Forecast Revisions Research Articles

Are Individual Analysts’ Responses to Management Forecasts Conditioned by Managers’ Forecasting Track Records?

We examine the extent to which analysts’ responses to management forecasts are influenced by the forecasting track records of the managers issuing the forecast, distinct from the track records of their firms. We find that analysts’ forecast revisions in response to management forecast news, measured at the analyst level, are positively conditioned by the manager’s track record for accuracy and firm’s track records for accuracy and consistency. We also find that analyst sensitivity to the conditioning effect of managers’ track records for accuracy increases with analyst experience. Additional analyses reveal that the nature of analyst responses to management forecasts, in terms of agreement or disagreement, are also influenced by manager and firm track records. Further, we find that the market tends to follow analysts’ leads in responding to management forecasts, consistent with analysts being a channel through which the market is informed of the conditioning effect of managers’ track records for accuracy.

The effect of ASC 606 adoption on value relevance of revenues: Early evidence

This study examines the effect of adopting the principle-based revenue recognition standard, i.e., ASC 606, on the value relevance of reported revenues. We hand-collect data on the cumulative effects of ASC 606 adoption on retained earnings to identify materially impacted firms. We find that, compared to firms not materially impacted by ASC 606 adoption, the value relevance of reported revenues has improved for firms materially impacted based on long-window tests, while the informativeness of reported revenues has not improved for firms materially impacted based on short-window tests around quarterly earnings announcements. We also find that analysts' revenue forecast revisions have increased incrementally for firms materially impacted by ASC 606 adoption. Overall, our results suggest that ASC 606 adoption has generally increased the value relevance and informativeness of reported revenues.

Analyst forecast revision consistency and reversal: Evidence of stock market efficiency

AbstractThis study reexamines the information content and market price discovery associated with individual analysts' earnings forecast revisions. We refine the definition of high‐innovation revisions as high‐innovation consistent forecast revisions and high‐innovation reversal forecast revisions. Cross‐sectional analysis shows that high‐innovation reversal good (bad) news revisions, although having larger forecast changes, cause fewer upward (downward) post‐revision price shifts than high‐innovation consistent good (bad) news revisions. We also find that the market can distinguish which group of analysts provides more useful information about firms’ true value, but it takes some time for the market to digest this useful information.

Does Political Uncertainty Obfuscate Narrative Disclosure?

ABSTRACT We examine the relation between political uncertainty and narrative disclosure complexity in conference calls. Using firm-level political uncertainty, we find that political uncertainty is positively associated with firms’ disclosures complexity as measured by the Fog index. Decomposing complexity into two latent components—information and obfuscation—we show that political uncertainty significantly increases the obfuscation but has no impact on the information. Further analysis reveals that complex disclosure is motivated by expected poor future performance amid political uncertainty. We also show that, during periods of heightened political uncertainty, obfuscated disclosure is associated with reduced earnings informativeness, increased dispersion in analyst forecasts, and higher volatility in forecast revisions. These findings are robust to including and excluding sentences containing complex political bigrams when calculating Fog. Further evidence shows that, during periods of political uncertainty, managers tend to use a more ambiguous tone and provide scripted and shorter (longer) responses to analysts’ questions (presentations).

Firms’ economic growth expectations and reactions to micro and macro news in Japan

ABSTRACT This study investigates whether firms’ expectations of aggregate output growth overreact or underreact to micro and macro news by examining firm surveys conducted by a Japanese newspaper company, Nikkei. We capture micro news measured by forecast revisions and macro news measured by a new variable, the Japanese government’s growth forecast and that of professional forecasters’ consensus. We found that firms’ economic growth expectations overreact only to macro news. Our results indicate the heterogeneity of firm expectations formation across target variables and news types. Our findings suggest that firms excessively rely on government forecasts in turbulent times. The overreaction of firms’ output growth expectations to the government’s forecast may be associated with their lower performance, and that the government’s macroeconomic forecasts play a role in macroeconomic performance through firms’ expectation channels.

