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Exploration and incorporation of virtual data positions for efficient forecasting of financial time series

Author

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  • S.C. Nayak
  • B.B. Misra
  • H.S. Behera

Abstract

Variations occur in the trends of financial time series data due to several reasons. Such random fluctuations lead to a sudden fall after a steady increase or a sudden rise after a gradual fall in the trend of financial time series data; this makes it difficult to predict. This research work explores the impact of virtual data positions (VDPs) on financial time series forecasting using three different data exploration techniques with artificial neural network (ANN) trained by back propagation. To train and validate these models, the daily closing prices of BSE, DJIA, NASDAQ, FTSE100, S%P500, TAIEX and SSE indices were used. Since none of the exploration techniques were found to be noticeably superior compared with the others, additional experiments were conducted using genetic algorithm (GA) to train ANN; this resulted in better accuracy.

Suggested Citation

  • S.C. Nayak & B.B. Misra & H.S. Behera, 2017. "Exploration and incorporation of virtual data positions for efficient forecasting of financial time series," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 26(1), pages 42-62.
    • Handle: RePEc:ids:ijisen:v:26:y:2017:i:1:p:42-62
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    Cited by:

    1. Sarat Chandra Nayak & Bijan Bihari Misra, 2020. "Extreme learning with chemical reaction optimization for stock volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-23, December.

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