Analyst Forecasts Research Articles

Information or Noise? Examining the Effect of Discretionary Disclosure of Accounting Estimates on Financial Analyst Forecasts

Whether discretionary disclosure of accounting estimates increases or decreases the decision usefulness of financial information is a fundamental issue in accounting and finance. Our paper speaks to this issue by examining the effect of discretionary disclosure of accounting estimates on the forecasting behaviour of financial analysts, who are sophisticated users of financial information and play a crucial role as an information intermediary in the capital markets. We find that discretionary disclosure of accounting estimates (made in the 10‐K management discussion and analysis and financial statement footnotes) reduces analyst forecast errors and analysts’ tendency to round their annual earnings forecasts. These effects are more pronounced when analysts issue forecasts for companies with high accounting reporting complexity. Overall, the results suggest that discretionary disclosure of accounting estimates reduces information costs by providing relevant information for analysts’ forecasting decisions.

From Hindsight to Foresight: Understanding and Overcoming Bias in Financial Forecasts

Abstract This study conducts a comprehensive benchmarking analysis on analyst forecasts of revenue and EBITDA for exchange-traded public companies in the U.S. spanning the period from 2009 to 2022. Our findings reveal a consistent and statistically significant upward bias present in both revenue and EBITDA forecasts, on average, as evidenced by various measures of forecast error. This bias is explored at both market sector and market-wide levels of aggregation to check for macro-consistency. Recognizing the significance of these biases, we propose bias-adjustment frameworks aimed at enhancing the accuracy of future analyst forecasts pertaining to revenue and EBITDA. The implications of our study underscore the importance of mitigating such biases for improved forecasting precision in financial markets.

Does analyst forecast informativeness affect managers’ financial reporting incentives?

This study investigates how the informativeness of analyst forecasts affects managers’ financial reporting incentives. Using a novel Match Index to estimate the earnings management induced by analyst forecasts, we find that when analyst forecasts are less informative, managers place less value on using them as a benchmark and thus, engage in less earnings management to meet that benchmark.

Analyst network centrality, forecast accuracy, and persistent influence

ABSTRACT This paper explores how analysts’ forecasting behaviour is related to their centrality within a dynamic information network. In this network, analysts who issued coverage reports on the same listed firms in clusters are connected. The social learning hypothesis and social capital theory suggest that financial analysts could learn from other analyst forecasts and obtain information from analyst reports. Employing a dynamic complex network methodology, we focus on analysts’ network centrality – degree, betweenness, and closeness – to represent their information access based on a sample of 819,539 analyst forecasts in the Chinese A-share market from 2018 to 2022. Our findings suggest that analysts with more central positions in the network produce more accurate earnings-per-share forecasts and have a longer persistent influence on other analysts. Our results support the perspective that the diffusion of information among analysts affects their forecasts and reporting behaviour.

Does data asset disclosure contribute to the market efficiency? Evidence from China

Information regarding firms' data assets and stock pricing has been more closely related when data is integrated into firms' value creation processes. However, since data asset disclosure can also be engaged in impression management by firms, its contribution to market efficiency may be impeded. Using a sample of Chinese A-share listed firms from 2014 to 2020, this study examines the impact of increased data asset information disclosure on the subsequent stock price volatility. The findings suggest that more data asset disclosure reduces stock return idiosyncratic volatility. The mechanism analysis shows that data asset disclosure reduces idiosyncratic volatility of stock prices by mitigating analyst forecast dispersion and noise trading induced by buy-sell imbalances. Moreover, this mitigating effect is more pronounced in firms with more analyst following, observable digital investments, and higher liquidity. But the effect is diminished if firms use a more positive abnormal tone in their annual reports.

Environmental, social and governance performance and equity mispricing: Does embedded information mediation matter?

We examine how environmental, social and governance (ESG) performance influences equity mispricing, based on a sample of listed firms in China from 2009 to 2022. We find that ESG performance alleviates equity mispricing, and the findings remain consistent after several robustness checks. We also analyze the role of different types of embedded information mediation (e.g., analysts, institutional investors, and auditors) in the relationship between ESG performance and equity mispricing. We find that ESG performance can alleviate equity mispricing by improving the accuracy and consistency of analyst forecasting, rational investment of institutional investors, and audit quality. We also find that ESG inconsistency over time can hinder the negative relationship between ESG performance and equity mispricing.

