RATIONAL EXPECTATIONS AND THE DYNAMIC ADJUSTMENT OF SECURITY ANALYSTS' FORECASTS TO NEW INFORMATION

We report the results of unbiasedness tests of security analysts' earnings forecasts. By examining how analysts incorporate new information into their updated earnings forecasts we can analyze directly the effect of new information on analysts' forecast revisions and evaluate whether these revised forecasts converge to rational expectations forecasts. The forecasts made by security analysts participating in the Institutional Brokers Estimate System (IBES) database are analyzed. Using standard statistical tests, we reject the simple form of the rational expectations hypothesis. However, by extending the standard tests used in previous studies, we obtain results that suggest that analysts' earnings forecasts conform to a dynamic form of rationality. The tendency of revised forecasts to converge stochastically toward the rational expectations forecast cautions against the rejection of more complicated forms of rationality.     

Causal language intensity in performance commentary and financial analyst behaviour

We use automated techniques to measure causal reasoning on earnings‐related financial outcomes of a large sample of MD&A sections of US firms and examine the intensity of causal language in that context against extent of analyst following and against properties of analysts’ earnings forecasts. We find a positive and significant association between a firm's causal reasoning intensity and analyst following and analyst earnings forecast accuracy respectively. Correspondingly, analysts’ earnings forecast dispersion is negatively and significantly associated with causal reasoning intensity. These results suggest that causal reasoning intensity provides incremental information about the relationship between financial performance outcomes and its causes, thereby reducing financial analysts’ information processing and interpreting costs and lowering overall analyst information uncertainty. Additionally, we find that decreases in analyst following are followed by more causal reasoning on performance disclosure. We also find that firms with a considerable increase of causal disclosure especially attract new analysts who already cover many firms. Overall, our evidence of the relationship between causal reasoning intensity and properties of analyst behaviour is consistent with the proposition that causal reasoning is a generic narrative disclosure quality characteristic, able to provide incremental information to analysts and guide analysts' behaviour.     

ABNORMAL RETURNS AND ANALYSTS' EARNINGS FORECAST REVISIONS ASSOCIATED WITH THE PUBLICATION OF “STOCK HIGHLIGHTS” BY VALUE LINE INVESTMENT SURVEY

We examine the reaction of stocks and the response of financial analysts' earnings forecasts to securities recommended as “Stock Highlights” by Value Line Investment Survey. Significant abnormal returns appear around the publication of stock highlights. Stock price responses are relatively efficient and permanent. Using earnings expectation data provided by the Institutional Brokers Estimate System, we find analysts raise their forecasts significantly following Value Line recommendations. Near-term forecast revisions are significantly related to stock returns at the time of the recommendations. Thus, an explanation for Value Line's security recommendation success is its ability to generate firm-specific earnings information.     

The supply of analysts and earnings forecasts

Using a unique set of hand-collected data, this study examines whether a larger supply of prospective analysts leads to existing financial analysts' higher earnings forecast accuracy. We analyze the effect of the analyst supply proxied by the number of universities and the number of finance and economics universities located in the same city as the headquarters of brokerage firms. Our findings suggest that brokerage firms located closer to a larger supply of potential analyst candidates are associated with higher analyst forecast accuracy, as observed by a lower forecast error. We further find that the degree of employed analysts' effort acts as a mediator between the supply of prospective analysts and the accuracy of their earnings forecasts.     

Relationship Incentives and the Optimistic/Pessimistic Pattern in Analysts' Forecasts

We examine whether analysts' incentives to maintain good relationships with management contribute to the optimistic/pessimistic within‐period time trend in analysts' forecasts. In our experiments, 81 experienced sell‐side analysts from two brokerage firms predict earnings based on historical information and management guidance. Analysts' forecasts exhibit an optimistic/pessimistic pattern across the two timing conditions (early and late in the quarter), and the effect is significantly stronger when the analysts have a good relationship with management than when their only incentive is to be accurate. Debriefing results indicate that analysts are aware of this pattern of forecasts, and believe that this benefits their future relationships with management and with brokerage clients. The analysts most frequently cite favored conference call participation and information access when describing benefits from maintaining good relationships with management. Our results suggest the following: The optimistic/pessimistic pattern in forecasts is in part a conscious response to relationship incentives, information access is perceived to be a major benefit of management relationships, and recent regulatory changes may have lessened but have not eliminated this conflict of interest source.     

