Decoding market instability: The interplay of attention deficits, internal information risks, and investor sentiment in shaping sustainable investments in Iran’s energy and technology sectors
市場不安定性の解読：注意欠陥、内部情報リスク、投資家感情がイランのエネルギー・テクノロジーセクターの持続可能な投資に与える影響 (AI 翻訳)
Mohammadtaghi Kabiri, Keramatollah Heydari Rostami, Hamidreza Talaie
🤖 gxceed AI 要約
日本語
本研究は、イランのエネルギー・テクノロジーセクターにおける持続可能な投資を阻害する要因として、投資家の注意欠陥、内部情報リスク、および感情の相互作用を検討する。テヘラン証券取引所のデータとディープニューラルネットワークを用いて、行動バイアスが市場の不安定性を増幅することを示す。結果は、マクロ経済要因よりも行動的非効率性が持続可能な投資に大きな影響を与えることを示唆している。
English
This study examines how investor attention deficits, internal information risks, and sentiment drive market volatility and hinder sustainable investments in Iran's energy and technology sectors. Using Tehran Stock Exchange data and deep neural networks, it finds that behavioral inefficiencies outweigh macroeconomic factors in amplifying instability. The research offers insights for policymakers to enhance transparency and promote sustainable transitions in sanctioned economies.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper provides a rare empirical analysis of sustainable investment barriers in a sanctioned economy, extending behavioral finance models to a context with high information asymmetry. While Iran-specific, the findings on attention deficits and internal risks offer parallels for other emerging markets facing similar disclosure challenges.
👥 読者別の含意
🔬研究者:Researchers can leverage the mixed-methods approach and DNN application for studying behavioral biases in sustainable finance.
🏛政策担当者:Policymakers in emerging markets may consider the role of attention deficits and information transparency in fostering stable sustainable investment environments.
📄 Abstract（原文）
Abstract This study investigates the interplay between investor attention deficits, internal information risks, and sentiment in driving market volatility and hindering sustainable investments in Iran’s energy and technology sectors. Drawing on behavioral finance and sustainable finance theories, we address a critical gap in emerging market literature by examining how cognitive biases and informational asymmetries amplify instability in the Tehran Stock Exchange (TSE). Employing a mixed-methods design, we analyze historical TSE data (2015–2023), conduct semi-structured interviews with 30 investors and project managers, and apply advanced models including dynamic panel regression, GARCH, structural equation modeling, and deep neural networks (DNNs). Findings reveal that attention deficits significantly heighten volatility, with an Attention Deficit Index exhibiting strong positive correlations to short- and long-term fluctuations. Internal risks, such as incomplete disclosures and managerial turnover, double the odds of market inefficiency. Investor sentiment mediates 48% of attention deficits’ effects on volatility, while DNNs (89% accuracy) uncover nonlinear interactions between biases and risks. Results demonstrate that behavioral inefficiencies outweigh macroeconomic drivers (e.g., exchange rates, oil prices) in fostering instability, underscoring the need for enhanced transparency and education. This research offers novel insights for policymakers to bolster sustainable transitions aligned with SDGs, extending behavioral models to sanctioned economies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1080/0013791x.2026.2620119first seen 2026-07-19 05:44:34
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