Design and Evaluation of Empirical Models for Stock Price Prediction
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.
|Date of creation:||Sep 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Prinsstraat 13, B-2000 Antwerpen|
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Burton G. Malkiel, 2005. "Reflections on the Efficient Market Hypothesis: 30 Years Later," The Financial Review, Eastern Finance Association, vol. 40(1), pages 1-9, 02.
When requesting a correction, please mention this item's handle: RePEc:ant:wpaper:2012017. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joeri Nys)
If references are entirely missing, you can add them using this form.