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

Author

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

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%.

Suggested Citation

  • Philip M. Tsang & Sin-Chun Ng & Reggie Kwan & Jacky Mak & Sheung-On Choy, 2007. "An empirical examination of the use of NN5 for Hong Kong stock price forecasting," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(3), pages 373-388.
    • Handle: RePEc:ids:ijelfi:v:1:y:2007:i:3:p:373-388
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    Cited by:

    1. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    2. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.

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