Combining machine learning and effective feature selection for real-time stock trading in variable time-frames
by A.K.M. Amanat Ullah; Fahim Imtiaz; Miftah Uddin Md Ihsan; Md. Golam Rabiul Alam; Mahbub Majumdar
International Journal of Computational Science and Engineering (IJCSE), Vol. 26, No. 1, 2023

Abstract: The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalised scheme. Many previous studies tried different techniques to build a machine learning model, which can make a significant profit in the US stock market by performing live trading. However, very few studies have focused on the importance of finding the best features for a particular period for trading. Our top approach used the performance to narrow down the features from a total of 148 to about 30. Furthermore, the top 25 features were dynamically selected before each time training our machine learning model. It uses ensemble learning with four classifiers: Gaussian naive Bayes, decision tree, logistic regression with L1 regularisation and stochastic gradient descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market.

Online publication date: Thu, 23-Feb-2023

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