A central question in corporate governance research is whether executive pay reflects efficient contracting or managerial rent extraction. The efficient contracting view, rooted in classical agency theory (Jensen and Meckling 1976), posits that boards design compensation packages to align managerial incentives with those of shareholders. From this perspective, pay levels simply reflect a competitive market for talent and economic determinants like firm size and value (Gabaix and Landier 2008).

In contrast, the managerial power or rent extraction view (Bebchuk and Fried 2003) argues that executive compensation is not a solution to the agency problem, but a consequence of it. Specifically, it posits that executives can earn excessive levels of compensation due to their power over the board. In line with this view, previous research has shown that excess compensation is associated with weak governance, as proxied by entrenched boards or CEO-chair duality (Core et al. 1999).

A central challenge to the rent extraction view is explaining how this practice persists in the face of mandatory compensation disclosures to enable investor monitoring. One potential mechanism is obfuscation, i.e., increasing the redundancy, complexity, and length of the provided information to draw attention away from key details and making it difficult for investors with limited attention and cognitive processing constraints to draw accurate conclusions (Blankespoor et al. 2019, 2020). In other words, if investors cannot easily process the disclosed compensation information, they are less likely to thoroughly evaluate undeserved excess pay.

In the United States, investors mainly rely on the CD&A section of the proxy statement to understand executive compensation packages. While mandated by the SEC to improve clarity and transparency (SEC 2011), the CD&A in practice is often long and complex. This raises the question whether CD&A sections contain bloat, i.e., irrelevant, boilerplate, and redundant content, and whether the extent of bloat is associated with excess compensation. Although earlier studies have attempted to identify obfuscation in other disclosure contexts using traditional textual analysis methods, such as readability or sentiment (Li 2008; Laksmana et al. 2012; Lawrence 2013), these approaches are not designed to assess contextual irrelevance.

Financial disclosures have grown substantially in length over the past two decades (Lesmy et al. 2019), driven mainly by regulation as well as firms voluntarily adding extra information. While increased disclosure should theoretically enhance transparency and reduce information asymmetry (Leuz and Verrecchia 2000), in practice, excessive length may reduce the informativeness and usefulness of financial reporting by overwhelming readers with irrelevant information. This phenomenon, where disclosures become unnecessarily wordy and include redundant information, is referred to as bloat (Kim et al. 2024). Bloat is especially problematic given that individuals have limited cognitive resources to process information (Albuquerque et al. 2024) and investors have restricted attention to analyzing complete financial data (Kim et al. 2024), meaning that they cannot evaluate every detail, particularly when disclosures are repetitive or strategically ambiguous.

Traditional textual analysis methods, such as document readability and length (e.g., Loughran and McDonald 2014), sentiment analysis (e.g., De Amicis et al. (2021)), and topic modelling (e.g., Dyer et al. (2017)), provide valuable insights into corporate reports. However, these tools use various dictionaries and rule-based approaches to determine the overall sentiment of the text without considering the relationship between words and sentences (Leippold 2023). As a result, they capture different aspects of textual complexity but are not designed to assess contextual relevance.

The introduction of generative AI represents a methodological shift and overcomes existing limitations through contextual understanding and reasoning. Unlike traditional NLP models, LLMs are pre-trained on a large set of data, meaning that the model learns patterns in language, such as grammar, word associations, sentence structures, and facts (Floridi and Chiriatti 2020). By filtering out redundancy and producing concise yet fully informative summaries, generative AI models reduce the cost, time, and computing resources required for textual analysis (Huang et al. 2023) and outperform other NLP models in various domains, including question-answering and translation (Brown et al. 2020).

Hence, LLMs’ main advantage over traditional models is their ability to perform tasks that involve human-like judgment by understanding the context surrounding each word and the relationships between sentences. These advantages of LLMs are expected to offer a new way of quantifying disclosure bloat in executive compensation disclosures and generate summaries that contain incrementally informative content associated with excess compensation beyond traditional textual measures.

