Download or read book Managerial Behavior and the Bias in Analysts Earnings Forecasts written by Lawrence D. Brown and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Determinants of Managerial Earnings Guidance Prior to Regulation Fair Disclosure and Bias in Analysts Earnings Forecasts written by Amy P. Hutton and published by . This book was released on 2005 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Earnings Predictability and Bias in Analysts Earnings Forecasts written by Somnath Das and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bias in Analysts Earnings Forecasts written by Seung-Woog (Austin) Kwag and published by . This book was released on 2003 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Are Markets Rational written by Seung-Woog Kwag and published by . This book was released on 2002 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Biased Forecasts or Biased Earnings The Role of Reported Earnings in Explaining Apparent Bias and Over Underreaction in Analysts Earnings Forecasts written by Jeffery S. Abarbanell and published by . This book was released on 2012 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Do Managers Bias Their Forecasts of Future Earnings in Response to Their Firm s Current Earnings Announcement Surprises written by Stephen P. Baginski and published by . This book was released on 2020 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Approximately 90 percent of managers' earnings forecasts are issued simultaneously with their firm's current earnings announcement - a practice referred to as the “bundling” of earnings information. We examine whether managers bias these forecasts conditional on the news conveyed in current earnings, and offer three findings. First, managers appear to release optimistically biased earnings forecasts with simultaneously released negative current earnings news. Second, managers appear to release pessimistically biased earnings forecasts with simultaneously released large positive current earnings news. Third, these results (especially for optimistic bias when current earnings news is negative) are stronger when managers: (1) face less analyst monitoring and lower litigation risk, which constrain the ability to bias their forecasts, and (2) face greater career concerns, which create incentives to alter investor perceptions about current earnings. Additional analysis suggests that investors are unable to identify the management forecast bias, but that they unravel the bias subsequently as it is revealed. While no archival study can ascertain management intent, we provide several results that cast doubt on the idea that this management forecast bias behavior is purely unintentional. Overall, our evidence suggests that managers issue biased forecasts with the earnings announcement to influence perceptions of their firm's current earnings news.

Download or read book A Theory of Analysts Forecast Bias written by Murugappa (Murgie) Krishnan and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we provide an equilibrium explanation for the observed optimism in analysts' earnings forecasts. Our analysis provides theoretical support to the widely held notion that analysts engage in earnings optimism to gain access to management's private information. We show that a strategic analyst, who is motivated by improving the combined accuracy of his forecasts, issues a biased initial forecast to extract information from management, but issues unbiased forecasts subsequently. The management, on the other hand, provides more access because this optimistic bias reduces the proprietary costs associated with disclosure at the margin. An important element of our model is the assumption that analysts also have private information relevant to assessing firm value. Despite rational expectations about analyst bias, analysts' private information cannot be fully unravelled by other agents due to the noise introduced by the diversity in analysts' incentives.

Download or read book An Empirical Investigation of Bias in Analysts Earnings Forecasts written by Hakan Saraoglu and published by . This book was released on 1996 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Investor Sentiment and Management Earnings Forecast Bias written by Helen Hurwitz and published by . This book was released on 2017 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates whether investor sentiment is associated with behavioral bias in managers' annual earnings forecasts that are generally issued early in the year when uncertainty is relatively high. I provide evidence that management earnings forecast optimism increases with investor sentiment. Furthermore, I find that managers' annual earnings forecasts are more pessimistic during low-sentiment periods than during normal-sentiment periods. Since managers lack incentives to further deflate stock prices during a low-sentiment period, this evidence indicates that sentiment-related management earnings forecast bias is likely to be unintentional. In addition, I find that the relation between management earnings forecast bias and investor sentiment is stronger for firms with higher uncertainty, consistent with investor sentiment having a greater influence on management earnings forecasts when uncertainty is higher.

Download or read book The Effect of Issuing Biased Earnings Forecasts on Analysts Access to Management and Survival written by Bin Ke and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study offers evidence on the earnings forecast bias analysts use to please firm management and the associated benefits they obtain from issuing such biased forecasts in the years prior to Regulation Fair Disclosure. Analysts who issue initial optimistic earnings forecasts followed by pessimistic earnings forecasts before the earnings announcement produce more accurate earnings forecasts and are less likely to be fired by their employers. The effect of such biased earnings forecasts on forecast accuracy and firing is stronger for analysts who follow firms with heavy insider selling and hard-to-predict earnings. The above results hold regardless of whether a brokerage firm has investment banking business or not. These results are consistent with the hypothesis that analysts use biased earnings forecasts to curry favor with firm management in order to obtain better access to management's private information.

