The downside risk premium of a stock is caused by the shape of the risk-neutral distribution and the Downside Risk Capital Asset Pricing Model (DR-CAPM) is accurately explained by the risk-neutral moments of stocks. Using a set of 179 million equity options, this thesis demonstrates that the risk-neutral variance, risk-neutral skewness and risk-neutral kurtosis determine stocks ex-ante exposure to downside risk and ex-ante returns. A risk-neutral representation of beta and downside beta is derived and it implies that the downside risk premium is a compensation for the non-normality of the underlying return distribution.

What is the return to investing in the stock market? Can we predict future stock market returns? How have equities performed over the last two centuries? The authors in this volume are among the leading researchers in the study of these questions. This book draws upon their research on the stock market over the past two dozen years. It contains their major research articles on the equity risk premium and new contributions on measuring, forecasting, and timing stock market returns, together with new interpretive essays that explore critical issues and new research on the topic of stock market investing. This book is aimed at all readers interested in understanding the empirical basis for the equity risk premium. Through the analysis and interpretation of two scholars whose research contributions have been key factors in the modern debate over stock market perfomance, this volume engages the reader in many of the key issues of importance to investors. How large is the premium? Is history a reliable guide to predict future equity returns? Does the equity and cash flows of the market? Are global equity markets different from those in the United States? Do emerging markets offer higher or lower equity risk premia? The authors use the historical performance of the world's stock markets to address these issues.

A timely approach to downside risk and its role in stock market investments When dealing with the topic of risk analysis, most books on investments treat downside and upside risk equally. Preparing for the Worst takes an entirely novel approach by focusing on downside risk and explaining how to incorporate it into investment decisions. Highlighting this asymmetry of the stock market, the authors describe how existing theories miss the downside and follow with explanations of how it can be included. Various techniques for calculating downside risk are demonstrated. This book presents the latest ideas in the field from the ground up, making the discussion accessible to mathematicians and statisticians interested in applications in finance, as well as to finance professionals who may not have a mathematical background. An invaluable resource for anyone wishing to explore the critical issues of finance, portfolio management, and securities pricing, this book: Incorporates Value at Risk into the theoretical discussion Uses many examples to illustrate downside risk in U.S., international, and emerging market investments Addresses downside risk arising from fraud and corruption Includes step-by-step instructions on how to implement the methods introduced in this book Offers advice on how to avoid pitfalls in calculations and computer programming Provides software use information and tips

Das Thema Risikoprämie für Aktien (Equity Risk Premium) wird hier zum ersten Mal verständlich erklärt. Die Risikoprämie für Aktien stellt einen Renditeausgleich dar für das erhöhte Risiko, das ein Anleger bei der Investition in Aktien eingeht, im Vergleich zu einer Investition in risikofreie Staatsanleihen. Die Risikoprämie ist zwar von der Theorie her einfach, jedoch in der Praxis ein sehr komplexes Phänomen. Für Finanzentscheidungen ist es von größter Bedeutung, daß man das Prinzip der Risikoprämie versteht und es anwenden kann. Cornell erläutert das Thema Schritt für Schritt sehr anschaulich und ohne terminologischen Ballast. Zunächst wird die Risikoprämie im Zusammenhang mit der Geschichte des Aktienmarktes betrachtet. Der Haussemarkt der 90er dient dabei als Fallstudie. Cornell zeigt, welche Rückschlüsse man durch die Analyse der Risikoprämie im historischen Verlauf für den Aktienmarkt ziehen kann, z.B. ob Aktienkurse steigen oder fallen oder ob sich der Aktienmarkt verändert. Vorausschauende Schätzungen der Risikoprämie werden anhand verschiedener konkurrierender Modelle analysiert, wobei die Vorzüge der jeweiligen Methode mitbewertet werden. 'Equity Risk Premium' ist das erste Buch, das dieses wichtige Prinzip der Risiko-Nutzen-Analyse erschöpfend behandelt. Es vermittelt einen tiefen Einblick und deckt alle Grundlagen ab, damit Investoren fundierte Finanzentscheidungen treffen können. Ein absolutes Muß für institutionelle Anleger, Geldmanager und Finanzvorstände, die auf eine fundierte Marktanalyse zurückgreifen müssen. (06/99)

A radical, definitive explanation of the link between loss aversion theory, the equity risk premium and stock price, and how to profit from it The Risk Premium Factor presents and proves a radical new theory that explains the stock market, offering a quantitative explanation for all the booms, busts, bubbles, and multiple expansions and contractions of the market we have experienced over the past half-century. Written by Stephen D. Hassett, a corporate development executive, author and specialist in value management, mergers and acquisitions, new venture strategy, development, and execution for high technology, SaaS, web, and mobile businesses, the book convincingly demonstrates that the equity risk premium is proportional to long-term Treasury yields, establishing a connection to loss aversion theory. Explains stock prices from 1960 through the present including the 2008/09 "market meltdown" Shows how the S&P 500 has consistently reverted to values predicted by the model Solves the equity premium puzzle by showing that it is consistent with findings on loss aversion Demonstrates that three factors drive valuation and stock price: earnings, long term growth, and interest rates Understanding the stock market is simple. By grasping the simplicity, business leaders, corporate decision makers, private equity, venture capital, professional, and individual investors will fully understand the system under which they operate, and find themselves empowered to make better decisions managing their businesses and investment portfolios.

The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.