Representation and learning in information retrieval
Representation and learning in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Information storage and retrieval
Information storage and retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
The Impact of E-Commerce Announcements on the Market Value of Firms
Information Systems Research
Forecasting Intraday Stock Price Trends with Text Mining Techniques
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
News and trading rules
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Financial Risk Manager Handbook (Wiley Finance)
Financial Risk Manager Handbook (Wiley Finance)
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A multi-agent decision support system for stock trading
IEEE Network: The Magazine of Global Internetworking
A neuro-evolutionary approach to intraday financial modeling
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Business intelligence in risk management: Some recent progresses
Information Sciences: an International Journal
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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The management of financial risk is one of the most challenging tasks of financial institutions. In the last two decades, diverse quantitative models and approaches have been developed and refined to address the impact of volatile markets on business. Whereas existing approaches have intensively utilized structured data such as historical price series, little attention has been paid to unstructured (textual) data, which could be a large source of information in this context. Previous empirical research has shown that certain news stories, such as corporate disclosures, can cause abnormal price behavior subsequent to their publication. On the basis of a data set comprising such news stories as well as intraday stock prices, this paper explores the risk implications of information being newly available to market participants. After showing that such events can significantly drive stock price volatilities, this research aims at identifying among the textual data provided those disclosures that have resulted in most supranormal risk exposures. To this end, four different learners - Naive Bayes, k-Nearest Neighbour, Neural Network, and Support Vector Machine - have been applied in order to detect patterns in the textual data that could explain increased risk exposure. Two evaluations are presented in order to assess the learning capabilities of the approach in the context of risk management. First, ''classic'' data mining evaluation metrics are applied and, second, a newly developed simulation-based evaluation method is presented. Evaluation results provide strong evidence that unstructured (textual) data represents a valuable source of information also for financial risk management - a domain in which, in the past, little attention has been paid to unstructured data. With regard to classification performance, it is also shown that there exist significant differences between the applied learning techniques.