Journal of Contemporary Accounting & Economics最新文献

This study investigates the effect of environmental and social (E&S) disclosure and managerial entrenchment on investment efficiency. E&S disclosure increases not only capital accessibility but also external monitoring of entrenched managers’ actions. We develop a theoretical model that demonstrates how these benefits and costs of firms’ voluntary E&S disclosure affect investment efficiency. Using a large sample of U.S.-listed firms over the period 2016–2022, we test the model’s predictions and provide empirical evidence suggesting that E&S disclosure is positively associated with investment efficiency and that this effect is stronger for firms with lower managerial entrenchment or those disclosing more financially material E&S information. Our study contributes to the investment efficiency literature by demonstrating the relevance of an incentive-compatible mechanism reflecting managers’ choice of E&S voluntary disclosure to the efficient capital allocation.

As an important institutional reform in the Chinese capital market, the registration system reform aims to improve the disclosure quality of public companies. Using the exogenous shock from the registration system reform as a quasi-natural experiment, we probe the effect of the registration system on disclosure in two stages: IPO admission and post-IPO supervision. We find that the registration system reform significantly reduces the performance deterioration of “incremental” companies in the IPO admission stage and improves the disclosure quality of “existing” companies in the post-IPO supervision stage. In addition, curbing earnings manipulation is a possible channel the registration system reform affects disclosure. Furthermore, these effects are more pronounced in firms with lower internal governance levels and less external media attention. Collectively, these findings respond to the theoretical controversy over whether the registration system will improve or worsen disclosure quality and confirm the reform has positive effects.

This study examines the association between corporate carbon performance (CCP) and firm risk using a sample of 9,212 firm-year observations from 13 countries in the Asia-Pacific region over the period 2002–2021. We also examine the moderating role of the quality of country-level governance in the association between CCP and firm risk. We find that CCP is negatively associated with a firm’s total, idiosyncratic and systematic risk and that country-level governance quality accentuates the negative association between CCP and firm risk. We also find that country-level business culture, emissions trading schemes, climate change performance and attention to carbon emissions accentuate the negative association between CCP and firm risk. Given the growing demands from regulatory bodies for increased transparency on carbon performance, the insights gained from our research hold significant relevance for regulators, policy makers, investors, financial analysts, scholars and businesses.

We investigate the joint effect of competitive strategies and the pay gap on ESG performance. We employ the Principal Component Analysis (PCA) to derive the two competitive strategies, namely product differentiation and cost leadership. Based on data from firms listed in S&P1500 from 2000 to 2022, and using the Three Stage Least Square (3SLS) model, we hypothesize and find that cost leadership companies have a negative relationship with ESG performance, and the pay gap exacerbates this negative relationship. In contrast, we predict and find that product differentiation companies have a positive relationship with ESG performance, and the pay gap makes this positive relationship more pronounced. Overall, we contribute to the literature and managerial practices in three ways. First, we contribute to the literature on the pay gap by considering the company’s competitive strategy, an important variable that previous studies tend to ignore. The findings of our study suggest that researchers need to consider competitive strategy when investigating the relationship between the pay gap and ESG performance. Second, our study uses ESG performance rather than financial performance as the dependent variable. As such, our study contributes to the limited literature on the relationship between the pay gap and ESG performance. Finally, for practice, our study sheds an important light on understanding the strategic reasons underlying managers’ motivation to invest in ESG activities.

This study investigates whether a target firm’s corporate social responsibility (CSR) influences the acquisition premium paid by an acquirer. Using the US public mergers and acquisitions (M&A) deals, this study finds that acquisition premium increases as targets’ perceived CSR quality increases, an effect incremental to previously documented drivers of such premiums. Additional findings reveal that the positive association between targets’ CSR quality and acquisition premiums is stronger for large targets and acquirers with high CSR performance. The study also documents that targets’ environmental performance has the strongest effects on acquisition premiums compared to other dimensions of CSR such as community, employees, diversity, or product. This study contributes to the literature by documenting the value of CSR in an unconventional manner using evidence from the M&A market. The findings are robust after controlling for the target-specific, acquirer-specific, deal-specific, and macro-economic variables and by using different proxies for CSR and acquisition premiums.

