EVA ("economic value added" - wirtschaftliche Wertschöpfung) ist eine Maßeinheit zur Ermittlung des echten Finanzergebnisses eines Unternehmens und eine Strategie zur Schaffung von Unternehmens- und Aktionärsvermögen. Das EVA-Prinzip besagt, daß ein Unternehmen kein Vermögen schafft, solange es nicht in der Lage ist, Gewinne einzufahren, die höher sind als die Kapitalkosten. EVA ist auch eine Methode, die Prioritäten innerhalb eines Unternehmens so zu gewichten, daß das Hauptaugenmerk auf der Schaffung von Vermögen liegt, und zwar zum Nutzen aller. Leicht verständlich geschrieben, mit zahlreichen Fallbeispielen bekannter Firmen und einem Minimum an Gleichungen und Finanzjargon. (11/98)

This book, first published in 2002, compiles psychologists' best attempts to answer important questions about intuitive judgment.

This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Financial Statement Analysis and Earnings Forecasting is the process of analyzing historical financial statement data for the purpose of developing forecasts of future earnings. This process is important because it is central to the valuation of companies and the securities they issue. After a short introduction, Section 2 delves into the question "Why earnings"? Focusing on dividend policy irrelevance, the author describes key analytical results that imply that expected earnings are the fundamental determinant of both equity and enterprise value. Section 3 examines the issues involved in selecting the earnings metric to forecast. Once an earnings metric has been chosen, the next question to ask is "How useful are historical accounting numbers for developing forecasts of that metric?" Sections 4 through 8 focus on this question. Section 4 discusses the general role of econometric modeling. Section 5 reviews time-series models. Section 6 examines the choices a researcher makes when using panel-data approaches and the author describes the advantages of these approaches. Section 7 reviews the role of accounting measurement in determining the usefulness of historical accounting numbers for developing forecasts of future earnings. Section 8 examines approaches for forecasting the higher moments of future earnings and section 9 provides a summary.

Tech Stock Valuation extends the R&D literature by providing detailed direct evidence on the market value implications of inventive and innovative output. Specifically, the text demonstrates that stock-price effects of patent output are most pronounced in the case of high-quality patents, where patent quality is measured by scientific merit. Scientific measures of patent quality give students a valuable new tool that can be used to measure R&D program effectiveness. At the same time, it gives investors a new tool to help them assess the value of hard-to-measure intangible assets. The book is an ideal resource for professionals working in finance and accounting; investment professionals and industry analysts who work for companies that engage in research and development; MBA students; economists working in industrial organizations, microeconomics, and contract theory. - Provides detailed direct evidence on the market value implications of inventive and innovative output - Based on recent research, much of which Dr. Hirschey has pioneered - Gives financial professionals a new tool for assessing R&D quality and its relation to market valuation