Menu Expand

Using Negations in Analyzing German Texts in Finance

  1. Journals
  2. Economics, Finance, Business & Management
  3. Credit and Capital Markets – Kredit und Kapital
  4. Vol. 56, Issue 3-4
  5. pp. 353–388

JOURNAL ARTICLE

Cite JOURNAL ARTICLE

Style
Pöferlein, M. Using Negations in Analyzing German Texts in Finance. Credit and Capital Markets – Kredit und Kapital, 56(3-4), 353-388. https://doi.org/10.3790/ccm.2023.1436301
Pöferlein, Matthias "Using Negations in Analyzing German Texts in Finance" Credit and Capital Markets – Kredit und Kapital 56.3-4, 2023, 353-388. https://doi.org/10.3790/ccm.2023.1436301
Pöferlein, Matthias (2023): Using Negations in Analyzing German Texts in Finance, in: Credit and Capital Markets – Kredit und Kapital, vol. 56, iss. 3-4, 353-388, [online] https://doi.org/10.3790/ccm.2023.1436301
Format
Download

Using Negations in Analyzing German Texts in Finance

Pöferlein, Matthias

Credit and Capital Markets – Kredit und Kapital, Vol. 56 (2023), Iss. 3-4 : pp. 353–388

Read Now Download (PDF)

Additional Information

Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.2023.1436301
Language
English
Pages
36
Keywords

