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A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange

Author

Listed:
  • Asif Perwej
  • Yusuf Perwej

    (Al-Baha University)

  • Nikhat Akhtar

    (BBD University)

  • Firoj Parwej

    (Singhania University)

Abstract

Forecasting is the process of computation in unknown situations from the historical data. Financial forecasting and planning is usually an essential part of the business plan, and would be done as part of setting up the organization and receive funds. Financial forecasting and planning is also an essential activity to confirm a good management, keeping the organization financially sound is a key objective. The prediction of stock market has been a long time tempting topic for researchers from different fields. Stock analysts use various forecasting methods to determine how a stock's price will move in the ensuing day. The purpose of this paper is to explore the radial basis function (RBF) and function linked artificial neural network (FLANN) algorithms for forecasting of financial data. We have based our models on data taken and compared those using historical data from the Bombay Stock Exchange (BSE). The RBF and FLANN parameters updated by Particle swarm optimization (PSO). In this paper, we have examined this algorithm on a number of various parameters including error convergence and the Mean Average Percentage Error (MAPE) and comparative assessment of the RBF and FLANN algorithms is done. The proposed method indeed can help investors consistently receive gains. Finally, a simple merchandise model is established to study the accomplishment of the proposed prediction algorithm against other criterion.

Suggested Citation

  • Asif Perwej & Yusuf Perwej & Nikhat Akhtar & Firoj Parwej, 2015. "A FLANN and RBF with PSO Viewpoint to Identify a Model for Competent Forecasting Bombay Stock Exchange," Post-Print hal-03362994, HAL.
    • Handle: RePEc:hal:journl:hal-03362994
    Note: View the original document on HAL open archive server: https://hal.science/hal-03362994
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