Managerial behavior differs considerably when managers report quarterly profits versus losses. When they report profits, managers seek to just meet or slightly beat analyst estimates. When they report losses, managers do not attempt to meet or slightly beat analyst estimates. Instead, managers often do not forewarn analysts of impending losses, and the analyst's signed error is likely to be negative and extreme (i.e., a measured optimistic bias). Brown (1997 Financial Analysts Journal) shows that the optimistic bias in analyst earnings forecasts has been mitigated over time, and that it is less pronounced for larger firms and firms followed by many analysts. In the present study, I offer three explanations for these temporal and cross-sectional phenomena. First, the frequency of profits versus losses may differ temporally and/or cross-sectionally. Since an optimistic bias in analyst forecasts is less likely to occur when firms report profits, an optimistic bias is less likely to be observed in samples possessing a relatively greater frequency of profits. Second, the tendency to report profits that just meet or slightly beat analyst estimates may differ temporally and/or cross-sectionally. A greater tendency to 'manage profits' (and analyst estimates) in this manner reduces the measured optimistic bias in analyst forecasts. Third, the tendency to forewarn analysts of impending losses may differ temporally and/or cross-sectionally. A greater tendency to 'manage losses' in this manner also reduces the measured optimistic bias in analyst forecasts. I provide the following temporal evidence. The optimistic bias in analyst forecasts pertains to both the entire sample and the losses sub-sample. In contrast, a pessimistic bias exists for the 85.3% of the sample that consists of reported profits. The temporal decrease in the optimistic bias documented by Brown (1997) pertains to both losses and profits. Analysts have gotten better at predicting the sign of a loss (i.e., they are much more likely to predict that a loss will occur than they used to), and they have reduced the number of extreme negative errors they make by two-thirds. Managers are much more likely to report profits that exactly meet or slightly beat analyst estimates than they used to. In contrast, they are less likely to report profits that fall a little short of analyst estimates than they used to. I conclude that the temporal reduction in optimistic bias is attributable to an increased tendency to manage both profits and losses. I find no evidence that there exists a temporal change in the profits-losses mix (using the I/B/E/S definition of reported quarterly profits and losses). I document the following cross-sectional evidence. The principle reason that larger firms have relatively less optimistic bias is that they are far less likely to report losses. A secondary reason that larger firms have relatively less optimistic bias is that their managers are relatively more likely to report profits that slightly beat analyst estimates. The principle reason that firms followed by more analysts have relatively less optimistic bias is that they are far less likely to report losses. A secondary reason that firms followed by more analysts have relatively less optimistic bias is that their managers are relatively more likely to report profits that exactly meet analyst estimates or beat them by one penny. I find no evidence that managers of larger firms or firms followed by more analysts are relatively more likely to forewarn analysts of impending losses. I conclude that cross-sectional differences in bias arise primarily from differential 'loss frequencies,' and secondarily from differential 'profits management.' The paper discusses implications of the results for studies of analysts forecast bias, earnings management, and capital markets. It concludes with caveats and directions for future research.

Prior to Regulation Fair Disclosure (Reg FD) some management spent considerable time and effort guiding analyst earnings estimates, often through detailed reviews of analysts' earnings models. In this paper I use proprietary survey data from the National Investor Relations Institute to identify firms that reviewed analysts' earnings models prior to Reg FD and those that did not. Under the maintained assumption that firms conducting reviews implicitly or explicitly guided analysts' earnings forecasts, I document firm characteristics associated with the decision to provide private managerial earnings guidance. Then, I document the characteristics of 'guided' versus 'unguided' analyst earnings forecasts. Findings demonstrate an association between several firm characteristics and guidance practices: managers are more likely to review analyst earnings models when the firm's stock is highly followed by analysts and largely held by institutions, when the firm's market-to-book ratio is high, and its earnings are important to valuation (high Industry-ERC R2), but hard to predict because its business is complex (high # of Segments). A comparison of guided and unguided quarterly forecasts indicates that guided analyst estimates are more accurate, but also more frequently pessimistic. An examination of analysts' annual earnings forecasts over the fiscal year does not distinguish between guidance and no guidance firms; both experience a quot;walk downquot; in annual estimates. To distinguishing between guidance and no guidance firms, one must examine quarterly earnings news: unguided analysts walk down their annual estimates when the majority of the quarterly earnings news is negative, guided analysts walk down their annual estimates even though the majority of the quarterly earnings news is positive.

This paper examines cross-sectional differences in the optimistic behavior of financial analysts. Specifically, we investigate whether the predictive accuracy of past information (e.g., time-series of earnings, past returns, etc.) is associated with the magnitude of the bias in analysts' earnings forecasts. We posit that there is higher demand for non-public information for firms whose earnings are difficult to accurately predict than for firms whose earnings can be accurately forecasted using public information. Assuming that optimism facilitates access to management's non-public information, we hypothesize that analysts will issue more optimistic forecasts for low predictability firms than for high predictability firms. Our results support this hypothesis.

If either economic incentives or psychological phenomena cause the bias in analysts' forecasts to persist long enough, it would be potentially discoverable and exploitable by investors. quot;Exploitationquot; in this context implies that investors, through examination of historical forecasting performance, can more or less reliably estimate the direction and extent of bias, and impute unbiased estimates for themselves, given analysts' forecasts. The absence of persistence in forecast errors would suggest that analysts' own behavior ultimately quot;self-correctsquot; within a time frame that eliminates the possibility that the patterns could be exploited by investors. We use two look-back methods that capture salient features of analysts' past forecasting behavior to form quintile portfolios that describe the range of analysts' forecasting behavior. Parametric and nonparametric tests are performed to determine whether the two portfolio formation methods provide predictive power with respect to subsequent forecast errors. The findings support a conclusion that analysts' behaviors in both optimistic and pessimistic extremes do not entirely self-correct, leaving open the possibility that investors may find historical forecast errors useful in making inferences about current forecasts.

We demonstrate the role of three empirical properties of cross-sectional distributions of analysts' forecast errors in generating evidence pertinent to three important and heretofore separately analyzed phenomena studied in the analyst earnings forecast literature: purported bias (intentional or unintentional) in analysts' earnings forecasts, forecaster over/underreaction to information in prior realizations of economic variables, and positive serial correlation in analysts' forecast errors. The empirical properties of interest include: the existence of two statistically influential asymmetries found in the tail and the middle of typical forecast error distributions, the fact that a relatively small number of observations comprise these asymmetries and, the unusual character of the reported earnings benchmark used in the calculation of the forecast errors that fall into the two asymmetries that is associated with firm recognition of unexpected accruals. We discuss competing explanations for the presence of these properties of forecast error distributions and their implications for conclusions about analyst forecast rationality that are pertinent to researchers, regulators, and investors concerned with the incentives and judgments of analysts.Previously titled quot;Biased Forecasts or Biased Earnings? The Role of Earnings Management in Explaining Apparent Optimism and Inefficiency in Analysts' Earnings Forecastsquot.

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.