Author Details

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

References

  1. Algaba, A./Ardia, D./Bluteau, K./Borms, S./Boudt, K. (2020): Econometrics meets sentiment: An overview of methodology and applications. Journal of Economic Surveys, Vol. 34(3), 512–547.  Google Scholar
  2. Allee, K. D./Deangelis, M. D. (2015): The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion. Journal of Accounting Research, Vol. 53(2), 241–274.  Google Scholar
  3. Alshorman, S. A. A./Shanahan, M. (2022): The voice of profit: exploring the tone of Australian CEO‘s letters to shareholders after the global financial crisis. Corporate Communications: An International Journal, Vol. 27(1), 127–147.  Google Scholar
  4. Aly, D./El-Halaby, S./Hussainey, K. (2018): Tone disclosure and financial performance: evidence from Egypt. Accounting Research Journal, Vol. 31(1), 63–74.  Google Scholar
  5. Bannier, C. E./Pauls, T./Walter, A. 2017): CEO-Speeches and Stock Returns.  Google Scholar
  6. Bannier, C. E./Pauls, T./Walter, A. (2019a): Content analysis of business specific text documents: Introducing a German dictionary. Journal of Business Economics, Vol. 89(1), 79–123.  Google Scholar
  7. Bannier, C. E./Pauls, T./Walter, A. (2019b): The Annual General Meeting revisited: The role of the CEO speech.  Google Scholar
  8. Berns, J./Bick, P./Flugum, R./Houston, R. (2022): Do changes in MD&A section tone predict investment behavior? Financial Review, Vol. 57(1), 129–153.  Google Scholar
  9. Bochkay, K./Hales, J./Chava, S. (2020): Hyperbole or Reality? Investor Response to Extreme Language in Earnings Conference Calls. The Accounting Review, Vol. 95(2), 31–60.  Google Scholar
  10. Borochin, P. A./Cicon, J. E./DeLisle, R. J./McKay Price, S. (2018): The effects of conference call tones on market perceptions of value uncertainty. Journal of Financial Markets, Vol. 40, 75–91.  Google Scholar
  11. Boudt, K./Thewissen, J. (2019): Jockeying for Position in CEO Letters: Impression Management and Sentiment Analytics. Financial Management, Vol. 48(1).  Google Scholar
  12. Brau, J. C./Cicon, J./McQueen, G. (2016): Soft Strategic Information and IPO Underpricing. Journal of Behavioral Finance, Vol. 17(1), 1–17.  Google Scholar
  13. Bushman, R. M./Hendricks, B. E./Williams, C. D. (2016): Bank Competition: Measurement, Decision-Making, and Risk-Taking. Journal of Accounting Research, Vol. 54(3), 777–826.  Google Scholar
  14. Chakraborty, B./Bhattacharjee, T. (2020): A review on textual analysis of corporate disclosure according to the evolution of different automated methods. Journal of Financial Reporting and Accounting, Vol. 18(4), 757–777.  Google Scholar
  15. Correa, R./Garud, K./Londono, J. M./Mislang, N. (2021): Sentiment in Central Banks’ Financial Stability Reports. Review of Finance, Vol. 25(1), 85–120.  Google Scholar
  16. Da Tonin, J. M. F./Scherer, L. M. (2022): Market reaction to the tones of earnings conference calls. Journal of Business Management, Vol. 62(1), 1–18.  Google Scholar
  17. Daniel, F./Lohrke, F. T./Fornaciari, C. J./Turner, R. (2004): Slack resources and firm performance: a meta-analysis. Journal of Business Research, Vol. 57(6), 565–574.  Google Scholar
  18. Davis, A. K./Piger, J. M./Sedor, L. M. (2012): Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language. Contemporary Accounting Research, Vol. 29(3), 845–868.  Google Scholar
  19. Davis, A. K./Tama-Sweet, I. (2012): Managers’ Use of Language Across Alternative Disclosure Outlets: Earnings Press Releases versus MD&A. Contemporary Accounting Research, Vol. 29(3), 804–837.  Google Scholar
  20. Druz, M./Petzev, I./Wagner, A. F./Zeckhauser, R. J. (2020): When Managers Change Their Tone, Analysts and Investors Change Their Tune. Financial Analysts Journal, Vol. 76(2), 47–69.  Google Scholar
  21. Feldman, R./Govindaraj, S./Livnat, J./Segal, B. (2008): The Incremental Information Content of Tone Change in Management Discussion and Analysis.  Google Scholar
  22. Ferguson, N. J./Philip, D./Lam, H. Y. T./Guo, J. M. (2015): Media Content and Stock Returns: The Predictive Power of Press. Multinational Finance Journal, Vol. 19(1), 1–31.  Google Scholar
  23. Ferris, S. P./Hao, Q./Liao, M.-Y. (2013): The Effect of Issuer Conservatism on IPO Pricing and Performance. Review of Finance, Vol. 17(3), 993–1027.  Google Scholar
  24. Frankel, R./Jennings, J./Lee, J. (2022): Disclosure Sentiment: Machine Learning vs. Dictionary Methods. Management Science, Vol. 68(7), 5514–55.  Google Scholar
  25. Fritz, D./Tows, E. (2018): Text Mining and Reporting Quality in German Banks: A Cooccurrence and Sentiment Analysis. Universal Journal of Accounting and Finance, Vol. 6(2), 54–81.  Google Scholar
  26. Gentzkow, M./Kelly, B./Taddy, M. (2019): Text as Data. Journal of Economic Literature, Vol. 57(3), 535–574.  Google Scholar
