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Title: Norn predictor — stock prediction using a neural oscillatory-based recurrent network
Authors: Lee, RST
Liu, JNK
Issue Date: 2001
Source: International journal of computational intelligence and applications, 2001, v. 1, no. 4, p. 439-451
Abstract: Financial prediction is one of the most typical applications in contemporary scientific study. In this paper, we present a fully integrated stock prediction system – NORN Predictor – 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 the NOEGM (Neural Oscillatory-based Elastic Graph Matching) model and its integration with the Time-difference recurrent neural network models. This will provide a fully integrated and automated tool for analysis and investigation of stock investment. From the implementation point of view, the stock pricing information of 33 major Hong Kong stocks in the period from 1990 to 1999 is being adopted for system training and evaluation. As compared with the contemporary neural prediction model, the proposed system has achieved challenging results in terms of efficiency and accuracy.
Keywords: Stock prediction
Neural oscillatory-based recurrent network
Neural networks
Publisher: Imperial College Press
Journal: International journal of computational intelligence and applications 
ISSN: 1469-0268
EISSN: 1757-5885
DOI: 10.1142/S1469026801000354
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