Analyst Forecasts Research Articles

A multi-dimensional assessment of the accuracy of analyst target prices

This paper provides the first assessment for the quality of analyst target forecasts within an emerging market. We find that analysts' target forecasts tend to commit systematically upward bias (9.4%), large absolute pricing error (24.8%), over-prediction of the actual price changes (21%), and a low proportion (54%) of correct directional forecasts. The forecasting quality decays over time as the information in the target prices becomes obsolete, long before the one-year expiry period indicated in the analyst reports. Nevertheless, analysts’ target forecasts consistently outperformed alternative forecasts generated solely based on historical data, which confirms the price discovery function of analyst forecasting. A number of factors are found to be influential to the target performance. Target quality is adversely associated with stock idiosyncratic risk, prior index volatility, the aggressiveness of the target forecasts, and the optimism of consensus targets. In addition, more accurate target prices are delivered by brokerages that have prior experience with the industry and the companies under review, but not by brokerages that possess monopoly power on the stock-specific information.

Forward-Looking Sustainability Information and Financial Analysts

ABSTRACT This paper empirically examines the relationship between forward-looking environmental and social information in sustainability reports (FLSI) and financial analysts’ forecast accuracy. To provide contextual insights, we conduct semistructured interviews with financial analysts. We find that FLSI quantity, measured by FLSI words and scope, and FLSI quality, measured by the precision, horizon, and direction of the environmental or social forecast, are associated with greater earnings forecast accuracy for short-horizon forecasts. Specifically, the precision and direction of FLSI affect analysts’ short- and midhorizon forecasts. Our results hold only for environmental FLSI and FLSI in externally assured sustainability reports. However, the results indicate that the provision of economic information in sustainability reports does not affect analyst forecasts. Collectively, our findings carry far-reaching implications for practitioners and—in particular—regulators around the world by shedding light on the potential costs and benefits of the new European Corporate Sustainability Reporting Directive (CSRD). Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41.

Central bank communication and macro information in analyst forecasts: Evidence from Chinese listed firms

AbstractThe paper examines how central bank communication affects the macro information in analyst forecasts. Using quarterly data of Chinese‐listed firms from 2007 to 2018, we find that richer and more frequent central bank communication increases the macro information contained in analyst forecasts. This effect is realized through the employment of in‐house economists by security firms. In addition, we document that the effect of central bank communication on macroeconomic information in analyst forecasts is more salient under a contractionary monetary policy regime, during a bear market, or when the economic policy is more uncertain. We also show that analyst forecasts are more sensitive to central bank communication when firms that they follow are state‐owned enterprises, have larger leverage ratios, or are located in more developed regions. In addition, analyst forecasts are more susceptible to central bank communication when the communication is in an informal oral format, when the public has more trust in the credibility of the central bank communication, and when the central bank pays more attention to expectation management after 2010. Finally, we show that richer and more frequent central bank communication also improves the accuracy of analyst forecasts.

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.

A balancing act between accuracy and timeliness: Evidence from analyst forecasts in China

AbstractThis paper investigates the impact of teamwork on analysts' ability to balance accuracy and timeliness. Surprisingly, team analysts demonstrate a lower ability compared to individual analysts. This trade‐off ability is even worse in diverse teams. Further, we find that Confucianism negatively affects this ability. However, this ability significantly improves when star analysts serve on the analyst team, or when analyst team members come from the province where the covered firm is headquartered, or when analyst team members graduate from the same college. Overall, these findings suggest that it is crucial to consider the two performance dimensions simultaneously.

Extreme Sentiment and Jumps in Analyst Forecast Dispersion

Extreme Sentiment and Jumps in Analyst Forecast Dispersion

Patent collateral and the trajectory of innovation

Taking the Chinese policy project of patent collateral policy as the quasi-natural experiment setting, we investigate the impact of patent collateral policy on the corporate innovation trajectory. We find that patent collateral increases applications of exploitative patent and its proportion in total patent applications, but no significant impact on exploratory innovation is observed. This finding is still robust in a series of robustness tests. The mechanism tests show that the impact of patent collateral promotes firms' exploitative innovation mainly by increasing the accessibility of firms' bank loans. The economic effects test shows that the patent collateral policy can reduce the deviation of analyst forecast and ease corporate financing constraints, which plays an important role in stabilizing capital market expectations. Heterogeneity analysis shows that patent collateral policy mainly promotes exploitative innovation of companies whose senior executives have no financial background, non-high-tech companies and non-SOEs. This paper enriches the research on the innovation incentive effects of patent collateral policy by providing an empirical investigation of the impact of patent collateral policy on corporate innovation trajectory. It helps to provide insights for emerging market countries as to improve patent collateral policy to promote the quality of corporate innovation.