Although the structure of performance-based incentives embedded within executive compensation contracts often serves as a mechanism to align managerial actions with shareholder interests (Bebchuk and Fried 2003), if executives benefit from rent extraction, they may have incentives to use disclosure bloat to mask excessive pay. Under the managerial power view, executives may use bloat in the CD&A section to earn compensation that is higher-than-justified by firm performance. First, when financial documents are less readable, shareholders that are limited in time and processing capacity may be less willing to thoroughly evaluate and scrutinize the compensation details, even if the information is relevant (Bloomfield 2002; Hooghiemstra et al. 2017). Second, bloat may focus investors’ attention on qualitative narratives framed by the firm, rather than relevant hard information. Consequently, we hypothesize:

H1: Disclosure bloat in the CD&A sections of proxy statements is positively associated with excess executive compensation.

This hypothesis is not without tension, because not all bloat is necessarily opportunistic, as compliance with regulatory requirements and legal risk management may require firms to include lengthy and redundant text to their disclosures.

While high executive compensation may be concealed by bloat, executives’ power over their compensation may be influenced by the strength of the corporate governance systems in place. Although monitoring and higher shareholder power can limit executives’ opportunistic behaviour (Eisenhardt 1989; Ruiz‐Verdú 2008), CEO duality or weak board oversight can facilitate obfuscation (Core et al. 1999; Basu et al. 2007), in line with managerial power theory. Moreover, directors may support executive-friendly pay because they value their board positions, are influenced by personal relationships and loyalty to CEOs, and benefit directly from the rewards CEOs can offer (Van Essen et al. 2015). Consequently, a less powerful and independent board may allow executives to exert greater influence over the compensation disclosure content and increase its length, which leads to higher bloat.

Thus, governance mechanisms can be possible determinants of disclosure bloat. Particularly, strong governance mechanisms, such as board gender diversity, should encourage more concise and transparent reporting, hence reducing bloat, while weaker governance, such as CEOs holding both CEO and board chair roles, can increase the opportunity for obfuscation and bloat. These arguments lead to the second hypothesis:

H2a: Board gender diversity is negatively associated with disclosure bloat.

H2b: CEO-Chair duality is positively associated with disclosure bloat.

The dataset comprises CD&A sections extracted from DEF 14A proxy filings in the SEC’s EDGAR database for S&P 1500 firms between 2011 and 2018, 7,786 firm-year observations. Data regarding the executive compensation and control variables are obtained from Compustat, ExecuComp, and BoardEx. Missing values are further retrieved from Refinitiv Eikon and the company’s annual reports.

Variable measurement consists of three main sets of variables and the procedure to construct the summaries. All continuous variables are winsorized at the 1^st and 99^th percentiles to mitigate the influence of outliers, except when bounded between 0 and 1.

To answer the research question and test H1, whether bloat is positively associated with excess compensation, the following OLS panel regression with fixed effects is employed:

Excess(Comp)_it = β₀ + β₁Bloat_it + β₂TraditionalMethods + β₃FirmControls_it + α_i + δ_t + θ_i + ɛ_it (1)

where α_i is firm fixed effect, δ_t is year fixed effect, θ_i is industry fixed effects using the Fama and French (1997) 48–industry classification, and ɛ_it is the error term.

To address H2a and H2b and find possible determinants of bloat, board gender diversity and CEO-chair duality are regressed with CEO pay slice (Bebchuk et al. 2011) and board size (Yermack 1996) as additional possible determinants and growth options, volatility, and loss (Kim et al. 2024) as control variables in the OLS regression:

Bloat_it = β₀ + β₁GovernanceMechanisms_it + β₂OtherFactors_it + β₃FirmFactors_it + δ_t + θ_i + ɛ_it (2)

Table 1 shows summary statistics for the main variables used in the analysis. The interpretation of excess compensation is the log ratio of actual to predicted compensation. Therefore, ExcessComp1 indicates that the median excess compensation is approximately 6.8 percent higher than predicted. All three proxy variables for excess executive compensation have a high standard deviation, meaning that the actual pay of some executives is more than their predicted pay, while for others, it is below predicted. Mean value of Bloat suggests that, on average, 87.4% of the original document content is not retained in the summary, implying a high level of redundancy in compensation disclosures.

The first analysis tests whether AI-generated summaries contain incrementally informative content beyond traditional textual measures in explaining excess compensation. Using a regression model in Equation (1), Table 2 shows that in column (1), a 0.01 point increase in net positive sentiment of SentimentLM (p < 0.001) is associated with an 8.89% (= e^8.518×0.01 – 1) increase in excess compensation, keeping other variables constant. While this result suggests that companies often use a more positive tone in their documents as a strategic tool to manage perception and shift readers’ attention away from things they want to hide, aligning with prior research (Huang et al. 2014, 2018), positive sentiment could coincide with the underlying justification for higher pay, such as strong performance.

