An empirical examination of the use of NN5 for Hong Kong stock price forecasting

  • Authors:

  • Philip M. Tsang;Sin-Chun Ng;Reggie Kwan;Jacky Mak;Sheung-On Choy

  • Affiliations:

  • Department of Engineering Science, The Open University of Hong Kong, A0923, 30 Good Shepherd Street, Homantin, Hong Kong, SAR, China.;Department of Computing and Mathematics, The Open University of Hong Kong, A0911, 30 Good Shepherd Street, Homantin, Hong Kong, SAR, China.;Department of Computing and Mathematics, The Open University of Hong Kong, A0911, 30 Good Shepherd Street, Homantin, Hong Kong, SAR, China.;Department of Computing and Mathematics, The Open University of Hong Kong, A0911, 30 Good Shepherd Street, Homantin, Hong Kong, SAR, China.;Department of Computing and Mathematics, The Open University of Hong Kong, A0911, 30 Good Shepherd Street, Homantin, Hong Kong, SAR, China

  • Venue:
  • International Journal of Electronic Finance
  • Year:
  • 2007

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Abstract

Reliable stock market movement prediction is a challenging task. The difficulty is mainly due to the close to random-walk behaviour of a stock time series. A number of published techniques have emerged in the trading community for prediction tasks. One of them is neural network, NN. In this paper, the theoretical background of neural networks and the backpropagation algorithm is reviewed. Subsequently, an attempt on building a stock buying/selling alert system using a backpropagation neural network, NN5, is presented. The system is tested with data from one of the Hong Kong stocks, The Hong Kong and Shanghai Banking Corporation (HSBC) holdings. The system is shown capable of achieving an overall hit rate of 78%.