Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Proceedings of the 2004 ACM symposium on Applied computing
Combining data and text mining techniques for analysing financial reports: Research Articles
International Journal of Intelligent Systems in Accounting and Finance Management
The language of quarterly reports as an indicator of change in the company's financial status
Information and Management
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
Novel Hybrid Hierarchical-K-means Clustering Method (H-K-means) for Microarray Analysis
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Training a Support Vector Machine in the Primal
Neural Computation
Statistical methods for automated generation of service engagement staffing plans
IBM Journal of Research and Development - Business optimization
A rough margin based support vector machine
Information Sciences: an International Journal
Combining News and Technical Indicators in Daily Stock Price Trends Prediction
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
Predicting Future Earnings Change Using Numeric and Textual Information in Financial Reports
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Predicting risk from financial reports with regression
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A parameter-free hybrid clustering algorithm used for malware categorization
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Analysis of stock price return using textual data and numerical data through text mining
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices
Data & Knowledge Engineering
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
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Stock price movement prediction is an appealing topic not only for research but also for commercial applications. Most of prior research separately analyzes the meanings of the qualitative or quantitative features, and does not consider the categorical information when clustering financial reports. Since quantitative or qualitative features contain only partial information, there may be no synergy by considering them individually. It is more appropriate to predict stock price movements by simultaneously taking both quantitative and qualitative features into account. Therefore, in this study, we utilize a weighting scheme to combine both qualitative and quantitative features of financial reports together, and propose a method to predict short-term stock price movements. The proposed method employs the categorical information to localize the clusters and improve the purity of each resultant cluster. We gathered 26,255 reports of companies listed in the S&P 500 index from the EDGAR database and conducted the GICS (Global Industrial Classification System) experiments based on the industry sectors. The empirical evaluation results show that the proposed method outperforms the SVM, naïve Bayes, and PFHC methods in terms of accuracy and average profit.