10-K Reports Research Articles

Economic policy uncertainty and narrative R&D disclosures

This study examines the impact of economic policy uncertainty on narrative R&D disclosure. Analyzing 10-K reports from 1997 to 2020, we find that the quantity of R&D disclosure increases with economic policy uncertainty. We also document that firms provide more forward-looking R&D disclosure during uncertain periods. Finally, we find that such impact is more pronounced for the firms with high return volatility and firms in the early-stage of business cycle. We contribute to the literature by showing how managers adjust R&D disclosures in response to economic policy uncertainty.

Environmental risk factor disclosures in the 10-K reports of U.S. energy companies

Purpose: This study examines changes in the volume and specificity of environmental risk factor disclosures in the 10-K reports of U.S. energy companies between 2017 and 2021. Methodology/approach: A total of 210 reports were analyzed using the content analysis method, a specificity index, the Wilcoxon signed-rank test and the Pearson correlation coefficient. Findings: Starting from the 2019 reports, the volume of environmental risk factor disclosures increased significantly year on year. In the 2021 reports, energy companies provided, on average, 31% more information than in 2017 reports (measured by word count). This growth was due to changes in the volume of disclosures on climate-related risk factors. However, the increase was not accompanied by an increase in detail. The level of specificity in the disclosure of both climate-related and other environmental risk factors was low (with the former being higher) and did not change significantly over the period considered. In general, boilerplate language was often used when dis-cussing environmental risk factors. Research limitations/implications: The findings suggest the need for regulators to detail requirements for specific disclosures on environmental risk factors. Originality/value: Previous studies based on 10-K reports examined the disclosures regarding any risk, tax risk, mergers and acquisitions risk, and security risk. This pa-per is the first to analyze the volume and specificity of disclosures devoted to environ-mental risk factors. In addition, it provides a more complete picture of environmental risk reporting practices by considering climate-related and non-climate-related risk factors separately.

FETILDA: Evaluation Framework for Effective Representations of Long Financial Documents

In the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission. These documents are typically very long and tend to contain valuable soft information about a company’s performance that is not present in quantitative predictors. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators. In recent years, there has been great progress in natural language processing via pre-trained language models (LMs) learned from large corpora of textual data. This prompts the important question of whether they can be used effectively to produce representations for long documents, as well as how we can evaluate the effectiveness of representations produced by various LMs. Our work focuses on answering this critical question, namely, the evaluation of the efficacy of various LMs in extracting useful soft information from long textual documents for prediction tasks. In this article, we propose and implement a deep learning evaluation framework that utilizes a sequential chunking approach combined with an attention mechanism. We perform an extensive set of experiments on a collection of 10-K reports submitted annually by U.S. banks, and another dataset of reports submitted by U.S. companies, to investigate thoroughly the performance of different types of language models. Overall, our framework using LMs outperforms strong baseline methods for textual modeling as well as for numerical regression. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs for representing long documents can improve the quality of representation of textual data and, therefore, help in improving predictive analyses.

CEO succession origin and annual reports readability

We examine the association between the origin of chief executive officer (CEO) succession (i.e., promoting a CEO from within the firm as opposed to recruiting from outside) and annual reports readability. Based on a sample of large U.S. companies during the period 2004–2020, we predict and find that companies with insider CEOs issue more readable 10-K reports compared to those who hire from outside. This positive association between insider CEOs and readability is less pronounced for insider CEOs who wield greater power. We further document that the association is stronger if CEOs have a financial background. To further explore the underlying mechanism, we test whether the association is driven by earnings management or firms’ business strategies. Our results show that the effect of CEO succession origin on readability is primarily attributable to business strategies. We address endogeneity issues and check the robustness of our results in several ways. Finally, we document important policy implications emerging from our study.

Trade secret laws and initial public offering underpricing

This paper examines the impact of trade secret laws on the underpricing of initial public offerings (IPOs) in the United States, where we focus on the Uniform Trade Secret Act and/or the inevitable disclosure doctrine. Given that trade secret laws help firms protect their know-how in the form of trade secrets, we propose that trade secrets protected by these laws may result in increased corporate opacity, leading to greater IPO underpricing. Empirically, the average first-day returns of IPOs in states with trade secret laws are 12.7% higher than those in states without such laws. Using the information disclosed in the 10-K report on whether the firm owns trade secrets, we suggest that the existence of trade secret laws enhances the protection effect of firm maintenance of trade secrets, which leads to greater IPO underpricing. Further analyses show that the effect of trade secret laws is reinforced for IPO firms operating in complex industries, IPO firms with R&D investments, and large IPO firms, suggesting that trade secret laws increase the difficulties investors face in assessing the intrinsic value of such firms.

