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Neural Computing and Applications

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01.07.2013 | Original Article

Forecasting IBEX-35 moves using support vector machines

verfasst von: Christian L. Dunis, Rafael Rosillo, David de la Fuente, Raúl Pino

Erschienen in: Neural Computing and Applications | Ausgabe 1/2013

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Abstract

This research aims at examining the application of support vector machines (SVMs) to the task of forecasting the weekly change in the Madrid IBEX-35 stock index. The data cover the period between 10/18/1990 and 10/29/2010. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) decision rules. The SVMs with given values of the RSI and MACD indicators are used in order to determine the best situations to buy or sell the market. The two outputs of the SVM are both the direction of the market and the probability attached to each forecast market move. The best result that it has been achieved is a hit ratio of 100% using the SVM classifier under some chosen risk-aversion parameters. However, these results are obtained analyzing recent periods rather than using all the dataset information.

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The software used is MATLAB 7.8.0 (R2009a).

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Titel: Forecasting IBEX-35 moves using support vector machines
verfasst von: Christian L. Dunis
Rafael Rosillo
David de la Fuente
Raúl Pino
Publikationsdatum: 01.07.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0821-9

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