Textual Analysis in Economics and Finance

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Practitioners and researchers face the challenge of having to extract information from the vast amount of unstructureddata available today. This data typically takes the form of text written in natural language, such ascorporate filings, transcripts of earnings calls, news articles, twitter feeds, etc.. Due to its size, it is infeasibleto manually process this data. Instead, computer-based methods of textual analysis are required. Textualanalysis, i.e. automated extraction of information from text, requires fundamentally different approaches comparedto analyzing structured data, such as tabulated time-series data. Academics in finance have startedto 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 onfirm performance and investor behavior.Despite these first successful attempts, textual analysis is not yet part of the standard toolset in financialeconomics research. This is due to methodological issues of this relatively new discipline, and due to the factthat textual analysis has yet to be proven useful in the various fields of financial economics. The scientificnetwork aims to address these challenges. To this end, three areas of a lack of existing research have beenidentified: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.