This study examines the effect of financial disclosure complexity on the Iranian capital market reaction, focusing on the moderating role of social media attention. Drawing on limited attention theory and information processing cost theory, the study hypothesizes that complex disclosures weaken immediate market reactions, but that pre-disclosure attention on Persian Twitter mitigates this negative effect. Using panel data from 128 non-financial firms listed on the Tehran Stock Exchange over 2016–2023 (1,024 firm-year observations), disclosure complexity is measured via a composite index of report length, numerical density, and ambiguity. Investor attention is proxied by the logarithm of firm-specific Twitter posts in the five days before annual report releases, extracted using Python and Persian NLP tools. Fixed-effects panel regressions with firm-clustered robust standard errors show that a one-standard-deviation increase in complexity reduces cumulative abnormal returns (CAR[0,+1]) by about 1.4%. More importantly, the interaction between complexity and attention is positive and significant: for firms in the highest attention quartile, the negative effect of complexity becomes statistically indistinguishable from zero. The direct effect of attention alone is insignificant, suggesting that attention mainly moderates rather than directly informs prices. The findings imply that social media attention can serve as a cognitive corrective in emerging markets, helping investors process complex disclosures. Regulators and firms should therefore simplify reporting and use digital channels to attract constructive attention.
Limited Attention, Behavioral Finance, Financial Disclosure Complexity, Social Media, Twitter, Market Reaction, Tehran Stock Exchange, Panel Data, Event Study
1. Introduction
This study investigates the impact of financial disclosure complexity on the reaction of the Iranian capital market, emphasizing the moderating role of social media attention. Grounded in behavioral finance, specifically limited attention theory and information processing costs, the research addresses two fundamental questions: (1) whether more complex financial disclosures lead to weaker market reaction in the Tehran Stock Exchange (TSE), and (2) whether attention generated on Persian Twitter moderates this negative relationship. The study provides empirical evidence from an emerging market context, contributing to the literature on cognitive biases in financial decision-making and the informational role of social media.
2. Literature Review
The theoretical framework integrates Limited Attention Theory (Kahneman, 1973; Hirshleifer & Teoh, 2003) and Information Processing Cost Theory (Bloomfield, 2008). Investors face cognitive constraints, operating as selective information processors. Complex disclosures—characterized by length, numerical density, and ambiguous language—elevate processing costs, leading to incomplete or delayed price adjustments. This phenomenon is particularly pronounced in emerging markets like Iran, where retail investors with lower financial literacy constitute a significant portion of market participants. Concurrently, the digital attention economy paradigm suggests social media platforms have become primary arenas for attracting investor attention. Platforms like Twitter facilitate collective discourse and cognitive facilitation, potentially mitigating the adverse effects of information complexity. Based on this synthesis, two hypotheses were developed: H1 posits a negative relationship between financial disclosure complexity and immediate market reaction (measured by cumulative abnormal returns and abnormal trading volume). H2 proposes that pre-disclosure social media attention positively moderates this relationship; the negative effect of complexity on market reaction is weaker for firms receiving higher levels of digital attention.
3. Methodology
The study employs a quantitative, event-study-based design with panel data spanning 2016–2023. The final sample comprises 1,024 firm-year observations from 128 non-financial companies listed on the TSE, selected through systematic screening. Disclosure complexity was measured using a multidimensional composite index, standardizing and averaging three proxies: (1) report length (logarithm of word count in explanatory notes), (2) numerical density (ratio of numerical digits to total words), and (3) degree of ambiguity (frequency of uncertain words such as "may," "probably," "risk"). Textual data were extracted from financial reports on the Codal database using Python libraries (PyPDF2, pdfplumber, BeautifulSoup) and processed with Hazm, a specialized Persian NLP library. Investor attention was quantified using data from Persian Twitter. Over 250,000 Persian tweets containing company-specific keywords were collected using snscrape and tweepy libraries. The attention metric (ATTN) was defined as the natural logarithm of the average daily count of unique, firm-specific posts during the five-day window preceding annual report releases. Control variables included firm size, book-to-market ratio, profitability (ROA), leverage, institutional ownership, Big Four auditor quality, and market return index. The analytical approach employed fixed-effects panel regression models with firm-clustered robust standard errors. The main model tested the interaction between COMP and ATTN, with robustness ensured through alternative variable constructions, different event windows, GMM estimation, and subsample analyses.
4. Results
The empirical analysis provides strong support for both hypotheses. First, a statistically significant (p < 0.01) negative coefficient was found for the complexity variable (COMP). Economically, a one-standard-deviation increase in complexity leads to an approximately 1.4% reduction in short-window cumulative abnormal returns (CAR[0,+1]). This confirms that greater disclosure complexity dampens the market's immediate price adjustment, supporting limited attention and information processing cost theories. Second, the interaction term between COMP and ATTN was positive and statistically significant (p < 0.05). Marginal effect analysis revealed that the negative slope of complexity on returns flattens as social media attention increases. For firms in the highest attention quartile, the adverse effect of complexity becomes statistically indistinguishable from zero, demonstrating that high levels of digital attention can effectively neutralize the information processing barrier created by complex disclosures. Notably, the direct effect of social media attention alone on returns was insignificant, highlighting that its primary role is moderating rather than directly price-informative. Robustness checks using alternative windows, volume measures (CAV), different complexity specifications, and subsample analyses consistently reaffirmed the core results. Subsample analysis showed that the moderating effect of attention was stronger for firms with lower institutional ownership, where retail investors predominate.
5. Conclusion
This research establishes that the efficiency of the Iranian capital market in incorporating complex financial information is not determined by disclosure complexity alone but is fundamentally contingent upon the level of investor attention it garners in the digital ecosystem. It provides compelling evidence that social media activity can function as a societal corrective to cognitive limitations, enhancing price discovery. The study advocates for an integrated perspective in market regulation and corporate strategy that simultaneously addresses the quality of formal disclosures and the dynamics of informal digital communication channels. For regulators, the evidence underscores the need for policies promoting simplification and readability in mandatory disclosures. For corporate managers, the results highlight the strategic importance of active investor relations in the digital sphere. For retail investors, the study serves as a caution about cognitive biases and suggests consulting credible informal channels when confronting complex reports. Future research could extend this work by incorporating multiple social media platforms, employing advanced NLP techniques to distinguish between productive and superficial attention, and exploring cross-country comparative studies.