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Pöferlein, M. Using Negations in Analyzing German Texts in Finance. Credit and Capital Markets – Kredit und Kapital, 99999(), 1-36. https://doi.org/10.3790/ccm.2024.1436301
Pöferlein, Matthias "Using Negations in Analyzing German Texts in Finance" Credit and Capital Markets – Kredit und Kapital 99999., 2024, 1-36. https://doi.org/10.3790/ccm.2024.1436301
Pöferlein, Matthias (2024): Using Negations in Analyzing German Texts in Finance, in: Credit and Capital Markets – Kredit und Kapital, vol. 99999, iss. , 1-36, [online] https://doi.org/10.3790/ccm.2024.1436301
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Using Negations in Analyzing German Texts in Finance

Pöferlein, Matthias

Credit and Capital Markets – Kredit und Kapital, Vol. (2024), Online First : pp. 1–36

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.2024.1436301
Language
English
Pages
36
Keywords

Author Details

Matthias Pöferlein (M.A.), University of Bayreuth, Chair of Finance and Banking, Universitätsstr. 30, 95447 Bayreuth.

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Abstract

Domain-specific dictionaries have prevailed, when conducting the dictionary-based approach to measure the sentiment of textual data in finance. Through the contributions of Bannier et al. (2019a) and Pöferlein (2021), two versions of a dictionary suitable for analyzing German finance-related texts are available (BPW dictionary). This paper conducts and tests further improvements of the given word lists by calculating the sentiment of German-speaking annual reports to forecast future return on assets and future return on equity. This corrected and expanded version provides more significant results. Despite the broad usage of negations, this type of improvement in combination with the BPW dictionary has not yet been tested when conducting the dictionary-based approach. Therefore, this paper additionally tests different negation lists to show that implementing negations can improve results.

Table of Contents

Section Title Page Action Price
Matthias Pöferlein: Using Negations in Analyzing German Texts in Finance 1
Abstract 1
I. Introduction 1
II. Literature Review 3
III. Data 4
1. Data Source 4
2. Used Dictionaries 5
3. Parsing 7
IV. Methodology 8
1. Measurement of Sentiment and Implementation of Negations 8
2. Empirical Approach 9
V. Results 10
1. Summary Statistics 10
2. Significance of Results 15
VI. Conclusion 23
Appendix 24
References 31

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