​Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?

Financial analysts provide information in their research reports and thereby help forming expectations of a firm’s future business performance. Thus, it is essential to recognize analysts who provide the most precise forecasts and the accounting literature identifies characteristics that help finding the most accurate analysts. Tanja Klettke detects new relationships and identifies two new determinants of earnings forecast accuracy. These new determinants are an analyst’s “general forecast effort” and the “number of supplementary forecasts”. Within two comprehensive empirical investigations she proves these measures’ power to explain accuracy differences. Tanja Klettke’s research helps investors and researchers to identify more accurate earnings forecasts.

Wall Street Research: Past, Present, and Future provides a timely account of the dramatic evolution of Wall Street research, examining its rise, fall, and reemergence. Despite regulatory, technological, and global forces that have transformed equity research in the last ten years, the industry has proven to be remarkably resilient and consistent. Boris Groysberg and Paul M. Healy get to the heart of Wall Street research—the analysts engaged in the process—and demonstrate how the analysts' roles have evolved, what drives their performance today, and how they stack up against their buy-side counterparts. The book unpacks key trends and describes how different firms have coped with shifting pressures. It concludes with an assessment of where equity research is headed in emerging markets, drawing conclusions about this often overlooked corner of Wall Street and the industry's future challenges.

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.

How well does it pay to own the Standard & Poor’s 500 Index’s best-performing stock of the year? Over the 2012-2021 period, the one-year total return ranged from 80% to 743%. This book identifies the quantitative and qualitative traits of stocks that made it to #1 and tells the stories of how they got there. A key indicator, the Fridson-Lee Statistic, makes its debut in these pages. Aiming for the massive upside of the #1 stocks entails substantial risk. It’s not something to do with more than a small percentage of your portfolio. But attempting to pick the coming year’s top performer can provide an outlet for speculative impulses that might otherwise spoil a prudent, long-term investment plan. And by investigating the statistically determined best candidates for #1, you’ll gain important insights into stock selection. The Little Book of Picking Top Stocks explains why conventional equity research provides only limited help in zeroing in on the index’s future top performer. Spotting the #1 stock isn’t Wall Street analysts’ focus, although the information they furnish about companies’ competitive strategies is quite helpful. Problematically, investment banks’ fundamental stock reports are structured around a valuation metric that was discredited nearly half a century ago—earnings per share. Author Martin Fridson’s previous writings on the stock market include the books It Was a Very Good Year and Investment Illusions, as well as articles such as “Ben Graham’s Value Approach: Can It Still Work?” He has received the CFA Society of New York’s Ben Graham Award and has been named the Financial Management Association International’s Financial Executive of the Year. The Green Magazine called his Financial Statement Analysis (co-authored with Fernando Alvarez) “one of the most useful investment books ever.”

Modern financial markets offer the real world's best approximation to the idealized price auction market envisioned in economic theory. Nevertheless, as the increasingly exquisite and detailed financial data demonstrate, financial markets often fail to behave as they should if trading were truly dominated by the fully rational investors that populate financial theories. These markets anomalies have spawned a new approach to finance, one which as editor Richard Thaler puts it, "entertains the possibility that some agents in the economy behave less than fully rationally some of the time." Advances in Behavioral Finance collects together twenty-one recent articles that illustrate the power of this approach. These papers demonstrate how specific departures from fully rational decision making by individual market agents can provide explanations of otherwise puzzling market phenomena. To take several examples, Werner De Bondt and Thaler find an explanation for superior price performance of firms with poor recent earnings histories in the tendencies of investors to overreact to recent information. Richard Roll traces the negative effects of corporate takeovers on the stock prices of the acquiring firms to the overconfidence of managers, who fail to recognize the contributions of chance to their past successes. Andrei Shleifer and Robert Vishny show how the difficulty of establishing a reliable reputation for correctly assessing the value of long term capital projects can lead investment analysis, and hence corporate managers, to focus myopically on short term returns. As a testing ground for assessing the empirical accuracy of behavioral theories, the successful studies in this landmark collection reach beyond the world of finance to suggest, very powerfully, the importance of pursuing behavioral approaches to other areas of economic life. Advances in Behavioral Finance is a solid beachhead for behavioral work in the financial arena and a clear promise of wider application for behavioral economics in the future.