Cluster Computing

, Volume 22, Supplement 6, pp 13159–13166 | Cite as

Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach

  • S. Kumar ChandarEmail author


Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1 week, simple moving average for 2 weeks, 14 days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks.


Neuro fuzzy approach Technical indicators Stock market prediction ANFIS training method 


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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Management StudiesChrist UniversityBengaluruIndia

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