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Stock Indices Prediction Using Radial Basis Function Neural Network

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

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Abstract

Aim of the paper is to efficiently predict the stock market data for future days ahead using Radial Basis Function (RBF) neural network. DJIA and S&P 500 stock indices have been taken to simulate the RBF model and also comparison has been done with results obtained from Functional Link Artificial Neural Network(FLANN) and Multilayer Perceptron, (MLP). From the simulation result it is observed that the proposed model is giving better results than other two neural network models interms of prediction accuracy.

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Author information

Authors and Affiliations

  1. Dept. of CSE /CA/IT, ITER, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, India

    Minakhi Rout, Usha Manasi Mohapatra & Rosalin Mahapatra

  2. Dept. of CSIT, G.G. Vishwavidyalaya, Central University, Bilaspur, India

    Babita Majhi

Authors
  1. Minakhi Rout
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  2. Babita Majhi
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  3. Usha Manasi Mohapatra
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  4. Rosalin Mahapatra
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

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© 2012 Springer-Verlag Berlin Heidelberg

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Rout, M., Majhi, B., Mohapatra, U.M., Mahapatra, R. (2012). Stock Indices Prediction Using Radial Basis Function Neural Network. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_34

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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