Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures

Evert S, Heinrich P, Henselmann K, Rabenstein U, Scherr E, Schmitt M, Schröder L (2019)


Publication Type: Journal article

Publication year: 2019

Journal

Pages Range: 309-330

DOI: 10.1007/s10849-019-09283-6

Abstract

We investigate an approach to improving statistical text classification by combining machine learners with an ontology-based identification of domain-specific topic categories. We apply this approach to ad hoc disclosures by public companies. This form of obligatory publicity concerns all information that might affect the stock price; relevant topic categories are governed by stringent regulations. Our goal is to classify disclosures according to their effect on stock prices (negative, neutral, positive). In the study reported here, we combine natural language parsing with a formal background ontology to recognize disclosures concerning particular topics from a prescribed list. The semantic analysis identifies some of these topics with reasonable accuracy. We then demonstrate that machine learners benefit from the additional ontology-based information when predicting the cumulative abnormal return attributed to the disclosure at hand.

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How to cite

APA:

Evert, S., Heinrich, P., Henselmann, K., Rabenstein, U., Scherr, E., Schmitt, M., & Schröder, L. (2019). Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures. Journal of Logic, Language and Information, 309-330. https://doi.org/10.1007/s10849-019-09283-6

MLA:

Evert, Stephanie, et al. "Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures." Journal of Logic, Language and Information (2019): 309-330.

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