Are professional forecasters inattentive to public discussions about inflation? The case of Argentina

AbstractWe evaluate whether professional forecasters incorporate valuable information from public discussions on social media. The study covers the case of inflation in Argentina for the period 2016–2022. We find solid evidence consistent with inattention. A simple indicator of attention to inflation on social media is shown to anticipate professional forecast errors. A one standard deviation increment in the indicator is followed by a rise of 0.4% in mean forecast errors in the subsequent month and by a cumulative increment of 0.7% over the next 6 months. Furthermore, social media content anticipates significant revisions in forecasts that target multiple months ahead inflation and calendar year inflation. These findings are different from previously documented forms of inattention. Consistent results are verified by implementing out‐of‐sample forecasts and using content from an alternative social network. The study has implications for the use of professional forecasts in the context of policymaking and sheds new evidence on the nature of imperfect information in macroeconomics.

Heterogeneous overreaction in expectation formation: Evidence and theory

Using firm-level earnings forecasts and managerial guidance data, we construct guidance surprises for analysts, i.e., differences between managerial guidance and analysts' initial forecasts. We document new evidence on expectation formation: (i) analysts overreact to managerial guidance and the overreaction is state-dependent, i.e., it is stronger for negative guidance surprises but weaker for surprises that are larger in size; and (ii) forecast revisions are neither symmetric in guidance surprises nor monotonic. We organize these facts with a model where analysts are uncertain about the quality of managerial guidance. We show that a reasonable degree of ambiguity aversion is necessary to account for the documented heterogeneous overreaction pattern.

Local information advantage and stock returns: Evidence from social media

AbstractWe examine the information asymmetry between local and nonlocal investors with a large dataset of stock message board postings. We document that abnormal relative postings of a firm, that is, unusual changes in the volume of postings from local versus nonlocal investors, capture locals' information advantage. This measure positively predicts firms' short‐term stock returns as well as those of peer firms in the same city. Sentiment analysis shows that posting activities primarily reflect good news, potentially due to social transmission bias and short‐sales constraints. We identify the information driving return predictability through content‐based analysis. Abnormal relative postings also lead analysts' forecast revisions. Overall, investors' interactions on social media contain valuable geography‐based private information.

Expectation-driven boom-bust cycles

Using data from the Survey of Professional Forecasters, we observe that a large fraction of analysts’ expectations about future economic growth is not due to technology or other shocks to fundamentals measured by the business cycle literature. We find that these unexplained changes in forecast revisions predict significant boom-bust dynamics in the key macroeconomic aggregates. We offer a novel theory where boom-bust dynamics stem from expectation shocks orthogonal to fundamentals.

On the informativeness of unexpected exclusions from street earnings

AbstractExclusions from street earnings can include both expected exclusions, forecasted ex ante by analysts, and unexpected exclusions, revealed after earnings are reported. While prior research largely examines total exclusions from street earnings, unexpected exclusions reflect the news or surprise in exclusions. We investigate the properties and informativeness of unexpected exclusions for future profitability, benchmark beating, analyst forecast errors, and future stock returns. We find that unexpected exclusions represent a mix of transitory and recurring items and are informative about future street earnings. In an analysis of hand‐collected analysts' reports, we find that unexpected exclusions are more likely to reflect misestimated recurring items when analysts forecasted exclusions, and unexpected transitory items when analysts did not forecast exclusions. We also examine benchmark‐beating behavior, in which street earnings meet street forecasts but GAAP earnings miss GAAP forecasts. We observe that benchmark beating is more likely to occur when analysts forecast exclusions than when they do not. Moreover, we find unexpected exclusions are more persistent when street earnings meet street forecasts but GAAP earnings miss GAAP forecasts. These findings are consistent with recurring earnings amounts being opportunistically shifted to excluded items to meet analysts' street forecasts. Finally, we find some evidence that analysts and investors react to, but do not fully incorporate, the information in unexpected exclusions, based on forecast revisions and stock price reactions.

