نوع مقاله : مقاله پژوهشی
نویسنده
گروه حسابداری، واحد سوسنگرد، دانشگاه آزاد اسلامی، سوسنگرد، ایران.
چکیده
چکیده
هدف این پژوهش بررسی تأثیر پیچیدگی افشای مالی بر واکنش بازار سرمایه ایران با تأکید بر نقش تعدیلگری توجه در شبکههای اجتماعی است. بر اساس چارچوب مالی رفتاری و نظریههای توجه محدود و هزینههای پردازش اطلاعات، انتظار میرود پیچیدگی افشا واکنش بازار را تضعیف کند، اما توجه ایجادشده در شبکههای اجتماعی میتواند این رابطه را تعدیل نماید. برای آزمون این فرضیهها، دادههای ۱۲۸ شرکت پذیرفتهشده در بورس اوراق بهادار تهران در بازه زمانی ۱۳۹۵ تا ۱۴۰۲ (۱۰۲۴ مشاهده شرکت-سال) با روش غربالگری سیستماتیک جمعآوری شد. پیچیدگی افشا با شاخص ترکیبی شامل طول گزارش (لگاریتم تعداد کلمات یادداشتهای توضیحی)، چگالی اعداد (نسبت ارقام عددی به کلمات) و درجه ابهام (فراوانی کلمات مبهم) اندازهگیری گردید. توجه سرمایهگذاران با استفاده از دادههای شبکه اجتماعی توئیتر فارسی و بهکمک روشهای پردازش زبان طبیعی (کتابخانههای Hazm و snscrape) کمیسازی شد. یافتههای حاصل از رگرسیون دادههای پانل با اثرات ثابت نشان داد که پیچیدگی افشا رابطه منفی و معناداری با بازده غیرعادی انباشته دارد؛ بهگونهای که یک انحراف معیار افزایش در پیچیدگی، بازده غیرعادی را ۱٫۴ درصد کاهش میدهد. همچنین، سطح توجه در توئیتر این رابطه منفی را بهطور معناداری تضعیف میکند و در سطوح بالای توجه، اثر بازدارنده پیچیدگی تقریباً خنثی میشود. این یافتهها بر لزوم سادهسازی گزارشهای مالی توسط تنظیمگران و بهرهگیری شرکتها از شبکههای اجتماعی برای جلب توجه سازنده تأکید میکند. نوآوری پژوهش در گذار از تحلیل مفهومی به آزمون تجربی با دادههای کلان و تلفیق دو حوزه گزارشگری مالی و اقتصاد توجه دیجیتال است.
کلیدواژهها
موضوعات
عنوان مقاله [English]
The Impact of Financial Disclosure Complexity on the Iranian Capital Market Reaction: The Moderating Role of Social Media Attention
نویسنده [English]
- Sajede HasanNejadNeysi
Department of Accounting, So.C., Islamic Azad University, Susangerd, Iran
چکیده [English]
Abstract
Purpose: This research 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 study addresses two 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.
Theoretical Framework and Hypothesis Development: 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 (Moradi et al., 2021). 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.
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. Twitter was selected due to its textual nature, relatively open API, and prevalence as a forum for stock exchange discussions in Iran. Over 250,000 Persian tweets containing company-specific keywords (stock symbols, full names, abbreviations) 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.
Key Findings: 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.
Discussion and Implications: The findings offer substantial contributions to theory and practice. Theoretically, the study extends limited attention theory to the digital age within an emerging market context, demonstrating that social media attention acts as a critical moderating variable capable of mitigating market inefficiencies stemming from information complexity. It bridges the literature on formal financial reporting with informal information diffusion through digital networks. It introduces a theoretical distinction between "mere attention" and "quality information processing," evidenced by the insignificant direct effect of attention versus its significant interactive effect with complexity. Methodologically, the research pioneers the use of Persian social media data and NLP techniques in accounting and finance research. Practically, the findings carry important implications. For regulators like the Securities and Exchange Organization of Iran, the evidence underscores the need for policies promoting simplification and readability in mandatory disclosures, such as setting maximum limits for report length or minimum readability scores. Regulators could establish "attention monitor" systems to identify firms experiencing attention deficits during critical disclosure periods. For corporate managers, the results highlight the strategic importance of active investor relations in the digital sphere; proactive engagement on social media—publishing simplified management summaries or hosting online Q&A sessions—can ensure complex financial information is effectively communicated. For retail investors, the study serves as a caution about cognitive biases and suggests consulting credible informal channels when confronting complex reports. For auditors, the findings emphasize considering "understandability" as a practical objective during financial reporting consultations.
Limitations and Future Research Directions: The study acknowledges several limitations. First, data collection was limited to Persian Twitter; future studies could incorporate other platforms like Instagram and Telegram. Second, attention was measured quantitatively (post counts); future research could employ advanced NLP techniques to analyze content quality, sentiment, and user influence, distinguishing between "productive" and "superficial" attention. Third, the focus was solely on annual reports; future studies could examine quarterly reports, earnings announcements, or event-based disclosures. Fourth, despite advanced econometric methods, the observational data precludes definitive causal claims; mixed-method approaches could provide deeper insights. Finally, cross-country comparative studies could explore how institutional differences shape the relationship between disclosure complexity, attention, and market reactions.
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.
کلیدواژهها [English]
- Limited Attention
- Behavioral Finance
- Financial Disclosure Complexity
- Social Media
- Market Reaction
- Tehran Stock Exchange
- Panel Data
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