Understanding Analysts' Use and Under-Use of Stock Returns and Other Analysts' Forecasts when Forecasting Earnings PDF Download
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Author: Michael B. Clement
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this study, we examine how analysts are affected by the public actions of investors and other analysts by closely examining how analysts revise their earnings forecasts after an earnings announcement. In particular, we hypothesize that analysts observe the actions of investors and other analysts in order to more accurately forecast earnings and have the expertise to determine when these actions are most informative about future earnings. Consistent with our hypotheses, we find that analysts revise their earnings forecasts more strongly in response to returns and other analysts' revisions when these signals are more informative about future earnings changes. We also find that, consistent with analysts being conservative while facing uncertain information, underreactions are strongest (not weakest) when analysts are responding most strongly to these signals (i.e., when the signals are most informative). Lastly, we find that analysts who are most sensitive to the informativeness of others' actions are relatively more accurate in forecasting earnings, suggesting that the ability to extract information from the actions of others serves as a source of expertise for at least some analysts.
Author: Michael B. Clement
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this study, we examine how analysts are affected by the public actions of investors and other analysts by closely examining how analysts revise their earnings forecasts after an earnings announcement. In particular, we hypothesize that analysts observe the actions of investors and other analysts in order to more accurately forecast earnings and have the expertise to determine when these actions are most informative about future earnings. Consistent with our hypotheses, we find that analysts revise their earnings forecasts more strongly in response to returns and other analysts' revisions when these signals are more informative about future earnings changes. We also find that, consistent with analysts being conservative while facing uncertain information, underreactions are strongest (not weakest) when analysts are responding most strongly to these signals (i.e., when the signals are most informative). Lastly, we find that analysts who are most sensitive to the informativeness of others' actions are relatively more accurate in forecasting earnings, suggesting that the ability to extract information from the actions of others serves as a source of expertise for at least some analysts.
Author: Sundaresh Ramnath
Publisher: Now Publishers Inc
ISBN: 1601981627
Category : Business & Economics
Languages : en
Pages : 125
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Book Description
Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.
Author: Marius del Giudice Rodriguez
Publisher:
ISBN:
Category : Corporate profits
Languages : en
Pages : 132
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Book Description
This dissertation contains three self-contained chapters dealing with specific aspects of financial analysts' earnings forecasts. After recent accounting scandals, much attention has turned to the incentives present in the career of professional financial analysts. The literature points to several reasons why financial analysts behave overoptimistically when providing their predictions. In particular, analysts may wish to maintain good relations with firm management, to please the underwriters and brokerage houses at which they are employed, and to broaden career choice. While the literature has focused more on analysts' strategic behavior in these situations, less attention has been paid to the implications these factors have on financial analysts' loss functions. The loss function dictates the criteria that analysts use in order to build their forecasts. Using a simple compensation scheme in which the sign of prediction errors affect their incomes differently, in the first chapter we examine the implications this has on their loss function. We show that depending on the contract offered, analysts have a strict preference for under-prediction or over-prediction and the size of this asymmetric behavior depends on the parameter that governs the financial analyst's preferences over wealth. This is turn affects the bias in their forecasts. Recent developments in the forecasting literature allow for the estimation of asymmetry parameters after observing data on forecasts. Moreover, they allow for a more general test of rationality once asymmetries are present. We make use of forecast data from financial analysts, provided by I/B/E/S, and present evidence of asymmetries and weak evidence against rationality. In the second chapter we study the evolution over time in the revisions to financial analysts' earnings estimates for the 30 Dow Jones firms over a 20 year period. If analysts' forecasts used information efficiently, earnings revisions should not be predictable. However, we find strong evidence that earnings revisions can in fact be predicted by means of the sign of the last revision or by using publicly available information such as short interest rates and past revisions. We propose a three-state model that accounts for the very different magnitude and persistence of positive, negative and `no change' revisions and find that this model forecasts earnings revisions significantly better than an autoregressive model. We also find that our forecasts of earnings revisions predict the actual earnings figure beyond the information contained in analysts' earnings estimates. Finally, the empirical literature on financial analysts' forecast revisions of corporate earnings has focused on past stock returns as the key determinant. The effects of macroeconomic information on forecast revisions is widely discussed, yet rarely tested in the literature. In the third chapter, we use dynamic factor analysis for large data sets to summarize a large cross-section of macroeconomic variables. The estimated factors are used as predictors of the average analyst's forecast revisions for different sectors of the economy. Our analysis suggests that factors extracted from macroeconomic variables do, indeed, improve on the current model with only past stock returns. In trying to explain what drives financial analysts' forecast revisions, the factors representing the macroeconomic environment must be considered to avoid a potential omitted variable problem. Moreover, the explanatory power and direction of such factors strongly depend on the industry in question.
