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Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

Jumpstart Our Business Startups Act 2012 (the JOBS Act) was passed in 2012. JOBS Act enables emerging growth companies (EGCs) to go public without being subject to the full vigorous range of regulations applicable to publicly traded companies. The purpose of this paper is to study financial performance, Tobin’s Q-ratio and value relevance of EGCs.

The sample includes 620 IPOs during the period from April 5, 2009 to April 5, 2015. The analyses use firm-quarter observations.

The results show that EGCs have both lower financial performance, and a lower Tobin’s Q-ratio compared to the financial performance and Tobin’s Q-ratio of non-EGCs. Moreover, the value relevance of accounting information for EGCs is lower than the value relevance of accounting information for non-EGCs.

This study contributes to the accounting regulation literature by documenting the inferior market performance and financial information quality of EGCs, i.e., the unintended consequences of the JOBS Act.

The purpose of this study is to examine the impact of goodwill impairment losses on bond credit ratings.

The authors use regression analysis to examine the relationship between goodwill impairment losses and bond credit ratings.

The empirical results show a negative relationship between the amount of goodwill impairment losses and bond credit ratings, suggesting that firms with goodwill impairment losses receive lower credit ratings. The authors perform various additional tests, including subsamples in good or bad market time, changes analysis, first time goodwill impairment firms vs subsequent impairment and the two-stage least squares regression analysis to address potential endogeneity issues. The main results persist.

This paper links and contributes to two streams of literature: goodwill impairment in accounting literature and bond credit ratings in finance literature. Whether a firm’s goodwill impairment losses affect the firm’s bond credit rating remains an interesting question that has not been examined previously. To the best of the authors’ knowledge, this is the first study that directly examines the relationship between goodwill impairment losses and bond ratings at the firm level.

This paper aims to examine how Chief Executive Officer (CEO) power affects firm-level labor productivity.

The authors rely on regression analysis to examine the relation between CEO power and labor productivity.

Following prior research (i.e. the sequential rank order tournament theory), the authors predict that powerful CEOs lead to high labor productivity. They find a significant and positive relationship between CEO power and labor productivity. They further decompose labor productivity into labor efficiency and labor cost components and find a positive (negative) relationship between CEO power and labor efficiency (cost) component, suggesting that more powerful CEOs better manage labor efficiency and control labor cost. The results are also robust to various additional tests.

This study contributes to two streams of research: the CEO power literature in finance and the labor productivity and cost literature in accounting. To the best of the authors’ knowledge, it is the first study that performs a direct empirical test on the relation between CEO power and labor productivity.

The purpose of this paper is to examine the impact of career concerns on CEOs’ use of expectations management to meet or beat analysts’ quarterly earnings forecasts. The authors posit that early career-stage CEOs are less (more) likely to use expectations management than are late career-stage CEOs if the market views expectations management as an opportunistic strategy (efficient process) due to reputational capital concerns.

The authors obtain data for CEO career stages and CEO compensation from ExecuComp, analyst earnings forecasts from the detailed I/B/E/S database, financial statement data from quarterly Compustat and stock returns from the daily CRSP database over the period 1992–2013.

The results are consistent with the opportunistic hypothesis and early-stage CEOs seeking to build reputational capital by avoiding the perception of engaging in an inefficient managerial strategy. The authors find robust evidence that late career-stage CEOs are more likely to engage in expectations management than early career-stage CEOs. Furthermore, the authors show that late career-stage CEOs tend to employ expectations management to boost the value of their equity-based compensation.

The findings have important implications because the authors document a different implication of the “horizon problem” related to CEOs’ opportunistic forecasting behavior and the manipulation of analysts’ forecasts for CEOs who are approaching retirement.

The results have practical implications for analysts who provide earnings forecasts for firms whose CEOs are in early or late career stages and for investors who use such analysts’ forecasts in firm valuation models.

The authors contribute to the literature on expectations management by documenting how reputational incentives of CEOs affect the likelihood that managers engage in expectations management. The authors show that an important managerial incentive to engage in expectations management is CEO career concerns. Furthermore, the authors show that CEOs who are in early stages of their careers choose not to engage in expectations management due to the market’s perceived degree of opportunism pertaining to this strategy.

The author discusses the paper by Ordyna (2020) and suggests a few areas for improvement. First, the author could think more about the financing scheme of the acquisition including hybrid debt securities. Second, the author could consider forecast reputation, the timing and frequency of earnings guidance. Third, the author may consider the difference-in-differences research design and identification strategy. Other model designs, robustness checks and alternative measures of key variables are discussed.

This study aims to examine the influence of five sports celebrity personality (SCP) attributes – attractiveness, expertise level, credibility, trustworthiness and character – on consumers' purchase intentions (CPI). It identifies celebrity brand congruence (CBC), endorsed brand celebrity (EBC) and transfer of brand image (TBI) as antecedents of CPI.

The purposive sampling technique was used to collect the data from 838 respondents. This study developed a multidimensional construct for SCP. The covariance-based structural equation modeling (SEM) technique was used to examine the relationship between SCP and the endorsed brand. The study used CBC as a mediator and EBC and TBI as partial mediators. The direct and indirect effect of SCP on CPI was investigated using CBC, EBC and TBI as mediators.

This study supports the importance of three antecedents (i.e. CBC, EBC and TBI) on CPI. It finds congruence across SCP and CBC variables, and a positive impact of SCP on EBC and TBI variables. Also, it exhibits a significant direct effect of CBC on EBC and TBI, whereas the direct effect of CBC on CPI is not substantial. The indirect effect of CBC through mediating variables EBC and TBI found to be significant.

This study concludes that sports celebrity endorsement is essential to transfer the positive celebrity image to the endorsed brand image. However, it is not merely sufficient to influence the buyers' purchase conduct; the brand credibility additionally assumes to take a role in changing their behavioral intentions.

This study contributes to the sports marketing literature by its novelty in analyzing the sports celebrity personality at a multidimensional level. It uses SCP's different attributes as one construct and studies its impact on CPI by taking CBC, EBC and TBI as mediators. The results of this study equip sports management professionals with the knowledge to build better long-term relationships with consumers.

A long-time ethical issue in financial accounting is earnings management. Two popular ways that earnings are managed include use of accruals (Kothari et al., 2016) and real activities management (RM). This study examines the association between RM and short selling and an association between short sellers and RM behavior related to earnings management. Instead of using accruals, RM is accomplished by timing investment or financing decisions and thereby alter reported earnings. Our results show that short sellers avoid targeting firms with a high level of RM, but this only holds for those firms that just meet analysts’ forecasts. This result suggests that short sellers interpret RM as a signal used by companies to convey their “good news” and confidence in their future performance. On the other hand, the authors document that heavily shorted firms engage in a lower amount of RM, which is consistent with the notion that short selling plays an external disciplinary role in constraining firms’ RM behavior for earnings management. This chapter would be of interest to anyone concerned with earnings management, such as financial market analysts, investors, academic researchers, and, in particular, regulators, who are involved in setting rules on short selling.

This paper examines the relationship between environmental uncertainty and tax avoidance at the firm level. We posit that managers faced with more uncertain environments are likely to engage in more tax avoidance activities. We find a significant and negative relationship between environmental uncertainty and effective tax rates, and our results persist through a battery of robust checks. We further find that managerial ability mitigates the above relationship. Moreover, we find that small, highly leveraged, and innovative firms operating in uncertain environments engage in more tax avoidance.