Impression management strategy — The relationship between accounting narrative thematic bias and financial graph distortion

Prior literature has examined 10-K narrative thematic bias and financial graph distortion as two independent outcomes that might arise from managements' efforts at impression management. Largely unexplored is an analysis of narrative thematic bias and financial graph distortion as joint and interrelated outcomes that would arise if management coordinates both in the same 10-K report as part of an impression management strategy. We fill this void in the literature by using a simultaneous equation system to examine the joint relationship between narrative thematic bias and graph distortion in the 10-K filings of S&P 500 firms from 2014 to 2018. We draw upon Paivio's (1986) dual coding theory to predict a positive association between narrative thematic bias and financial graph distortion based on the insight that graph distortion helps reinforce the effects of thematic bias, and vice versa. Consistently, we find a simultaneous and positive relationship between thematic bias and graph distortion. Further, we find that this complementary relationship is more pronounced in firms with weak corporate governance and weak external monitoring. Our findings suggest that management may exploit the reinforcing effects of thematic bias and graph distortion to leave financial statement users with a more favorable impression of firm performance.

Management’s tone change in MD&A and tax avoidance

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

Accounting fraud detection using contextual language learning

Accounting fraud is a widespread problem that causes significant damage in the economic market. Detection and investigation of fraudulent firms require a large amount of time, money, and effort for corporate monitors and regulators. In this study, we explore how textual contents from financial reports help in detecting accounting fraud. Pre-trained contextual language learning models, such as BERT, have significantly advanced natural language processing in recent years. We fine-tune the BERT model on Management Discussion and Analysis (MD&A) sections of annual 10-K reports from the Securities and Exchange Commission (SEC) database. Our final model outperforms the textual benchmark model and the quantitative benchmark model from the previous literature by 15% and 12%, respectively. Further, our model identifies five times more fraudulent firm-year observations than the textual benchmark by investigating the same number of firms, and three times more than the quantitative benchmark. Optimizing this investigation process, where more fraudulent observations are detected in the same size of the investigation sample, would be of great economic significance for regulators, investors, financial analysts, and auditors.

CFO Accounting Expertise and SEC Comment Letter Process

In this study, we explore the expanded role of chief financial officers (CFOs) in facilitating corporate disclosure by examining how their accounting expertise influences the resolution process of Securities and Exchange Commission (SEC) comment letters issued on 10-K reports. Using CFO professional profile data collected extensively through EDGAR, Lexis-Nexis, and several online searches, we find that CFO accounting expertise improves the resolution of SEC comment letters by reducing the remediation costs and the likelihood of unfavorable resolution outcomes. We also show that the effect of CFO accounting expertise is more pronounced when the CFO’s involvement in the resolution process is greater, and the SEC comment letter includes comments on accounting topics. Our findings are robust to a battery of robustness tests. Overall, this study highlights the importance of CFOs’ role and function in regulatory compliance. JEL codes: G14; G30; M40; M41; M48

Accounting executives and readability of 10-K reports

ABSTRACT This study investigates how the presence of accounting executives is related to the readability of narratives in 10-K reports. We find that companies with accounting executives tend to prepare more readable 10-K reports than those without. Furthermore, we document that the higher the accounting executives’ positions within the executive group hierarchy, the better the readability of the 10-K reports. Overall, findings suggest that the accounting executives’ characteristic of pursuing precise information explains the information delivery pattern of 10-K reports. The results expand two strands of literature: one for the determinants of 10-K readability and the other for the role of the accounting profession in the executive team.

