Determinants of Earnings Forecast Error, Earnings Forecast Revision and Earnings Forecast Accuracy


Book Description

​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?




Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)


Book Description

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.




Earnings Management


Book Description

This book is a study of earnings management, aimed at scholars and professionals in accounting, finance, economics, and law. The authors address research questions including: Why are earnings so important that firms feel compelled to manipulate them? What set of circumstances will induce earnings management? How will the interaction among management, boards of directors, investors, employees, suppliers, customers and regulators affect earnings management? How to design empirical research addressing earnings management? What are the limitations and strengths of current empirical models?




Expectations and the Structure of Share Prices


Book Description

John G. Cragg and Burton G. Malkiel collected detailed forecasts of professional investors concerning the growth of 175 companies and use this information to examine the impact of such forecasts on the market evaluations of the companies and to test and extend traditional models of how stock market values are determined.




Analyzing the Analysts


Book Description







Profits in the Long Run


Book Description

Discovers that there are persistent differences in market power among large U. S. companies by analyzing data for the 1000 largest manufacturing firms in 1950 and 1972. Considers the influence of risk, sales, diversification, growth and managerial control on long run profitability.







Estimating the Cost of Capital Implied by Market Prices and Accounting Data


Book Description

Estimating the Cost of Capital Implied by Market Prices and Accounting Data focuses on estimating the expected rate of return implied by market prices, summary accounting numbers, and forecasts of earnings and dividends. Estimates of the expected rate of return, often used as proxies for the cost of capital, are obtained by inverting accounting-based valuation models. The author describes accounting-based valuation models and discusses how these models have been used, and how they may be used, to obtain estimates of the cost of capital. The practical appeal of accounting-based valuation models is that they focus on the two variables that are commonly at the heart of valuations carried out by equity analysts -- forecasts of earnings and forecasts of earnings growth. The question at the core of this monograph is -- How can these forecasts be used to obtain an estimate of the cost of capital? The author examines the empirical validity of the estimates based on these forecasts and explores ways to improve these estimates. In addition, this monograph details a method for isolating the effect of any factor of interest (such as cross-listing, fraud, disclosure quality, taxes, analyst following, accounting standards, etc.) on the cost of capital. If you are interested in understanding the academic literature on accounting-based estimates of expected rate of return this monograph is for you. Estimating the Cost of Capital Implied by Market Prices and Accounting Data provides a foundation for a deeper comprehension of this literature and will give a jump start to those who have an interest in these topics. The key ideas are introduced via examples based on actual forecasts, accounting information, and market prices for listed firms, and the numerical examples are based on sound algebraic relations.