NORN finance forecaster: a neural oscillatory-based recurrent network for finance prediction

Authors:
Raymond S. T. Lee;James N. K. Liu
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Hong Kong
Venue:
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Year:
2001

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Abstract

Financial prediction is so far the most important applications in contemporary scientific study. In this paper, we present a fully integrated stock prediction system - NORN Finance Forecaster - A Neural Oscillatory-based Recurrent Network for finance prediction system to provide both a) Long-term trend prediction, and b) Short-term stock price prediction. One of the major characteristics of the proposed system is the automation of the conventional financial technical analysis technique such as market pattern analysis via NOEGM (Neural Oscillatory-based Elastic Graph Matching) model and its integration with the Time-difference recurrent neural network model. This will provide a fully integrated and automated tool for analytic and investigation of stock investment. From the implementation point of view, the stock pricing information of 33 major Hong Kong stocks in the period of 1990 to 1999 are being adopted for system training and evaluation. As compared with contemporary neural prediction model, the proposed system has achieved challenging results in terms of efficiency and accuracy.