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Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach

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  • Maciej Wujec

    (AI Lab, Science and Technology Park in Opole, Technologiczna 2, 45-839 Opole, Poland)

Abstract

An important role in the fundamental analysis is played by the acquisition and analysis of various types of information about the company. Text documents are an increasingly important source of this information. Their accurate and quick analysis is an increasingly important challenge for financial analysts. Research in the area of financial text analysis is based on sentiment analysis. The deep neural networks and the stocks’ cumulative abnormal return are used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require manual labeling of data or the creation of dictionaries and is free from the subjective assessment of the researcher. Taking into account the broad context of words and their meaning in financial texts, it also eliminates the problem of ambiguity of words in various contexts. The sentiment of financial texts presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability, the deep learning model gives predictions with a precision of 62% for the positive class and 55% for the negative class. The event study results show that the sentiment calculated under the proposed method can be successfully used to determine the probable direction of the market reaction to the information contained in current reports with a 1 percent significance level. The results can be used in market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

Suggested Citation

  • Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:12:p:582-:d:694175
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