Monetary Policy Shocks: Data or Methods?

Different series of high-frequency monetary shocks can have a correlation coefficient as low as 0.5 and the same sign in only two-thirds of observations. Both data and methods drive these differences, which are starkest when the federal funds rate is at its effective lower bound. Methods that exploit the differential responsiveness of short- and long-term asset prices can incorporate additional information. After documenting differences in monetary shocks, we explore their consequence for inference. We find that empirical estimates of monetary policy transmission from local projections and VARs are less affected by shock choice than forecast revision specifications.

NEWS AND INFORMATION RIGIDITY: FURTHER EVIDENCE FROM GDP GROWTH FORECASTS

The study investigates the existence and the extent of information rigidity in gross domestic product (GDP) growth forecasts among 25 developed and 18 developing economies during 2002–2017 period utilizing a survey dataset never explored before on this issue. In general, the study finds ample evidence of information rigidity. However, it is not as common and as severe as it is in earlier studies. Our study also finds ample evidence of overreaction to new information. Information rigidity is present during the recession period for the developed countries, and we find some evidence of information gathering picking up during the recession period. Overall, we notice less forecast rigidity or inefficiency in our study compared to some earlier studies. Our multi-country test results show that forecast revisions depend on both own country and cross-country lagged revisions. In general, forecast revisions of developed economies, especially USA, prompt revisions in other developed and developing economies. Similarly, some developing countries’ forecast revisions prompt revisions in other developed and developing countries although at smaller magnitudes. Therefore, we confirm from earlier studies that one source of forecast rigidity is not to incorporate overseas events in forecast revisions quickly and completely.

Tone complexity and analyst forecast behaviors: evidence from earnings conference calls

PurposeBoth the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.Design/methodology/approachUsing approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.FindingsConsistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.Originality/valueThis study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

Market Reaction to CEOs’ Dynamic Hemifacial Asymmetry of Expressions

Neuropsychological studies propose that listeners unconsciously assess speakers’ trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOs’ dynamic hemifacial asymmetry of expressions (HFAsy) shown in CNBC’s video interviews about corporate earnings. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that (1) the stock market reacts negatively to the CEO’s HFAsy shown in the interview video, and (2) the market reaction to favorable earnings news is negatively associated with the CEO’s HFAsy. We also find a significantly positive association between the short-window abnormal bid-ask spreads and HFAsy, suggesting a larger investor disagreement for firms with high-HFAsy CEOs. Furthermore, we show that analyst forecast revisions are negatively associated with CEOs’ HFAsy. Our results are robust to conducting a falsification test using the previous quarter as a pseudo-event, employing an alternative measure of HFAsy, and using more granular trading data at the second level. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appearing on CEOs’ faces. This paper was accepted by Brian Bushee, accounting. Funding: Financial support from the National Natural Science Foundation of China [Grants 71772123, 72132006, 72172038, 72241425, 72273088, 72372103, and 72310107002], the National Social Science Foundation of China [Grant 23ZDA039], Fudan University (Sci-Tech Innovation Foundation of School of Management), and the Research Fund of Shanghai Data Exchange Corporation is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4922 .

Why does operating profitability predict returns? New evidence on risk versus mispricing explanations

AbstractThis study develops new evidence on risk versus mispricing explanations of the well‐known profitability premium. First, we examine whether exposure to expected downside risk is a plausible explanation. We find that high profitability is associated with both lower ex ante and ex post probabilities of future price crashes. Thus, less profitable firms exhibit greater downside risk than highly profitable firms, making a downside risk explanation implausible. Although this fact is overlooked by the market in general, it is anticipated by options traders; we find that put options of low profitability firms are relatively more expensive. Simultaneously, these firms do not exhibit greater probability of jumps, indicating that volatility(risk)‐based explanations for the profitability premium are unlikely to be descriptive. Second, we find that the sticky‐expectations model of Bouchaud et al. (2019, The Journal of Finance, 74, 639–674) only partially explains the profitability premium. While on average, analysts' forecast revisions correct in the same direction as recent profitability, the profitability premium still exhibits a strong relationship to the non‐sticky component of analysts' forecast revisions. Third, institutional investors trade profitability‐based signals but do so with a delay, likely contributing to the premium. Overall, our evidence favours the explanation that the profitability premium is related to investor mispricing of potential downside risk and provides greater clarity on recent findings in the literature.

