The nature of statistical learning theory
The nature of statistical learning theory
Language models for financial news recommendation
Proceedings of the ninth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Currency exchange rate forecasting from news headlines
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
NewsCATS: A News Categorization and Trading System
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Review: Expert systems and evolutionary computing for financial investing: A review
Expert Systems with Applications: An International Journal
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
A quantitative stock prediction system based on financial news
Information Processing and Management: an International Journal
How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations
Information Systems Research
Applying text and data mining techniques to forecasting the trend of petitions filed to e-People
Expert Systems with Applications: An International Journal
Improving stock market prediction by integrating both market news and stock prices
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Media coverage in times of political crisis: A text mining approach
Expert Systems with Applications: An International Journal
The Journal of Machine Learning Research
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Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information, recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance. We discuss some of the (dis)advantages of the most widely used performance metrics and conclude that is difficult to assess the external validity of performance using some of these measures. Moreover, there remain many questions as to the real-world applicability of these empirical models. In the first part of this study we design novel stock price prediction models, based on state-of-the-art text-mining techniques to assert whether we can predict the movement of stock prices more accurately by including indicators of irrationality. Along with this, we discuss which metrics are most appropriate for which scenarios in order to evaluate the models. Finally, we discuss how to gain insight into text-mining-based stock price prediction models in order to evaluate, validate and refine the models.