Firm characteristics and analyst following

TL;DR: The authors examined how sell-side analyst coverage influences the quantity, timing, and content of firm-specific media coverage and found that exogenous reductions in analyst coverage decrease the overall quantity of media coverage, while media coverage around earnings announcements actually increases, albeit not enough to offset the decrease in coverage at other times.

TL;DR: In this article, the authors investigated if financial analysts perceive accounting information differently depending on the method chosen conditioned to the disclosure of the required information in the notes, and found that the choice of accounting regime does not affect the bias and accuracy of earnings forecasts, nor target prices nor stock recommendations, no matter if firms provide or not information in notes.

TL;DR: In this article, the authors address the paradox of risk management: managers hedge in order to avoid negative earnings surprises, yet they tend to hedge risks uninformative of the value of the company; and the presence of options in managers' compensation distorts their incentive to hedge, inducing them to expose the company to too much risk.

TL;DR: In this article, the authors investigated the long-term earnings growth forecast (hereafter, LTG forecast) for some firms but not others and why some analysts issue LTG forecasts while others do not.

TL;DR: In this article, the null hypothesis of no misspecification was used to show that an asymptotically efficient estimator must have zero covariance with its difference from a consistent but asymptonically inefficient estimator, and specification tests for a number of model specifications in econometrics.

TL;DR: In this paper, the authors propose a model in which there is an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation.

TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.

TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.