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%.
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