Empirical Models of Analyst Forecasts

Empirical Models of Analyst Forecasts
Author: Youfei Xiao
Publisher:
Total Pages:
Release: 2016
Genre:
ISBN:

This dissertation is comprised of two studies on analyst forecasts. The first study provides empirical evidence about the objective function underlying analysts' choice of forecasts. Assumptions about sell-side analysts' objective function are critical to empirical researchers' understanding of their incentives and resulting behavior. In contrast to approaches used in previous papers which rely exclusively on statistical properties of forecasts, I compare theoretical models with alternate objective functions based on their ability to explain observed forecasts. A linear loss objective function which incorporates the effect future analysts' actions on analysts' deviation from peer forecasts is best rationalized by the data. I find that assumptions about the objective function have a substantial impact on the conclusions from empirical tests about analysts' incentives and behavior. The second study provides empirical estimates of uncertainty and disagreement about future earnings that underly analyst forecast dispersion. A parsimonious model which assumes that analysts' payoffs are jointly determined by forecast error and deviation from consensus reproduces many of the descriptive facts observed about forecast dispersion in the data. The strategic behavior that arises from the model distorts both the levels of forecast dispersion and the sensitivity of the measure with respect to cross-sectional variation in uncertainty. The estimated parameters perform better at predicting forecast dispersion out-of-sample than approaches based solely on regressions that use firm characteristics. Counterfactual simulations indicate that analysts' strategic incentives, together with the sequential forecast setting, plays a first-order role in determining forecast dispersion relative to the firm's information environment. The model-implied estimates of earnings uncertainty exhibit a substantially less negative association with future returns relative to the association generated by forecast dispersion. This finding partially reconciles the findings from previous studies with theories about the asset pricing implications of uncertainty and disagreement.

Empirical Implications of Analyst Forecast Dispersion to the Information Dynamics of Valuation Models

Empirical Implications of Analyst Forecast Dispersion to the Information Dynamics of Valuation Models
Author: Daniel M. Bryan
Publisher:
Total Pages:
Release: 2015
Genre:
ISBN:

Ohlson (1995) models firm value as a function of abnormal earnings, net book value and other unspecified information. Ohlson (2001) proposes consensus analyst forecasts as a proxy for the previously unspecified other information in his model, which we test using a two stage approach. The first stage identifies information in analyst forecasts that is reflected in current earnings and net book value, and the second stage regresses the first-stage residuals as the proxy for other new information. Our initial results using price-levels regressions concur with Dechow et al.'s (1999) findings that short-run consensus analyst forecasts are effective proxies for other information, and that the proposed model is no more descriptive than capitalizing short-run forecasts in perpetuity. We find that with high forecast dispersion, however, the effectiveness of analyst forecasts as well as the association between earnings and market values are diminished. Overall, we find that the descriptive ability of both the Ohlson model and the capitalized forecast model is dampened with high forecast dispersion, but the dampening is more severe for the capitalized forecast model, suggesting that the descriptive ability of Ohlson's valuation framework is strongest, relative to capitalized analyst forecasts, when uncertainty and information asymmetry are most severe. In contrast to our (and Dechow et al.'s) price-levels regression results, we find with returns regressions that Ohlson's model is consistently and significantly more descriptive than a model that simply capitalizes changes in analyst forecasts.

Expert Adjustments of Model Forecasts

Expert Adjustments of Model Forecasts
Author: Philip Hans Franses
Publisher: Cambridge University Press
Total Pages: 145
Release: 2014-10-09
Genre: Business & Economics
ISBN: 1107081599

Brings together current theoretical insights and new empirical results to examine expert adjustment of model forecasts from an econometric perspective.

A Multivariate Analysis of Earnings Forecasts Generated by Financial Analysts and Univariate Time Series Models

A Multivariate Analysis of Earnings Forecasts Generated by Financial Analysts and Univariate Time Series Models
Author: William S. Hopwood
Publisher:
Total Pages: 36
Release: 1978
Genre: Econometrics
ISBN:

The study provides evidence on the relative accuracy of forecasts of earnings generated from five sources including statistical models and financial analysts. The statistical models were chosen on the basis of their usage in recent studies in the literature. The results indicate that the five types of forecasts are not significantly different using a multivariate testing procedure.

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

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.

Business Analysis and Valuation

Business Analysis and Valuation
Author: Sue Joy Wright
Publisher:
Total Pages: 720
Release: 2014
Genre: Business enterprises
ISBN: 9780170261951

Business Analysis and Valuation has been developed specifically for students undertaking accounting Valuation subjects. With a significant number of case studies exploring various issues in this field, including a running chapter example, it offers a practical and in-depth approach. This second edition of the Palepu text has been revitalised with all new Australian content in parts 1-3, making this edition predominantly local, while still retaining a selection of the much admired and rigorous Harvard case studies in part 4. Retaining the same author team, this new edition presents the field of valuation accounting in the Australian context in a clear, logical and thorough manner.