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Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

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Listed:
  • A. K. M. Amanat Ullah
  • Fahim Imtiaz
  • Miftah Uddin Md Ihsan
  • Md. Golam Rabiul Alam
  • Mahbub Majumdar

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.

Suggested Citation

  • A. K. M. Amanat Ullah & Fahim Imtiaz & Miftah Uddin Md Ihsan & Md. Golam Rabiul Alam & Mahbub Majumdar, 2021. "Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction," Papers 2107.13148, arXiv.org, revised Aug 2023.
  • Handle: RePEc:arx:papers:2107.13148
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    References listed on IDEAS

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    1. Mishra, Ritesh Kumar & Sehgal, Sanjay & Bhanumurthy, N.R., 2011. "A search for long-range dependence and chaotic structure in Indian stock market," Review of Financial Economics, Elsevier, vol. 20(2), pages 96-104, May.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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