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Purpose The purpose of this paper is to investigate whether financial companies of the USA are inclined to manipulate the management discussion and analysis (MD&A) tone and thus to follow impression management behaviours. Also, the paper proposes a tone analysis of MD&As conducted by comparing the tone of MD&As of one year with financial conditions of the same year and the next. Design/methodology/approach The tone analysis is conducted on two sub-samples of US-listed financial companies, unhealthy firms and healthy firms, which experienced different financial conditions between 2002 and 2011. Findings With regard to healthy firms, MD&A tone is useful to explain the current year’s performance and helps to predict next year performance, whereas, with reference to unhealthy companies, managers use the tone to pursue impression management strategies, by using more positive words and more future-oriented words than healthy companies. Research limitations/implications This study analyses the correlation between MD&A tone at time t and financial performance at time t and t+1, it does not investigate other time spans. The empirical results of this study cannot be generalized to other countries. Practical implications Main implications are addressed to regulators and policy makers, which may contrast impression management through a more effective regulation. Another implication regards investors, who cannot fully rely on MD&As of unhealthy companies. Originality/value This study analyses financial companies, rather neglected by the literature on MD&A tone. Results suggest that financial firms are also inclined to engage in impression management. This research would be useful for investors who base their decisions on qualitative analysis, interested in understanding to what extent the MD&A narratives are reliable.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value This study associates the manager’s emphasis on audit, measured with computational linguistics from the MD&A, with realized audit quality.

PurposeThis study examines the impact of retail investor attention on the quality of management discussion and analysis (MD&A) sections in the annual reports of A-share main board companies listed in China, framed within the context of limited attention theory.Design/methodology/approachThis study analyzes textual data from the MD&A sections of annual reports and interaction data from the investor-firm interaction platforms of the Shanghai and Shenzhen Stock Exchanges in China, covering 2015–2019. It applies the Continuous Bag of Words (CBOW) model and t-SNE dimensionality reduction technique to construct an indicator for MD&A information content. Additionally, the study assesses the moderating effects of factors, including state ownership, media coverage, regulatory inquiry letters and economic policy uncertainty, on the relationship between retail investor attention and MD&A quality.FindingsThe results show that retail investor attention significantly enhances MD&A quality, primarily through the mechanism of signal transmission. This relationship is moderated by factors including state ownership, media coverage, regulatory inquiry letters and economic policy uncertainty. Moreover, the study indicates that the positive impact of retail investor attention on MD&A quality significantly reduces the risk of stock price crashes and improves firm performance.Originality/valueThis study contributes to the literature by providing new insights into the role of retail investor attention in enhancing MD&A disclosure quality, highlighting retail investors as an informal monitoring mechanism that supports capital market stability. It addresses a gap by incorporating limited attention theory into the analysis of retail investor attention and MD&A quality and introduces an innovative approach to measure MD&A information content. The findings offer valuable implications for improving disclosure practices among listed companies.

The aim of this study is to examine the impact of corporate characteristics on voluntary disclosures of management discussion and analysis (MDA) reports in India. Using a formal tone, the data was extracted from the annual reports of the top 100 listed firms available on the CMIE Prowess database for seven years (2016–2022). After excluding 23 companies from the financial and insurance sector, a panel regression method with the assistance of Gretl software was employed to investigate the relationship between the Management Discussion and Analysis Disclosure Index (MDADI) for voluntary aspects and various corporate attributes, with a total of 490 firm years of balanced observations. In India, firms follow the mandatory compliance of the MDA reports, but voluntary disclosures are somehow those which are not much emphasized but are a good indication of firm performance and their accountability towards their stakeholders (Mayew et al., 2015). Our empirical findings reveal that profitability as a proxy to firm performance has a significant positive relationship with MDA voluntary disclosures. Further, an insignificant association between VDS (Voluntary Disclosure Score) and the board size, presence of independent directors and firm size was found. This indicates that firm performance plays a significant role in adding more voluntary disclosures in MDA reports. The possible reason for this could be the use of “Management Impression Strategy” in the MDA reports, which means managers disclose more only when the firm has earned more and use impressive language to attract stakeholders. The outcomes of this research offer valuable insights for regulators, policymakers, and listed companies in India, aiding in the enhancement of MDA reporting quality. Additionally, this study provides a roadmap for future research on MDA reporting quality and corporate attributes in other emerging countries that have similar regulatory frameworks. This paper makes a timely and pertinent contribution to the scholarly discourse by shedding light on the relationship between MDA disclosures and firm attributes. Its findings provide valuable insights for both academia and industry.

