Journal of Corporate Accounting and Finance最新文献

This study examines the financial performance of North American firms, accentuating the significant role of total assets in determining profitability, measured through EBITDA. By incorporating traditional econometric models—Fixed Effects and Random Effects—with machine learning techniques, exclusively Support Vector Machines (SVM), the research assesses both explanatory and predictive capabilities. The findings reveal that total assets significantly influence EBITDA, with the Fixed Effects model offering robust explanatory power (adjusted R² = 77.86%) and SVM exhibiting superior predictive accuracy (89.4%). Additionally, the nuanced effects of financial ratios, such as liquidity and leverage, emphasize their indirect contributions to financial performance. These results offer actionable insights for stakeholders to optimize asset utilization and financial strategies. This study's implications extend to advancing predictive methodologies in financial analysis, providing a scalable framework for analyzing profitability across diverse firms and contexts. This research specifies constructive insights for stakeholders, investors, and policymakers, enhancing their understanding of the financial dynamics of North American firms.

This study examines the effects of the deliberation and adoption of SFAS 141 and 142 on loan spreads. The deliberation process for guidance may trigger uncertainty and an ultimate adoption leads to revised guidance. Each of these activities can impact capital costs. Using industries with insignificant goodwill as a comparison group, I analyze the changes in loan spreads in industries with significant goodwill and find an increase in loan spreads during the deliberation period of the standards and no change after the adoption. These results contribute to our understanding of the effects of goodwill accounting on credit market participants as well as the contracting effects of the deliberation process.

Numerical anchoring and conformity to a known view are two prominent biases exhibited by auditors. While both influence auditors’ judgments and reduce decision quality, research has not explored a common source: their creation by a superior to whom one is accountable. It is unclear how these biases interact and how they can be mitigated. We use three experiments to examine how accountability and experience influence numerical anchoring in auditors’ risk assessments with a known perspective. We also test an intervention to reduce auditors’ anchoring and conformity. In Experiment 1, we investigate the impact of a manager-provided anchor on experienced and inexperienced auditors, finding both are susceptible to conforming to the manager's numerical perspective. However, inexperienced auditors are more vulnerable to this bias. In Experiment 2, we increase thoughtfulness in inexperienced auditors by having them perform a risk factor evaluation before their client-specific risk assessments. Such intervention helped inexperienced auditors generate risk assessments that were more consistent with those of experienced auditors under accountability. In our final experiment, we demonstrate that primed thoughtfulness and reduced accountability helped inexperienced auditors reach judgments consistent with experienced auditors in a control group with no known views.

Using minute-by-minute trading volume data from five ETFs (SPY, DIA, IWM, DDM, and DOG) over the period from 2013 to 2021, this paper examines the intraday trading puzzle and uncovers patterns beyond the typical U-shape. Even after controlling for potential spurious effects—such as market open/close and Wednesdays—we find persistent, albeit with reduced magnitude, statistically significant trading volume spikes at 5-minute intervals across all ETFs. Hence, while open and close, as well as Wednesdays, contribute to the pattern, they do not fully explain it. The spikes are more pronounced during negative return periods, and our models suggest increased randomness in the distribution of trading activity over time. While these predictable spikes may stem from algorithmic trading biases, we propose they could be a legacy of older algorithms influencing newer ones as they feed from historical data. The results suggest that a puzzling trading pattern persists even for highly traded ETFs; hence, it provides important findings for investors exposed to these securities.

As digitalization reshapes corporate structures and capital markets, traditional methods of assessing firm valuation and market risk are increasingly insufficient, especially for technology-intensive companies. This study investigates whether the financial characteristics that define technology firms, such as high Price to Earnings Ratios (PER) and market volatility, are rooted in sectoral identity or in fundamental financial indicators. Using a cross-sectional dataset of publicly traded firms from multiple sectors, we conduct both comparative descriptive statistics and two multiple linear regression models to explain PER and 5 year beta (Beta5). The results reveal company-specific indicators, such as return on equity, price momentum, pre-tax earnings, and the composition of intangible assets, are the main drivers of both valuation and systematic risk. Sector classification itself does not exert a statistically significant influence once these variables are controlled for. Moreover, technology firms exhibit elevated PER and market capitalization due to superior growth and profitability, not speculative excess. Conversely, firms from traditional sectors display higher operational efficiency, as indicated by stronger EBITDA margins. These findings challenge the notion that digital firms are systematically overvalued and highlight the need for integrated valuation risk frameworks that account for intangible-driven value in the modern economy.