  27. González, M./Guzmán, A./Téllez, D. Fernando/Trujillo, M. Andrea (2019): What you say and how you say it: Information disclosure in Latin American firms. Journal of Business Research, Vol. 127(3), 427–443.  Google Scholar
  28. Hart, R. P. (2000): DICTION 5.0.  Google Scholar
  29. Henry, E. (2006): Market Reaction to Verbal Components of Earnings Press Releases: Event Study Using a Predictive Algorithm. Journal of Emerging Technologies in Accounting, Vol. 3, 1–19.  Google Scholar
  30. Henry, E. (2008): Are Investors Influenced By How Earnings Press Releases Are Written? Journal of Business Communication, Vol. 45(4), 363–407.  Google Scholar
  31. Henry, E./Leone, A. J. (2016): Measuring Qualitative Information in Capital Markets Research: Comparison of Alternative Methodologies to Measure Disclosure Tone. The Accounting Review, Vol. 91(1), 153–178.  Google Scholar
  32. Henry, E./Thewissen, J./Torsin, W. (2021): International Earnings Announcements: Tone, Forward-looking Statements, and Informativeness. European Accounting Review, 1–35.  Google Scholar
  33. Hillert, A./Jacobs, H./Müller, S. (2018): Journalist disagreement. Journal of Financial Markets, Vol. 41, 57–76.  Google Scholar
  34. Huang, X./Teoh, S. H./Zhang, Y. (2014): Tone Management. The Accounting Review, Vol. 89(3), 1083–1113.  Google Scholar
  35. Iqbal, J./Riaz, K. (2022): Predicting future financial performance of banks from management’s tone in the textual disclosures. Quality & Quantity, (56), 2691–2721.  Google Scholar
  36. Jandl, J.-O./Feuerriegel, S./Neumann, D. (2014): Long- and Short-Term Impact of News Messages on House Prices: A Comparative Study of Spain and the United States, Auckland.  Google Scholar
  37. Jegadeesh, N./Wu, D. (2013): Word power: A new approach for content analysis. Journal of Financial Economics, Vol. 110(3), 712–729.  Google Scholar
  38. Kang, Y./Cai, Z./Tan, C.-W./Huang, Q./Liu, H. (2020): Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, Vol. 7(2), 139–172.  Google Scholar
  39. Kang, T./Park, D.-H./Han, I. (2018): Beyond the numbers: The effect of 10-K tone on firms’ performance predictions using text analytics. Telematics and Informatics, Vol. 35(2), 370–381.  Google Scholar
  40. Kaya, D./Maier, C./Böhmer, T. (2020): Empirische Kapitalmarktforschung zu Conference Calls: Eine Literaturanalyse. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, Vol. 72, 183–212.  Google Scholar
  41. Kaye, J./Druin, A./Lampe, C./Morris, D./Hourcade, J. P. (Hg.)): Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, New York, NY, USA 2016.  Google Scholar
  42. Kearney, C./Liu, S. (2014): Textual Sentiment in Finance: A Survey of Methods and Models. International Review of Financial Analysis, (33), 171–185.  Google Scholar
  43. King, D. R./Dalton, D. R./Daily, C. M./Covin, J. G. (2004): Meta-analyses of post-acquisition performance: indications of unidentified moderators. Strategic Management Journal, Vol. 25(2), 187–200.  Google Scholar
  44. Koelbl, M. (2020): Is the MD&A of US REITs informative? A textual sentiment study. Journal of Property Investment & Finance, Vol. 38(3), 181–201.  Google Scholar
  45. Krause, J./Perer, A./Ng, K. (2016): Interacting with Predictions, in: Kaye, Jofish/Druin, Allison/Lampe, Cliff/Morris, Dan/Hourcade, Juan Pablo (Hrsg.): Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, New York, NY, USA 2016, 5686–5697.  Google Scholar
  46. Lewis, C./Young, S. (2019): Fad or future? Automated analysis of financial text and its implications for corporate reporting. Accounting and Business Research, Vol. 49(5), 587–615.  Google Scholar
  47. Li, F. (2010): Textual Analysis of Corporate Disclosures: A Survey of the Literature. Journal of Accounting Literature, (29), 143–165.  Google Scholar
  48. Loughran, T./McDonald, B. (2011): When Is a Liability Not a Liability?: Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, Vol. 66(1), 35–65.  Google Scholar
  49. Loughran, T./McDonald, B. (2015): The Use of Word Lists in Textual Analysis. Journal of Behavioral Finance, Vol. 16(1), 1–11.  Google Scholar
  50. Loughran, T./McDonald, B. (2016): Textual Analysis in Accounting and Finance: A Survey. Journal of Accounting Research, (54), 1187–1230.  Google Scholar
  51. Luo, Y./Zhou, L. (2020): Textual tone in corporate financial disclosures: a survey of the literature. International Journal of Disclosure and Governance, Vol. 17, 101–110.  Google Scholar
  52. Meier, T./Boyd, R. L./Pennebaker, J. W./Mehl, M. R./Martin, M./Wolf, M./Horn, A. B. (2018): “LIWC auf Deutsch”: The Development, Psychometrics, and Introduction of DE-LIWC2015.  Google Scholar
  53. Mishev, K./Gjorgjevikj, A./Vodenska, I./Chitkushev, L. T./Trajanov, D. (2020): Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers. IEEE Access, Vol. 8, 131662–131682.  Google Scholar
