بررسی پویایی حباب‌های بازار سهام تهران: رویکردی مبتنی بر مدل DS-LPPLS
فصلنامه بورس اوراق بهادار

فصلنامه بورس اوراق بهادار

بررسی پویایی حباب‌های بازار سهام تهران: رویکردی مبتنی بر مدل DS-LPPLS

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استاد، گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
2 دانشیار، گروه اقتصاد، دانشگاه اشرفی اصفهانی، اصفهان، ایران و دانشگاه پیام نور، ایران.
3 دانشجوی دکتری، گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
چکیده
در این پژوهش، حباب‌های بازار سهام را در بورس اوراق بهادار تهران بررسی کردیم و از مدل DS-LPPLS (مدل تکینگی قانون توان لگاریتمی-دوره‌ای سیستم‌های دینامیکی) برای تحلیل داده‌های سال‌های ۲۰۰۹ تا ۲۰۲۴ استفاده کردیم. این مدل با تشخیص رشد فوق‌نمایی قیمت‌ها و نوسانات لگاریتمی-دوره‌ای، حباب‌های مثبت و منفی را شناسایی می‌کند و شاخص‌های اطمینان و اعتماد را به عنوان سیگنال‌های هشدار زودهنگام به کار می‌گیرد.
نتایج تجربی چندین حباب کلیدی را آشکار کرد؛ به ویژه حباب بزرگ مثبت سال ۱۳۹۹ (۲۰۲۰) که شاخص تقریباً ۵۰۰ درصد جهش داشت و با عواملی مثل تزریق گسترده نقدینگی، ورود انبوه سرمایه‌گذاران خرد و بحران کووید-۱۹ همزمان بود. حباب‌های دیگر هم اغلب با رویدادهای ژئوپلیتیکی مانند تحریم‌ها و برجام مرتبط بودند. جالب این که مدل توانست نقاط بحرانی را با دقت پیش‌بینی ex-ante بیش از ۸۰ درصد در این بازار پرتلاطم نوظهور پیش‌بینی کند.
در نهایت، یافته‌ها نشان می‌دهند DS-LPPLS نسبت به روش‌های سنتی عملکرد بهتری دارد و نقش شوک‌های کلان اقتصادی در شکل‌گیری حباب‌ها را به خوبی برجسته می‌کند. این مطالعه پیشنهادهای عملی برای نظارت بهتر و مدیریت ریسک سرمایه‌گذاران ارائه می‌دهد
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Investigating the Dynamics of Stock Market Bubbles in Tehran: A DS-LPPLS Model Approach

نویسندگان English

mohammad nabi Shahiki Tash 1
Mohammadreza Ghasemi 2
Malihe heydari 3
1 Professor, Department of Economics, Faculty of Economics and Administrative Sciences, University of Sistan and Baluchestan, Zahedan, Iran.
2 Associate Professor, Department of Economics, Ashrafi Esfahani University, Isfahan, Iran; and Payame Noor University, Iran
3 Ph.D. Student, Department of Economics, Faculty of Economics and Administrative Sciences, University of Sistan and Baluchestan, Zahedan, Iran.
چکیده English

In this study, we explored stock market bubbles in the Tehran Stock Exchange (TSE) by applying the Dynamical Systems Log-Periodic Power Law Singularity (DS-LPPLS) model to data from 2009 to 2024. What stands out about this model is how it picks up on super-exponential price surges and those distinctive log-periodic oscillations, allowing us to spot both positive bubbles (sharp upward rallies) and negative ones (sudden crashes). We used the Confidence and Trust indices as early warning tools.
Our empirical findings highlighted several key bubble periods. The most striking was the massive positive bubble in 1399 (2020), where the index jumped nearly 500%, perfectly aligning with heavy monetary injections, a huge influx of retail investors, and the disruptions from the COVID-19 pandemic. Other bubbles tied closely to geopolitical events, like sanctions and the JCPOA negotiations. Remarkably, the model achieved over 80% ex-ante accuracy in forecasting critical turning points in this highly volatile emerging market.
Overall, the results show DS-LPPLS outperforms traditional methods, clearly illustrating how macroeconomic shocks fuel bubble formation. This work provides practical insights for better regulatory monitoring and helping investors manage risks in turbulent conditions.

کلیدواژه‌ها English

Stock market bubbles
DS-LPPLS
Confidence Index
Trust Index
Tehran Stock Exchange
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