Version 1
: Received: 26 September 2021 / Approved: 28 September 2021 / Online: 28 September 2021 (12:38:54 CEST)
How to cite:
Wujec, M. A. Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network. Preprints2021, 2021090472. https://doi.org/10.20944/preprints202109.0472.v1
Wujec, M. A. Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network. Preprints 2021, 2021090472. https://doi.org/10.20944/preprints202109.0472.v1
Wujec, M. A. Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network. Preprints2021, 2021090472. https://doi.org/10.20944/preprints202109.0472.v1
APA Style
Wujec, M. A. (2021). Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network. Preprints. https://doi.org/10.20944/preprints202109.0472.v1
Chicago/Turabian Style
Wujec, M. A. 2021 "Sentiment Analysis of Current Reports Texts with Use of Cumulative Abnormal Return and Deep Neural Network" Preprints. https://doi.org/10.20944/preprints202109.0472.v1
Abstract
The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning 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 BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.
Keywords
fundamental analysis supported by machine learning; financial texts sentiment analysis; natural language processing in finance
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