Lead indicator models and UK analysts' earnings forecasts

This study examines the predictive ability of models which adjust random walk forecasts of corporate earnings, to incorporate past changes in economic lead indicators. The results suggest that changes in the broad money supply measure M4 contain predictive ability, beyond equivalent changes in other lead indicators or an individual firm's earnings. When forecasts from the broad-money model are compared with forecasts generated by financial analysts a size effect is evident: the superiority of analysts' forecasts is apparent much earlier for large firms than for small firms. This result is consistent with studies suggesting a size-related differential in the collection and dissemination of information by market participants.     

The use of management forecasts to dampen analysts' expectations by Chinese listed firms

This paper studies the use of management earnings forecasts (MEF) to dampen analysts' expectations, i.e. expectation management, by Chinese listed companies. We reveal several important findings: Firstly, information asymmetry is positively associated with the use of MEF to dampen analysts' expectations. State control has been found to moderate this relationship. Secondly, dampening analysts' expectations using MEF leads to negative stock return reactions and downward analysts' forecast revisions. Thirdly, the effectiveness of “pre-empting bad news through MEF” appears mixed and dependent on the information content of MEF and measures of actual earnings surprises. Finally, firms that disclose MEF are found to engage in more earnings management to meet the forecasts than firms that do not.     

Does revenue-expense matching play a differential role in analysts’ earnings and revenue forecasts?

This paper investigates whether matching has differential implications for the accuracy of analysts' earnings and revenue forecasts. We construct a novel measure of firm-level matching and document that matching improves analysts' earnings forecasts to a greater extent than their revenue forecasts. We also document matching's differential impact on analysts' earnings and sales forecasts by proposing a new count metric capturing a wedge in the accuracy of earnings and revenue forecasts. In additional tests, we report that the differential impact of matching is less (more) pronounced in a situation where the balance sheet (income statement) orientation likely dominates. We also report that matching's differential role is weaker (stronger) when firms have high intangible intensity (analysts have appropriate resources or expertise). In short window tests, matching's role in analysts' forecast revisions is more pronounced for earnings than sales forecasts. Overall, these results show how analysts benefit from better revenue-expense matching.     

Properties of Market Expectations of Accounting Earnings by Financial Analysts: UK versus US

This paper compares patterns and properties of financial analysts' forecasts (FAFs) of earnings between the UK and the US. Using 299 UK and 400 US firms from the same data source—Institutional Brokers Estimate System tapes—the accuracy of forecasts is examined and the sources of errors analysed. The results reveal that FAFs errors in the UK are much smaller than those in the US, and that this disparity persists even after controlling for firm size and industry effects. Further, FAFs of US firms demonstrate consistent overestimation of earnings over the sample period 1988–1990, while UK firms show overestimation in only one of three years. A model developed to test the determinants of forecast error reveals that forecasters of UK firms are influenced in a somewhat different fashion from those of US firms. While dispersion, predisclosure information (market capitalisation or number of analysts), and industry are common determinants of forecast error for both countries, the percentage of forecasts revised is a major determinant only for UK firms.     

Do Publicly Signalled Earnings Management Incentives Affect Analyst Forecast Accuracy?

