Yazar "Saritas, Merve Mert" seçeneğine göre listele

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    Are Shocks to the Grazing Land Footprint Permanent or Transitory? Evidence from a Machine Learning-Based Unit Root Test
    (Mdpi, 2025) Yilanci, Veli; Ozgur, Onder; Saritas, Merve Mert
    Understanding the dynamic behavior of the grazing land footprint (GLF) is critical for sustainable land management. This study examines the GLF in 92 countries to determine if the series is stationary, a statistical property indicating that shocks have transitory effects, or non-stationary, which implies that shocks have permanent, cumulative impacts (a phenomenon known as persistence). We employ a novel machine learning framework that uses an XGBoost algorithm to synthesize information from multiple conventional tests and time-series characteristics, enhancing analytical robustness. The results reveal significant cross-country heterogeneity. The GLF exhibits stationary behavior in a subset of nations, including China, India, and Norway, suggesting that their ecosystems can absorb shocks. However, for most countries, the GLF is non-stationary, indicating that ecological disruptions have lasting and cumulative impacts. These findings underscore that a one-size-fits-all policy approach is inadequate. Nations with a stationary GLF may find short-term interventions effective, whereas those with non-stationary series require profound structural reforms to mitigate long-term degradation. This highlights the critical role of advanced methodologies in shaping evidence-based environmental policy.
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    Detecting and explaining bubbles in Islamic stock markets: A dual approach with LPPLS and machine learning
    (Borsa Istanbul Anonim Sirketi, 2026) Saritas, Merve Mert; Özgür, Önder; Yilanci, Veli
    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 and 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. © 2026 Borsa İstanbul Anonim Şirketi.
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    Weak-form efficiency in Islamic equity markets: a multi-frequency analysis incorporating non-linearity and structural breaks
    (Emerald Group Publishing Ltd, 2025) Yilanci, Veli; Cetinkaya, Asli; Saritas, Merve Mert
    PurposeThis study aims to examine the weak-form market efficiency of Islamic stock indices across 11 countries. It aims to contribute to the Islamic finance literature by applying a rigorous methodological framework designed to overcome limitations identified in previous research.Design/methodology/approachThis study uses daily data from the Dow Jones Islamic Market indices. The methodological approach involves an enhanced testing framework. This framework incorporates pretesting for nonlinearity and structural breaks to ensure the appropriate selection of unit root tests and wavelet decomposition to analyze market efficiency across distinct investment horizons (short term, medium term and long term).FindingsThe empirical results of this study consistently show that the Islamic stock indices under examination deviate from weak-form market efficiency. This inefficiency persists across all tested investment horizons. Furthermore, the evidence reveals complex nonlinear dependencies and persistent violations of the random walk hypothesis within these markets. These findings suggest the potential for exploitable price predictability.Originality/valueThis study offers a significant contribution to the Islamic finance literature by implementing a methodologically robust framework for assessing market efficiency in Sharia-compliant markets. The use of pretesting for nonlinearity and structural breaks, combined with wavelet decomposition analysis, addresses critical shortcomings in prior studies. The findings highlight the importance of methodological rigor when testing market efficiency hypotheses within ethical investment contexts. They also offer valuable implications for portfolio management, investment strategy formulation and regulatory policy development within the Islamic financial ecosystem.