Climate Disasters and Analysts’ Earnings Forecasts: Evidence from the United States

ABSTRACT We examine the relationship between climate disasters and analysts’ earnings forecasts in the United States. We find that climate disasters are associated with deteriorated analyst forecast properties proxied by forecast errors and forecast dispersion. We reason that the volatility of return on assets and of cash flows, and lower financial statement comparability, are three potential channels through which climate disasters influence analyst forecast properties. We also find that this relationship is more pronounced for firms in climate-vulnerable industries. Results from the market reaction tests further support our main findings by showing that the stock market responds less strongly to positive earnings surprises during periods of high climate disasters. Our results are robust to a battery of sensitivity tests, including a two-stage least squares approach and a difference-in-differences specification. Overall, the results shed light on the association between climate disasters and analysts’ earnings forecasts, which has significant implications for academics, investors, and standard setters.

Accounting for bubbles: A discussion of Arif and Sul (2024)

A signal that identifies asset pricing bubbles would be valuable so investors could reposition their portfolios to weather the bubble, yet ex ante bubble identification has proven illusory. Arif and Sul (2024) study whether industry-level investment in net operating asset (NOA) accruals is an ex ante accounting-based signal that predicts bubbles. Using industry-level price run-ups across 49 countries, they find that NOA accruals predict a higher likelihood of a future crash, lower future returns, and larger analyst forecast errors, especially following run-up periods. The authors attribute these patterns to sentiment-driven overinvestment. My discussion summarizes the contributions of Arif and Sul's findings to the asset pricing bubbles, behavioral finance, and aggregate accruals literatures. I also outline empirical challenges faced by studies investigating bubbles and recommend approaches to further strengthen inferences. Finally, I propose opportunities for future research to integrate deeper accounting knowledge into bubble research.

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).

A Study on Disclosure of Corrections and Financial Analyst Forecasts

A Study on Disclosure of Corrections and Financial Analyst Forecasts

Digital Technology Innovation, Surplus Management and Analyst Forecast Bias

With the rapid development of digital economy, digital technology, as its technical support, has an important impact on market information environment and analysts' forecast deviation. Using the data of listed companies from 2007 to 2021, this paper investigates the impact of corporate digital technology innovation on analyst forecast bias. We find that enterprise digital technology innovation has a restraining effect on analyst forecast bias, and this conclusion is still valid after a series of robustness tests. Heterogeneity analysis shows that this effect is more obvious in firms with low media attention, investor attention and institutional ownership. Mechanism analysis shows that digital technology innovation inhibits firms' earnings management behavior, thus reducing analyst forecast bias. These results are consistent with the logic that digital technology innovation enables managers to use visual earnings management to suppress earnings manipulation and reduce analyst forecast bias. The conclusion of this paper expands the research scope of the economic effect of technological innovation in the digital era, and has certain significance for enterprises to optimize digital innovation strategy and improve market transparency.

Managing Climate Change Risks: Sea-Level Rise and Mergers and Acquisitions

Using a large sample over the period 1986 to 2017, we show that companies with higher exposure to climate change risk induced by sea-level rise (SLR) tend to acquire firms that are unlikely to be directly affected by SLR. We find that acquirers with higher SLR exposure experience significantly higher announcement-period abnormal stock returns. Analyses using failed merger bids as an exogenous shock show that post-merger, analyst forecasts become more accurate and environmental-related as well as overall ESG scores improve.

Synchronicity and Sentiment: Decoding Earnings Quality and Market Returns in EU under Economic Policy Uncertainty

This study investigates the impact of economic policy uncertainty on earnings quality and market returns in the European Union, a topic of growing importance in the wake of fluctuating global economic conditions. Using data from 14 EU countries spanning the period from 2000 to 2022, the findings of this study are significant, revealing a clear correlation between heightened economic policy uncertainty and altered investor behavior, as well as a deterioration in earnings quality. We also found that (a) stock market returns are threatened by the shroud of economic uncertainty, (b) higher market sentiment anticipates a negative return but, on the other hand, lower market sentiment expects an increase in the return, (c) stock market returns are higher in the case of highly accurate analyst forecasts, and (d) stock market returns are higher in the case of a higher level of stock return synchronicity. These insights have profound implications for investors, policymakers, and scholars, offering a deeper understanding of market dynamics under uncertainty and guiding more informed decision-making in turbulent economic times.

The effects of manager sentiment in financial disclosure: Perspectives of operational efficiency and market reaction

This study constructs a measure of managerial optimism using textual and nonparametric estimation methods, exploring its impact on company operational efficiency and market reactions. Findings indicate that managerial optimism positively influences stock price returns over the subsequent year by enhancing internal operational efficiency and reducing analyst forecast dispersion, with a more pronounced effect in state-owned enterprises. Utilizing information from the MD&A (Manager Discussion and Analysis) section of annual reports, this research demonstrates the role of managerial optimism in company operations and market reactions, offering empirical evidence for corporate behavioral finance research and practical implications for management practice.

