Essays on Qualitative and Quantitative Risk Management

This book is based on the Ph.D. thesis "Essays on Qualitative and Quantitative Risk Management" written by the author of this book. It consists out of three essays on text mining applications in finance and the validation of a credit risk model. To be more precise, the three essays address the following research questions: What kind of text mining measures are suitable in the finance area for analyzing text such as annual reports and can we use these measures to predict short-term performance or the reporting quality? Can we measure the tone of a document by using automatically calculated sentiment scores? How can we build a sentiment score, that captures keywords within a larger context? Do the chapters/sections of an annual report have a different influence on the whole content of the report? How can banks validate their credit risk model with a special focus on an analytical model? This book addresses practitioners, consultants, analysts, and bankers as well as students, researchers, and lecturers with focus on text mining applications in finance and the validation of credit risk models.

Autorentext
David Fritz was born in Duisburg and studied the subjects mathematics, computer science, and pedagogics at the University of Cologne. He is not only a research associate at the University of Cologne but also an external lecturer at diverse public and private universities. His main research areas are text and data mining with a special application to the finance sector and the management of credit and market risks.

Klappentext

This book is based on the Ph.D. thesis "Essays on Qualitative and Quantitative Risk Management" written by the author of this book. It consists out of three essays on text mining applications in finance and the validation of a credit risk model. To be more precise, the three essays address the following research questions: What kind of text mining measures are suitable in the finance area for analyzing text such as annual reports and can we use these measures to predict short-term performance or the reporting quality? Can we measure the tone of a document by using automatically calculated sentiment scores? How can we build a sentiment score, that captures keywords within a larger context? Do the chapters/sections of an annual report have a different influence on the whole content of the report? How can banks validate their credit risk model with a special focus on an analytical model? This book addresses practitioners, consultants, analysts, and bankers as well as students, researchers, and lecturers with focus on text mining applications in finance and the validation of credit risk models.