Using a panel of listed Australian firms for the years 1999–2007, this paper investigates whether analysts' forecast efficiency is improved by the occurrence of a publicly observable event, such as a CEO appointment, which signals a firm's earnings management incentives. Two supporting hypotheses are also tested: first, that CEO appointments are associated with income‐decreasing earnings management; and second, that analyst forecast errors increase with the level of earnings management present in current period financial statements. Consistent with prior literature, we find income‐decreasing earnings management in the year of CEO appointment. Earnings management, as a general phenomenon, is found to be significantly related to analyst forecast errors in the period in which the earnings management occurs. However, we present evidence that analyst forecasts for current year earnings are significantly more accurate with respect to earnings management in cases where a CEO is appointed during the current financial period.     

Sources of earnings variability and their effect on earnings forecasts

Previous research shows that analysts’ forecasts of earnings do not fully incorporate information contained in reported earnings variability. This study investigates whether the inefficient forecast is because of a failure to incorporate observable information on two components of earnings variability: variability in operating performance and income smoothing. Our results show that analysts’ forecasts fully incorporate information contained in earnings variability for firms with high income smoothing and for firms with low operating variability. A smaller serial correlation of forecast errors is observed for firms with low operating variability, which suggests that analysts recognize the permanence in earnings for such firms.     

The role of accounting conservatism in management forecast bias

We investigate whether management earnings forecasts fully incorporate information in historical accounting conservatism. We find that management earnings forecasts are more optimistic for firms with greater accounting conservatism in the previous year. We further examine whether this conservatism-related optimistic bias in management earnings forecasts varies with managers’ difficulty predicting earnings accurately, managers’ opportunistic incentives, and the firms’ litigation risk. We find that the negative association between management forecast errors and conservatism increases, to various extent, with the firms’ operating cycles, earnings volatility, and the width of forecast range but does not change with proxies for opportunistic incentives or litigation risk. These results suggest that forecast difficulty is the primary reason for managers’ failure to incorporate conservatism fully in their earnings forecasts.     

Performance commitments and the properties of analyst earnings forecasts: Evidence from Chinese reverse merger firms

We investigate whether the performance commitments in Chinese reverse merger (RM) transactions affect the properties of analyst earnings forecasts. All RM firms in China are required to make performance commitments for a limited number of years after being publicly listed. As performance commitment is an important piece of public information, it can influence analysts' understanding of firms and their efforts to forecast earnings. Using manually assembled information on RM transactions, we find that, in comparison to the control firms, RM firms exhibit an increase in analyst forecast error and dispersion after the end of performance commitment. This effect is more pronounced in firms with lower levels of information transparency. We also document that the public information contents of analyst forecasts decrease and forecast revisions increase in the post-commitment period, while the private information content of analyst forecasts and the number of their firm visits remain unchanged. Overall, our findings suggest that analysts rely greatly on public information; they have important implications for academics and policymakers in understanding how performance commitments in RM transactions affect the market information environment.     

Mandatory disclosure of comment letters and analysts' forecasts

Comment letters (CLs) have been adopted as the main supervision mechanism for information disclosure by the two main Chinese stock exchanges since 2013. Both CLs and firms' responses have been publicly disclosed since the end of 2014. Using nonfinancial listed firms from 2013 to 2019 as our sample, we investigate the impact of CLs and their mandatory disclosure on analysts' forecast quality. The results show that, in the pre-disclosure period, there is no significant relation between CLs and analysts' forecast quality. However, in the post-disclosure period, CLs are positively (negatively) correlated with analysts' forecast accuracy (optimism). The quality of analysts' forecasts is much higher when CLs contain more questions. In addition, the impact of CLs is larger for samples with a lower percentage of star analysts or samples with higher earnings volatility. CL recipients tend to disclose more information on their internal and external risks, which can offer additional information to analysts.     

