Analysing Trading Strategies and Uses of Machine Algorithm to Predict Stock Prices

Abstract

The investment banking and financial sectors have undergone significant transformation from the 19th century to the present, driven by technological advancements and evolving trading strategies. Traditional methods of buying and selling stocks have been replaced or supplemented by automated algorithms and machine learning techniques, enabling more precise stock market predictions. This review paper consolidates historical and contemporary strategies employed by traders and investors to maximize profits in the stock market. It emphasizes the application of machine learning in predicting stock market trends and decision-making for buying and selling securities.

A systematic review of journal articles published between 2016 and 2022 was conducted to identify the primary markets, stock indices, and trading strategies utilized in stock market predictions. The paper contributes to the literature by providing (1) a detailed analysis of trading strategies and market indicators, and (2) a comprehensive review of machine learning techniques employed in stock market forecasting. Furthermore, a bibliometric analysis highlights the most influential studies in this domain.

Technical analysis tools, such as moving averages and candlestick patterns, are explored alongside modern methodologies. The study also identifies existing gaps in trading strategy research, offering insights for future advancements in this field. This review serves as a valuable resource for understanding the intersection of traditional trading methods, machine learning algorithms, and stock market prediction.