Author: Patricia C. O'Brien
Publisher: Forgotten Books
ISBN: 9780666405524
Category : Mathematics
Languages : en
Pages : 74
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Book Description
Excerpt from Analysts' Forecasts as Earnings Expectations Analysts' forecasts of earnings are increasingly used in accounting and finance research as expectations data, to proxy for the unobservable market expectation of a future 'realization. 'since a diverse set of forecasts is available at any time for a given firm's earnings. Composites are used to distill the information from the diverse set into a single expectation. This paper considers the relative merits of several composite forecasts as expectations data. One of the primary results is that the most current forecast available outperforms more commonly used aggregations such as the mean or the median. Mthis result is consistent-with forecasters incorporating information from others' previous predictions into their own. It also suggests that the forecast date, which previous research has largely ignored, is a characteristic relevant for distinguishing better forecasts. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Brian Michael Bratten
Publisher:
ISBN:
Category :
Languages : en
Pages : 198
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Book Description
This dissertation examines the general research issue of whether the components of earnings are informative and specifically 1) how analysts consider earnings components when predicting future earnings and 2) whether the information content in, and analysts' use of, earnings components have changed through time. Although earnings components have predictive value for future earnings based on each component's persistence, extant research provides only a limited understanding of whether and how analysts consider this when forecasting. Using an integrated income statement and balance sheet framework to estimate the persistence of earnings components, I first establish that disaggregation based on the earnings components framework in this study is helpful to predict future earnings and helps explains contemporaneous returns. I then find evidence suggesting that although analysts consider the persistence of various earnings components, they do not fully integrate this information into their forecasts. Interestingly, analysts appear to be selective in their incorporation of the information in earnings components, seeming to ignore information from components indicating lower persistence, which results in higher forecast errors. Conversely, when a firm's income is concentrated in high persistence items, analysts appear to incorporate the information into their forecasts, reducing their forecast errors. I also report that the usefulness of components relative to aggregate earnings has dramatically and continuously increased over the past several decades, and contemporaneous returns appear to be much better explained by earnings components than aggregate earnings (than historically). Finally, the relation between analyst forecast errors and the differential persistence of earnings components has also declined over time, indicating that analysts appear to recognize the increasing importance of earnings components through time.
Author: Malwina Woznik
Publisher: GRIN Verlag
ISBN: 3656478236
Category : Business & Economics
Languages : en
Pages : 38
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Book Description
Seminar paper from the year 2011 in the subject Business economics - Controlling, grade: 1,3, University of Cologne (Seminar für allgemeine BWL und Controlling), language: English, abstract: Since the beginning of the 90s research on issues referring to analysts’ practise grew rapidly to such an extent that even several publications are concerned with giving an overview of this development. Besides the principal-agent problematic between the firm’s managers and the equity investors, investors are dependent on analysts’ information in times where equity trading soared and the trading turnover in 2008 was 35 times higher than in 1980. That is why shareholders are not able to analyse the amount of information regarding a company due to lack of time or ability. Therefore analysts advise investors to make a profitable decision by publishing a report including for instance stock recommendations or earnings forecasts. Another reason why there is so much research about analysts’ practise is the fact that their information influences investors’ trading behaviour. Thus, it is crucial to know how reliable those statements are and accordingly to be able to assess the quality of the outputs. However, to answer the question of analysts’ process of transforming various information of stock recommendations have to be examined in detail. Recent investigations rather focus on the single properties of analysts’ analyses as earnings forecasts and stock recommendations, but did not connect those two values. Prior studies deal with research questions like the effect of earnings forecasts on the stock prices or the use of stock recommendations to foretell abnormal return. Bradshaw (2004) is the first research paper which follows the question whether there is a link and if so how analysts incorporate the earnings forecasts into their stock recommendation. Because of the importance of Bradshaw (2004), this paper reviews the main issues and embeds them into the existing literature concerning the role of analysts. The rest of this paper is organized as follows. The first chapter focuses on the character of analysts and potential key input factors which might be used by analysts for issuing recommendations. Then a brief review of Bradshaw (2004) is given to present the main results. This enables a discussion about potential and realized extensions in literature which follows in the third chapter. The final chapter concludes.
