Air pollution,affect, and forecasting bias: Evidence from Chinese financial analysts

We document a negative relation between air pollution during corporate site visits by investment analysts and subsequent earnings forecasts. After accounting for analyst, weather, and firm characteristics, an extreme worsening of air quality from “good/excellent” to “severely polluted” is associated with a more than 1 percentage point lower profit forecast, relative to realized profits. We explore heterogeneity in the pollution-forecast relation to understand better the underlying mechanism. Pollution only affects forecasts that are announced in the weeks immediately following a visit, indicating that mood likely plays a role, and the effect of pollution is less pronounced when analysts from different brokerages visit on the same date, suggesting a debiasing effect of multiple perspectives. Finally, there is suggestive evidence of adaptability to environmental circumstances – forecasts from analysts based in high pollution cities are relatively unaffected by site visit pollution.     

Analyst characteristics, timing of forecast revisions, and analyst forecasting ability

We first examine whether analysts with certain characteristics that prior research has identified are related to superior forecasting ability systematically time their forecast revisions later in the fiscal quarter. We then examine whether this superior ability persists after controlling for the timing advantage by using relative forecast error, a measure that largely eliminates the timing advantage of recent forecasts. Using a sample of quarterly earnings forecast revisions over the 20-year period from 1990 to 2009, we find that analysts with more firm-specific and general experience and more accurate prior-period forecasts, analysts employed by larger brokerage firms, and analysts who follow fewer industries and companies tend to revise forecasts later in the quarter. We also find that analyst characteristics that are positively correlated with revision timing are negatively related to relative forecast errors. These results are consistent with analyst characteristics being useful proxies for analyst forecasting ability and analysts with greater ability revising forecasts later in the quarter.     

Range has it: decoding the information content of forecast ranges

Range forecasts have emerged as the predominant form of management forecasts, but prior research has overlooked the information conveyed by forecast ranges. This study fills this void by examining the information content of the extent to which managers’ forecast ranges overlap with the range of individual analysts’ pre-existing estimates (i.e., overlap). We expect managers to signal their superior private information by issuing low-overlap forecasts. We predict and find that, compared with high-overlap forecasts, low-overlap forecasts are associated with stronger market reactions and higher accuracy of management forecasts relative to analyst estimates. Moreover, when responding to low-overlap management forecasts, analysts with prior estimates out of management forecast ranges are more likely to revise into the management forecast range, less likely to revise toward the consensus, and more likely to improve in revised forecast accuracy. Our findings suggest that investors and analysts view low-overlap management forecasts as signals of superior private information.     

Analyst reputation and management earnings forecasts

Prior studies show that analysts with high reputation are influential in the market. This paper examines whether managers consider analyst reputation in shaping their voluntary disclosure strategy. Using Institutional Investor magazine’s All-American (AA) rankings as a proxy for analyst reputation, we find that the coverage of AA analysts is positively associated with the likelihood of quarterly management earnings forecasts (MEFs). We also find that AA analysts’ forecast optimism is more positively associated with the likelihood of MEFs than non-AA analysts’ forecast optimism when the firm is covered by AA analysts. Analyses based on AA analyst coverage changes and AA status changes confirm the relation between analyst reputation and MEFs. We further find that analyst reputation influences other MEF properties, such as forecast news, bias, and revisions, and that our results are robust to alternative measures of analyst reputation. Further analyses show that market reactions at quarterly earnings announcements are more positive (negative) when firms meet/beat (miss) AA analysts’ forecasts than when firms meet/beat (miss) non-AA analysts’ forecasts. Collectively, our findings suggest that managers strategically provide voluntary forecasts by taking into account the reputation of individual analysts following their firms.     

When does analyst reputation matter? Evidence from analysts’ reliance on management guidance

We investigate the joint effects of analyst reputation, uncertainty and guidance news valence on analysts’ reliance on management guidance. We find that, compared to less reputable analysts, reputable analysts rely less on guidance when they issue earnings forecasts. This analyst reputation effect is stronger when earnings and information uncertainty are higher or when the guidance contains good news. Further analysis suggests that both reputable and less reputable analysts sacrifice their forecast accuracy when they rely less on guidance; however, reputable analysts are compensated to a greater extent by the increased informativeness of their forecasts. Finally, we find that analysts’ future career advancement is enhanced when their reliance is low.     

Determinants of analysts’ revenue forecast accuracy

This study investigates financial analysts’ revenue forecasts and identifies determinants of the forecasts’ accuracy. We find that revenue forecast accuracy is determined by forecast and analyst characteristics similar to those of earnings forecast accuracy—namely, forecast horizon, days elapsed since the last forecast, analysts’ forecasting experience, forecast frequency, forecast portfolio, reputation, earnings forecast issuance, forecast boldness, and analysts’ prior performance in forecasting revenues and earnings. We develop a model that predicts the usefulness of revenue forecasts. Thereby, our study helps to ex ante identify more accurate revenue forecasts. Furthermore, we find that analysts concern themselves with their revenue forecasting performance. Analysts with poor revenue forecasting performance are more likely to stop forecasting revenues than analysts with better performance. Their decision is reasonable because revenue forecast accuracy affects analysts’ career prospects in terms of being promoted or terminated. Our study helps investors and academic researchers to understand determinants of revenue forecasts. This understanding is also beneficial for evaluating earnings forecasts because revenue forecasts reveal whether changes in earnings forecasts are due to anticipated changes in revenues or expenses.     

