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Motivated by significant global developments affecting the sell-side industry, in particular a shift toward passive investments and growing regulation, this study examines whether financial analyst coverage declined over the past decade and if any loss of analyst coverage is associated with a change in forecast accuracy.

After investigating, and confirming, a general decline in analyst following, the authors calculate the loss of analyst coverage relative to the firm-specific maximum between 2009 and 2013. In multivariate analyses, the authors then examine whether this loss of coverage differs across geographic region, firm size and capital market development, and whether it is associated with consensus analyst accuracy.

Results indicate that between 2011 and 2021, firm-specific analyst coverage globally declined 17.8%, while the decline in the EU was an even greater, 28.5%. Within the EU, results are most pronounced for small-cap firms. As a consequence of the loss of coverage, the authors observe a global decline in forecast accuracy, with EU small-cap firms and firms domiciled in EU non-developed capital markets faring the worst.

This study is the first to document a concerning global decline in analyst coverage over the past decade. The study results provide broad-based empirical support for anecdotal reports that smaller firms in the EU and those in EU non-developed capital markets bear the brunt of consequences stemming from changes in the sell-side analyst industry.

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.

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).

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.

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.

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.

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.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

As the influence of institutional investors over managerial decision-making grows, so does the importance of understanding the effect of institutional investor ownership (IO) on firm outcomes. The authors take a comprehensive approach to studying the effect of IO on earnings management (EM).

The authors study the relation between IO and EM using a sample of 59,503 listed U.S. firm-year observations from 1981–2019. The authors proxy EM with earnings surprises and with accrual-based and real activity measures. The authors test for nonlinear relations and analyze changes resulting from the passage of the Sarbanes–Oxley Act.

The findings support a positive IO-EM relation overall, but show that the relation is dynamic and heavily context-dependent with evidence of nonlinearity. The authors also find evidence that IO positively affects accrual-based EM and real activities EM negatively.

To the authors’ knowledge, this is the first study of the IO-EM relation to consider evidence of nonlinearity in the U.S. context, measuring changes to the relation over time, and with the use of several measures of EM.

The purpose of this paper is to be the first empirical article to provide necessary standard deviation inputs for adoption in probabilistic prognostications of hotel revenues and expenses, i.e. prognostications that consider risk. Commonly accepted methodologies to develop hotel financial projections resulting in point estimates of upcoming performance have been perceived as egregiously insufficient because they do not consider risk in lodging investments. Previous research has recommended the use of probabilistic methodologies to address this concern, and it has been recommended that analysts use Monte Carlo simulation. This methodology requires the estimation of standard deviations of specific, future hotel revenue and expense items, and this paper provides such inputs based on a large sample of actual, recent data.

This study provides actual standard deviations using a sample of recent hotel profit and loss (P&L) statements for over 3,000 hotels (Over 19,000 P&L statements) to provide analysts with empirically-supported standard deviations that may be applied to Uniform System of Accounts for the Lodging Industry (USALI) hotel revenues and expenses in hotel financial (revenue and expense) prognostications.

Findings are presented for standard deviations based on typical line items as defined in the USALI, and these findings may be used by practitioners as inputs for hotel financial projections. Findings also include that hotel revenue items generally have higher standard deviations than expense items. Findings are presented in detail in the manuscript, including overall findings, as well as findings based on hotel class.

Rather than practitioners adopting standard deviations of hotel revenue and expense line items based on guesswork or judgment, which is the current “state of the art” in hotel financial projections, this paper provides practitioners with actual standard deviations which may be adopted in probabilistic prognostications of hotel revenues and expenses.

This paper may be the first to provide practitioners with actual standard deviations, based on typical USALI line items, for adoption in probabilistic prognostications of hotel revenues and expenses.

It has been two decades since the first academic paper shone a spotlight on non-GAAP earnings. The past 20 years of research investigates concerns over the misuse of these disclosures and resulted in some significant changes to accounting and reporting standards across the globe. This paper aims to document the history of non-GAAP reporting and outline the emerging themes of the now matured practice of non-GAAP reporting.

