Performance Evaluation of Two Algorithms for Learning in ANN Based on a Real Financial Prediction

Yadira SOLANO  Hiroaki IKEDA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E80-A   No.2   pp.407-412
Publication Date: 1997/02/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural networks,  backpropagation,  learning,  financial forecasting,  

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The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a proposed modification to the first one by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show not only that NBP performs better than OBP since the former speeds up convergence time to a given error value, but also NBP shows a good generalization performance.