The characteristics of individual analysts’ forecasts in Europe

This study investigates the determinants of financial analysts’ forecasts differential accuracy in 14 different European stock markets. Using the I/B/E/S Detail History Database, I find that European financial analysts forecast accuracy is positively associated with analyst firm specific experience. Forecast accuracy is negatively associated with the number of countries followed by analysts and the age of the forecast. Surprisingly, I find no relationship between forecast accuracy and analysts’ job experience and the size of the bank employing the analyst.     

Disclosure of internal control material weaknesses and optimism in analyst earnings forecasts

This paper investigates how analyst forecast optimism is associated with disclosures of internal control material weaknesses (ICMWs) and their remediation under Section 404 of the Sarbanes–Oxley Act (SOX). Drawing on agency theory, I hypothesize that analysts are likely to issue earnings forecasts that are more optimistic for firms with ICMW disclosures than for those without ICMW disclosures. Using a sample of 20,875 firm-year observations with 10-K (10-Q) reports from 2004 to 2018, I find a positive association between ICMW disclosures and analyst forecast optimism. This positive association is partially driven by investors’ inability to unravel analyst forecast bias and analysts’ intentions to curry favor with management for private information. In addition, analysts are found to issue less optimistic forecasts for firms with ICMW remediation disclosures compared with those without ICMW remediation disclosures. A series of propensity score matching and regression analyses are conducted to test the robustness of my inferences. Overall, the paper suggests that analysts have incentives to take the opportunity of firms disclosing ICMWs to bias their forecasts upward for self-interest. The findings have the potential to assist regulators in guiding analyst behavior and educating investors to unravel positive bias in analyst forecasts.     

Do product market threats affect analyst forecast precision?

We examine how product market threats influence the precision of analyst forecasts. Greater competitive threats may make forecasting more difficult by increasing the uncertainty regarding future cash flows and by influencing the quality of financial disclosure. Using a firm-specific measure of product market threats (i.e., fluidity), we find that analysts are more likely to be less precise forecasting earnings for highly fluid firms and that the lack of precision is not fully explained by performance volatility. Our findings further suggest that firms with fluid products have lower accruals quality and that they are more likely to withheld information regarding contract terms and sales from major customers. Cross-sectional analysis further suggests that the effect of fluidity on analyst forecasts is more pronounced when firms have flexibility in disclosure choices. Using significant changes in tariff rates as a quasi-natural experiment, we find that analyst forecast precision is significantly lower following tariff reductions.     

Analyst Pessimism and Forecast Timing

In this study, we show that on average relatively pessimistic analysts tend to reveal their earnings forecasts later than other analysts. Further, we find this forecast timing effect explains a substantial proportion of the well‐known decrease in consensus analyst forecast optimism over the forecast period prior to earnings announcements, which helps explain why analysts’ longer term earnings forecasts are more optimistically biased than their shorter term forecasts. We extend the theory of analyst self‐selection regarding their coverage decisions to argue that analysts with a relatively pessimistic view–compared to other analysts–are more reluctant to issue their earnings forecasts, with the result that they tend to defer revealing their earnings forecasts until later in the forecasting period than other analysts.     

High-Technology Intangibles and Analysts' Forecasts

This study examines the association between firms' intangible assets and properties of the information contained in analysts' earnings forecasts. We hypothesize that analysts will supplement firms' financial information by placing greater relative emphasis on their own private (or idiosyncratic) information when deriving their earnings forecasts for firms with significant intangible assets. Our evidence is consistent with this hypothesis. We find that the consensus in analysts' forecasts, measured as the correlation in analysts' forecast errors, is negatively associated with a firm's level of intangible assets. This result is robust to controlling for analyst uncertainty about a firm's future earnings, which we also find to be higher for firms with high levels of internally generated (and expensed) intangibles. Given that analyst uncertainty increases and analyst consensus decreases with the level of a firm's intangible assets, we also expect and find that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst's forecast increases with a firm's intangible assets. Finally, additional analysis reveals that lower levels of analyst consensus are associated with high-technology manufacturing companies, and that this association is explained by the relatively high R&D expenditures made by these firms. Overall, our results are consistent with financial analysts augmenting the financial reporting systems of firms with higher levels of intangible assets (in terms of contributing to more accurate earnings expectations), particularly R&D-driven high-tech manufacturers.     

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.     