  54. Myšková, R./Hájek, P. (2020): Mining risk-related sentiment in Corporate annual reports and its effect on financial performance. Technological and Economic Development of Economy, Vol. 26(6), 1422–1443.  Google Scholar
  55. Patelli, L./Pedrini, M. (2014): Is the Optimism in CEO’s Letters to Shareholders Sincere? Impression Management Versus Communicative Action During the Economic Crisis. Journal of Business Ethics, Vol. 124, 19–34.  Google Scholar
  56. Pengnate, S./Lehmberg, D. G./Tangpong, C. (2020): Top management‘s communication in economic crisis and the firm‘s subsequent performance: sentiment analysis approach. Corporate Communications: An International Journal, Vol. 25(2), 187–205.  Google Scholar
  57. Picault, M./Renault, T. (2017): Words are not all created equal: A new measure of ECB communication. Journal of International Money and Finance, (79), 136–156.  Google Scholar
  58. Pöferlein, M. (2021): Sentiment Analysis of German Texts in Finance: Improving and Testing the BPW Dictionary. Journal of Banking and Financial Economics, Vol. 16(2), 5–24.  Google Scholar
  59. Price, M. S./Doran, J. S./Peterson, D. R./Bliss, B. A. (2012): Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, Vol. 36(4), 992–1011.  Google Scholar
  60. Remus, R./Quasthoff, U./Heyer, G. (2010): SentiWS – a Publicly Available German-language Resource for Sentiment Analysis. Proceedings of the 7th International Language Ressources and Evaluation (LREC’10), 1168–1171.  Google Scholar
  61. Renault, T. (2017): Intraday online investor sentiment and return patterns in the U.S. stock market. Journal of Banking & Finance, Vol. 84, 25–40.  Google Scholar
  62. Rice, D. R./Zorn, C. (2019): Corpus-based dictionaries for sentiment analysis of specialized vocabularies. Political Science Research and Methods, Vol. 67, 1–16.  Google Scholar
  63. Röder, F./Walter, A. (2019): What drives Investment Flows into Social Trading Portfolios? The Journal of Financial Research, Vol. 42(2), 383–411.  Google Scholar
  64. Ruscheinsky, J. R./Lang, M./Schäfers, W. (2018): Real estate media sentiment through textual analysis. Journal of Property Investment & Finance, Vol. 36(5), 410–428.  Google Scholar
  65. Schmeling, M./Wagner, C. (2016): Does Central Bank Tone Move Asset Prices?  Google Scholar
  66. Shapiro, A. H./Sudhof, M./Wilson, D. J. (2022): Measuring news sentiment. Journal of Econometrics, Vol. 228(2), 221–243.  Google Scholar
  67. Stone, P. J./Dunphy, D. C./Smith, M. S./Ogilvie, D. M. (1966): The General Inquirer: A Computer Approach to Content Analysis. The M.I.T. Press, Cambridge, Massachusetts, London.  Google Scholar
  68. Taboada, M./Brooke, J./Tofiloski, M./Voll, K./Stede, M. (2011): Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, Vol. 37(2), 267–307.  Google Scholar
  69. Tetlock, P. C. (2007): Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, Vol. 62(3), 1139–1168.  Google Scholar
  70. Tetlock, P. C./Saar-Tsechansky, M./Macskassy, S. (2008): More Than Words: Quantifying Language to Measure Firms’ Fundamentals. The Journal of Finance, Vol. 63(3), 1437–1467.  Google Scholar
  71. Tillmann, P./Walter, A. (2018): ECB vs Bundesbank: Diverging Tones and policy Effectiveness.  Google Scholar
  72. Tillmann, P./Walter, A. (2019): The effect of diverging communication: The case of the ECB and the Bundesbank. Economics Letters, Vol. 176©, 68–74.  Google Scholar
  73. Universität Leipzig (2022): Deutscher Wortschatz: Korpus News 2020. Accessed: 15.04.2022, https://​corpora.uni-leipzig.de​/​de​?​corpusId=​deu_news_2020.  Google Scholar
  74. Vojinović, Ž./Milutinović, S./Leković, B. (2020): Micro-specific Profitability Factors of the Serbian Insurance Industry: A Panel Data Estimation. Ekonomie a Management, Vol. 23(1), 135–155.  Google Scholar
  75. Wolf, M./Horn, A. B./Mehl, M. R./Haug, S./Pennebaker, J. W./Kordy, H. (2008): Computergestützte quantitative Textanalyse: Äquivalenz und Robustheit der deutschen Version des Linguistic Inquiry and Word Count. Diagnostica, Vol. 54(2), 85–98.  Google Scholar
  76. Yuthas, K./Rogers, R./Dillard, J. F. (2002): Communicative Action and Corporate Annual Reports. Journal of Business Ethics, Vol. 41(1-2), 141–157.  Google Scholar

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 353
Abstract 353
I. Introduction 353
II. Literature Review 355
III. Data 356
1. Data Source 356
2. Used Dictionaries 357
3. Parsing 359
IV. Methodology 360
1. Measurement of Sentiment and Implementation of Negations 360
2. Empirical Approach 361
V. Results 362
1. Summary Statistics 362
2. Significance of Results 367
VI. Conclusion 375
Appendix 376
References 383

Contact

Duncker & Humblot GmbH Carl-Heinrich-Becker-Weg 9 12165 Berlin, Deutschland T: +49 30 790006-0

Quick Navigation

2024 Duncker & Humblot GmbH

Powered by CloudPublish

We use cookies to ensure that you get the best experience on our website. Learn more about how we use cookies.