Change in Systematic Bias and News as Drivers of Earnings Forecast Revision - A New Forecast Revision Model

2010 ◽  
Author(s):  
Sebastian Gell ◽  
Carsten Homburg ◽  
Katja Schulze
1998 ◽  
Vol 13 (3) ◽  
pp. 271-274 ◽  
Author(s):  
Lawrence D. Brown

This paper tackles an interesting question; namely, whether dispersion in analysts' earnings forecasts reflects uncertainty about firms' future economic performance. It improves on the extant literature in three ways. First, it uses detailed analyst earnings forecast data to estimate analyst forecast dispersion and revision. The contrasting evidence of Morse, Stephan, and Stice (1991) and Brown and Han (1992), who respectively used consensus and detailed analyst data to examine the impact of earnings announcements on forecast dispersion, suggest that detailed data are preferable for determining the data set on which analysts' forecasts are conditioned. Second, it relates forecast dispersion to both analyst earnings forecast revision and stock price reaction to the subsequent earnings announcement. Previous studies related forecast dispersion to either analyst forecast revision (e.g., Stickel 1989) or to subsequent stock price movements (e.g., Daley et al. [1988]), but not to both revision and returns. Third, it includes the interim quarters along with the annual report. In contrast, previous research focused on the annual report, ignoring the interims (Daley et al. [1988]).


2020 ◽  
Vol 4 (1) ◽  
pp. 88
Author(s):  
Cicilia Erna Susilawati

Trading in the stock market occur due to differences in opinion on the expected value of the securities. In the other side, asymmetric information between investors and companies caused the stock price does not reflect the real price. So, asymmetric information should be reduced. Information by securities analysts is an information that is expected to reduce that. This study investigates the performance of securities analysts through its role in reducing asymmetric information. This is motivated by some previous studies that stated that the Indonesian capital market is inefficient, because high levels of asymmetric information. Analysts is considered as inform market participant who can reduce the asymmetric information so as to make capital market to be efficient. The role of securities analysts is seen through the product. There are stock recommendation and earnings forecast revision. Testing the consistency of the analyst's stock recommendations and earnings forecast revision before testing their impact on asymmetric information. The results showed that output in the form of stock securities analysts and earnings forecast recommendation are consistent but has not been able to reduce the asymmetry of information that occurs between investors and companies.


2006 ◽  
Vol 18 (1) ◽  
pp. 37-51 ◽  
Author(s):  
Michael J. Eames ◽  
Steven M. Glover ◽  
Jane Jollineau Kennedy

Recent scandals and controversies have focused substantial attention on the behavior of financial analysts. Responses such as the Sarbanes-Oxley Act, new regulations at securities exchanges, and massive legal settlements are consistent with the perception that analysts' research and stock recommendations exhibit significant self-serving bias. While anecdotal and legal evidence support the allegations that some analysts have intentionally mislead the investing public, recent archival research suggests unintentional cognitive processes also contribute to systematic bias in analysts' forecasts (Eames et al. 2002). However, studies based on stock-market data cannot distinguish between unintentional cognitive processes and intentional bias stemming from economic incentives (e.g., trade boosting). In a laboratory experiment we eliminate economic incentives and find that cognitive processes unintentionally lead to earnings forecast bias. Our results suggest that recent regulations and policy changes by Congress, the Securities and Exchange Commission, exchange markets, and brokerage firms will not totally eliminate bias in analysts' earnings forecasts.


2013 ◽  
Vol 03 (03n04) ◽  
pp. 1350014
Author(s):  
Dongmei Li

One of the many challenges facing financial economists is to distinguish the theories explaining momentum. Brav and Heaton (2002) show that it is very difficult to distinguish the "rational" models of structural uncertainty (SU) from "behavioral" models of conservatism (C). In this paper, I reexamine the SU model and the C model proposed by Brav and Heaton (2002) in explaining short-run momentum. Based on simulated data, I find that they differ from each other in the relation between agent's earnings forecast revision and the lagged earnings change. This relation is significantly negative for the SU model and significantly positive for the C model. Empirical evidence provides support for the SU model.


2007 ◽  
Vol 82 (5) ◽  
pp. 1227-1253 ◽  
Author(s):  
Michael B. Mikhail ◽  
Beverly R. Walther ◽  
Richard H. Willis

Regulators' interest in analyst reports stems from the belief that small investors are unaware of the conflicts sell-side analysts face and may, as a consequence, be misled into making suboptimal investment decisions. We examine who trades on security analyst stock recommendations by extending prior research to focus on investor-specific responses to revisions. We find that both large and small traders react to analyst reports; however, large investors appear to trade more than small traders in response to the information conveyed by the analyst's recommendation and earnings forecast revision (proxied by the magnitudes of the recommendation change and the earnings forecast revision, respectively). We also find that small investors do not fully account for the effects of analysts' incentives on the credibility of analyst reports, as captured by the type of recommendation (i.e., upgrade versus downgrade or buy versus sell). In particular, small investors not only trade more than large investors following upgrade and buy recommendations, but also trade more following upgrade and buy recommendations than they do following downgrade and hold/sell recommendations. Furthermore, we observe that, on average, small traders are net purchasers following recommendation revisions regardless of the type of the recommendation; large traders tend to be net sellers following downgrades and sells. Consequently, large traders generate statistically positive returns from their trading, while small traders generate statistically negative returns from their trading. These findings are consistent with large investors being more sophisticated processors of information, and provide some support for regulators' concerns that analysts may more easily mislead small investors.


2019 ◽  
Author(s):  
Joel L Pick ◽  
Nyil Khwaja ◽  
Michael A. Spence ◽  
Malika Ihle ◽  
Shinichi Nakagawa

We often quantify a behaviour by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2, repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed (e.g. as a function of the observation period) as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then review recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.


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