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One Day Prediction of NIKKEI Index Considering Information from Other Stock Markets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3070)

Abstract

A task of a stock index prediction is presented in this paper. Several issues are considered. The data is gathered at the concerned stock market (NIKKEI) and two other markets (NASDAQ and DAX). The data contains not only original numerical values from the markets but also indicators pre-processed in terms of technical analysis, i.e. the oscillators are calculated and the structures of a value chart are extracted. Selected data is input to a neural network that is functionally divided into separate modules. The prediction goal was next day opening value of Japanese stock market index NIKKEI with consideration of German and USA stock markets’ indexes. The average prediction error on the test set equals 43 points and the average percentage prediction error is equal to 0.27% while the average index volatility equals 0.96%.

Keywords

Stock Market Independent Component Analysis Technical Analysis Data Stock Market Index Average Percentage Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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