In recent years, global economic uncertainty has intensified, making it crucial for export-oriented enterprises to bolster their export resilience to withstand external risks. This study uses the 2012 release of the “Green Credit Guidelines” by the former China Banking Regulatory Commission as a quasi-natural experiment, using data from China's listed manufacturing firms between 2009 and 2016. We conduct a difference-in-differences model to investigate experimentally how green financing policies affect export resilience. According to the findings, green finance policies considerably improve the export resilience of non-heavily polluting enterprises (NHPEs) relative to their heavily polluting counterparts (HPEs). We conduct a number of robustness tests to guarantee the validity of our conclusions. Mechanism analyses reveal that green finance policies improve export resilience through the effects of green innovation, alleviating financing constraints, and enhancing ESG performance. Heterogeneity tests indicate stronger impacts in state-owned enterprises, large-scale firms, and firms with longer operating histories. Additionally, higher levels of regional digital inclusive finance and greater industry market concentration further reinforce export resilience. Further analysis demonstrates that green finance policies not only improve NHPEs' risk resistance but also enhance their export recovery capabilities. For businesses looking to use green finance to increase export resilience, these findings provide insightful information.

In this study, we experimentally examine the effects of the critical audit matter (CAM) industry norm and auditor-CEO social tie on investors’ financial reporting risk (FRR) assessments. Using online participants, we find that non-professional investors assess the lowest FRR when the CAM is consistent with the industry norm and the auditor-CEO social tie is absent than when the CAM is inconsistent with the industry norm or when the auditor-CEO social tie is present. Our findings also suggest that the presence of social tie negatively affects investors’ investment interests, lowers their perceptions toward audit quality and the auditor, and increases auditor liability for a CAM-related misstatement. Moreover, we find that a norm-consistent CAM leads to better investment decisions, higher reliance on audit reports, but also higher perceived management liability for a CAM-related misstatement. The findings of our study can be informative to management and auditors, as well as regulators.

This study assesses the impact of carbon emissions on firms' default risk in 44 major countries for overcoming the “tragedy of the horizon.” Carbon emissions affect firms' risk profiles at risk horizons through physical and transitional climate risks. A critical management issue is the likelihood and magnitude of climate change's impacts affecting firms' credit risk. We propose a random-effects logistic generalized least squares regression model for panel data to examine the impacts and the firm heterogeneity by country and sector. The global and country-wise panel data analyses show that carbon emissions increase firms' PDs in the global and four-country markets for all risk horizons, whereas the sector-wise analyses show that high carbon emissions reduce firms' PDs only in the industrials sector out of four sectors. In addition, for three countries (the United States, the United Kingdom, and Japan) and four sectors, the longer the risk horizon, the more the heterogeneity represented by the between-firm residual variance decreases, whereas for China, the heterogeneity with Scope 3 fluctuates. Overall, this study provides an effective credit risk analysis methodology that considers carbon emissions for related entities, such as firms, lenders, investors, regulators, and policymakers.

This study analyzes how Critical Audit Matter (CAM) disclosures affect investor perceptions and market responses to discretionary reporting, by examining market reactions to the interaction between the textual characteristics of CAM disclosures and both discretionary (DLLP) and non-discretionary (NDLLP) loan loss provisions in the banking sector. The results show that discretionary loan loss provisions are generally associated with a positive market reaction, as they may signal managerial confidence or financial strength. However, this reaction is reduced when CAM disclosures indicate audit complexity, contain a negative tone, or include uncertain language—leading to increased return volatility around the 10-K filing date. These findings highlight the role CAM disclosures play in shaping how investors interpret managerial judgment in financial reporting. More broadly, the results carry regulatory implications by offering evidence on the informational value of CAM disclosures and their effectiveness in supporting investor decision-making in complex reporting environments.