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A New Approach to Predicting Analyst Forecast Errors

A New Approach to Predicting Analyst Forecast Errors
Author: Mr. Eric Chi-Ying So
Publisher:
Total Pages:
Release: 2012
Genre:
ISBN:

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I provide evidence that investors overweight analyst forecasts by demonstrating that prices do not fully reflect predictable components of analyst errors, which conflicts with conclusions in prior research. I highlight estimation bias in traditional approaches and develop a new approach that reduces this bias. I estimate 'characteristic forecasts' that map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts analyst forecast errors and revisions. I document abnormal returns to strategies that sort firms by predicted forecast errors, consistent with investors overweighting analyst forecasts and predictable biases in analyst forecasts influencing the information content of prices.


Business Forecasting

Business Forecasting
Author: Michael Gilliland
Publisher: John Wiley & Sons
Total Pages: 417
Release: 2015-12-15
Genre: Business & Economics
ISBN: 1119228298

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A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field. Forecasting performance is ultimately limited by the 'forecastability' of the data. Yet failing to recognize this, many organizations continue to squander resources pursuing unachievable levels of accuracy. This book provides a wealth of ideas for improving all aspects of the process, including the avoidance of wasted efforts that fail to improve (or even harm) forecast accuracy. Analyzes the most prominent issues in business forecasting Investigates emerging approaches and new methods of analysis Combines forecasts to improve accuracy Utilizes Forecast Value Added to identify process inefficiency The business environment is evolving, and forecasting methods must evolve alongside it. This compilation delivers an array of new tools and research that can enable more efficient processes and more accurate results. Business Forecasting provides an expert's-eye view of the field's latest developments to help you achieve your desired business outcomes.


Analyst Forecasting Errors and Their Implications for Security Analysis

Analyst Forecasting Errors and Their Implications for Security Analysis
Author: Lawrence D. Brown
Publisher:
Total Pages: 8
Release: 2014
Genre:
ISBN:

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Dreman and Berry (1995) have offered a perspective on analyst earnings forecast errors and their implications for security analysis. Among other arguments, they contend that the errors are too large to be reliably used by investors, the forecasts are less accurate than forecasts by time-series models, the errors are increasing over time, the analysts' forecasts are too optimistic, and the investment community relies too heavily on analyst forecasts. An alternative perspective on these issues is offered. The argument is that analysts' forecast errors are within 3% of an appropriate benchmark (namely, stock price), that their forecasts generally are significantly more accurate than forecasts by naive or sophisticated time-series models, that analyst forecast errors have not been increasing over time, that analysts have been too pessimistic in recent years, and that the investment community, by placing too much weight on forecasts made by time-series models, relies too little on analysts' forecasts.


Refining Financial Analysts' Forecasts by Predicting Earnings Forecast Errors

Refining Financial Analysts' Forecasts by Predicting Earnings Forecast Errors
Author: Tatiana Fedyk
Publisher:
Total Pages: 30
Release: 2018
Genre:
ISBN:

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Prior research on financial analyst' quarterly earnings forecasts has documented serial correlation in forecast errors. This paper examines the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm's industry and the analyst's experience and brokerage house affiliation. Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, I model consensus forecast errors as an autoregressive process. I demonstrate that the model of forecast errors that best fits the data is AR(1), and use the obtained autoregressive coefficients to predict consensus forecast errors. Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors, and proposes a series of refinements to the consensus. These refinements were not presented in prior literature, and can be useful to financial analysts and investors.


Analyst's Forecasts as Earnings Expectations

Analyst's Forecasts as Earnings Expectations
Author: Patricia C. O'Brien
Publisher: Palala Press
Total Pages: 74
Release: 2015-09-11
Genre:
ISBN: 9781342370051

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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.


New Evidence on Serial Correlation in Analyst Forecast Errors

New Evidence on Serial Correlation in Analyst Forecast Errors
Author: Cintia M. Easterwood
Publisher:
Total Pages:
Release: 2009
Genre:
ISBN:

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We reexamine the serial correlation of forecast errors using a method that allows analysts to react differently to good and bad news. Our method also controls for the influence of a normal non-zero, firm-specific component of forecast error. Our results indicate that forecast errors exhibit positive serial correlation when there is bad news in the prior forecast error, negative serial correlation when there is good news in the prior forecast error, and no serial correlation when there is no news in the prior forecast error. These findings are consistent with analysts having optimistic reactions to new information.


Forecasting in the Social and Natural Sciences

Forecasting in the Social and Natural Sciences
Author: Kenneth C. Land
Publisher: Springer Science & Business Media
Total Pages: 376
Release: 2012-12-06
Genre: Science
ISBN: 9400940114

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Social and natural scientists often are called upon to produce, or participate, in the pro duction of forecasts. This volume assembles essays that (a) describe the organizational and political context of applied forecasting, (b) review the state-of-the-art for many fore casting models and methods, and (c) discuss issues of predictability, the implications of forecaSt errors, and model construction, linkage and verification. The essays should be of particular interest to social and natural scientists concerned with forecasting large-scale systems. This project had its origins in discussions of social forecasts and forecasting method ologies initiated a few years ago by several social and natural science members of the Social Science Research Council's Committee on Social Indicators. It became appar ent in these discussions that certain similar problems were confronted in forecasting large-scale systems-be they social or natural. In response, the Committee hypothesized that much could be learned through more extended and systematic interchanges among social and natural scientists focusing on the formal methodologies applied in forecasting. To put this conjecture to the test, the Committee sponsored a conference at the National Center for Atmospheric Research in Boulder, Colorado, on June 10-13, 1984, on forecasting in the social and natural sciences. The conference was co-chaired by Committee members Kenneth C. Land and Stephen H. Schneider representing, respectively, the social and natural science mem bership of the Committee. Support for the conference was provided by a grant to the Council from the Division of Social and Economic Science of the National Science Foundation.


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

Determinants of Earnings Forecast Error, Earnings Forecast Revision and Earnings Forecast Accuracy
Author: Sebastian Gell
Publisher: Springer Science & Business Media
Total Pages: 144
Release: 2012-03-26
Genre: Business & Economics
ISBN: 3834939374

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​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?