Analyst ability and research effort: non-EPS forecast provision as a research quality signal

The range of non-EPS forecast types provided by individual analysts to I/B/E/S has increased dramatically over time but varies considerably across firms. We propose that in providing a broader range of forecast types, analysts can signal superior research ability and research effort. Consistent with this hypothesis, we document positive associations between the number of forecast types (NFT) an analyst provides and common proxies for research quality, including earnings forecast accuracy, price target accuracy, stock recommendation profitability, market reactions to stock recommendation revisions, and analyst career outcomes. The effects of NFT are incremental to other quality proxies used in the literature and are distinct from the issuance of specific non-EPS forecast types studied previously (e.g., cash flow forecasts). We demonstrate the information value of NFT to investors by examining the out-of-sample performance of portfolios formed conditional on NFT and exploiting revisions in consensus earnings forecasts and individual analysts’ stock recommendations. We conclude that the number of forecast types an analyst provides for a firm is a readily available proxy for the quality of her research.

A Tale of Two Forecasts: An Analysis of Mandatory and Voluntary Effective Tax Rate Forecasts

ABSTRACT Disclosure theory predicts that the likelihood of voluntary disclosures increases with the noise level in mandatory disclosures. We test this prediction by exploiting a unique setting where firms simultaneously provide two forecasts of the same metric—annual effective tax rates (ETRs). We find that managers are more likely to issue voluntary ETR forecasts when mandatory ETR forecasts contain more noise due to tax complexity, suggesting that managers resort to voluntary disclosure when mandatory disclosure constrains their ability to convey private information. Using analysts’ ETR forecast revisions to assess the informativeness of the two ETR forecasts, we find that both forecasts are incrementally informative. In addition, analysts weight voluntary ETR forecasts more heavily, especially when voluntary ETR forecasts are non-GAAP based and when discrete items are present. Overall, we provide evidence on the relation between and the informativeness of voluntary and mandatory disclosures by examining two competing forecasts issued simultaneously.

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.

The Effect of Analyst Conservatism on Meeting the Consensus via Earnings Management

SYNOPSIS Conservative analysts react more to negative news than positive news, and the market response is greater for forecast revisions from conservative analysts (Hugon and Muslu 2010; Keskek and Tse 2018). Little is known about how firms and managers respond to a lower benchmark resulting from having a more conservative analyst following. We examine the effect of analyst conservatism on firms just meeting or beating the benchmark via accrual-based earnings management. We find that firms with a more conservative analyst following have lower earnings benchmarks and are more likely to just meet or beat the consensus. Additionally, these firms meet the lower benchmark with lower levels of earnings management, with this effect being strongest in poor information environments. Collectively, our results suggest that management’s benchmark meeting behavior is impacted by the conservatism of the firm’s analyst following. JEL Classifications: G14; G24; M40; M41.

Do analysts anchor on public signals in forecasting the target price of disruptive technology firms?

Public signals serve as an anchor for higher order beliefs when forecasting stock price, a target that is determined by the average beliefs of investors. We predict that the anchoring effect is particularly strong when forecasting the stock price of disruptive technology firms, a fundamentally ambiguous target. Consistent with this prediction, we find that analysts’ price forecasts revisions are more responsive to earnings news for disruptive than non-disruptive technology firms. In contrast, this differential responsiveness does not exist for earnings forecasts, as earnings realizations do not depend on the average beliefs of investors.