Investigating the Impact of financial leverage manipulation on the complexity of non-financial information disclosure: The moderating role of financial constraint and stock price crash risk

Objective: The disclosure of non-financial information, such as board of directors’ reports, plays a vital role in transparency and investor decision-making. However, leverage manipulation—often motivated by the intent to conceal financial distress—can increase the complexity and opacity of such disclosures. This study aims to examine the relationship between leverage manipulation and the complexity of non-financial disclosures, while accounting for financial constraints and stock price crash risk. By focusing on publicly listed firms in the Tehran Stock Exchange, this research seeks to identify the mechanisms through which managers obscure non-financial information and explores the impact of such complexity on market perception.

Methods: This applied study employed a descriptive-correlational approach. The statistical population comprised board activity reports from 129 companies across 19 industries listed on the Tehran Stock Exchange, covering the period from 2007 to 2023 (1386–1402 in the Persian calendar), yielding 2,193 firm-year observations. Data were extracted from the Codal database and analyzed using a panel data approach. Leverage manipulation was measured based on the Zhu (2006) model, while complexity indicators were selected through theoretical literature and factor analysis. Partial Least Squares (PLS) modeling and ordinary least squares (OLS) regression were used to evaluate the relationships, allowing for the examination of cross-sectional dependencies and complex interactions.



Results: The main findings of the research proved there is a positive and significant relationship between leverage manipulation and difficulty and complexity of text of non-financial information. For the purpose of covering manipulations, some managers with various motivations make the text of non-financial information difficult, in this case leverage ratios will be less than the truth. The study revealed, there is a significant difference among industries. when it comes to leverage manipulation that some companies are likely to manipulate leverage. In terms of motivation, financial constraint and risk of stock price crash play key role in this case. Additionally, control variables have impact on modeling result which refers some factors have influence and connections with difficulty and leverage manipulation variables. Based on structural equation modeling and variables, three hypotheses are discussed in this article, all of which have been confirmed.



Conclusion: This study demonstrates that leverage manipulation affects not only quantitative metrics but also the qualitative dimensions of corporate reporting. Managers may employ linguistic techniques such as passive constructions, excessive verbosity, and technical jargon to diminish the clarity of non-financial information. This tendency intensifies under conditions of financial constraints and stock price crash risk, ultimately distorting investor perception and undermining transparency. Unlike quantitative manipulations, which are often detectable via standard audit procedures, qualitative obfuscation is subtler and harder to identify—making it a critical area for scrutiny. Aligned with signaling theory, the findings show that increasing disclosure complexity functions as a negative signal, reducing investor trust and elevating information asymmetry. This research contributes to the body of knowledge by integrating financial manipulation with linguistic opacity, offering a multi-dimensional framework for understanding disclosure practices. From a practical standpoint, enhancing the financial literacy and accounting knowledge of investors is essential, as non-financial reports assume a foundational understanding of accounting concepts. In addition, policymakers are encouraged to establish clearer reporting standards and enforce disclosure simplicity to prevent undue complexity. The study’s limitations include its focus on Iranian publicly listed firms and the exclusion of international comparisons. Future research may extend this model using structural equation modeling or machine learning techniques across diverse markets and sectors. Ultimately, this research offers a theoretical and empirical framework for enhancing reporting transparency and mitigating information asymmetry in capital markets.