This study investigates a novel dimension of ESG (environmental, social, and governance), namely the degree of inequality in the distribution of a firm’s overall ESG performance across the three pillars. By grounding our arguments on the agency theory, we argue that such a dimension can discern the degree of authenticity of managers’ ESG awareness. A more unequal distribution might be due to a discretionary and self-interested adoption of ESG principles in order to win the favour of key stakeholders. Using a sample of U.S. listed companies, we provide empirical evidence that disparity in ESG scores between pillars detracts value from ESG engagement. Moreover, such a negative moderating effect worsens in companies that are more prone to agency problems (e.g. higher cash holdings), lack ESG-based compensation, have lower leverage, and are more exposed to the investor spotlight (e.g. higher analyst coverage). Overall, our findings suggest the importance of accounting for managerial motivations to engage in ESG and support the idea that a lower perceived authenticity of these programmes results in lower value outcomes.

Using data on firms’ carbon emissions in 28 countries, we document that a firm’s initiation of carbon reduction activities relates to lower tax payments. This result endorses a perception of legitimacy theory suggesting that companies can gain legitimacy with tax authorities by adopting carbon reduction strategies. Our study also shows that the positive relationship between lower tax payments and a firm’s carbon reduction leads to higher Return on Assets (ROA), particularly for firms with lower operating performance. Moreover, our study documents that firms that engage in carbon reduction activities can mitigate their tax burden in countries that impose a carbon tax, have higher media freedom, judicial independence, and robust legal systems. These various institutions within a country can influence the relationship between a company’s carbon emission reductions, lower tax payments, and its overall performance. Our findings are robust even when using alternative measures of carbon emissions, control variables for corporate social responsibility, and the entropy-balance or propensity score matching sample.

Using World Bank survey data, we document that banks extend more favorable loan terms to borrowers in countries with more lending corruption. This relation is stronger when borrowers have financing constraints but weaker in countries with stronger monitoring of foreign bank ownership or with stronger religiosity. We also find that banks in countries with high lending corruption have poor loan quality and earnings performance and are more susceptible to trouble during a financial crisis. Overall, our findings suggest that corruption “greases the wheels” for borrowers but is detrimental to bank shareholders.

This paper investigates how major customer firms, managed by highly capable managers, can gain bargaining power over their suppliers. Our results document a positive association between managerial ability and the supply chain power a major customer firm holds over its suppliers. The results are robust to endogeneity concerns, tested through two-stage least squares (2SLS) regressions and difference-in-differences estimates surrounding forced CEO turnovers. We find the positive association to be stronger for durable goods manufacturers and higher ability managers engaged in socially responsible activities and corporate innovation. We provide evidence that higher-ability managers use their enhanced bargaining power to secure greater supplier trade credit.

This study uses machine learning techniques to construct financial distress prediction (FDP) models for Chinese A-listed construction companies and compares their classification performance with conventional Z-Score models. Three machine learning algorithms (Classification and Regression Tree, AdaBoost, and CUSBoost) are used to generate machine-learning-based classifiers, and four Z-Score models (Altman Z-Score, Sorins/Voronova Z-Score, Springate, and Z-Score of Ng et al.) are selected for comparison. The sample comprises 1782 firm-year observations from Chinese A-listed construction companies on the Shenzhen and Shanghai Stock Exchanges from 2012 to 2021. The out-of-sample predicting performance of the classifiers are measured using the areas under the receiver operating characteristic curve (AUC) and under the precision-recall curve (AUPR). In additional tests, Pearson's correlation coefficients and the variance inflation factor are utilized to identify correlations among the raw financial predictors, while principal component analysis is used to address high-correlation issues among the features. Results confirm that machine learning classifiers can effectively predict financial distress for Chinese A-listed construction companies and are more accurate than Z-Score models. Furthermore, the CUSBoost classifier is identified as the most precise model based on the AUC and AUPR metrics in both primary and additional tests. This study addresses the gap concerning the application of machine learning in FDP for Chinese-listed construction companies. Additionally, the CUSBoost Algorithm is introduced into the field of FDP research for the first time. Through the comparison of machine learning and Z-Score models, this study also contributes to the literature related to the contrast between machine learning and statistical modeling techniques.