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Project
Contributions to the analysis of corporate information: Robustness, sustainability and textual analysis.
Investors are in the business of acquiring information and using that information to manage a portfolio of assets. Information asymmetry, however, plays a central role in investors' information acquisition and occurs when one group of participants has better or more timely information than other groups. Typically, the source of the information asymmetry is the superior knowledge that managers have about the firm's prospects, while the investors in the firm comprise the uninformed group. With examples such as the accounting scandals of Enron, Lernout & Hauspie, Worldcom, it is obvious today that information on capital markets is asymmetrically distributed and that information differential between the managementand investors can lead to a suboptimal allocation of resources within the firm. Clearly, managers, investors and regulators concerned with financial supervision are in need of methods to manage information asymmetryproblems.</>
This Ph.D. dissertation contributes to the development of new methods to mitigate information asymmetry on capital markets and focuses on three specific sources of information, each of them corresponding to one part of the dissertation. In Part I, I define a prediction model that helps investors reduce information asymmetry by predicting financial analysts' forecast error. In Part II, I focus on the value of corporate social responsibility (CSR) information, provided by the Kinder, Lyndenberg and Domini Research and Analytics database (KLD). In Part III, the objective is to decrease information asymmetry by defining more accurate measures of tone (or sentiment) in the narrative sections of a firm's voluntary disclosures, such as earnings press releases and CEO letters to shareholders. Overall, we show that investors, managers and regulators can manage and reduce information asymmetries on capital markets by either using advanced econometric methods, new databases on a firms stakeholder activities or the textual content of a firm's financial disclosures.</>
This Ph.D. dissertation contributes to the development of new methods to mitigate information asymmetry on capital markets and focuses on three specific sources of information, each of them corresponding to one part of the dissertation. In Part I, I define a prediction model that helps investors reduce information asymmetry by predicting financial analysts' forecast error. In Part II, I focus on the value of corporate social responsibility (CSR) information, provided by the Kinder, Lyndenberg and Domini Research and Analytics database (KLD). In Part III, the objective is to decrease information asymmetry by defining more accurate measures of tone (or sentiment) in the narrative sections of a firm's voluntary disclosures, such as earnings press releases and CEO letters to shareholders. Overall, we show that investors, managers and regulators can manage and reduce information asymmetries on capital markets by either using advanced econometric methods, new databases on a firms stakeholder activities or the textual content of a firm's financial disclosures.</>
Date:1 Oct 2010 → 18 Aug 2014
Keywords:corporate finance, information asymmetries
Disciplines:Applied economics
Project type:PhD project