Detecting and Explaining Bubbles in Islamic Stock Markets: A Dual Approach with LPPLS and Machine Learning

Mert Sarıtaş M., Özgür Ö., Yılancı V.

BORSA ISTANBUL REVIEW, sa.Forthcoming, ss.1-16, 2026 (SSCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.bir.2026.100790
  • Dergi Adı: BORSA ISTANBUL REVIEW
  • Derginin Tarandığı İndeksler: Scopus, Social Sciences Citation Index (SSCI), EconLit, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-16
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

This study investigates the presence and predictability of price bubbles in Islamic stock markets, challenging the

proposition that their Sharia-compliant principles provide inherent resilience against such phenomena.

Employing a dual methodology, we first apply the Log-Periodic Power Law Singularity (LPPLS) model to detect

crash periods in the daily Dow Jones Islamic Market indices for Canada, Japan, the United Kingdom, and the

United States from 1996 to 2025. Subsequently, we utilize an Extreme Gradient Boosting (XGBoost) algorithm

to identify the key macro-financial drivers of these identified bubble episodes. The results from the LPPLS

analysis confirm that these indices exhibit significant bubble dynamics. The XGBoost model incorporated

imbalance-aware learners further reveals that the probability of a bubble is systematically linked to a

combination of market-based and macroeconomic variables, with the stock price index, intraday volatility, long-

term interest rates, and exchange rates emerging as the most significant predictors, albeit with country-specific

variations.