Does Analyst Forecast Dispersion Represent Investors Perceived Uncertainty Toward Earnings
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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.
The Predictive Qualities of Earnings Volatility and Earnings Uncertainty
Author | : Dain C. Donelson |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
This study examines the differential predictive power of past earnings volatility for analyst forecast errors and future returns. Past earnings volatility jointly captures two correlated, but distinct, earnings properties: time-series earnings variation and uncertainty in future earnings. To distinguish between these two earnings properties, we develop a forward-looking measure of earnings uncertainty that has a minimal mechanical link to variation in prior-period earnings realizations and does not rely on analyst forecasts. Our results suggest that future earnings uncertainty, and not time variation in earnings, is associated with overly optimistic future earnings expectations of equity analysts and investors. We provide the first empirical evidence on the relevance of future earnings uncertainty to analysts and investors over one-year horizons. In addition, we provide empirical evidence showing that forecast dispersion is a poor measure of earnings uncertainty.
Analysts' Forecasts as Proxies for Investor Beliefs in Empirical Research
Author | : Jeffery S. Abarbanell |
Publisher | : |
Total Pages | : 56 |
Release | : 1993 |
Genre | : Investment analysis |
ISBN | : |
Uncertainty and Investment
Author | : Stephen Bond |
Publisher | : |
Total Pages | : 58 |
Release | : 2004 |
Genre | : Capital investments |
ISBN | : |
Does Uncertainty Boost Overconfidence? The Case of Financial Analysts' Forecasts
Author | : Véronique Bessière |
Publisher | : |
Total Pages | : 1 |
Release | : 2014 |
Genre | : |
ISBN | : |
This article examines the link between uncertainty and analysts' reaction to earnings announcements for a sample of European firms during the period 1997-2007. In the same way as Daniel, Hirshleifer and Subrahmanyam (1998), we posit that overconfidence leads to an overreaction to private information followed by an underreaction when the information becomes public. Psychological findings suggest that this effect is more prominent in an uncertain environment. Our tests are based on the relationship between forecast revisions and forecast errors. When analysts excessively integrate information in their revisions (i.e. overreact), their forecast revisions are too intense, and the converse occurs when they underreact. As a proxy for uncertainty we analyze two subsamples: high-tech and low-tech firms. Our results support the overconfidence hypothesis. We jointly observe the two phenomena of under- and overreaction. Overreaction occurs before the public release and disappears after it. Our results also show that both effects are more significant for the high-tech subsample. For robustness, we sort the sample using analyst forecast dispersion as a proxy for uncertainty and obtain similar results. We also document the fact that the high-tech stock crash in 2000-2001 moderated analysts' overconfidence.
Uncertainty About Future Earnings as a Determinant of Bias in Analysts'Earnings Forecasts
Author | : Bong-Heui Han |
Publisher | : |
Total Pages | : |
Release | : 2000 |
Genre | : |
ISBN | : |
Researchers have identified numerous factors associated with security analysts' optimistic bias, including size, earnings-to-price ratio, forecast dispersion, past returns, and past forecast errors. These factors are viewed as having future earnings uncertainty as a common attribute. Empirical evidence consistent with this view is presented. Using these factors as proxies for future earnings uncertainty, univariate tests show that analysts' bias increases as uncertainty increases. Multivariate tests indicate that each of the uncertainty proxies incrementally explains bias, after controlling for the other variables. A model is developed which significantly improves accuracy by reducing both forecast bias and forecast error variance in tests on holdout samples.
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.