This systematic literature review searches two popular databases to identify the academic publications relating to non-GAAP reporting between 2002 and 2022. The paper uses bibliographic mapping to present the key statistics of the non-GAAP reporting field of research.

The non-GAAP reporting environment started out as the “wild West’ but, through regulation and public awareness, emerged as an important supplement to the traditional outputs of financial reporting. Current consensus is recent non-GAAP earnings are informative to users but there is lack of research into qualitative non-GAAP disclosures and the vast body of archival research needs triangulating with more experimental studies.

This paper contributes to the literature by documenting the past 20 years of non-GAAP reporting and identifying the important existing and emerging research areas concerning non-GAAP earnings disclosures.

An important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI).

The first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field.

The author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field.

This study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries.

Core AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors.

The social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field.

This paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field.

This study examines the relation between pay inequalities in top management teams and how efficiently firms convey valuation-relevant information to investors. Given that reward comparisons with reference groups create feelings of inequity, top management team pay inequalities can impair the information environment. This manifests into lengthier or less readable financial reports.

This paper employs an ordinary least squares (OLS) regression model to test whether and how the pay distribution in the top management team is associated with the readability of the annual report. It also employs a two-stage least squares (2SLS) regression model to further address the endogeneity concern. Lastly, it conducts cross-sectional analyses to examine heterogeneity in the observed relation.

Using the intra-firm pay gap as a proxy for pay inequality and file size, Bog Index and Fog Index of the 10-K filings as a proxy for financial report readability, the author finds that firms with larger pay gaps exhibit lengthier or less readable 10-K filings. The main findings are robust to the use of an instrumental variable approach. She finds some evidence that the relation is less pronounced when pay gaps are justified by explicit legal authority of CEOs or high ability of CEOs. The main results are robust to considerations of alternative explanations.

This paper adds a new dimension to the debate on pay inequality by studying pay gaps in the top management team and financial report readability. The author's findings have important implications for executive compensation policies and for corporate disclosure policies.

The digital revolution has changed consumer–service provider interaction, spawning a new generation of FinTech. This paper analyzes consumers' reactions to artificial intelligence (AI) (vs human) decisions.

The authors tested their predictions by conducting two experimental studies with FinTech consumers (n = 503).

The results reveal that consumers' responses to AI (vs human) credit decisions depend on the type of credit product. For personal loans, the rejection by an AI provider triggers higher levels of satisfaction compared to a credit analyst. This effect is explained via the perceived role congruity. In addition, the findings reveal that consumers’ rejection sensitivity determines how they perceive financial services role congruity.

To the best of the authors' knowledge, this research is the first to jointly examine AI (vs human) credit decisions in FinTech and role congruity, extending prior research in the field.

This study examines whether country-level financial integration affects firms' accounting choices and the quality of financial information.

This study employs Propensity Score Matching (PSM), and panel regressions of a large sample of data from 20 emerging markets over the period 1987–2018.

This study finds evidence that increased level of financial integration is significantly positively associated with firms' accruals earnings management (AEM) and real earnings management (REM).

Findings in the study have implications for standard-setting bodies that aim to enhance the usefulness of financial reporting quality. The study also has implications for various initiatives by governments in emerging markets aimed at raising investor confidence and fostering stock market development through greater financial integration.

Findings in the study have implications for standard-setting bodies that aim to enhance the usefulness and quality of financial reporting. The findings can be of interest to analysts, auditors and other monitoring institutions who play a crucial role in detecting earnings management and reducing information asymmetry. Finally, the study has implications for various initiatives by governments in emerging markets aimed at raising investor confidence and fostering stock market development through greater financial integration.

Findings in the study reveal how country-level financial integration affects accruals and real earnings management in a sample of firms from 20 emerging markets. Further, the study adds to the growing body of literature on emerging markets where capital markets mechanisms, regulatory environment and firm's corporate governance are distinct to developed markets.