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Efficient forecasting of financial time-series data with virtual adaptive neuro-fuzzy inference system

Author

Listed:
  • Sarat Chandra Nayak
  • Bijan Bihari Misra
  • Himansu Sekhar Behera

Abstract

Uncertainties and non-linearity associated with the stock index make it difficult to predict its behaviour and hence it remains a challenging task for researchers. Newly developed intelligent machine learning techniques have been applied to this area and these have established as efficient forecasting models. This paper presents a Virtual Adaptive Neuro-Fuzzy Inference System (VANFIS) for efficient forecasting of stock market indices. VANFIS works in a virtual environment where the Adaptive Neuro-Fuzzy Inference System (ANFIS) is exposed to virtual data positions to infer the future stock price. This model does not take any actual data at any point of time as its input, but works completely in the virtual environment. To validate the performance of the proposed model, 15 years' data from ten stock markets are taken and five different performance metrics are evaluated. Simulation results show that VANFIS significantly improves forecasting performance in comparison to ANFIS.

Suggested Citation

  • Sarat Chandra Nayak & Bijan Bihari Misra & Himansu Sekhar Behera, 2016. "Efficient forecasting of financial time-series data with virtual adaptive neuro-fuzzy inference system," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(4), pages 379-402.
    • Handle: RePEc:ids:ijbfmi:v:2:y:2016:i:4:p:379-402
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    Citations

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    Cited by:

    1. Sanjib Kumar Nayak & Sarat Chandra Nayak & Subhranginee Das, 2021. "Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach," FinTech, MDPI, vol. 1(1), pages 1-16, December.
    2. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.

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