Stock market prediction using evolutionary support vector machines: an application to the ASE20 index

Karathanasopoulos, A., Theofilatos, K. A., Sermpinis, G., Dunis, C., Mitra, S. and Stasinakis, C. (2016) Stock market prediction using evolutionary support vector machines: an application to the ASE20 index. European Journal of Finance, 22(12), pp. 1145-1163. (doi:10.1080/1351847X.2015.1040167)

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Abstract

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named Evolutionary Support Vector Machine (ESVM) Stock Predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a Naïve strategy, a buy and hold strategy, a MACD and an ARMA models), and a MLP neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100, SP&500 indices.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stasinakis, Dr Charalampos and Sermpinis, Professor Georgios
Authors: Karathanasopoulos, A., Theofilatos, K. A., Sermpinis, G., Dunis, C., Mitra, S., and Stasinakis, C.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
College of Social Sciences > Adam Smith Business School > Economics
Journal Name:European Journal of Finance
Publisher:Routledge
ISSN:1351-847X
ISSN (Online):1466-4364
Published Online:29 July 2015
Copyright Holders:Copyright © 2015 Informa UK
First Published:First published in The European Journal of Finance 22: 12(1145-1163)
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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