Download or read book Analysts Response to Earnings Management written by Xiaohui Liu and published by . This book was released on 2004 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous literature studies analysts' earnings forecasts without considering firms' response to analysts' forecasts. This study improves upon previous research by considering firms' earnings management with respect to analysts' forecasts. I hypothesize that analysts understand these earnings management practices, and incorporate firms' expected behavior into their forecasts. I demonstrate that for firms with high tendencies and flexibilities to manage earnings downwards, and/or firms with negatively skewed earnings, analysts account for earnings management practices by lowering the otherwise optimal forecasts. Comparing analysts' consensus forecasts with proxy for non-strategic forecasts (otherwise optimal forecasts), I find that analysts' forecasts are systematically below the non-strategic forecasts for firm-quarters that have: high accounting reserves available to manage earnings downwards, high unmanaged earnings, low debt to equity ratios, negative forecasted earnings, and negatively skewed unmanaged earnings. These results suggest that analysts forecast below the non-strategic level in order to avoid the large optimistic forecast errors that occur when firms who cannot meet forecasts manage earnings downward. The test results also suggest that analysts forecast above the non-strategic forecasts when earnings are positively skewed, and/or when firms have high tendencies and flexibilities to manage earnings upwards.

Download or read book Analysts Incentives and Systematic Forecast Bias written by Senyo Y. Tse and published by . This book was released on 2008 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The likelihood that earnings announcements meet or beat analyst expectations differs substantially and systematically across firms. Prior research explores managers incentives to meet analyst expectations. In this paper, we examine analysts incentives to issue systematically biased earnings forecasts and thereby influence the likelihood that firms report good earnings news. We first document that forecast biases are systematically different, as large firms and firms with low forecast dispersion - labeled high-information firms - are more likely to report positive earning surprises, while small firms and firms with large forecast dispersion - labeled low-information firms - tend to have optimistically biased forecasts that often lead to negative earnings surprises. We also show that potential financing needs induce more optimistic forecasts for low-information firms, but this effect is greatly mitigated for high-information firms. We find that career concerns help explain analysts' systematic forecast bias. An analyst's career longevity is enhanced by issuing pessimistic forecasts for high-information firms and optimistic forecasts for low-information firms. Optimistic forecast bias for high-financing-need firms has no consequence for an analyst's career longevity, but optimistic bias for low-financing-need firms hurts. Our results suggest that career concerns contribute to a systematic pattern of forecasting that aligns with managerial preferences.

Download or read book Management Earnings Forecast Bias and Insider Trading written by Afshad J. Irani and published by . This book was released on 2001 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates the association between bias in earnings forecasts released by managers of financially distressed firms and subsequent insider trading. Prior studies have documented optimism in such forecasts. Given this finding, this study investigates whether this optimism is systematically related to opportunistic management behavior or a sincere belief (by management) that their firm's financial situation is going to get better. Abnormal insider trading in the post management forecast period is examined to test these alternative explanations. The findings for the full sample are consistent with the opportunistic view, however the trading activity of non-managerial insiders seems to be the primary driver.

Download or read book Quantifying Cognitive Biases in Analyst Earnings Forecasts written by Geoffrey C. Friesen and published by . This book was released on 2006 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by cognitive biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for cognitive biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to cognitive dissonance bias. But they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman (1999). We also demonstrate the existence of these biases in international data.

Download or read book Earnings Management and Expectations Management written by Srinivasan Sankaraguruswamy and published by . This book was released on 2012 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prior literature has argued that managers manipulate earnings; other literature suggests that managers manipulate analysts (market) expectations of earnings. These two streams treat the two types of manipulation as occurring independently of each other. Our paper suggests that managers manipulate both earnings and expectations at the same time, and model them as jointly determined. In this model, errors in earnings and forecasts are positively correlated. In prior research the data reject the null that earnings forecasts are rational; in terms of this paper's model, these rejections are to be expected; given the positive correlation in errors between managers and analysts, prior tests are mis-specified. A non-zero forecast error on average is taken to mean that analysts are biased; this paper's model of joint determination predicates a non-zero forecast error on average. Similarly, this paper's model predicts rejection of rationality in other standard tests.