Research Grant Awarded: Scientific Network “Textual Analysis in Economics and Finance”

Together with eleven other researchers from LMU Munich, Mannheim, HU Berlin, Max Planck Society Munich, Georgia Tech, and Aalto University, we have been awarded a Deutsche Forschungsgemeinschaft (DFG) research grant for our Scientific Network “Textual Analysis in Economics and Finance”. Congratulations everybody.

The purpose of a scientific network

The DFG Scientific networks offer early career researchers the opportunity to engage in scientific exchange and cooperation on topics of common interest. A network consists of a group of people, who, over a defined period of up to three years, will work on a common research topic to attain a specific outcome. Network members may include early career as well as experienced researchers from Germany or from abroad. This international programme component serves to strengthen the ties to the international research community. The grant covers the costs for three workshops, where members of the scientific network and invited guests come together and share their insights from the latests research using textual analysis in economics and finance.

Textual analysis in economics and finance

Practitioners and researchers face the challenge of having to extract information from the vast amount of unstructured data available today. This data typically takes the form of text written in natural language, such as corporate filings, transcripts of earnings calls, news articles, twitter feeds, etc.. Due to its size, it is infeasible to manually process this data. Instead, computer-based methods of textual analysis are required. Textual analysis, i.e. automated extraction of information from text, requires fundamentally different approaches compared to analyzing structured data, such as tabulated time-series data. Academics in finance have started to adopt methods of textual analysis from linguistics and machine learning for their research. For instance, textual analysis is being applied to extract the tone or complexity of financial texts and analyze their effect on firm performance and investor behavior.

Despite these first successful attempts, textual analysis is not yet part of the standard toolset in financial economics research. This is due to methodological issues of this relatively new discipline, and due to the fact that textual analysis has yet to be proven useful in the various fields of financial economics. The scientific network aims to address these challenges. To this end, three areas of a lack of existing research have been identified:

1. Testing the ability of existing methods to select and classify information in financial texts.

2. Application of textual analysis in the context of capital markets and corporate finance applications.

3. Application of textual analysis in the international context, i.e. application to text written in foreign languages.

 

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