Evolving Systems

, Volume 10, Issue 4, pp 567–592 | Cite as

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

  • S. C. NayakEmail author
  • B. B. Misra
  • H. S. Behera
Original Paper


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.


Stock market forecasting Functional link artificial neural networks Artificial chemical reaction optimization Back propagation neural network 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKommuri Pratap Reddy Institute of TechnologyHyderabadIndia
  2. 2.Department of Information TechnologySilicon Institute of TechnologyBhubaneswarIndia
  3. 3.Department of Computer Science Engineering and Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia

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