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Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation

Author

Listed:
  • V. Vismayaa

    (Amrita Vishwa Vidyapeetham)

  • K. R. Pooja

    (Amrita Vishwa Vidyapeetham)

  • A. Alekhya

    (Amrita Vishwa Vidyapeetham)

  • C. N. Malavika

    (Amrita Vishwa Vidyapeetham)

  • Binoy B. Nair

    (Amrita Vishwa Vidyapeetham)

  • P. N. Kumar

    (Amrita Vishwa Vidyapeetham)

Abstract

Recommender systems that can suggest the user when to buy and sell stocks can be of immense help to those who wish to trade in stocks but are constrained by their limited knowledge of stock market dynamics. Traditionally, the trading recommendations have been generated on the basis of technical analysis. However, recent research in the field indicates that soft computing/data mining based recommender systems are also capable of generating profitable trading recommendations. An attempt has been made in this study to generate novel classifier based stock trading recommender systems that employ historical stock price data and technical indicators as input features. Moreover, there have been very few studies on the effectiveness recommender systems in the context of India, the world’s sixth largest economy and home to one of the world’s largest stock exchanges: the Bombay Stock Exchange (BSE). This study presents an empirical evaluation the effectiveness of five single classifier and six ensemble classifier based recommender systems on a total of 293 stocks drawn from the BSE. Recommender system performance for each stock is evaluated based on classification accuracy and eight economic performance measures. Results indicate that the proposed approach can indeed be used successfully for generating profitable trading recommendations.

Suggested Citation

  • V. Vismayaa & K. R. Pooja & A. Alekhya & C. N. Malavika & Binoy B. Nair & P. N. Kumar, 2020. "Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 901-923, March.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:3:d:10.1007_s10614-019-09922-x
    DOI: 10.1007/s10614-019-09922-x
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    References listed on IDEAS

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    1. Guglielmo Caporale & Luis Gil-Alana & Alex Plastun & Inna Makarenko, 2016. "Intraday Anomalies and Market Efficiency: A Trading Robot Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 275-295, February.
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    Cited by:

    1. Arvind Kumar Sinha & Pradeep Shende, 2024. "Uncertainty Optimization Based Feature Selection Model for Stock Marketing," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 357-389, January.

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