Artificial Neural Network (ANN), Min-Max Normalization, Iterative Approach, Stock Price Predication


Predicting stock price of a particular stock is a difficult non-linear problem. Artificial Neural Network (ANN) is a tool to solve this kind of problem and has received much attentions in the field of financial modeling in recent years. This paper proposes an enhanced ANN for predicting stock prices with a novel Max-Min normalization method as well as an iterative approach. Our experimental results confirm that the predication accuracy outperforms other existing ANN predication mechanisms.

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  • Anastasiadis, Magoulas &Vrahatis., 2005. New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing, [e-journal] 64. 253-270. Available through: ScienceDirect website
  •  <> [Access 8 May 2016].
  • Graupe, D., 2013. Principles of Artificial Neural Networks. 3rd ed. Singapore: World Scientific Publishing Company.
  • Hang Seng Indexes, (2016). Hang Seng Index and Sub-indexes. [online] Available at:
  • [Accessed 9 May. 2016].
  • Yahoo! Finance, (2016). Historical Prices. [online] Available at:
  • [Accessed 2 Apr. 2016].