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A Stock Trading Recommender System Based on Temporal Association Rule Mining

Author

Listed:
  • Binoy B. Nair
  • V. P. Mohandas
  • Nikhil Nayanar
  • E. S. R. Teja
  • S. Vigneshwari
  • K. V. N. S. Teja

Abstract

Recommender systems capable of discovering patterns in stock price movements and generating stock recommendations based on the patterns thus discovered can significantly supplement the decision-making process of a stock trader. Such recommender systems are of great significance to a layperson who wishes to profit by stock trading even while not possessing the skill or expertise of a seasoned trader. A genetic algorithm optimized Symbolic Aggregate approXimation (SAX)–Apriori based stock trading recommender system, which can mine temporal association rules from the stock price data set to generate stock trading recommendations, is presented in this article. The proposed system is validated on 12 different data sets. The results indicate that the proposed system significantly outperforms the passive buy-and-hold strategy, offering scope for a layperson to successfully invest in capital markets.

Suggested Citation

  • Binoy B. Nair & V. P. Mohandas & Nikhil Nayanar & E. S. R. Teja & S. Vigneshwari & K. V. N. S. Teja, 2015. "A Stock Trading Recommender System Based on Temporal Association Rule Mining," SAGE Open, , vol. 5(2), pages 21582440155, April.
    • Handle: RePEc:sae:sagope:v:5:y:2015:i:2:p:2158244015579941
    DOI: 10.1177/2158244015579941
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    References listed on IDEAS

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    1. 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.
    2. Roberto Iannaccone & Edoardo Otranto, 2003. "Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter," Econometrics 0311002, University Library of Munich, Germany.
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    Cited by:

    1. Youngbin Lee & Yejin Kim & Javier Sanz-Cruzado & Richard McCreadie & Yongjae Lee, 2024. "Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling," Papers 2404.07223, arXiv.org, revised Aug 2024.

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