The positive direction of File_Length (p < 0.001) and Res_File_Size (p < 0.001) coefficients is not surprising since larger file lengths and sizes suggest higher textual complexity. Every additional 1,000 words is associated with a 0.7% increase in excess compensation. Moreover, the significance of the Res_File_Size implies that firms that use more complex formatting and other textual characteristics beyond the word count are also more likely to provide longer and more detailed information, not for the sake of transparency, but to overwhelm the readers and obscure important information (Li 2008; Lawrence 2013). Therefore, less scrutiny can allow excess executive compensation to go unnoticed.

The coefficient of Leverage (–0.257, p < 0.001) indicates that higher debt constrains executives’ ability to inflate pay because creditors, particularly banks, act as additional monitors when they provide substantial financing. Hence, increased oversight limits executives’ ability to earn excess compensation. The Fog_Index coefficient is insignificant (p > 0.1), meaning that it cannot explain excess compensation. This conclusion opposes the initial expectation based on Li (2008) but shows that traditional readability metrics fail to capture textual complexity relevant to excess compensation, consistent with Loughran and McDonald (2014).

When industry-fixed effects replace firm-fixed effects in Columns (3) and (4), SentimentLM, Leverage, and Bloat lose significance, and the adjusted R² decreases from 60.6% to 9.4%, implying that industry fixed effects provide a much weaker control structure than firm fixed effects. Firm fixed effects absorb all stable, unobserved firm-level heterogeneity, whereas industry fixed effects only control for broad sector differences. As a result, switching to industry fixed effects introduces additional noise and reduces explanatory power, making coefficient estimates less precise.

Table 3 displays the results of Equation (1) with ExcessComp2 as a dependent variable. The findings are consistent with Table 2. Firm_Size becomes significant at the 5% level, previously 10%, suggesting larger firms are positively associated with excess executive compensation due to that larger firms have longer and more complex disclosures, which offer executives greater opportunity for obfuscation (Li 2008).

Similarly, Columns (3) and (4) in Table 3 are consistent with the results in Columns (3) and (4) in Table 2, but the Fog_Index coefficient becomes significant (p < 0.001) when industry-fixed effects are included, which implies that the variation in Fog_Index that matters for excess compensation occurs between firms, not within a firm over time. This finding differs from previous outcomes but aligns with Li (2008), who argues that higher textual complexity should enable executives to hide details and increase their compensation.

The results align with theoretical expectations. As argued before, most traditional measures rely on keywords and rules, while ignoring contextual meaning (Leippold 2023). Since compensation contracts are often written in verbose and complex language (Albuquerque et al. 2024), in line with the management obfuscation hypothesis, shareholders are less likely to thoroughly evaluate compensation structures due to the time constraints they face, leading to excess compensation. In contrast, LLMs are designed to grasp the contextual significance, as Floridi and Chiriatti (2020) argue, allowing them to filter out unnecessary and repetitive language and extract essential informational content from complex disclosures.

Following this rationale, the added explanatory power of the Bloat variable is a reasonable outcome of using a technology that more accurately replicates human judgment and intent when determining relevant content (Dong et al. 2024). Hence, the main implication of H1 is that LLMs can improve the quality of corporate disclosures and help address the principal-agency problem by assisting stakeholders in better understanding the company’s financial statements and reports.

To address whether summaries predict excess compensation, given that Bloat varies over time within firms, I use column (2) in Tables 2, 3 with firm-fixed effects to test H1. The coefficient of Bloat in column (2) of Table 2 is positive and significant at a 1% significance level; a 0.01 unit increase in Bloat is associated with a 0.52% (= e^0.520×0.01 – 1) increase in ExcessComp1 and a 0.95% (= e^0.944×0.01 – 1) increase in ExcessComp2, when all other variables are kept constant. Hence, firms with more bloated CD&A disclosures are more likely to give executives compensation higher than what is justified by the firm’s performance. As a result, H1 is not rejected.

