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This paper aims to investigate whether the managers’ emphasis on audit in the management’s discussion and analysis (MD&A) section of the 10-K filing, as part of the firm’s “tone at the top,” is linked to audit quality.

Adopting a computational linguistics approach, the authors measure the manager’s audit emphasis as the frequency of audit-related words in the MD&A. The authors then assess the relationship between audit emphasis and audit quality with ordinary least squares and probit regression models.

This study finds that the manager’s audit emphasis, proxied by the count of audit-related words, is positively associated with audit fees, audit delay, the appointment and retention of Big 4 and industry-specialist auditors, and the probability of switching to Big 4 auditors, while negatively linked to abnormal accruals and the possibility of financial misstatements.

The audit emphasis measure suffers from limitations. The computer program determining audit emphasis may misinterpret words in the MD&A. Researchers need to consider procedures to minimize misinterpretations.

Frequency of audit words in the MD&A reflects the firm’s aspiration for audit quality. Auditors, regulators and investors could ascertain such aspiration from past and current MD&As.

This study associates the manager’s emphasis on audit, measured with computational linguistics from the MD&A, with realized audit quality.

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.

This article examines rule amendments issued by the US Securities and Exchange Commission in November 2020, as part of the SEC’s ongoing “disclosure effectiveness initiative”, that revise in significant respects the requirements for financial disclosures presented in SEC filings as Management’s Discussion and Analysis of Financial Condition and Results of Operations.

This article provides an in-depth analysis of the rule amendments in the context of contrasting perspectives expressed by the SEC, individual SEC Commissioners who dissented from adoption of the amendments, and market participants regarding the merits of the SEC’s movement away from prescriptive disclosure requirements towards a more principles-based approach to disclosure.

Although the SEC’s rules have long reflected a mix of principles-based and prescriptive disclosure elements, the principles-based emphasis in this latest stage of the SEC’s disclosure modernization project accords the managements of filing companies greater latitude to determine whether financial information is material to investors and how such information should be presented.

This article provides expert guidance on a major new SEC disclosure development from an experienced securities lawyer.

The goal of the study is to examine the effects of management discussion and analysis (MD&A) sentiment in public companies' annual reports on corporate investment incentives in developing economies.

The authors use sentiment analysis of MD&A texts based on Loughran and McDonald (2011) and combination of panel data regression, logit model and random forest. The text data consists of 3,511 annual reports of Chinese listed companies for the period from 2010 to 2019.

This paper provides empirical evidence of signaling theory that sentiment of annual reports and MD&A influences corporate decisions on both M&A and internal investments. The authors found that comparing to annual reports MD&A sentiment has more stable and significant explanatory and predictive power.

This paper confirms the importance of MD&A sentiment for corporate investment decision taking and provides practical techniques for analysts and researchers to study corporate investment incentives from the point of view of signaling theory.

The study aims to expand the domains of signaling theory and corporate investment valuation by including a broader range of data on companies' M&A and internal investments in developing economies. To explore the impact of MD&A sentiment on corporate investment, a state-of-the-art set of text mining and machine learning techniques is used. The authors' results confirm that MD&A has signaling effect and can get a positive market response. Furthermore, this study enhances the empirical evidence of overconfidence theory, i.e. optimistic management whose MD&A tend to positive overestimates the management's investments decision and also underestimate the potential risk to the firm.

Research has shown evidence of the use of impression management strategies in corporate disclosures as a means of presumably tempering and swaying investors’ perceptions. These impression management strategies include shifts in the tone used when providing disclosures. However, recent research also provides evidence that such techniques can have a contrary effect when the tone of the message appears to be “too good to be true.” This study explores how the use of optimism and certainty in the Management Discussion and Analysis (MD&A) portion of the annual report affects nonprofessional investors’ investment decisions – a class of investors known to heavily rely on the MD&A portion of annual reports. We theorize a bifurcated effect where optimism and certainty have a positive and direct effect on investor willingness to invest, but at the same time optimism and certainty have a negative indirect effect on willingness to invest that is mediated through decreased perceptions of disclosure credibility. The results provide evidence supporting such a bifurcated effect from the use of tone in management disclosures.

This paper reports the results of an extensive study of the effectiveness of MD&As. Our primary research question can be stated as follows: To what extent do MD&As, as currently issued, meet the self‐perceived needs of individual investors? To answer this question, we examine the responses to a survey questionnaire. The content of the questionnaire was based on an earlier survey conducted in 1973 by Epstein (1975). Our results are based on a random sample of shareholders owning at least 100 shares of one stock on either the New York Stock Exchange or the American Stock Exchange. We conclude that the MD&A section of annual reports is a potentially useful investment tool. In its current state, however, investors read it less, and rank it as less useful than the financial statements. Our survey indicates that it is not difficult to understand. We attribute its poor performance, in part, to a lack of credibility, and, to a larger extent, to a lack of prospective information. Evidence on the relationship between demographic characteristics and usefulness shows that wealthy, inexperienced investors are the only group of investors who are currently using the MD&A in any substantial way.

Recent public policy initiatives seek greater transparency in financial reporting through an honest, balanced and thorough management discussion of company performance in the annual report. Management’s discussion invariably includes key performance indicators, such as financial ratios, relevant to external stakeholders. We model the impact of accounting estimates, assumptions, choices and errors on the risk of misleading financial ratios. This framework is illustrated through good and bad examples of financial reporting practices and by simulation of financial data of public companies. We provide a structured approach to inform policymakers, auditors and other stakeholders of the incremental financial reporting risk that accompanies current regulatory efforts.

This study provides experimental evidence on whether and how management's use of self-promotion, as a type of proactive impression management strategies in its disclosures, influences nonprofessional investors’ judgments and decisions. The results show that management's use of self-promotion influences nonprofessional investors so that investors (1) expect management's future performance to be better and (2) are likely to invest more in the company. These positive effects are more prominent when management's credibility is perceived to be high than when it is low. The findings of this study provide implications for both practice and research.

Part IV provides readers with the extant requirements for the application of materiality to recognition, measurement, presentation, and disclosure in the financial statements. This part also includes a detailed critical review of the recent Practice Statement on materiality, the FASB’s proposed ASU on the notes and the amendments to the Conceptual Framework proposed by the IASB and the FASB.

The part expands to issues that are typical of Management Commentary, including the SEC guidance on materiality in Management Discussion and Analysis.

It informs about the complexities and subtle differences between financial statements and bookkeeping and the different standards of reasonableness versus materiality.

A section moves from materiality to material misstatements and covers the application of materiality in auditing.

Another section goes in depth on internal control over financial reporting, showing the linkages between materiality and risk appetite and risk tolerance and the related application guidance.

The purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.

This study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.

Experimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.

This study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.