New Determinants of Analysts’ Earnings Forecast Accuracy


Book Description

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.




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?




Forecasting: principles and practice


Book Description

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.




Analyzing the Analysts


Book Description




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.




The Accuracy of Analyst Forecasts


Book Description

Inhaltsangabe:Abstract: This paper investigates the quality of financial analysts' earnings forecasts for companies which conducted initial public offerings (IPOs) during the years 1997 to 1999. The Neue Markt in Frankfurt offers a good setting to also study the development of a young market from the beginning of its operation onwards. I find support for the notion that initial returns and analysts' forecast accuracy are negatively related. I find that analysts' forecasts were by no means accurate. Mean forecast deviation, measured as percent deviation from actual earnings per share for the fiscal year, is 186.61 percent for the average broker. The sample is inhibited by serious availability problems, but all the same allows significant findings. Inhaltsverzeichnis:Table of Contents: 1.Introduction5 2.Literature10 2.1Banking systems the German framework10 2.2Conflict of interest as regulated in the German legal system12 2.3The quality of analysts' forecasts and conflicts of interest16 2.4The long-run underperformance phenomenon23 2.5Predicting the aftermarket performance of IPOs27 2.6Summary39 3.Data41 4.Method49 5.Empirical Results53 5.1IPOs differentiated by year of issue53 5.2Disparities of actual values58 5.3Earning per share found in annual reports as basis62 5.4IPOs differentiated by industry classification67 5.5Percentage deviations differentiated by Brokers73 6.Additional Results80 6.1Large German banks seasoned vs. IPO companies80 6.2The time factor86 6.3The relevance of accounting policy88 7.Summary and Conclusion92 8.References95




Accounting for Income Taxes


Book Description

Accounting for Income Taxes is the most comprehensive review of AFIT research. It is designed both to introduce new scholars to this field and to encourage active researchers to expand frontiers related to accounting for income taxes. Accounting for Income Taxes includes both a primer about the rules governing AFIT (Sections 3-4) and a review of the scholarly studies in the field (Sections 5-8). The primer uses accessible examples and clear language to express essential AFIT rules and institutional features. Section 3 reviews the basic rules and institutional details governing AFIT. Section 4 discusses ways that researchers, policymakers, and other interested parties can use the tax information in financial statements to better approximate information in the tax return. The second half of the monograph reviews the extant scholarly studies by splitting the research literature into four topics: earnings management, the association between book-tax differences and earnings characteristics, the equity market pricing of information in the tax accounts, and book-tax conformity. Section 5 focuses on the use of the tax accounts to manage earnings through the valuation allowance, the income tax contingency, and permanently reinvested foreign earnings. Section 6 discusses the association between book-tax differences and earnings characteristics, namely earnings growth and earnings persistence. Section 7 explores how tax information is reflected in share prices. Section 8 reviews the increased alignment of accounting for book purposes and tax purposes. The remainder of the paper focuses on topics of general interest in the economics and econometric literatures. Section 9 highlights some issues of general importance including a theoretical framework to interpret and guide empirical AFIT studies, the disaggregated components of book-tax differences and research opportunities as the U.S. moves toward International Financial Reporting Standards (IFRS). Section 10 discusses econometric weaknesses that are common in AFIT research and proposes ways to mitigate their deleterious effects.




Business Forecasting


Book Description

Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.




Principles of Forecasting


Book Description

This handbook summarises knowledge from experts and empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. Includes a comprehensive forecasting dictionary.




SAS for Forecasting Time Series, Third Edition


Book Description

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.