Lorsbach, Tobias (2019). Three essays on the performance of earnings forecasts, bankruptcy predictions and textual analysis. PhD thesis, Universität zu Köln.
|
PDF
Dissertation_LorsbachTobias_062019.pdf - Published Version Download (1MB) | Preview |
Abstract
In summary, Chapter 2 of my thesis finds that quantitative information from interim financial earnings disclosures fundamentally improves the earnings forecast accuracy of mechanical models and levels the playing field when comparing model forecasts to analyst forecasts. Similarly, quantitative financial disclosure information is a key input in most bankruptcy prediction models. Using web crawling techniques to aggregate bankruptcy information of German companies, Chapter 3 shows that the reliability of widely used bankruptcy prediction models is determined by the quality of the underlying bankruptcy data. Accordingly, bankruptcy prediction models can be improved by collecting accurate bankruptcy data and discarding incorrect information. Complementary, Chapter 4 applies a new perspective to information provided in corporate disclosures by examining qualitative or textual content. This section introduces a novel framework focusing on analysis of the textual content of annual reports. Using the immediacy of market reactions and investor responses to textual information enables quantifying the qualitative content of financial disclosures.
Item Type: | Thesis (PhD thesis) | ||||||||
Creators: |
|
||||||||
URN: | urn:nbn:de:hbz:38-102333 | ||||||||
Date: | 2019 | ||||||||
Language: | English | ||||||||
Faculty: | Faculty of Management, Economy and Social Sciences | ||||||||
Divisions: | Faculty of Management, Economics and Social Sciences > Business Administration > Finance > Professorship for Business Administration and Corporate Finance | ||||||||
Subjects: | Management and auxiliary services | ||||||||
Uncontrolled Keywords: |
|
||||||||
Date of oral exam: | 6 June 2019 | ||||||||
Referee: |
|
||||||||
Refereed: | Yes | ||||||||
URI: | http://kups.ub.uni-koeln.de/id/eprint/10233 |
Downloads
Downloads per month over past year
Export
Actions (login required)
View Item |