This module introduces methods for analyzing textual information in business and economics, building on the influential work of Loughran and McDonald, who developed dictionaries to capture sentiment, uncertainty, and vagueness in financial communication. It examines how corporate disclosures, such as annual reports, ad hoc announcements, and earnings conference calls, are used to study economic outcomes, such as future firm performance. The module also discusses how firms adapt their communication strategies, for example, by adjusting language to influence how information is detected and interpreted. Recent advances in large language models such as ChatGPT and FinBERT have further improved the ability to quantify financial text data. Students learn to analyze financial documents using dictionary-based methods and FinBERT, and apply these techniques in empirical exercises using Python.
- Explain the importance of (text) information for financial markets
- Apply text processing methods like regular expressions to identify and extract relevant information from documents and/or to clean documents for further analyses like sentiment analysis
- Analyze documents using the well-established bag-of-words/dictionary method
- Modify documents and dictionaries to obtain economically meaningful results
- Relate text-based variables (e.g., manager sentiment) to financial market outcomes
- Discuss how machine learning methods work and apply (finance-specific) large language models
- Evaluate whether a model/approach is suitable for a specific problem
This course is part of the prestigious part-time Master in Finance program, conducted in English on Fridays and Saturdays on Campus Westend. It offers a valuable opportunity to network and gain expertise without committing to a full degree program. Upon completion, participants receive a Certificate of Participation. This course may be credited towards the Certificate of Advanced Studies (CAS) in Financial Technology Management when completed together with the other required courses within one academic year. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alum, explore our attractive alumni discount options.
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Next course offering: Summer Semester 2027. New dates will be announced soon.
Prof. Dr. Alexander Hillert
Prof. Dr. Alexander Hillert is Professor of Finance and Data Science at the Leibniz Institute for Financial Research SAFE and Goethe University Frankfurt, where he also serves as Program Director of SAFE’s Research Data Center. His research focuses on empirical asset pricing, corporate finance, and behavioral finance, with a particular interest in using computer science methods to analyze unstructured information and study how investors process and interpret text-based information. His work has been published in leading journals, including the Review of Financial Studies and the Journal of Financial Economics.