Social media information and analyst forecasts

Social Media Information and Analyst Forecasts

This study examines whether the improvement in analyst forecast accuracy around mandatory IFRS adoption is associated with the improvement in the accuracy of financial statement-based forecasts. We find significant out-of-sample improvement in financial statement-based forecast accuracy around mandatory IFRS adoption and significant improvement in analyst forecast accuracy only in countries that made concurrent improvements to financial reporting enforcement. We show that the improvement in analyst forecast accuracy is associated with the improvement in financial statement-based forecast accuracy around IFRS adoption. We also show that analyst forecasts, particularly for firms whose analysts forecast under favorable conditions (i.e., analysts who are less busy with more experience and resources), have a greater association with financial statement-based forecasts, after mandatory IFRS adoption in countries with concurrent changes in enforcement.

This study examines the impact of a firm’s life cycle on the accuracy of analyst forecasts by considering the life cycle as a proxy of time-series change in a firm’s environment. The results show that the accuracy of analyst forecasts is lower in introduction and decline stages than the other stages. In contrast, forecasts in the growth and mature stages show relatively higher accuracy. Furthermore, the systematic association between life cycle stage and the accuracy of analyst forecast is derived from firms reporting high quality accruals and clients of Big 4 auditors. The results are consistent in case of using the two-stage regression tests to mitigate endogeneity concern existing in firms between analyst-followed and non-followed. Also, the results are robust when using the forecasts that are close to the earnings announcement date and those that are not. This study extends prior research that finds the effect of firm-level characteristics on analysts forecasting activities by exploring the macro-level consequences through life cycle stages.

This study provides initial empirical evidence on whether the readability of Key Audit Matters (KAMs) enhances analyst forecast accuracy following the adoption of ISA 701 in Indonesia. KAMs are intended to improve the communicative value of audit reports by highlighting matters that, in the auditor's professional judgement, were of most significance in the audit. While prior studies have largely focused on the presence or quantity of KAM disclosures, this paper shifts attention to their linguistic clarity, a dimension critical to the practical usefulness of audit reports yet often overlooked in empirical auditing research. Using a sample of 188 firm‐year observations from 2022 to 2023, we measure KAM readability using the Flesch Reading Ease Score and conduct robustness checks using the Flesch–Kincaid Grade Level and Gunning Fog Index. The findings reveal a significant negative association between KAM readability and analyst forecast error, indicating that clearer audit language contributes to more accurate forecasts. Additional analysis reveals that this effect is more pronounced in firms audited by non‐Big 4 auditors, suggesting that readable KAMs play a compensatory role in lower‐assurance environments. When audit brand credibility is limited, stakeholders appear to rely more heavily on the accessibility and clarity of audit disclosures to assess financial reporting risks. By offering early evidence from an emerging market context, this study contributes to the auditing literature by reframing readability as a core determinant of KAM effectiveness and provides practical implications for auditors and regulators seeking to enhance the communicative value of audit reports. In sum, this analysis shows that KAM readability helps improve analyst forecast accuracy, not just as a matter of style, but as a useful signal of information quality, especially when audit credibility is limited.

SUMMARY The accuracy and other properties of analyst earnings forecasts represent potentially useful proxies for the impact of audit quality on client financial reports. Extant research in the auditing literature, however, is characterized by diametrically opposite predictions and inconsistent findings regarding the relationship between audit quality and analyst forecast accuracy. We argue that a potential reason for the inconsistency in the literature reflects these studies' focus on end-of-year forecast accuracy, which is subject to competing effects of audit quality. High-quality auditors may simultaneously improve forecast accuracy through their impact on the decision usefulness of clients' prior period reports, and reduce forecast accuracy by constraining client attempts to manage earnings in the direction of the consensus forecast. We argue and present evidence in support of the conjecture that analysts' beginning-of-year forecasts are a superior metric for identifying the impact of audit quality on the properties of analyst forecasts because the decision usefulness effect of audit quality should be dominant with respect to those forecasts. Data Availability: Data are available from sources identified in the article.