The result is consistent with expectations from prior literature. According to agency theory, executives are incentivized to exploit their managerial power and camouflage the structure of their compensation (Bebchuk and Fried 2003), and bloated disclosures can be a mechanism to earn excess pay without detection by investors. Since investors and shareholders face cognitive and informational constraints (Albuquerque et al. 2024; Kim et al. 2024), they may give less scrutiny to compensation disclosures. Laksmana et al. (2012) also find lower CD&A readability in the 2007 proxy statements when the CEO’s compensation exceeds what is justified by the firm’s performance.

Similar to the results in Kim et al. (2024), Redundancy and Boilerplate are not significant in any of the columns. This finding further highlights the advantage of generative AI in better understanding the textual complexities. Nevertheless, the size and significance of the SentimentLM, File_Length, and Res_File_Size variables stay mostly constant, and the within R² does not substantially increase after incorporating the Bloat variable into the regression, even though the coefficient of Bloat is statistically significant. All of these suggest that the Bloat does not correct for omitted variable bias; instead, it reflects a unique but very small component of disclosure complexity that is not fully explained by sentiment or document length.

Untabulated tests on sentiment tone reveal that the negative sentiment coefficient is insignificant (p > 0.1), while the positive sentiment coefficient is significant (p < 0.001) in the main regression results. This suggests that firms use a more positive tone to obscure information since highlighting positive events can make investors judge higher executive compensation less, consistent with Huang et al. (2018), who show that managers use a positive tone to enhance earnings management.

The robustness check, where excess executive compensation is calculated using an alternative proxy of the Larcker et al. (2011) model, shows that the estimate of Bloat remains significant and its magnitude is similar to the one in Column (2) of Table 2. Moreover, as before, Bloat is significant only with firm-fixed effects, not with industry-fixed effects.

Regarding the economic magnitude of the Bloat variable, using the estimates from column (2) of Table 3, a one standard deviation increase in Bloat is associated with a 3.16% (= e^{0.033 × 0.944} – 1) increase in excess executive compensation. Although 3.16% might appear small, a slight change in executive pay can amount to thousands of dollars for CEOs in larger firms, which highlights the practical importance and LLM’s potential for increasing transparency and accountability in corporate governance.

We find no significant relationship between BoardDiversity or CEO_Duality and Bloat, suggesting that formal governance structures may not effectively constrain redundancy. Hence, H2a and H2b are rejected. Overall, the model explains only 0.9% of the variation in Bloat, further indicating the heterogeneous complexity of disclosure practices. However, the findings align with managerial power theory (Eisenhardt 1989) that since board members value their positions and personal relationships with CEOs, they are less likely to disagree with the CEO’s pay structure and reduce redundant compensation content.

This research improves both social and scientific understanding of corporate disclosure by demonstrating the value of LLMs in identifying complex textual relationships that traditional metrics often fail to identify (Leippold 2023). It is the first study to use LLMs to quantify disclosure bloat in corporate governance and examine its relationship with excess executive compensation. The results can offer a solution to a longstanding problem of excessive executive compensation by highlighting the potential of generative AI to address it.

Our findings suggest important implications for regulators by showing that lengthy or more complex executive compensation disclosures do not automatically improve transparency. They also matter for investors, who can leverage generative AI to analyze the key elements of the compensation disclosure sections faster and more easily. Finally, corporate boards should emphasize the significance of clear and less complex communication in financial documents.

We acknowledge some limitations that offer opportunities for future research. First, the output of the LLM varies depending on the model and prompt used. As a result, the length of the summarized documents reflects the LLM’s judgment of contextual relevance and can change depending on the assigned task to a specific LLM, which may also affect the measure of the bloat variable. Future research can utilize different and more advanced models to assess whether the findings are generalizable.

Moreover, the analysis is restricted to S&P 1500 firms from 2011–2018, limiting generalizability to smaller or international firms with different disclosure practices. Expanding the sample and improving CD&A data extraction would strengthen external validity. Future research can also examine non-financial reports, such as sustainability reports, to investigate whether management obscures relevant information when the content is less numerical.

Although our empirical analysis is based on U.S. firms, the main message that quality matters more than length is highly relevant to the European context. Future research could apply the bloat measure to European remuneration reports to see whether the results hold under the Shareholder Rights Directive.

Finally, as with most empirical corporate governance research, absent an exogenous shock endogeneity remains a concern. We therefore do not claim that our associations imply causality. Nevertheless, the findings provide an important starting point for deepening the understanding of how the complexity of text in executive pay disclosures may relate to governance outcomes and remain valuable for theory development and policy discussions.