Analyst forecast,accounting conservatism and the related valuation implications

This study investigates whether financial analysts incorporate accounting conservatism into their earnings forecasts and whether it is more difficult for them to forecast earnings for less conservative firms, and then examines the impact of the findings on the return predictability of the value‐to‐price (V/P) ratio. After controlling for the other factors affecting forecast accuracy, such as earnings predictability and information uncertainty, I find that analysts incorporate accounting conservatism into their earnings forecasts and that forecasting earnings is more difficult for less conservative firms. Consequently, the return predictability of the V/P ratio is stronger for more conservative firms, and previously reported return predictability of the V/P ratio is an average across firms with differing levels of conservatism.     

Do Managers Always Know Better? The Relative Accuracy of Management and Analyst Forecasts

We examine the relative accuracy of management and analyst forecasts of annual EPS. We predict and find that analysts’ information advantage resides at the macroeconomic level. They provide more accurate earnings forecasts than management when a firm's fortunes move in concert with macroeconomic factors such as Gross Domestic Product and energy costs. In contrast, we predict and find that management's information advantage resides at the firm level. Their forecasts are more accurate than analysts’ when management's actions, which affect reported earnings, are difficult to anticipate by outsiders, such as when the firm's inventories are abnormally high or the firm has excess capacity or is experiencing a loss. Although analysts are commonly viewed as industry specialists, we fail to find evidence that analysts have an information advantage over managers at the industry level. The two have comparable abilities to forecast earnings for firms with revenues or earnings that are more synchronous with their industries.     

Financial Analyst Characteristics and Herding Behavior in Forecasting

This study classifies analysts' earnings forecasts as herding or bold and finds that (1) boldness likelihood increases with the analyst's prior accuracy, brokerage size, and experience and declines with the number of industries the analyst follows, consistent with theory linking boldness with career concerns and ability; (2) bold forecasts are more accurate than herding forecasts; and (3) herding forecast revisions are more strongly associated with analysts' earnings forecast errors (actual earnings—forecast) than are bold forecast revisions. Thus, bold forecasts incorporate analysts' private information more completely and provide more relevant information to investors than herding forecasts.     

Analysts' Forecasts Following Forced CEO Changes

This paper examines analysts' earnings forecasts during the period of uncertainty following a change of chief executive officer (CEO). It distinguishes between forced and non‐forced CEO changes, and examines whether analysts utilize their information advantage to reduce the heightened uncertainty of a forced change of CEO. Examining a sample of Australian companies followed by analysts between 1999 and 2009, we find that forecasting accuracy is lower and earnings forecasts are more optimistic for firms experiencing forced CEO turnover compared to firms not undergoing such a change. However, dispersion is not statistically different. The results suggest that forced CEO turnover events provide a challenge to the forecasting environment for analysts. During CEO changes, investors should be aware that forecasts are less accurate and have an optimistic bias.     

Natural disasters and analysts' earnings forecasts

The effects of natural disasters on capital markets have been investigated by limited evidence even though these calamities bring considerable damages or loss of life. To fill this gap, we investigate the impacts of natural disasters, particularly earthquakes, on security analysts' earnings forecasts for affected firms in China. We obtain three key findings. First, analysts' optimism significantly decreases for firms located in neighborhood areas. Second, earthquakes do not significantly affect firm earnings and stock returns, thereby indicating that post-earthquake analyst pessimism is not based on rational judgment. Third, media attention promotes irrational pessimism among analysts, and post-earthquake pessimism is a result of heuristics bias attributable to psychological shocks. However, analysts correct the bias after initial irrational forecasts. Taken together, our findings contribute to the broader psychology and economics literature on the effects of natural disasters on analyst forecasts.     

Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts

This paper compares the relative predictive ability of several statistical models with analysts' forecasts. It is one of the first attempts to forecast quarterly earnings using an autoregressive conditional heteroskedasticity (ARCH) model. ARCH and autoregressive integrated moving average models are found to be superior statistical forecasting alternatives. The most accurate forecasts overall are provided by analysts. Analysts have both a contemporaneous and timing advantage over statistical models. When the sample is screened on those firms that have the largest structural change in the earnings process, the forecast accuracy of the best statistical models is similar to analysts' predictions.