FORECASTING PRICE INCREMENTS USING AN ARTIFICIAL NEURAL NETWORK

Filippo Castiglione ()
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Filippo Castiglione: Center for Advanced Computer Science, University of Cologne, ZPR/ZAIK, Weyertal 80, D-50931 Köln, Germany

Advances in Complex Systems (ACS), 2001, vol. 04, issue 01, 45-56

Abstract: Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by "black box" methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to forecast the sign of the price increments with a success rate slightly above 50%canbe found. Target series are the daily closing price of different assets and indexes during the period from about January 1990 to February 2000.

Keywords: Forecasting; neural networks; financial time series; detrending analysis (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:04:y:2001:i:01:n:s0219525901000097

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DOI: 10.1142/S0219525901000097

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