Study of machine learning algorithms for potential stock trading strategy frameworks - Agarwal, Aakash (International Journal of Financial, Accounting, and Management, 2021)
Purpose: This paper discusses major stock market trends and provides information on stock market forecasting. Stock market forecasting is essentially an attempt to forecast the future value of the stock market. Doing this manually can be a strenuous task, and thus we need some software and algorithms to make our task easier. This paper also lists a few of those algorithms, formulas, and calculations associated with them. These algorithms and models primarily revolve around the concept of Machine Learning (ML) and Deep Learning. Research Methodology: This study is based on descriptive, quantitative, and cross-sectional research design. We used a multivariate algorithm model and indicators to examine stocks for investing or trading and their efficiency. It concludes with the recommendations for enhancing trading strategies using machine learning algorithms. Results: This study suggests that after comparing and combining the various algorithms using experimental analysis, the random forest algorithm is the most suitable algorithm for forecasting a stock's market prices based on various data points from historical data. Limitations: The applicability of the study was only hampered by unforeseeable tragic events such as economic crisis, market collapse, etc Contribution: Successful stock prediction will be a substantial benefit for stock market institutions and provide real-world answers to the challenges that stock investors face. As a result, gaining significant knowledge on the subject is quite beneficial for us.