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Forecasting Price Increments Using An Artificial Neural Network

Author

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  • FILIPPO CASTIGLIONE

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

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.

Suggested Citation

  • Filippo Castiglione, 2001. "Forecasting Price Increments Using An Artificial Neural Network," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 45-56.
    • Handle: RePEc:wsi:acsxxx:v:04:y:2001:i:01:n:s0219525901000097
    DOI: 10.1142/S0219525901000097
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    Cited by:

    1. V. V. Kondratenko & Yu. A Kuperin, 2003. "Using Recurrent Neural Networks To Forecasting of Forex," Papers cond-mat/0304469, arXiv.org.

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