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Over9K: Predicting Stock Volatility through News and Events

Over9K is a system designed to predict stock future volatility by analyzing news and information from the internet. Aiming to extract impactful events such as reorganizations, bankruptcies, product releases, and earnings reports, users can efficiently search and browse through data that affects stock volatility. Utilizing a unique architecture comprising a web interface, MySQL, and an Internet crawler based on Nutch, Over9K incorporates various Information Extraction (IE) tools including Gate and CRF++ for optimal data processing. Future improvements focus on controlled crawling and enhancing feature extraction.

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Over9K: Predicting Stock Volatility through News and Events

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  1. Over9K Alex Meng Chunshi Jin Elliott Conant Jonathan Fung

  2. Agenda • What is Over9K about • Architecture • Crawler • IE/Classifier • Web Interface • Summary

  3. What is Over9K about? • Original Goal: A system to predict stock’s future volatility based on the news and information gathered from Internet. • Over ambitious because of ignorant. • What we have done: extract information/events which may affect the volatility of stocks . User can search and browse it.

  4. Events to Extract • Reorganization • Bankruptcy • Product release • Earning report

  5. Architecture Web Interface MySQL IE/Classifier Internet Crawler

  6. Crawler • Based on nutch • Crawled web sites: • …

  7. IE/Classifier • Tried several systems for IE • Gate • OpenCalais • CRF++ • Classifier • Mallet

  8. Comparison of IE tools • OpenCalais: • Web service. Easy to use. No machine learning process. • Not extensible • Fairly good precision/recall • Gate: • ANNIE( a Nearly New IE system ): • Tokenizer, Sentence Splitter, POSTagger, Gazetteer, NE • JAPE: Gate’s rule engine. • Extensible with JAPE. Easy to use for its regex like syntax. Deterministic behavior. • High precision/recall for defined patterns, low for undefined patterns.

  9. Comparison of IE tools (cont.) • CRF++ • Need tools to preprocess content: • HTML to text • POS Tag/NE (Stanford NLP library) • Extract other features when necessary • Convert file to the required train/test format of CRF++ • Template file to define dependencies of feature and label. • Labeling training set is laborious • Fairly good precision/recall. “Intelligence” may emerge. • Need big set of training set.

  10. Web Interface

  11. Lessons and Thoughts • A realistic goal is critical. • Right tools are important. • Future Improvement • Controlled crawling • Improve feature extraction qualities: POSTagger/NE etc.

  12. Q&A Thanks!

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