Corporate Transparency: Unstructured Soft Information, Gossip, and Fake News

Van Lent studies the effects of soft information on corporate transparency. He uses machine-learning technology to explore how partly standardized and unstandardized qualitative information is disseminated by firms and organizations, and how market participants process and act on this information. The project examines how users perceive information and thereby influence market-wide transparency. A key objective of the project is to explore to what extent the market mechanism is able to process qualitative information so that rumors, biased narratives and resulting biased beliefs are revised.

  • Forschungsfrage

    How does soft information affect transparency, and how does transparency of soft information affect price discovery in equity markets as well as product and factor markets?

  • Motivation

    Corporate transparency is often discussed in the context of financial statement information released by companies in response to legal requirements or market demands. While information linked to financial statements is perhaps the form of transparency that gains the most attention in the market place, it is also clear that corporate financial reports are just one of many information sources emanating from firms that are used by investors and other stakeholders. Hence, the full picture of corporate transparency requires a careful examination of (1) the disclosure channels of soft information, (2) their unique characteristics compared to financial statement disclosures, (3) the market response to soft information, and (4) the ultimate consequences of soft information based transparency in financial, factor, and product markets.

  • Forschungsprogramm

    This project studies the effects of soft information on corporate transparency. We use machine-learning technology to explore how partly standardized and unstandardized qualitative information is disseminated by firms and organizations, and how market participants process and act on this information. We examine how users perceive information and thereby influence market-wide transparency. A key objective is to explore to what extent the market mechanism is able to process qualitative information so that rumors, biased narratives and resulting biased beliefs are revised. This project aims at refining machine learning techniques to collect data, categorize data in economically-meaningful classes, and analyze both text and non-text data to overcome the empirical challenges posed by working with soft information.

  • Contribution

    Our project helps to build a better understanding of corporate transparency in a time when traditional forms of disclosures (through annual reports) appear to lose ground.

Ergebnis

The project has produced firm-level measures of risk and sentiment for US publicly listed firms. These measures are designed in particular to capture political risk, including specific topics such as health care, environment, and economic policy.  The data are made publicly available on www.firmlevelrisk.com and the machine learning algorithm used to generate the measures is part of the replication package which is available on https://doi.org/10.7910/DVN/OBNRBP.

Zugehörige Beiträge

Measuring Political Risk and its Effects on Firms

Prof. Dr. Laurence van Lent

A team of international researchers developed a measure for a firm’s exposure to political risk by using information gleaned from discussions between management and financial analysts in earnings conference calls. Weiterlesen

Beteiligte Institutionen

Die Hauptstandorte vom TRR 266 sind die Universität Paderborn, die HU Berlin und die Universität Mannheim. Alle drei Standorte sind seit vielen Jahren Zentren für Rechnungswesen- und Steuerforschung. Hinzu kommen Wissenschaftler der LMU München, der Frankfurt School of Finance and Management, der WHU – Otto Beisheim School of Management, der European School of Management and Technology in Berlin und der Goethe-Universität Frankfurt, die die gleiche Forschungsagenda verfolgen.