Crowdsourced Forecasts and the Market Reaction to Earnings Announcement News

ABSTRACT This study examines whether crowdsourced forecasts of earnings and revenues help investors unravel bias in earnings announcement news, which is commonly derived from analyst forecasts. Our results suggest that investors, on average, understand and price the predictive signals reflected in crowdsourced forecasts about the bias in analyst-based earnings and revenue surprises. Using the staggered addition of firms to the Estimize platform, we find that crowdsourced coverage is associated with reductions in the mispricing of forecast bias and declines in earnings announcement premia. We further find some evidence that managers use income-increasing accruals to meet the crowdsourced forecast benchmark and that they respond to crowdsourced coverage through increased downward earnings and revenue guidance. Overall, we conclude that user-generated content on crowdsourced financial information platforms helps investors discount biases in traditional equity research and thereby better process the news in earnings announcements. JEL Classifications: G14; G20; M41.

Analyst forecasting and market liquidity dynamics: A comparative analysis of U.S. and Chinese markets pre and post COVID-19

This study investigates the influence of analyst forecasting on market liquidity and information efficiency within developed U.S. and emerging Chinese markets, both before and during the global COVID-19 pandemic. Market liquidity, a crucial aspect of financial market functioning, is assessed using key indicators, including Amihud’s liquidity, turnover and bid/ask spreads. The findings reveal notable disparities in analysts’ forecasts of market dynamics before and after the onset of the COVID-19 crisis. During the pre-COVID-19 period, our analysis confirms a statistically significant relationship between analyst forecasting and market liquidity. However, as the COVID-19 pandemic emerged and evolved, analysts’ forecasting impacts on market liquidity showed a diminishing trend. Furthermore, the effects of analyst forecasting vary across quantiles in the Chinese market. In the United States, buy-sell recommendations and various analyst forecasting variables were significant before the pandemic. These findings underscore the nuanced interplay between analysts’ forecasts and the market dynamics within the studied regions. Most notably, the U.S. and Chinese markets experienced decreased information efficiency during the COVID-19 crisis. This shift resulted in a notable decoupling of analysts’ forecasts from information efficiency during this period. These outcomes have significant implications for scholars and investors alike, enhancing their comprehension of market conditions and investor behaviour when confronted with heightened market stressors, such as the COVID-19 pandemic, which has disrupted international financial markets.

Analysts’ cash flow forecasts and firms’ information environment: evidence from bid-ask spread

PurposeThis study aims to investigate whether the cash flow forecasts (CFF) of analysts can disseminate valuable information to the information environments of companies.Design/methodology/approachThe author uses empirical archival methodology to conduct differences-in-difference analyses.FindingsIt is found that information asymmetry decreases in the treatment group following the initiation of CFF during the postperiod, which is consistent with the hypothesis of this paper.Originality/valueTo the best of the author’s knowledge, this study is the first among the cash flow forecast studies to demonstrate the usefulness of CFF in the mitigation of information asymmetry, a friction that is widespread in capital markets.

A comparison of analysts’ and investors’ information efficiency of corporate social responsibility activities

Purpose This paper aims to examine the extent to which sell-side analysts efficiently incorporate firms’ corporate social responsibility (CSR) activities into their earnings forecasts. In addition, this paper also investigate the CSR information efficiency of analysts vis-à-vis that of investors. Design/methodology/approach This paper measures CSR activities by using CSR strength and CSR concern scores from the Morgan Stanley Capital International Environmental, Social and Governance database. This paper uses analysts’ earnings forecast errors and dispersion as proxies for their information efficiency. To compare the CSR information efficiency of analysts to that of investors, this paper uses the Vt/Pt ratio, which is the equity value estimates inferred from analysts’ earnings forecasts (a proxy for analysts’ CSR information efficiency) to the stock price of the focal company (a proxy for investors’ CSR information efficiency). Findings The regression analysis indicates that analysts’ earnings forecasts are optimistically biased and more dispersed for firms with positive CSR activities. The paper also finds that analysts’ forecasts are more optimistically biased than investors in interpreting CSR activities. Practical implications The lack of standardized protocols in CSR reporting and activities has raised the risk of mispricing by analysts, threatening the stability of sustainable investments. This paper suggests that regulators and standard-setters should establish a uniform framework governing firms’ CSR activities, along with their reporting and measurement, to ensure more consistent and reliable evaluations of CSR practices. Social implications Analysts’ mispricing of CSR activities may distort sustainable investing, as it can overly focus on the positive impacts of stakeholder theory, overlooking agency theory’s warnings about managerial self-interest. Investors need to assess CSR efforts with a dual perspective, acknowledging their societal value but also examining their alignment with shareholder interests. Originality/value To the best of the authors’ knowledge, this research is the first to assess the efficiency of analysts versus investors in processing CSR information amidst growing sustainable investment interests. Furthermore, building on Dhaliwal et al. (2012), which found that voluntary CSR disclosures correlate with more accurate analyst forecasts, this research provides fresh perspectives on the evolving nature of how analysts assimilate CSR information over time.

