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The purpose of this study is to examine the effect of audit committee (AC) tenure on corporate governance, a topic that has been long debated. Social capital theory explains how directors’ effectiveness varies through tenure. Consistent with this theory, this paper argues that AC tenure has an inverted U-shaped relationship with AC governance.

This paper estimates a quadratic function that regresses constructs for AC governance on the average AC, the AC chair, and nonchair tenure, and their respective square terms. The constructs for AC governance include financial reporting quality measures and perceived auditor independence measures.

This paper finds that average AC, AC chair, and nonchair tenure have inverted U-shaped relationships with financial reporting quality, consistent with social capital theory. This paper also finds similar associations when examining perceived auditor independence. The results are generally consistent with AC directors accumulating knowledge and social capital, which improves AC governance to an optimal level, following which entrenchment and familiarity occur and AC governance declines.

To the best of the authors’ knowledge, this is the first study in AC governance literature to show a nonlinear relationship between AC tenure and AC governance. This paper extends Huang and Hilary (2018) by demonstrating that a nonlinear effect is also present in the AC, a key board committee responsible for monitoring financial reporting quality and appointing auditors and approving their services. This paper further documents that the AC subsumes the effect of the overall board in some areas of AC oversight, and reconciles the inconclusive findings of prior research by showing a nonlinear relationship between AC tenure and AC governance.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

Drawing from agency and comprehension theories, this paper aims to examine the influence of annual report readability (ARR) on financial reporting quality (FRQ), with a focus on how information asymmetry moderates this relationship.

The study uses a sample of 467 listed firms in Vietnam from 2015 to 2021. To analyze the relationship between ARR and FRQ, this paper employs a Generalized Method of Moments (GMM) regression, incorporating information asymmetry as a moderating factor.

The research findings show that ARR has a positive and significant impact on the FRQ of Vietnamese-listed firms. This paper also finds that information asymmetry significantly and partially moderates the relationship between ARR and FRQ. Specifically, ARR can help alleviate the level of information asymmetry and contributes to improved FRQ.

From a practical perspective, this paper provides empirical evidence for managers, investors and related government departments to evaluate the effects of ARR and offers regulators a method to help improve the transparency of the stock market. More importantly, the results of this study have reference value for scholars and practitioners in developing countries like Vietnam.

From a theoretical perspective, our study adds to the growing literature on ARR, expands the scope of ARR research, elaborates on relevant economic consequences of ARR and complements the literature on the determinants of FRQ.