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ACFLN: artificial chemical functional link network for prediction of stock market index

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Abstract

Uncertainty and complexity associated with the stock data make the exact determination of future prices impossible. Successful prediction of a stock future price requires an efficient prediction system. This paper proposes an artificial chemical reaction optimization based functional link network termed as ACFLN for stock market forecasting. The efficiency of the proposed model has been evaluated by forecasting five real stock market prices such as BSE, DJIA, NASDAQ, TAIEX and FTSE. Different experiments are conducted to evaluate the performance of the proposed model such as forecasting the stock price 1 day ahead, 1 week ahead, and 1 month ahead. Data is obtained for all the working days in a year and for each data the said experiments are conducted. From simulation studies, it is revealed that the proposed model achieves better forecasting accuracies over others.

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Acknowledgements

The authors are very much thankful to the reviewers and the chief editor for their constructive suggestions which significantly facilitated the quality improvement of this article.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Hyderabad, 500088, India

    S. C. Nayak

  2. Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, 751024, India

    B. B. Misra

  3. Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    H. S. Behera

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  1. S. C. Nayak
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Correspondence to S. C. Nayak.

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Nayak, S.C., Misra, B.B. & Behera, H.S. ACFLN: artificial chemical functional link network for prediction of stock market index. Evolving Systems 10, 567–592 (2019). https://doi.org/10.1007/s12530-018-9221-4

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