ABSTRACT: This study examines the relative accuracy of analyst earnings forecasts prepared both before (prior forecasts) and after (posterior forecasts) voluntary management earnings forecasts. The motivation for the study stems directly from recent studies which document statistically significant stock price reactions associated with management forecasts. The results of these studies suggest that management conveys new (or inside) information about earnings through its forecasts. Based on these results, one would expect that management forecasts would be more accurate than prior analyst forecasts while management and posterior analyst forecasts would be equally accurate. This paper reports results fully consistent with both of these predictions. These results, however, are conditional upon caveats about the estimation dates for analyst forecasts used to classify them as prior or posterior Several previous studies have also investigated similar issues but have produced conflicting results. The tests reported here are based on larger samples than those found in previous studies. This suggests that any accuracy differences between management and analysts are of such magnitude that they can be detected only in relatively large samples.

PurposeThe purpose of this paper is to examine how family firms affect analyst forecast dispersion, accuracy and optimism and how earnings smoothness as the moderating factor, affects these relationships in an emerging market context.Design/methodology/approachThis paper uses the population sample of firms listed on the Taiwan Stock Exchange from 2009 to 2010 as the research sample, which includes 963 firm-year observations.FindingsThe findings show that analysts following family firms are more likely to have more dispersed, less accurate and more optimism biased forecasts than those following nonfamily firms. Earning smoothness is mainly used by nonfamily firms as a signaling strategy to improve analyst forecast quality. In contrast, earnings smoothness is mainly used by families as a garbling strategy, stimulating forecast optimism. Only earnings smoothness in family firms with a high level of family ownership concentration is likely to be signaling-oriented to improve analyst forecast accuracy and mitigate analyst optimism biases.Originality/valueEmerging markets are not only featured by prevailing principal-principal conflicts but also have multiple levels of agency conflicts among large shareholders, minority shareholders and professionally hired managers. This research reveals the multiple governance roles of family owners in affecting analyst forecast quality, including their entrenchment role in extracting private benefits of control through opaque environments and market discipline distortion role in aligning interests between managers and families without prioritizing meeting or beating analyst forecasts, both at the cost of minority shareholders. This research further disentangles the intertwined signaling oriented and garbiling oriented incentives associated with earnings smoothness under family governance.

The Effect of Integrated Reporting Assurance and Combined Assurance Model on Market Liquidity and Analyst Forecast Accuracy

This study examines the predictive power of self-disclosed social media information on borrowers’ default in peer-to-peer (P2P) lending and identifies social deterrence as a new underlying mechanism that explains the predictive power. Using a unique data set that combines loan data from a large P2P lending platform with social media presence data from a popular social media site, borrowers’ self-disclosure of their social media account and their social media activities are shown to predict borrowers’ default probability. Leveraging a social media marketing campaign that increases the credibility of the P2P platform and lenders disclosing loan default information on borrowers’ social media accounts as a natural experiment, a difference-in-differences analysis finds a significant decrease in loan default rate and increase in default repayment probability after the event, indicating that borrowers are deterred by potential social stigma. The results suggest that borrowers’ social information can be used not only for credit screening but also for default reduction and debt collection.

Incentives or Irrationality? International Evidence from the Impact of Individualism on Analyst Forecast Bias

Financial derivatives, analyst forecasts, and stock price synchronicity: Evidence from an emerging market

Phd Thesis: International Perspective: The Impact of Corporate Social Responsibility Reports on Analysts' Coverage and Forecast Properties: Evidence from Ft Global 500

The Transparency of Environmental, Social and Governance Disclosures, Integrated Reporting, and the Accuracy of Analyst Forecasts

Solace in Social Media: Women Unite Under COVID-19

Navigating Social Media in #Ophthalmology