based ML models in forecasting stock market trends. The research seeks to determine the factors affecting model efficacy and investment strategy. This research examines techniques for incorporating technology to improve forecasting models. This investigation utilizes a qualitative methodology in conjunction with reliable online resources and academic publications. The data includes several viewpoints on AI's ability to predict the future of finance, enabling a thorough assessment of current approaches and their effectiveness. Machine learning models demonstrate enhanced efficacy in particular market conditions, with data quality and feature selection being critical for accurate forecasts. This analysis examines the ramifications of these findings for real investors and politicians. The report concludes with recommendations for improving AI-based stock market trend predictions. It illustrates how machine learning could improve financial decision-making and suggests directions for future research. This study offers ideas to improve the predictive efficacy of AI-driven investment methods, ultimately helping investors make more informed choices. The ramifications reach beyond investors, financial institutions, and politicians navigating complex markets.