US-Russia Geopolitical Risk and Corporate Cybersecurity Risk: Evidence from 10-K Reports

US-Russia Geopolitical Risk and Corporate Cybersecurity Risk: Evidence from 10-K Reports

Discovering High-Risk Bank Risk Factors Based on Risk Matrix

Bank risk management is a crucial issue in the stability of the financial system. How to select high-risk factors that make banks in trouble and how these factors affect bank risks have always been a core problem. Previous studies comprehensively identified bank risk factors from textual risk disclosures and used the disclosure frequency of risk factors to determine important factors to which banks should pay more attention. This paper creatively constructs the textual risk matrix with frequency and sentiment of risk factors to divide bank risk factors into the high-risk category, mid-risk category, and low-risk category. Then we explore the impact of different categories of risk factors on bank risk and the risk perception of investors. Based on 457,383 sentences of 2,735 Form 10-K reports of 240 American commercial banks from 2006 to 2020, 33 bank risk factors were identified. Three risk factors belong to in high-risk category, including loan loss risk, regulation risk, and interest rate risk. Three factors are classified in the mid-risk category and 27 risk factors are low-risk factors. The regression results show that compared with individual bankruptcy risk, risk factors have better prediction and interpretive ability on the systemic risk. The disclosure of bank risk factors will affect the investors’ risk perception, especially the worse risk situation of the high-risk factors will increase the risk perceived by investors.

Textual disclosure complexity and analysts’ weighting of information

This study investigates the influence of textual complexity in firms’ annual reports (Form 10-K) on financial analysts’ weighting of information. Drawing on a sample of U.S.-listed companies spanning from 1994 through 2020, we demonstrate that analysts tend to under-weight private information when 10-K reports are more readable, especially when the disclosed events indicate positive news, and when analysts face high workloads. Additionally, we find that analysts can identify managers’ opportunistic disclosure practices and rationally allocate weight between private and public information. Our findings suggest that analysts are affected by the underlying reporting and business complexity but not misled by the discretionary readability of 10-K reports when weighting information.

When attention to climate change matters: The impact of climate risk disclosure on firm market value

Concerns about climate change and its potential economic impact have prompted policymakers and institutions to introduce standards and principles to encourage the disclosure of company risks related to climate change. Past research demonstrated that such disclosure positively impacts firm stock prices. This study analyzes the magnitude and significance of climate risk disclosures within 10-K and 10-Q reports of U.S. companies through the application of novel methods in text mining and social network analysis. Furthermore, it measures the level of attention directed towards climate change at the firm level by analyzing transcripts from earnings conference calls. The study contributes to the literature by investigating the effect of climate risk disclosure on firm market value and considering where such disclosure occurs. The findings demonstrate a positive relationship between climate risk disclosures and firm value. However, this relationship can turn negative when the attention to climate change intensifies. The results of this study are of particular relevance for practitioners and policymakers who are provided with a novel instrument to quantify the magnitude of climate change risk disclosure in textual data. Regulators can identify firms particularly exposed to climate change and create incentives for disclosure, especially when firms may be disincentivized to share information about climate change risks.

Financial Analysis and Valuation on Southwest Airlines

This essay offers a thorough analysis of Southwest Airlines' recent performance, especially in light of the COVID-19 pandemic, which had a major effect on the aviation sector. The paper analyzes Southwest's financial statements using accounting and ratio analysis to evaluate the company's liquidity, leverage, profitability, and efficiency. The results are compared to those of its competitors using data from the 10-K reports. The paper also assesses important accounting policies and notes made by the company to provide a thorough understanding of its financial performance. Furthermore, the paper evaluates the airline industry's recovery and how it is expected to affect Southwest's future performance. The research highlights how the decreased number of travel restrictions has led to the industry's gradual recovery and the potential for increased demand for air travel. The paper also discusses how Southwest Airlines can leverage this opportunity to enhance its performance in the future. Finally, the paper provides investment recommendations based on the findings of the analysis. The paper argues that Southwest Airlines is a worthy investment due to its solid financial position, strong liquidity, and efficient operational performance. The paper also identifies potential risks that investors should be aware of before investing in the company. Overall, this paper is a valuable resource for investors who are interested in making informed decisions about investing in the airline industry.

Does XBRL Tagging Indicate Disclosure Quality? The Relationship Between XBRL Standard and Extension Tags and Stock Return Synchronicity

ABSTRACT We propose new measures of financial disclosure quality based on XBRL standard and extension tag counts. XBRL is designed to allow users to analyze more financial statement detail, thereby increasing reporting transparency. Thus, firms that use more XBRL standard tags should produce more detailed and comparable financial reports. To test this assertion, we examine the association between XBRL tag counts and stock return synchronicity, which measures the market’s reaction to the quality of firms’ disclosures. We find that firms that use more standard taxonomy tags in their 10-K reports have higher stock return synchronicity after the 10-K filing. However, we find that stock return synchronicity decreases as XBRL extension tag counts increase, suggesting that the use of extension tags reduces disclosure quality. Our findings contribute to the growing body of research on XBRL disclosure. Data Availability: Data are available from the public sources cited in the text.