The Effects of Disclosure and Analyst Regulations on the Relevance of Analyst Characteristics for Explaining Analyst Forecast Accuracy

We posit and find an effect of disclosure and analyst reporting regulations implemented from 2000 through 2003 (including Regulation Fair Disclosure, the Sarbanes‐Oxley Act and the Global Settlement Act) on the importance of analyst and forecast characteristics for analyst forecast accuracy. Following the enactment of these regulations, more experienced analysts and All‐Star analysts do not maintain their superior forecast accuracy, and analysts employed by large brokerage houses perform worse than other analysts. In addition, we find a decrease in the importance of analyst effort, the number of industries and firms followed, days elapsed since the last forecast, and forecast horizon. While the importance of bold upward forecast revisions does not change, bold downward revisions lose their relevance for forecast accuracy after 2003. Finally, we find an increase in the importance of prior forecast accuracy. We find that the importance of these characteristics varies with the precision of publicly available information. Specifically, the decrease in the importance of most analyst and forecast characteristics and the increase in the importance of prior forecast accuracy are greater when the precision of publicly available information is low. Overall, our results suggest that the positive effects of experience, effort, brokerage house size and All‐Star status on forecast accuracy in the pre‐regulation period were because of the information advantages that these analysts enjoyed (rather than their ability to generate private information). In contrast, our results suggest that prior forecast accuracy is related to analysts’ ability to generate private information.     

The effect of repeat restructuring charges on analysts’ forecast revisions and accuracy

The primary objective of this study is to examine the effect of prior restructuring charges on analyst forecast revisions and accuracy. We find evidence that analysts respond differently to first-time restructuring firms than to repeat restructuring firms. Analysts revise their forecasts of both one-year-ahead earnings and five-year long-term growth in earnings more negatively for first-time restructuring firms than for firms with prior charges. When we examine forecast errors in the year subsequent to the restructuring, we find that current charges complicate analysts’ earnings forecast task. We further find that the decline in analyst forecast accuracy is mitigated by prior charges within past two years.     

Is Working Capital Information Useful for Financial Analysts? Evidence from China

Financial analysts are important information intermediaries in the capital market. This study investigates whether information about working capital management is useful for financial analysts of Chinese firms. With a sample of listed companies from 2004 to 2014, we find that the efficiency of working capital management is positively associated with the number of analyst following and analyst forecast accuracy, and negatively associated with analyst forecast dispersion. Specifically, when the cash conversion cycle becomes longer, number of analyst following and the accuracy of their mean forecasts decrease, while the forecast dispersion increases. The findings of this study indicate a potential mechanism through which information about working capital management is incorporated in stock price in emerging markets such as China.     

Analysts’ accrual-related over-optimism: do analyst characteristics play a role?

Bradshaw et al. (J Acc Res 39:45–74, 2001) find that analyst forecast over-optimism is greater for firms with high accruals. This “accrual-related over-optimism” is generally interpreted as evidence that analyst forecasts do not fully incorporate predictable earnings reversals associated with high accruals. We investigate whether analyst experience, access to resources (brokerage size), and portfolio complexity moderate the relation between over-optimistic forecasts and high accruals. We demonstrate the robustness of accrual-related over-optimism to controls for cash flow and prior forecast errors. We find that accrual-related over-optimism is lower for analysts with greater general experience and for analysts following fewer firms but find only limited evidence of lower accrual-related over-optimism for analysts from larger brokerages and for analysts following fewer industries.     

Do analysts who understand accounting conservatism exhibit better forecasting performance?

This study investigates the performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., asymmetric forecast timeliness) with the asymmetric timeliness of firms’ reported earnings (i.e., asymmetric earnings timeliness). We find that better timeliness‐matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better timeliness‐matching analysts issue less biased earnings forecasts, more profitable stock recommendations and have more favorable career outcomes. Overall, our results indicate that analysts’ ability to incorporate conditional conservatism into their earnings forecasts is an important reflection of analyst expertise and professional success.     

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.     

Political contributions and analyst behavior

We show that the personal traits of analysts, as revealed by their political donations, influence their forecasting behavior and stock prices. Analysts who contribute primarily to the Republican Party adopt a more conservative forecasting style. Their earnings forecast revisions are less likely to deviate from the forecasts of other analysts and are less likely to be bold. Their stock recommendations also contain more modest upgrades and downgrades. Overall, these analysts produce better quality research, which is recognized and rewarded by their employers, institutional investors, and the media. Stock market participants, however, do not fully recognize their superior ability as the market reaction following revisions by these analysts is weaker.     

Qualitative audit materiality and earnings management

This study investigates auditors’ propensity to rely on quantitative materiality thresholds to the exclusion of qualitative materiality thresholds. Specifically, we examine whether auditors are more likely to allow earnings management that is less than typical quantitative materiality thresholds but that nonetheless is qualitatively material. We use changes in tax expense as a proxy for earnings management. Our results indicate that companies with pre-managed earnings that would have missed the consensus analyst forecast are more likely to decrease their tax expense when the magnitude of the decrease is less than quantitative audit materiality thresholds. The results also indicate that firms are more likely to meet or beat the forecast when the amount of earnings management necessary to meet the analyst forecast is less than quantitative materiality. These results are consistent with auditors relying on quantitative materiality thresholds to the exclusion of qualitative materiality thresholds, i.e., the importance of meeting or beating the analyst forecast. Finally, we find that the ability to use tax expense reduction within quantitative materiality to meet or beat analysts’ consensus forecasts was significantly reduced by the SEC’s guidance on materiality in SAB-99 and by the passage of the Sarbanes–Oxley Act.