Identifying the News in Analysts’ Earnings Forecasts Revisions: An Alternative to the Random Walk Expectation

Previous research indicates that analysts’ forecasts are superior to time series models as measures of investors’ earnings expectations. Nevertheless, research also documents predictable patterns in analysts’ forecasts and forecast errors. If investors are aware of these patterns, analysts’ forecast revisions measured using the random walk expectation are an incomplete representation of changes in investors’ earnings expectations. Investors can use knowledge of errors and biases in forecasts to improve upon the simple random walk expectation by incorporating conditioning information. Using data from 2005 to 2015, we compare associations between market-adjusted stock returns and alternative specifications of forecast revisions to determine which best represents changes in investors’ earnings expectations. We find forecast revisions measured using a ‘bandwagon expectations’ specification, which includes two prior analysts’ forecast signals and provides the most improvement over random-walk-based revision measures. Our findings demonstrate benefits to considering information beyond the previously issued analyst forecast when representing investors’ expectations of analysts’ forecasts.

Integrated reporting and analyst behaviour in diverse institutional settings

Purpose Given the limited studies that have started to focus on contexts where integrated reporting (IR) is voluntarily adopted, this paper aims to explore the moderating role of institutional characteristics on the association between voluntary report release and analyst forecast accuracy. Design/methodology/approach This study uses a quantitative empirical research method grounded on voluntary disclosure theory to provide empirical evidence on an international sample of companies choosing to release integrated reports. Preliminarily, a cluster analysis is used to group countries according to institutional patterns. Multivariate analyses detect the associations between report release choice and analysts’ forecast accuracy across clusters. Multiple econometric approaches are used to address the endogeneity concerns. Findings IR release is not informative for the market unless considering systematic variations across different institutional settings. Analysts’ forecast is more accurate for IR adopters located in strong institutional enforcement settings than for all the other companies. In the strong institutional setting that is also characterized by a pluralistic society, IR release benefits for the market are conditioned by the fact that the choice to release IR depends on environmental, governance and social disclosure-based managers remuneration and disclosure requirements. In weak institutional settings, IR release is not beneficial for the forecast accuracy. Research limitations/implications Academics and practitioners can gain understanding of the usefulness of voluntary IR across different institutional settings. Originality/value The study advances the understanding of the IR’s informativeness, overcoming the common dichotomous distinctions between strong and weak institutional settings.

How Completely Do Analysts Incorporate Firm-Specific and Industry Information in Their Forecasts? Evidence and Implications for Post-Forecast Revision Drift

Prior studies find a positive relation between analyst forecast revisions and upcoming news, suggesting that analysts’ forecast revisions are incomplete with respect to available information. In this study, we use the association between forecast revisions and upcoming news to measure forecast completeness and show that post-forecast-revision drift is higher when forecasts are incomplete. We follow Hui and Yeung’s (2013) approach to separate forecast revision news into industry-wide and firm-specific components because they find that drift is primarily associated with the industry component. We find that forecast revisions are less complete for industry-wide news than for firm-specific news. Furthermore, analysts’ industry-wide revisions are less complete early in the year and when the underlying news is bad, and we find stronger post-forecast-revision drift in those cases. We also show that analysts who were optimistic in prior periods tend to issue forecasts that are less complete and that generate stronger drift than forecasts by other analysts. Our findings provide an explanation for the drift that contrasts with prior studies that attribute the drift to investors’ slow assimilation of the news in forecast revisions. Thus, our study sheds light on analysts’ role in conveying firm-specific and industry-wide news to investors and on the implications for post-forecast-revision drift.

Types of politically connected firms and analysts' earnings forecast

PurposeThis study examined the effect of different types of politically connected (PCON) Malaysian firms on analysts' forecast accuracy and dispersion.Design/methodology/approachThe study identified different types of PCON firms according to Wong and Hooy's (2018) classification, which divided political connections into government-linked companies (GLCs), boards of directors, business owners and family members of government leaders. The sample covered the period 2007–2016, for which earnings forecast data were obtained from the Institutional Brokers' Estimate System (IBES) database and financial data were extracted from Thomson Reuters Fundamentals. We deleted any market consensus estimates made by less than three analysts and/or firms with less than three years of analyst forecast information to control for the impact of individual analysts' personal attributes.FindingsThe study found that PCON firms were associated with lower analyst forecast accuracy and higher forecast dispersion. The effect was more salient in GLCs than in other PCON firms, either through families, business ties or boards of directors. Further analyses showed that PCON firms—in particular GLCs—were associated with more aggressive reporting of earnings and poorer quality of accruals, hence providing inadequate information for analysts to produce accurate and less dispersed earnings forecasts. The results were robust even after addressing endogeneity issues.Research limitations/implicationsThis study found new evidence of the impact of different types of PCON firms in exacerbating information asymmetry, which was not addressed in prior studies.Practical implicationsThis study has a significant practical implication for investors that they should be mindful of high information asymmetry in politically connected firms, particularly government-linked companies.Originality/valueThis is the first study to provide evidence of the impact of different types of PCON firms on analysts' earnings forecasts.

Characterizing and Predicting Reviews for Effective Product Marketing and Advancement

Online surveys have become a significant wellspring of data for clients prior to settling on an educated buy choice. Early audits of an item will in general exceptionally affect the ensuing item deals. In this paper, we step up and study the conduct qualities of early reviewer through their posted audits on our shopping gateway. In explicit, we partition item lifetime into three back to back stages, in particular early, lion's share. A client who has posted a survey in the beginning phase is considered as an early analyst. We quantitatively describe early reviewer dependent on their rating practices, the supportiveness scores got from others and the relationship of their surveys with item prevalence. We have tracked down that (1) an early analyst will in general relegate a higher normal rating score; and (2) an early reviewer will in general post more supportive audits. Our examination of item surveys additionally demonstrates that early reviewers appraisals and their got support scores are probably going to impact item prominence. By survey audit posting measure as a multiplayer rivalry game, we propose a novel edge based implanting model for early analyst forecast. Broad investigations on two diverse web based business datasets have shown that our proposed approach beats various cutthroat baselines.