A survey on net asset value prediction using artificial neural network and its variants

  • Abstract
  • Keywords
  • References
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  • Abstract

    Net Asset Value (NAV) prediction is one of the important financial time series forecasting task as various investors are interested to invest their money in mutual funds. NAV specifies the price at which investors can buy or sell units of a fund. It is calculated on a daily basis. Since the financial market is dynamic and chaotic in nature, it becomes very difficult to predict the NAV. From the literatures, it is found that different nonlinear models using computational intelligence methods have been proposed to predict the financial time series data i.e. stock market index, exchange rate, NAV. The basic objective of different time series prediction models using computational intelligence methods is to improve the prediction accuracy and to reduce the model complexity. This survey is primarily focused on the usage of Artificial Neural Network model and their variants on NAV prediction. The ability to map the nonlinear relationship and the self-adaptive nature of ANN makes it useful in predicting different financial time series data. It is concluded that the performance of different neural network models is superior to other linear models in NAV prediction.



  • Keywords

    Functional Link Artificial Neural Network; Multilayer Perceptron; Nonlinear; Recursive Least Square; Soft Computing.

  • References

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Article ID: 19140
DOI: 10.14419/ijet.v7i4.19140

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