Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine Learning
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent information triage
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining and Knowledge Discovery
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Case studies: Commercial, multiple mining tasks systems: gainsmarts
Handbook of data mining and knowledge discovery
The Journal of Machine Learning 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
NewsCATS: A News Categorization and Trading System
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Journal of the American Society for Information Science and Technology
Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Prediction in financial markets: The case for small disjuncts
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of nonlinear methods for predicting earnings surprises and returns
IEEE Transactions on Neural Networks
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In this study we evaluate the effectiveness of augmenting numerical market data with textual-news data, using data mining methods, for forecasting stock returns in intraday trading. Integrating these two sources of data not only enriches the information available for the forecasting model, but it can potentially capture joint patterns that may not otherwise be identified when each data source is employed separately. We start with market data and then gradually add various textual data representations, going from simple representations, such as word counts, to more advanced representations involving sentiment analysis. To find the incremental value of each data representation, we build an end-to-end recommendation process including data preprocessing, modeling, validation, trade recommendations and economic evaluation. Each component of the modeling process is optimized to remove human bias and to allow us to impartially compare the results of the various models. Additionally, we experiment with several forecasting algorithms to find the one that yields the ''best'' results according to a variety of performance criteria. We employ data representation procedures and modeling improvements beyond those used in previous related studies. The economic evaluation of the results is conducted using a simulation procedure that inherently accounts for transaction costs and eliminates biases that have potentially affected previous related data-mining studies. This research is one of the largest-scale data-mining studies for evaluating the effectiveness of integrating market data with textual news data for the purpose of stock investment recommendations. The results of our study are promising in that they show that augmenting market data with advanced textual data representation significantly improves stock purchase decisions. Best results are achieved when the approach is implemented with a nonlinear neural network forecasting algorithm.