Environmental Uncertainty, Analyst Forecasts and Investments Efficiency

Environmental Uncertainty, Analyst Forecasts and Investments Efficiency

Financial analysts’ coverage, forecast accuracy, and climate change vulnerability

The effects of climate change are real. The understanding of how these effects manifest in business operations is still nascent, but even more so, how they affect users of company information. This study sought to determine whether and how climate change vulnerability relates to an important business stakeholder, the financial analyst. We hypothesize that climate change vulnerabilities reduce both analysts’ following and analysts’ forecast accuracy. Using data from the Center for Research in Security Prices (CRSP), Compustat, Audit Analytics, Institutional Shareholder Services (ISS), and London Stock Exchange Group (LSEG), we construct a sample of 3,754 firm-year observations comprising 1,269 unique firms for the years 2019–2022. Our proxy for climate change vulnerability is the environmental, social, and governance (ESG) controversies score. We estimate cross-sectional regression models to test our hypotheses. We find support for our hypotheses. Also, we find that firms with high climate change vulnerability have significantly lower analyst coverage than those with low vulnerability. We also find that financial analyst forecasts are significantly less accurate for firms with higher vulnerability. However, this effect is only observable in industries classified as more exposed to the effects of climate change. We recognize the noisy nature of our proxy for vulnerability to climate change. Cognizant of this, we conduct further analysis to allay concerns of bias in our findings. We make important contributions to the existing literature by not only showing that ESG controversies score is an appropriate proxy for climate change vulnerability but also by adducing empirical evidence that climate change vulnerability affects how analysts react to and use company financial information. We discuss the significance and limitations of our results and make recommendations for further research.

Risk disclosure and stock price crash risk: Evidence from Chinese listed firms

This study constructs a risk-related dictionary to quantify firm risk disclosure and explores its association with stock price crash risk. Based on Chinese A-share listed companies from 2013 to 2020, we find that (1) risk disclosure is negatively associated with stock price crash risk; (2) this association is more significant in non-state-owned firms, firms in regions with poor legal systems, and during bull markets; and (3) analyst forecasts and investor sentiment have mediating effects. Evidence shows that mandatory requirements for risk disclosure are meaningful and suggests that regulators improve risk disclosure norms to promote Chinese capital market development.

Question Dodging, Information Environment, and Analyst Forecasts

Question Dodging, Information Environment, and Analyst Forecasts

Reakcije ulagača na objave tromjesečne dobiti na Zagrebačkoj burzi

This study investigates the investors’ reactions to the publication ofquarterly earnings for companies at the Zagreb Stock Exchange. The empiricalresearch based on the event study and 409 firm-year observationsconfirms that investors react to the publication of quarterly earnings, whilethe Friedman test indicates that the reactions are most frequent for earningsannouncements from the second quarter (Q2). The occurrence of abnormalstandardized returns on announcement day suggests that the efficient markethypothesis in semi-strong form does not hold for the Croatian capital market.Moreover, there is evidence of gradual price adjustment after announcementday. Longitudinal analysis in the period 2010-2022 indicated that investors’reactions are stable and that there is no trend of growth or decline in standardizedabnormal returns. The estimated pool regression model revealedthat several firm characteristics influence the information content of quarterly earnings. The earnings surprise variable positively affects standardizedabnormal returns, while firm size and reporting of losses variables negativelyaffect standardized abnormal returns. The results of the study confirm tocapital market regulators the practical benefits of quarterly financial reporting,to firms that they can use quarterly financial reports to reduce informationasymmetry and to investors that after the quarterly earnings publicationthere is the possibility of achieving abnormal returns. The limitation of thisstudy is relatively small sample in comparison with similar studies. Findingsabout slow price adjustment after the day of earnings release might be challengedby the occurrence of other price-sensitive information. Some futureresearch might improve findings by use of additional independent variables,like management analysis and analyst forecast, which could shed new lighton the quarterly earnings information content at ZSE.

Banking Liberalization and Analyst Forecast Accuracy

Banking Liberalization and Analyst Forecast Accuracy

Hate Crimes and Analyst Forecast Behaviors Amidst the COVID-19 Pandemic

Hate Crimes and Analyst Forecast Behaviors Amidst the COVID-19 Pandemic

Managerial overconfidence and classification shifting

This study examines whether overconfident CEOs engage in classification shifting. We find that managerial overconfidence is related to an increase in unexpected core earnings via reclassifying recurring expenses as special items. This finding is robust to controlling for firms’ propensity to engage in accruals management and real earnings management, using a subsample of non-zero income-decreasing special items, employing alternative measures of overconfidence, and controlling for CEO and firm fixed effects. We further find that overconfident CEOs are more likely to engage in classification shifting when the incentives are stronger, i.e., when CEOs want to meet or beat analyst forecasts, when CEOs’ pay is highly sensitive to core earnings, and when CEOs have low managerial ability. Overconfident CEOs are also more likely to engage in classification shifting when there are more opportunities for misconduct, i.e., when financial statement comparability is low and when the CEO can use opportunistic special items to misclassify expenses. Overall, we find that overconfident CEOs are intentionally, rather than unintentionally, engaging in classification shifting to boost core earnings.

Social Jetlag and Heuristic Analyst Forecasts

Social Jetlag and Heuristic Analyst Forecasts