Design and implementation of NN5 for Hong Kong stock price forecasting

Abstract

A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN). In this paper, the theoretical background of NNs and the backpropagation algorithm is reviewed. Subsequently, an attempt to build a stock buying/selling alert system using a backpropagation NN, NN5, is presented. The system is tested with data from one Hong Kong stock, The Hong Kong and Shanghai Banking Corporation (HSBC) Holdings. The system is shown to achieve an overall hit rate of over 70%. A number of trading strategies are discussed. A best strategy for trading non-volatile stock like HSBC is recommended.