Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

A. K. M. Amanat Ullah, Fahim Imtiaz, Miftah Uddin Md Ihsan, Md. Golam Rabiul Alam and Mahbub Majumdar

Papers from arXiv.org

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 trading period. 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 regularization, 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 of making trading decisions in the stock market.

Date: 2021-07, Revised 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-isf
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Published in Int. J. Computational Science and Engineering, Vol. 26 No.1, (2023)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2107.13148

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