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.

Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.

Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.

The first real-world guide for training equity research analysts—from a Morgan Stanley veteran Addresses the dearth of practical training materials for research analysts in the U.S. and globally Valentine managed a department of 70 analysts and 100 associates at Morgan Stanley and developed new programs for over 500 employees around the globe He will promote the book through his company's extensive outreach capabilities

The financial crisis and the recession that followed caught many people off guard, including experts in the financial sector whose jobs involve predicting market fluctuations. Financial analysis offices in most international banks are supposed to forecast the rise or fall of stock prices, the success or failure of investment products, and even the growth or decline of entire national economies. And yet their predictions are heavily disputed. How do they make their forecasts—and do those forecasts have any actual value? Building on recent developments in the social studies of finance, Stories of Capitalism provides the first ethnography of financial analysis. Drawing on two years of fieldwork in a Swiss bank, Stefan Leins argues that financial analysts construct stories of possible economic futures, presenting them as coherent and grounded in expert research and analysis. In so doing, they establish a role for themselves—not necessarily by laying bare empirically verifiable trends but rather by presenting the market as something that makes sense and is worth investing in. Stories of Capitalism is a nuanced look at how banks continue to boost investment—even in unstable markets—and a rare insider’s look into the often opaque financial practices that shape the global economy.

A Brookings Institution Press and Nomura Institute of Capital Markets Research publication Developed country capital markets have devised a set of institutions and actors to help provide investors with timely and accurate information they need to make informed investment decisions. These actors have become known as "financial gatekeepers" and include auditors, financial analysts, and credit rating agencies. Corporate financial reporting scandals in the United States and elsewhere in recent years, however, have called into question the sufficiency of the legal framework governing these gatekeepers. Policymakers have since responded by imposing a series of new obligations, restrictions, and punishments—all with the purpose of strengthening investor confidence in these important actors. Financial Gatekeepers provides an in-depth look at these new frameworks, especially in the United States and Japan. How have they worked? Are further refinements appropriate? These are among the questions addressed in this timely and important volume. Contributors include Leslie Boni (University of New Mexico), Barry Bosworth (Brookings Institution), Tomoo Inoue (Seikei University), Zoe-Vonna Palmrose (University of Southern California), Frank Partnoy (University of San Diego School of Law), George Perry (Brookings Institution), Justin Pettit (UBS), Paul Stevens (Investment Company Institute), Peter Wallison (American Enterprise Institute).

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.