Artificial Neural Network Algorithms based Nonlinear Data Analysis for Forecasting in the Finance Sector

  • Authors

    • Jitendra Kumar Jaiswal
    • Raja Das
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20829
  • Moving Average, Forecasting, Neurons, Backpropagation, RBFN, and Sliding Window

  • The involvement of big populace in the quantitative trading has been increased remarkably since the wired and wireless systems have become quite ubiquitous in the fields of finance and economics. Statistical, mathematical and technical analysis in parallel with machine learning and artificial intelligence are frequently being applied to perceive prices moving pattern and forecasting. However stock price do not follow any deterministic regulatory function, factor or circumstances rather than many considerations such as economy and finance, political environments, demand and supply, buying and selling tendency, trading and investment, etc. Historical data assist remarkably for prices forecasting as an important option for mathematicians and researchers. In this paper, we have followed backpropagation and radial basis function neural network for predicting future prices by modifying these techniques as per requirements. We have also performed a comparative analysis of the two ANN techniques for existing and our modified models.

     

     

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  • How to Cite

    Kumar Jaiswal, J., & Das, R. (2018). Artificial Neural Network Algorithms based Nonlinear Data Analysis for Forecasting in the Finance Sector. International Journal of Engineering & Technology, 7(4.10), 169-176. https://doi.org/10.14419/ijet.v7i4.10.20829