Loss Firms and Analysts' Earnings Forecast Errors PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Loss Firms and Analysts' Earnings Forecast Errors PDF full book. Access full book title Loss Firms and Analysts' Earnings Forecast Errors by Lee-Seok Hwang. Download full books in PDF and EPUB format.
Author: Lee-Seok Hwang Publisher: ISBN: Category : Languages : en Pages : 13
Book Description
An examination of analysts' accuracy in predicting annual earnings for firms reporting losses and firms reporting profits finds that analysts are ten times more accurate in predicting the earnings of profit firms. They have also improved their predictive ability for profit firms since the mid-1980s. The sample includes eighteen years of coverage by IBES and Compustat analysts.
Author: Lee-Seok Hwang Publisher: ISBN: Category : Languages : en Pages : 13
Book Description
An examination of analysts' accuracy in predicting annual earnings for firms reporting losses and firms reporting profits finds that analysts are ten times more accurate in predicting the earnings of profit firms. They have also improved their predictive ability for profit firms since the mid-1980s. The sample includes eighteen years of coverage by IBES and Compustat analysts.
Author: Sudipta Basu Publisher: ISBN: Category : Languages : en Pages : 25
Book Description
We argue that accounting conservatism makes earnings forecasting difficult by introducing transitory components in reported earnings. These transitory components are likely to be disproportionately represented in firms reporting losses. We show that analysts' mean forecast errors and absolute forecast errors for loss firms are substantially greater than those for profit firms in every single quarter, regardless of the forecast horizon. We argue that auditors' legal liability incentives make it likely that fourth quarter earnings are more conservative than interim quarter earnings. Forecast errors are always higher for loss firms in the fourth quarter compared to earlier quarters. Using special items to proxy for transitory components induced by conservatism, we document similar results for firms reporting special items, partitioned by the sign of the special items. Our results are consistent with auditor conservatism affecting fourth quarter earnings differentially, which causes analysts' earnings forecasts to be poorest for the fourth quarter.
Author: Sudipta Basu Publisher: ISBN: Category : Languages : en Pages : 40
Book Description
Prior research concludes that financial analysts do not process public information efficiently in generating their earnings forecasts. The OLS regression-based tests used in prior studies assume implicitly that analysts face a quadratic loss function, or that analysts minimize their squared forecast errors. In contrast, we argue that analysts face a linear loss function, or that they minimize their absolute forecast errors. We conduct and compare rational expectations tests conditioned on these two alternative loss functions. While we replicate prior findings of inefficiency with OLS regressions, we find virtually no evidence of forecast inefficiency with Least Absolute Deviation regressions, where we explicitly assume a linear loss function.
Author: Mark Clatworthy Publisher: ISBN: Category : Languages : en Pages :
Book Description
Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors' valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimal forecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution.
Author: Gia Marie Chevis Publisher: ISBN: Category : Languages : en Pages :
Book Description
Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms that consistently meet or exceed analysts' earnings expectations and those that do not. I then analyze the extent to which the market incorporates this (in)efficiency into its earnings expectations. Consistent with my hypotheses, I find that analysts are relatively less efficient with respect to prior returns for firms that do not consistently meet expectations than for firms that do follow such a strategy, especially when prior returns convey bad news. However, forecast errors for firms that consistently meet expectations do not appear to be serially correlated to a greater extent than those for firms that do not consistently meet expectations. It is not clear whether the market considers such inefficiency when setting its own expectations. While the evidence suggests they may do so in the context of a shorter historical pattern of realized forecast errors, other evidence suggests they may not distinguish between predictable and surprise components of forecast error when the historical forecast error pattern is more established.
Author: Cheng Few Lee Publisher: World Scientific ISBN: 9811202400 Category : Business & Economics Languages : en Pages : 5053
Book Description
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.
Author: Lawrence D. Brown Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Jing Li Publisher: ISBN: Category : Languages : en Pages : 42
Book Description
This study examines how accounting conservatism may affect the information environment of analysts' earnings forecasts, taking into account the interaction between unconditional and conditional conservatism. Unconditional conservatism preempts conditional conservatism in the later period and reduces the uncertainty in loss recognition associated with bad news. Through a simple analyst forecast model, I demonstrate that: 1) unconditional conservatism is negatively correlated with analysts' forecast errors for good news or mild bad news firms, but positively correlated with analysts' forecast errors for extreme bad news firms; and 2) unconditional conservatism reduces the overall uncertainty in analysts' forecasts. The empirical results are consistent with the predictions. Moreover, the evidence shows that the impact of unconditional conservatism on analysts' forecasts is greater for early forecasts, when the information uncertainty is high, than for late forecasts.
Author: Stanimir Markov Publisher: ISBN: Category : Languages : en Pages : 33
Book Description
Prior research on financial analysts' consensus earnings forecast errors has tended to explore either incentives-based or inefficient information use-based explanations for the properties of the analysts' forecast errors. This has limited our understanding of financial analysts' expectation formation process as incentives and cognitive biases are likely to simultaneously affect the properties of the analysts' consensus forecast errors. Our main contribution is in separating these two effects. In particular, using consensus quarterly earnings forecast data, we document that analysts have asymmetric loss function, and that they do not fully use past earnings and forecast errors information in minimizing their expected loss.
Author: Lee-Seok Hwang
Author: Lee-Seok Hwang
Author: Sudipta Basu
Author: Michael L. Ettredge
Author: Sudipta Basu
Author: Mark Clatworthy
Author: Gia Marie Chevis
Author: Cheng Few Lee
Author: Lawrence D. Brown
Author: Jing Li
Author: Stanimir Markov