Design and Evaluation of Empirical Models for Stock Price Prediction

Enric Junqué de Fortuny, Tom de Smedt, David Martens and Walter Daelemans

Working Papers from University of Antwerp, Faculty of Business and Economics

Abstract: The efficient market hypothesis and related theories claim that it is impossible to predict future stock prices. Even so, empirical research has countered this claim by achieving better than random prediction performance. Using a model built from a combination of text mining and time series prediction, we provide further evidence to counter the efficient market hypothesis. We discuss the difficulties in evaluating such models by investigating the drawbacks of the common choices of evaluation metrics used in these empirical studies. We continue by suggesting alternative techniques to validate stock prediction models, circumventing these shortcomings. Finally, a trading system is built for the Euronext Brussels stock exchange market. In our framework, we applied a novel sentiment mining technique in the design of the model and show the usefulness of state-of-the-art explanation-based techniques to validate the resulting models.

Keywords: Stock prediction; Support Vector Machine; Text mining; Opinion mining (search for similar items in EconPapers)
Pages: 24 pages
Date: 2012-09
New Economics Papers: this item is included in nep-cba and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:ant:wpaper:2012017

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