Social Media Information and Analyst Forecasts

PurposeThe purpose of this paper is to examine whether corporate social media information helps improve analysts’ forecast accuracy.Design/methodology/approachThis study uses hand-collected information on S&P 500 firms’ official Facebook pages and uses posts and reactions to such posts to measure corporate Facebook information. Multivariate regression models are estimated to test the relationship between analysts’ forecast accuracy and corporate Facebook information.FindingsThe results indicate that analysts forecast accuracy is unresponsive to posts. However, analyst forecast errors are decreasing in reactions to posts. These findings are robust to the inclusion of control variables, firm and time fixed effects, and alternative specifications of forecast errors and different pre-forecast time windows.Research limitations/implicationsThis study has some limitations. It focuses only on the S&P 500 firms, which are large and generally provide better information to the market. The sample period coincides with the early period of the corporate Facebook culture. However, more recent data sets are likely to provide stronger results.Practical implicationsThe findings of this study provide support for “information generation” role of social media and show that reactions to corporate Facebook posts are the new and unique information generated from corporate social media activities, which help information intermediaries in improving their forecasting accuracy.Originality/valueThis study makes an important contribution to the literature by separating the information dissemination role of social media from information generation role and establishes the first evidence on how corporate social media information affects forecast accuracy of financial analysts.

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The brain gain of CFOs in China: The case of analyst forecasts

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

Solace in Social Media: Women Unite Under COVID-19

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.

A Re-Examination of the Importance of Analyst and Forecast Characteristics for Forecast Accuracy: The Effects of Analyst Disagreement, Stale Forecasts, and the Information Environment

Mean reversion in profitability and growth is a well-documented phenomenon; however, comparatively less is known about the level of analysis that best captures mean reversion. In this study, we find that analyzing firms by life cycle stage improves the out-of-sample accuracy of profitability and growth forecasts over analyzing firms pooled across the economy and analyzing firms by industry. The improved accuracy is robust to both short-term and long-term forecasts of profitability and growth. We also find that the improvement in accuracy of forecasts from life cycle analyses is greatest for firms in the introduction and decline stages and for firms with greater uncertainty. Finally, we examine market participants' use of life cycle information in forming expectations. We find inefficient use of life cycle information in analyst forecasts, but not in management forecasts. We also find that life cycle forecasts are associated with year-ahead abnormal stock returns.

We examine the association between investor expectations and its components and sell-side analysts’ short-run quarterly earnings forecast bias and forecast accuracy. To measure investor expectations, we use the Index of Consumer Expectations survey and decompose it into the “fundamental” component related to underlying economic factors (FUND) and the “sentiment” component unrelated to underlying economic factors (SENT). We find that analysts are the most optimistic and the least accurate when SENT is higher. Management long-horizon earnings forecasts attenuate the effects of SENT on forecast optimism and forecast accuracy. Analysts are also the most accurate when FUND is higher. Last, the market places more weight on unexpected earnings when SENT is high. These findings suggest that analysts are affected by investor sentiment and the market reacts more strongly to unexpected earnings when analyst forecasts are the least accurate. The last result potentially explains why prior research (for example, Baker and Wurgler, The Journal of Finance 61:1645–1680, 2006) finds an association between investor sentiment and cross-sectional stock returns.

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.

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.

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.

Brain Trust

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

Social media allows for easy access and sharing of information in real-time. Since the beginning of the coronavirus disease (COVID-19) pandemic, social media has been used as a tool for public health officials to spread valuable information. However, many Internet users have also used it to spread misinformation, commonly referred to as “fake news.” The spread of misinformation can lead to detrimental effects on the infrastructure of healthcare and society. The purpose of this scoping review was to identify the sources and impact of COVID-19 misinformation on social media and examine potential strategies for limiting the spread of misinformation. A systemized search of PubMed, Embase, and Web of Science electronic databases using search terms relevant to the COVID-19 pandemic, social media, misinformation, or disinformation was conducted. Identified titles and abstracts were screened to select original reports and cross-checked for duplications. Using both inclusion and exclusion criteria, results from the initial literature search were screened by independent reviewers. After quality assessment and screening for relevance, 20 articles were included in the final review. The following three themes emerged: (1) sources of misinformation, (2) impact of misinformation, and (3) strategies to limit misinformation about COVID-19 on social media. Misinformation was commonly shared on social media platforms such as Twitter, YouTube, Facebook, messaging applications, and personal websites. The utilization of social media for the dissemination of evidence-based information was shown to be beneficial in combating misinformation. The evidence suggests that both individual websites and social media networks play a role in the spread of COVID-19 misinformation. This practice may potentially exacerbate the severity of the pandemic, create mistrust in public health experts, and impact physical and mental health. Efforts to limit and prevent misinformation require interdisciplinary, multilevel approaches involving government and public health agencies, social media corporations, and social influencers.