Do Family Firms Issue More Readable Annual Reports? Evidence From the United States

Using a sample of 22,442 firm-year observations for 3,721 U.S. listed firms, we show that family firms, on average, issue annual reports with higher readability than non-family firms. Higher readability could occur due to lower obfuscation or less information conveyance. By controlling complexity and choosing readability measures linked to obfuscation, we attribute the higher readability to lower obfuscation. Our investigation into the heterogeneity in family firms shows that the positive effect of family control on reporting readability exists for eponymous family firms but not for non-eponymous family firms. We also find that family firms managed by founders or heirs issue more readable 10-K reports than non-family firms, but family firms managed by outsiders do not exhibit such a difference. Cross-sectional analyses show that the difference in readability between family and non-family firms diminishes for firms with more earnings manipulation, weaker board governance, and dual-class shares. Further, we find that investors perceive family firms’ annual reports with higher readability to be more informative. Finally, we use state-level succession tax cuts as an exogenous shock to link the higher readability to family insiders’ incentives and preferences. Our findings are consistent with the view that family insiders’ incentive to maintain family reputation contributes to lower obfuscation in 10-K narrative disclosures.

Does better firm information disclosure enhance institutional blockholder monitoring on information asymmetry? Evidence from 10-K readability

Using the Plain Writing Act of October 2010 as a source of exogenous variation in 10-K readability, we examine whether improvements in 10-K reports enhance institutional investor monitoring of firms' information environments. This study reveals that the negative association between institutional monitoring and information asymmetry is more robust when firms provide 10-K reports with enhanced readability. Since the implementation of the Act, the impact of improved readability on the relationship between institutional monitoring and information asymmetry has become increasingly pronounced. This finding confirms our hypothesis that improved readability provides better firm information for institutional investor monitoring of firms, thus decreasing information asymmetry.

Generalist CEOs and the readability of the 10-K report

In this paper, we investigate the association between the general managerial ability of CEOs and the readability of 10-K reports. We find that the readability of 10-K reports is lower for firms managed by CEOs with general managerial ability. Our result is robust to change analysis, an alternate readability measure, various fixed effects, an instrumental variable approach, a propensity score approach, and an entropy balancing approach. Our additional analysis reveals that general managerial ability is negatively associated with the readability of management discussion and analysis (MD&A). Moreover, the disclosure tone of 10-K reports and MD&A is conservative when firms are managed by generalist CEOs. Our findings also reveal that CEO tenure moderates the positive association between the general ability index and Gunning Fog index of 10-K reports. Finally, we find that high investment level and misstatement strengthen the association between the general ability index and the readability of 10-K reports, thus supporting the obfuscation hypothesis. We, therefore, conclude that firms incur costs in the form of lower disclosure quality when they opt for a generalist CEO.

When ESG talks: ESG tone of 10-K reports and its significance to stock markets

Since the ESG topic consistently gains on importance in the investment universe, companies provide investors with information regarding recent and future ESG activities through different reporting channels. The most recent research finds relevance of ESG-related corporate activities for formation of investors' opinion regarding companies' valuations and growth prospects. Based on a sample of more than seventeen thousand unique 10-K reports of US companies filed with SEC in period 2013 to 2019 and the word-power methodology proposed by Jegadeesh and Wu (2013), this study also shows evidence for significant relation of ESG textual tone of 10-K reports to stock market returns of filing companies around the report filing dates. Using the ESG linguistic dictionary recently proposed by Baier, Berninger, and Kiesel (2020), this study shows evidence for significant relation of social and governance-related topics disclosure to stock returns, while environmental narratives being ignored by the markets. When looking at individual words from the ESG lexicon, such words as “community”, “health”, “control” imply positive reaction of markets, while “discrimination”, “embezzlement”, and “crime” are related to negative returns. The robustness analysis based on the inverse document frequency word weightings and actual ESG performance scores confirms the significance of ESG information disclosure of 10-K reports for investors. Thus, this study sheds light on the mechanics of ESG information perception and its influence on capital markets.