Learning by doing or catering: Firm-specific experience and analyst forecast accuracy

This article explores catering behavior among security analysts by examining how firm-specific experience impacts forecast accuracy. Using analyst forecasts on listed companies in China from 2014 to 2018, we find that firm-specific experience significantly reduces analyst forecast accuracy, which indicates the existence of catering behavior. Heterogeneity analysis reveals that catering behavior primarily exists for analysts employed by large brokerage firms or who issue forecasts for large companies. Further analysis shows that catering behavior is more prevalent when analysts make forecasts for SOEs. These findings suggest multi-faced implications of firm-specific experience and call for better regulations on refraining analysts' catering behavior.

Friends in media: Implications of media connections for analyst forecast optimism

We investigate the effect of corporate executives' connections with financial media on analyst forecast optimism. Some studies document that managers use their connections to benefit the firm. Alternatively, other studies show that managers use their connections to benefit themselves at the expense of the firm. Thus, it is difficult to predict ex-ante whether executives use their media connections to enhance information efficiency or distort information environment. We propose two hypotheses on the impact of financial media connections: information efficiency and manipulation. We estimate managerial media connection by using the BoardEx database, and apply standard OLS regression, mediation analysis, propensity score matching, 2-SLS instrumental variable regressions, and difference-in-difference regressions to test the two alternate hypothesis. Consistent with the manipulation hypothesis, our results suggest that corporate executives use media connections to exacerbate analyst forecast optimism. Mediation analysis suggests that media connections are related to not only more news coverage but also more positive news sentiment. We further find that managerial incentives and board monitoring moderates the relationship between media connections and analyst forecast optimism. Our study makes several contributions to academic literature and is interesting to regulators and investors. As corporate executives tend to exploit their friendship with the media to achieve enhanced analyst forecast optimism, it would have a negative impact on the efficiency of the capital markets. Our results suggest that regulators and investors should more closely scrutinize the social ties of corporate executives.

Earnings management and analyst forecast

This study explores the relationship between surplus management, analysts’ attention, and earnings forecasts, which we empirically analyze by selecting selected A-share listed companies in China from 2015 to 2022 as a research sample. Our findings indicate that the higher the degree of the company's surplus management, the lower the degree of analysts’ attention. Furthermore, the higher the degree of the company's earnings management, the lower the degree of analysts’ over-attention. Moreover, the higher the branch's earnings management degree, the lower the analysts’ earnings forecasting accuracy. Finally, substantial differences exist in the relationship between earnings management and analysts’ forecasts, such as business property rights.

Consequences of firm-specific stock price crashes on analyst forecasts: Evidence from China

The phenomenon of stock price crash events (SPCs) has always attracted market attention, but existing research mainly focuses on its causes or determinants and rarely examines the consequences of SPCs. This study aims to fill this gap by examining the consequences of firm-specific stock price crash events (SPCs) on analyst forecast accuracy. Using the sample of analysts and listed companies from 2001 to 2020 and employing a difference-in-differences design, we find that after the company’s stock price crashed, the analyst forecast error decreased and the accuracy increased. For analysts who have not conducted site visits before SPC, and have no geographical advantages, this effect is more obvious, thus validating our hypothesis following analyst attention theory. Additional analysis shows that SPCs stimulate forecast accuracy more significantly for lowly-reputed analysts than for highly-reputed analysts. Channel analysis documents that, for the aforementioned group of analysts, the enhancement effect of SPCs on analyst forecasts is mediated by analyst effort increasing. This study adds to the evidence that SPCs bring positive externalities from the analyst perspective, that is, SPCs attract the attention of some analysts and improve their forecast accuracy. This study also enhances our understanding of analyst behavior under uncertainty, finally enriching the literature on the determinants of analyst forecast accuracy.

Dividend smoothing and financial transparency

The study explores the effect of financial transparency on dividend smoothing behavior. We analytically show that dividend smoothing should increase when shareholders cannot observe cash flow realizations. Using exogenous variation in financial transparency created by the SOX, we find that less transparent firms as measured by higher dispersion of analyst forecasts and lower excess audit fees reduced dividend smoothing more after the legislative change, as compared to other dividend-paying firms in the sample. Our study complements recent studies considering the firm's dividend smoothing as an alternative corporate governance mechanism.