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Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects

Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects

František Dařena, Jonáš Petrovský, Jan Přichystal, Jan Žižka
Copyright: © 2021 |Pages: 28
ISBN13: 9781799890201|ISBN10: 1799890201|EISBN13: 9781799890218
DOI: 10.4018/978-1-7998-9020-1.ch053
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MLA

Dařena, František, et al. "Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects." Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, edited by Information Resources Management Association, IGI Global, 2021, pp. 1056-1083. https://doi.org/10.4018/978-1-7998-9020-1.ch053

APA

Dařena, F., Petrovský, J., Přichystal, J., & Žižka, J. (2021). Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects. In I. Management Association (Ed.), Research Anthology on Strategies for Using Social Media as a Service and Tool in Business (pp. 1056-1083). IGI Global. https://doi.org/10.4018/978-1-7998-9020-1.ch053

Chicago

Dařena, František, et al. "Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects." In Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, edited by Information Resources Management Association, 1056-1083. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-9020-1.ch053

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Abstract

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

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