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Detailed Management Earnings Forecasts

Detailed Management Earnings Forecasts
Author: Kenneth J. Merkley
Publisher:
Total Pages: 48
Release: 2014
Genre:
ISBN:

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We provide archival evidence on how a particular type of supplementary information affects the credibility of management earnings forecasts. Managers often provide detailed forecasts of specific income statement line items to shed light on how they plan to achieve their bottom-line earnings targets. We assess the effect of this forecast disaggregation on the credibility of management earnings forecasts. Based on a relatively large hand-collected sample of 900 management earnings forecasts, we find that disaggregation increases analysts' sensitivity to the news in managers' earnings guidance, suggesting that analysts find the guidance more credible. More importantly, we identify several factors that influence this relation. First, disaggregation plays a more important role when earnings are otherwise more difficult to forecast. Second, disaggregation is more important after Regulation Fair Disclosure prohibited selective disclosure, especially for firms that were more affected because they had previously provided more private guidance. Finally, in contrast to common assertions in the prior literature, we find that in more recent years, disaggregation matters more for guidance that conveys bad news. Managers as well as researchers should be interested in evidence suggesting that financial analysts find disaggregation especially helpful in contexts where managers' credibility is particularly important.


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.


Management Earnings Forecasts

Management Earnings Forecasts
Author: Hwa Deuk Yi
Publisher:
Total Pages: 236
Release: 1994
Genre: Corporate profits
ISBN:

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Risk-Based Forecasting and Planning and Management Earnings Forecasts

Risk-Based Forecasting and Planning and Management Earnings Forecasts
Author: Christopher D. Ittner
Publisher:
Total Pages: 67
Release: 2017
Genre:
ISBN:

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This study examines the association between a firm's internal information environment and the accuracy of its externally-disclosed management earnings forecasts. Internally, firms use forecasts to plan for uncertain futures. The risk management literature argues that integrating risk-related information into forecasts and plans can improve a firm's ability to forecast future financial outcomes. We investigate whether this internal information manifests itself in the accuracy of external earnings guidance. Using detailed survey data and publicly-disclosed management earnings forecasts from a sample of publicly-traded U.S. companies, we find that more sophisticated risk-based forecasting and planning processes are associated with smaller earnings forecast errors and narrower forecast widths. These associations hold across a variety of different planning horizons (ranging from annual budgeting to long-term strategic planning), providing empirical support for the theoretical link between internal information quality and the quality of external disclosures.


Management Earnings Forecasts

Management Earnings Forecasts
Author: D. Eric Hirst
Publisher:
Total Pages: 50
Release: 2008
Genre:
ISBN:

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In this paper, we provide a framework in which to view management earnings forecasts. Specifically, we categorize earnings forecasts as having three components - antecedents, characteristics, and consequences that roughly correspond to the timeline associated with an earnings forecast. By evaluating management earnings forecast research within the context of this framework, we render three conclusions. First, forecast characteristics appear to be the least well-understood component of earnings forecasts - both in terms of theory and empirical research - even though it is the component over which managers have the most control. Second, much of the prior research focuses on how one forecast antecedent or characteristic influences forecast consequences and does not study potential interactions among the three components. Third, much of the prior research ignores the iterative nature of management earnings forecasts - that is, forecast consequences of the current period influence antecedents and chosen characteristics in subsequent periods. Implications for researchers as well as educators, managers, investors, and regulators are provided.