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Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

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  • Yan Chen
  • Dongxu Mo
  • Feipeng Zhang

Abstract

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction.

Suggested Citation

  • Yan Chen & Dongxu Mo & Feipeng Zhang, 2022. "Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 5971-5996, December.
    • Handle: RePEc:taf:reroxx:v:35:y:2022:i:1:p:5971-5996
    DOI: 10.1080/1331677X.2022.2043762
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