Narrative Risk in Crypto Disclosures: Textual Evidence from an Emerging Market - Nurlaela, Lina (Yayasan Sinergi Kawula Muda, 2023)
This study examines how specific narrative features in crypto related corporate disclosures influence firm specific crash risk among Indonesian public firms. Set within the backdrop of Indonesia’s regulatory transition under POJK 27/2024, the research focuses on five textual dimensions: negative tone, uncertainty, forward looking language, readability, and regulatory salience. Using a panel dataset covering 2019–2025, we conduct textual analysis on firm disclosures written in Bahasa Indonesia, drawn from IPO prospectuses, financial reports, and material announcements. Natural Language Processing tools, adapted to the local linguistic context, were applied to extract narrative metrics. These metrics were then linked to two crash risk measures: Negative Conditional Skewness (NCSKEW) and Down to Up Volatility (DUVOL). Regression results reveal that negative tone and uncertainty significantly elevate crash risk, while forward looking language and regulatory references reduce it by reinforcing investor confidence and signaling stronger managerial commitment to regulatory compliance. Additionally, the implementation of POJK 27/2024 coincides with more structured, regulation oriented narratives. The study contributes to textual finance by developing a language sensitive risk assessment model tailored to an emerging market context. It underscores the importance of regulatory anchoring and narrative clarity in managing investor expectations in volatile sectors like crypto.