Author: Sati P. Bandyopadhyay
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
Little attention has been paid to a principal decision context in which analysts' earnings forecasts are prepared, namely, as an input to their recommendations. We use two data sets, Value Line, USA, and Research Evaluation Service, Canada, and examine the importance of analysts' earnings forecasts for their stock price forecasts via three hypotheses: (1) analysts' earnings forecasts are important for their stock price forecasts; (2) analysts' long-term earnings forecasts are more important than their short-term earnings forecasts for their predictions of stock prices over a particular stock price forecast horizon; (3) the importance of analysts' earnings forecasts for their stock price forecasts rises as the joint earnings and stock price forecast horizon increases. We show that: (1) when the earnings forecast horizon is the next fiscal year, forecasted earnings explain only 30% of the variation in forecasted price; (2) the importance of forecasted earnings for forecasted price rises as the earnings forecast horizon increases; (3) in the long run, (i.e. three to five years hence), forecasted earnings explain about 60% of the variation in forecasted price. Decision usefulness is an ex ante concept, but tests regarding the usefulness of earnings for stock price generally have used actual (not expectational) data. Our evidence suggests that earnings expectations are decision useful, where the decision context is sell-side analysts' stock price forecasts. Our results are potentially important to users of sell-side analyst research reports. When a stock recommendation is accompanied only by short-run earnings forecasts, investors need to closely examine estimates of non-earnings variables to assess the quality of stock recommendations. In contrast, when stock recommendations are accompanied by both short-run and long-run earnings forecasts, investors need to examine estimates of non-earnings information variables less closely.
Author: Xia Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
Prior studies (e.g., Lys and Sohn 1990; Ali, Klein and Rosenfeld 1992) have documented a positive association between analysts' forecast errors and past stock returns and suggested cognitive bias on the part of analysts as a possible explanation. In this paper, we separately analyze the association between forecast errors and past negative returns and that between forecast errors and past positive returns. We find that forecast errors are only positively associated with past negative returns and are not associated with past positive returns. These results are robust to a series of sensitivity tests. They are inconsistent with analysts being subject to cognitive bias; instead, they are consistent with several explanations related to accounting conservatism or analysts' incentives: analysts having difficulty in forecasting discretionary charges associated with past negative returns, analysts not exerting effort in forecasting earnings of firms with poor performance, or analysts ignoring bad news in order to please managers.
Author: Brian R. Bruce
Publisher: Irwin Professional Publishing
ISBN:
Category : Business & Economics
Languages : en
Pages : 398
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Book Description
Author: Yaowen Shan
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
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Book Description
This study proposes and validates “other information” in analysts' forecasts as a legitimate proxy for future cash flows, and examines its incremental role in explaining stock return volatility. We suggest that “other information” contains information about fundamentals beyond that reflected in current financial statements, and reflects firms' fundamentals on a more timely basis than dividends or earnings. The link between “other information” and volatility can be derived from a combination of the accounting version of the Campbell-Shiller model (Campbell and Shiller 1988a, 1988b; Vuolteenaho 2002) and Ohlson's (1995) linear information dynamics. Using standardized regressions we find volatility increases when current “other information” is more uncertain, and increases more in response to unfavorable news compared to favorable news. Variance decomposition analysis shows that the variance contribution of “other information” dominates that of expected-return news. The incremental role of “other information” is at least half of the effect of earnings in explaining future volatility. The results are valid for measures of both systematic and idiosyncratic volatility, and are more pronounced for firms with poor information environments. Overall, our results highlight the importance of including “other information” as an additional cash-flow proxy in future studies of stock prices and volatility.
