Download or read book Company Valuation and Bankruptcy Prediction written by Jan Klobucnik and published by GRIN Verlag. This book was released on 2013-11-18 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2013 in the subject Economics - Finance, grade: summa cum laude, University of Cologne, language: English, abstract: The contribution of this study is manifold and relevant for academics and practitioners alike. It adds to the literature in the fields of corporate finance, financial accounting and stochastic modeling. In particular, this dissertation provides answers to the following questions: given the less efficient markets, can specialists as financial analysts provide additional information, which contain investment value? How can the true value of a company be determined with publicly available data and can discrepancies between fundamental and market values be exploited? Finally, is it possible to assess the firm’s financial health and its likelihood of failure several years into the future? Adressing these questions, the study first illustrates the company valuation assessment by financial analysts as summarized in their target prices and the information processing by analysts and investors in detail. Second, this thesis offers a novel empirical implementation of a model for fundamental company valuation that employs accounting data. In this context it demonstrates severe over- and undervaluation from a fundamental perspective in the U.S. technology sector over the last 20 years. Both the analysts’ company valuation captured by their target prices and the implementation of the fundamental company valuation model translate into significant investment value before and after transaction costs, which supports the notion of non-efficient markets. Finally, one major contribution is to evaluate a new approach for bankruptcy prediction that is based on stochastic processes. It is theoretically appealing and performs better especially for longer forecast horizons than standard methods.