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The authors investigate how managers adapt their financial reporting and disclosure practices in response to the COVID-19 pandemic through changes in accounting estimates (CAEs).

The authors define the pandemic period as starting on March 1, 2020. The sample consists of 9,575 CAEs disclosed in quarterly (10-Qs) and annual (10-Ks) financial reports by US firms between January 1, 2004 and May 31, 2022. The authors perform multivariate analyses of the impact of the COVID-19 pandemic on the incidence of CAEs and on whether the impact of CAEs on firms' financial performance and reporting quality changes during the pandemic.

In the examination of the CAE footnote disclosures in the quarterly (10-Qs) and annual (10-Ks) reports of US companies, the authors find no evidence that the incidence of CAEs in 10-Ks or the number of firms reporting CAEs are significantly different in the pre-pandemic and pandemic periods, but the incidence of CAEs in 10-Qs is significantly higher in the pandemic period than in the pre-pandemic period. The authors also find that the number of CAEs related to revenue recognition increase significantly in the pandemic period, but CAEs in other categories decrease, with the sharpest drop seen in the liabilities category. Further investigation suggests that although the dollar impact of 10-K CAEs on current financial statements is higher during the pandemic period, firms with CAEs, especially positive CAEs, in either 10-Ks or 10-Qs are less likely to use CAEs to boost earnings in the pandemic period. However, the authors find evidence that firms tend to use CAEs to “big bath” current earnings and create reserve for future period. The authors have not observed any significant differences in how the various phases of the pandemic affect the reporting of CAEs. Additionally, there is no evidence to suggest that financially distressed firms report more or fewer CAEs during the pandemic.

The results are consistent with the notion that, during the pandemic, firms exercise greater caution in their CAE disclosures, refraining from using CAEs as a means of boosting earnings but as a strategy to create reserve for future period. The paper highlights the challenges that various stakeholders face when assessing a company's current and future financial performance based on management's accounting estimates.

This study captures the impact of the COVID-19 pandemic on the incidence of CAEs and CAEs' impact on the financial performance and financial reporting quality of firms during the pandemic.

This study aims to provide measurable information that evaluates a company’s ESG performance based on the conceptual connection between ESG, non-financial elements of a company and the UN Sustainable Development Goals (SDGs) for resolving global issues.

A novel data processing method based on the BERT is presented and applied to analyze the changes and characteristics of SDG-related ESG texts from companies’ disclosures over the past decade. Specifically, ESG-related sentences are extracted from 93,277 Form 10-K filings disclosed between 2010 and 2022 and the similarity between these extracted sentences and SDGs statements is calculated through sentence transformers. A classifier is created by fine-tuning FinBERT, a financial domain-specific pre-trained language model, to classify the sentences into eight ESG classes.

The quantified results obtained from the classifier reveal several implications. First, it is observed that the trend of SDG-related ESG sentences shows a slow and steady increase over the past decade. Second, large-cap companies relatively have a greater amount of SDG-related ESG disclosures than small-cap companies. Third, significant events such as the COVID-19 pandemic greatly impact the changes in disclosure content.

This study presents a novel approach to textual analysis using neural network-based language models such as BERT. The results of this study provide meaningful information and insights for investors in socially responsible investment and sustainable investment and suggest that corporations need a long-term plan regarding ESG disclosures.

The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms.

The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations.

The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance.

This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics.

The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

This study examines whether investors value the timing and/or information of mandatory disclosures in a unique research setting of listed companies in Thailand.

The authors adopt an event-study based approach. Abnormal stock returns are calculated using an OLS market model to measure market reactions to three types of mandatory reports issued by listed Thai firms: financial statements, Form 56-1 and Form 56-2. These reports are released sequentially but contain overlapping information content. Multivariate regression models are employed to examine the market reactions to these regulatory reports and explore the characteristics of firms that affect the market response.

The stock market reacts differentially to these reports. The financial statements, which are filed the earliest and are the most concise, prompt the strongest reaction. Investors similarly react significantly to Form 56-1 and Form 56-2, although Form 56-2 provides additional information beyond Form 56-1. The market reactions to small firms are stronger. Collectively, equity investors focus on the timeliness of disclosures rather than the information disclosed in the mandatory reports.

The evidence provides support for ongoing regulatory initiatives aimed at improving the timeliness of mandatory disclosures in emerging economies.

Prior studies on disclosure regulation investigate either the effect of information content or the timing of mandatory disclosures in isolation. The authors differentiate the effect of information content from disclosure timing and extend the literature by suggesting that investors incrementally value timeliness of disclosures. Investors perceive the benefit of the timely release of quantitative information compared to subsequent narrative disclosures. Between Form 56-1 and Form 56-2, the earlier release of the narrative non-financial information is incrementally traded into share prices.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

This study investigates the influence of nonfinancial 8-K disclosures released during the earnings announcement window on the abnormal trading activities of individual investors.

We employ regression analysis in this empirical study to examine the impact of nonfinancial 8-K filings on individual investors' abnormal trading activities.

Our results reveal that individual investors exhibit higher levels of abnormal trading activities when firms release nonfinancial 8-Ks during the (0,1) window of earnings announcements. This effect is observed for both buyer-initiated and seller-initiated transactions and is particularly pronounced for firms reporting an operating loss. Negative sentiment in 8-Ks significantly intensifies such effect. Additionally, we find that buy-sell consensus increases significantly with concurrent nonfinancial 8-Ks. This suggests that 8-Ks may reduce information noise, leading individuals to trade with greater conviction.

Our study examines the joint influence of nonfinancial 8-Ks and earnings announcements on individual investors' trading activities, thereby providing a novel perspective on the mechanisms through which 8-K filings affect individual investors' trading behaviors.

The process performed during a financial statement audit is critical but is unobservable to external stakeholders. This can create challenges in assessing the quality of individual audit engagements. This study’s objective is to introduce and investigate an archival measure based on publicly available information that proxies for audit process ineffectiveness.

We proxy for audit process ineffectiveness using errors in the audit report. We examine audit reports to identify errors because the audit report represents the auditor’s primary communication with financial statement users and is subject to rigorous preparation and review. We first examine if typical factors influencing audit process ineffectiveness are associated with audit report errors. We then examine whether audit reports containing errors are associated with audit quality measures.

We find that errors are more likely to be present in audit reports when time pressure exists and less likely when auditors exert more effort and when audit engagement risk is higher. Results also show that errors in audit reports are positively associated with financial reporting misstatements, measured by subsequently disclosed Big R restatements and out-of-period adjustments.

Collectively, our evidence suggests that an audit report containing an error is a suitable proxy for audit process ineffectiveness. This proxy has audit quality implications because inattentiveness in one area of the audit process could indicate inattentiveness in another area.

This case uses an array of carefully selected and excerpted revenue recognition related information contained in Salesforce.com's January 31, 2019, 10-K. Maria, the fictional protagonist, is seeking to understand those disclosures as part of her preparation for an upcoming job interview with the company. As such, she is relying on those disclosures to provide insights as to the company's main product/service lines, the events that signal when and how much revenue the company has earned (i.e., the essence of its business model), along with the related official generally accepted accounting principles (GAAP) criteria pertinent to the valuing and timing of recorded revenues.