Forecasting in stock market with Machine Learning: A State of Art

Machine learning has become increasingly crucial in stock market forecasting due to its capacity to analyze large volumes of data, detect patterns, and make predictions based on historical trends. It offers several benefits, including handling complex datasets, recognizing and predicting patterns, adaptability and scalability, automating trading strategies, risk management and portfolio optimization, and more. However, it’s important to recognize that stock markets are influenced by numerous unpredictable factors, and there are inherent limitations and uncertainties in predicting market movements. Therefore, machine learning should be utilized as one tool among many in the investor’s decision-making process, complementing human judgment and domain expertise. This survey article explores the use of machine learning techniques in stock market price prediction, examining different models and their success rates across various countries and datasets. It also delves into the commonly used data sources for stock price prediction and discusses methods for processing this data. Through this study, it becomes evident how machine learning techniques have the potential to enhance stock price prediction and contribute to a deeper understanding of financial markets.