Analyst Earnings Forecasts and the Accuracy of Expectational Data PDF Download
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Author: Richard H. Bushnell
Publisher:
ISBN:
Category :
Languages : en
Pages : 88
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Author: Richard H. Bushnell
Publisher:
ISBN:
Category :
Languages : en
Pages : 88
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Book Description
Author: Tanja Klettke
Publisher: Springer Science & Business
ISBN: 3658056347
Category : Business & Economics
Languages : en
Pages : 120
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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.
Author: Patricia C. O'Brien
Publisher: Forgotten Books
ISBN: 9780666405524
Category : Mathematics
Languages : en
Pages : 74
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Book Description
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.
Author: Andrew Stotz
Publisher:
ISBN:
Category :
Languages : en
Pages : 122
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Book Description
Over the past 12 years, financial analysts across the world have been optimistically wrong with their 12-month earnings forecasts by 25.3%. This study may be the first of its kind to assess analyst earnings forecast accuracy at all listed companies across the globe, covering 70 countries. A review of prior research shows little uniformity in the preparation of the data set, yet differences in how outliers are treated, for example, can create substantially different results. This research lays out six specific steps to prepare the data set before any analysis is done.Three main conclusions come from this research: First, analyst earnings forecasts globally were 25.3% optimistically wrong, meaning on average, analysts started each year forecasting company profits of US$125, but 12 months later that company reported profits of US$100. Second, analysts had a harder time forecasting earnings for companies in emerging markets, where they were 35% optimistically wrong. Third, that analyst optimism mainly occurred when the companies they forecasted experienced very low levels of actual earnings growth, analysts did not make an equal, but opposite error for fast growth companies.
Author: Patricia C. O'brien
Publisher: Forgotten Books
ISBN: 9781333752545
Category : Mathematics
Languages : en
Pages : 38
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Book Description
Excerpt from Forecast Accuracy of Individual Analysts: A Nine-Industry Study Second, because the error terms in the fixed effects model are severely non normal, a non-parametric approach is taken. The non-parametric tests compare, for each industry, the observed distribution of analysts' average (through time) ranks with the distribution which would be expected if all analysts are alike, and each year is an independent observation. The non-parametric tests fail to reject the hypothesis that the observed distribution is identical to the expected distribution, in eight of the nine industries. Continuing research will investigate the source of the differences in the single industry in which the null hypothesis is rejected. 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.
Author: Lawrence D. Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 12
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Book Description
Author: PATRICIA C. O'BRIEN
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
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Book Description
Author: Wenjuan Xie
Publisher:
ISBN:
Category :
Languages : en
Pages : 138
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Book Description
Author: Sundaresh Ramnath
Publisher: Now Publishers Inc
ISBN: 1601981627
Category : Business & Economics
Languages : en
Pages : 125
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Book Description
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.
Author: Sundaresh Ramnath
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Book Description
This paper compares Value Line and I/B/E/S analyst earnings forecasts in terms of accuracy, rationality, and as proxies for market expectations. Using more recent data and forming consensus forecasts from the I/B/E/S detail files, we reach different conclusions than Philbrick and Ricks [1991], who found that Value Line provided more reliable actual EPS data, but forecasts from the two databases were of similar quality. We find that I/B/E/S actual quarterly EPS data are no longer less reliable than Value Line EPS data, and I/B/E/S quarterly earnings forecasts significantly outperform Value Line in terms of accuracy and as proxies for market expectations. We find that I/B/E/S forecasting superiority can be explained by the combination of I/B/E/S's timing advantage and the mitigation of idiosyncratic error through consensus building. We also evaluate long-term forecasts from both databases and find that I/B/E/S forecasts are less biased and more accurate. Our results have implications for research evaluating the information content of quarterly earnings announcements, research using long-term forecasts in valuation models, and research seeking to generalize evidence regarding Value Line analysts' forecasting behavior.