This study examines the evolving information environment in a sample of post-IPO firms from 2010 to 2020 by exploring the relationship between firm-level manager sentiments and excess buy-and-hold stock returns. We confirm that there is a negative relationship between the full-text manager sentiment and the long-term excess buy-and-hold returns from 1 to 12 months after corporate filings. However, there is no positive association between manager sentiment and contemporaneous (e.g., 4-day) event-window announcement returns as documented in prior studies. The mispricing effect captured by the firm-level manager sentiment is less severe in the later years post IPOs, indicating the degree of information asymmetry is a dynamic phenomenon as the firms become more established over time. We also find the manager sentiment from the section of Management Discussion and Analysis (MD&A) is less optimistic about the firm performance than the full-text manager sentiment, and there is no mispricing effect of the MD&A manager sentiment on stock returns. These findings shed valuable insights on the dynamic information environment in the post-IPO firms from a unique perspective.

An accurate prediction of financial distress is beneficial to investors and allows banks and other financial institutions to build an early warning system to avoid risk contagion. This study investigated financial distress prediction using textual sentiment extracted from listed firms’ annual reports in the Chinese market. The sentiments reflected by the firms’ management discussions and analysis (MD&A) sections and audit reports were extracted separately through the application of deep learning algorithms. We found that the sentiment score extracted from MD&A sections was more optimistic compared with that extracted from audit reports. Moreover, the experimental results demonstrated that the modeling performance was significantly improved with the incorporation of textual sentiment scores, and the inclusion of sentiment from audit reports lead to a more significant incremental improvement than that from the MD&A sections. However, when both sentiment scores were included in the modeling input, the improvement in predictive accuracy was insignificant compared to the model using audit report scores only. Our study highlights the predictive power of textual information in annual reports, and shows that the textual sentiment of annual reports should be applied in distress modeling. The results provide implications for the utilization of soft information in credit risk modeling in the context of Chinese market, and such application can be further explored in other areas of operational research studies.

Beginning in 2005, the Securities and Exchange Commission (SEC) mandated firms to include a “risk factor” section in their Form 10-K to discuss “the most significant factors that make the company speculative or risky.” In this study, we examine the information content of this newly created section and offer two main results. First, we find that firms facing greater risk disclose more risk factors, and that the type of risk the firm faces determines whether it devotes a greater portion of its disclosures towards describing that risk type. That is, managers provide risk factor disclosures that meaningfully reflect the risks they face. Second, we find that the information conveyed by risk factor disclosures is reflected in systematic risk, idiosyncratic risk, information asymmetry, and firm value. Overall, our evidence supports the SEC’s decision to mandate risk factor disclosures, as the disclosures appear to be firm-specific and useful to investors.

We use machine learning methods to create a comprehensive measure of credit risk based on qualitative information disclosed in conference calls and in management’s discussion and analysis section of the 10-K. In out-of-sample tests, we find that our measure improves the ability to predict credit events (bankruptcies, interest spreads, and credit rating downgrades), relative to credit risk measures developed by prior research (e.g., z-score). We also find our measure based on conference calls explains within-firm variation in future credit events; however, we find little evidence that the measures of credit risk developed by prior research explain within-firm variation in credit risk. Our measure has utility for both academics and practitioners, as the majority of firms do not have readily available measures of credit risk, such as actively-traded CDS or credit ratings. Our study also adds to the growing body of research using machine-learning methods to gather information from conference calls and MD&A to explain key outcomes.

This study proposes an AI-based model to predict corporate management performance by combining financial data with strategic information extracted from CEO messages in sustainability reports. Using a dataset of 1,271 listed companies on Korea’s KOSPI and KOSDAQ markets (2016–2023), we applied eight machine learning and deep learning classifiers: KNN, SVM, GBM, CatBoost, GAN, RNN, LSTM, and Transformer. Financial variables were selected based on prior accounting research, while strategic variables were derived via text mining of CEO messages and categorized using the Sustainable Balanced Scorecard (SBSC) framework. Results show that models incorporating both financial and strategy-based variables outperformed those using financial data alone. Notably, the Transformer model achieved the highest predictive accuracy, followed by LSTM and RNN. These findings provide actionable insights for investors and corporate stakeholders while advancing interdisciplinary research between accounting and AI. Under 5-fold cross-validation, the best-performing hybrid model (Transformer with SBSC features) achieved Accuracy = 0.8467, AUC = 0.8481, and F1 = 0.8572, and adding SBSC strategy indicators improved mean performance across models (ΔAccuracy=+0.0121; ΔAUC=+0.0092; ΔF1=+0.0119).

This paper examines the information content of the forward-looking statements (FLS) in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naïve Bayesian machine learning algorithm.