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New Determinants of Analysts’ Earnings Forecast Accuracy

New Determinants of Analysts’ Earnings Forecast Accuracy
Author: Tanja Klettke
Publisher: Springer Science & Business
Total Pages: 120
Release: 2014-04-28
Genre: Business & Economics
ISBN: 3658056347
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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.

Analysts' Forecasts as Earnings Expectations (Classic Reprint)

Analysts' Forecasts as Earnings Expectations (Classic Reprint)
Author: Patricia C. O'Brien
Publisher: Forgotten Books
Total Pages: 74
Release: 2018-02-26
Genre: Mathematics
ISBN: 9780666405524
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Excerpt from Analysts' Forecasts as Earnings Expectations Analysts' forecasts of earnings are increasingly used in accounting and finance research as expectations data, to proxy for the unobservable market expectation of a future 'realization. 'since a diverse set of forecasts is available at any time for a given firm's earnings. Composites are used to distill the information from the diverse set into a single expectation. This paper considers the relative merits of several composite forecasts as expectations data. One of the primary results is that the most current forecast available outperforms more commonly used aggregations such as the mean or the median. Mthis result is consistent-with forecasters incorporating information from others' previous predictions into their own. It also suggests that the forecast date, which previous research has largely ignored, is a characteristic relevant for distinguishing better forecasts. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

Financial Analysts' Forecasts and Stock Recommendations

Financial Analysts' Forecasts and Stock Recommendations
Author: Sundaresh Ramnath
Publisher: Now Publishers Inc
Total Pages: 125
Release: 2008
Genre: Business & Economics
ISBN: 1601981627
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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.

Analyst's Forecasts as Earnings Expectations

Analyst's Forecasts as Earnings Expectations
Author: Patricia C O'Brien
Publisher: Palala Press
Total Pages: 74
Release: 2018-03-04
Genre:
ISBN: 9781379241669
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This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

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

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)
Author: Cheng Few Lee
Publisher: World Scientific
Total Pages: 5053
Release: 2020-07-30
Genre: Business & Economics
ISBN: 9811202400
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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.

Financial Analysts' Heterogeneous Earnings Expectations and Their Stock Recommendations

Financial Analysts' Heterogeneous Earnings Expectations and Their Stock Recommendations
Author: Steven Lustgarten
Publisher:
Total Pages:
Release: 2019
Genre:
ISBN:
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In this study we test whether financial analysts' use their earnings forecasts to make stock recommendations. We hypothesize that if analysts use earnings forecasts as a basis for stock recommendations, the likelihood of a buy (sell) recommendation ought to increase (decrease) when the analyst's earnings forecast becomes more optimistic (pessimistic) relative to the market's expectation. The data supports this hypothesis. We also test the extent to which analysts' stock recommendations are based on public and/or on private earnings information. Private information is measured as the difference between the analysts own earnings forecast and the consensus forecasts of other analysts. Public information is measured as the difference between the consensus forecast and the random walk forecast. Our data show that stock recommendations are related to both private and public earnings information, private information is more important. We also find that the relationship between recommendations and forecasts is stronger where earnings are more value relevant. Factors such as higher earnings persistence and growth opportunities, lower market risk and larger firm size make stock recommendations more responsive to earnings forecasts. Stock recommendations are related to forecasted earnings surprises even when the forecast revision is held constant.