Designing a Turkey Financial Stress Index Optimized via the Artificial Bee Colony Algorithm

Financial stress indexes quantify stress levels in financial markets, differentiate between crisis and stable periods, and provide early warning signals for policymakers. Existing research often employs non-generalizable indicators and insufficiently addresses indicator sensitivity during crisis periods. To address this gap, the present study applies the Artificial Bee Colony (ABC) algorithm, a metaheuristic optimization approach, to assign dynamic weights to the Turkey Financial Stress Index (TFSI). The index is constructed using monthly data from January 2000 to September 2025, incorporating MSCI Turkey Index volatility, maximum drawdown, the Sharpe ratio, and the beta coefficient relative to the MSCI World Index. Empirical findings reveal that the ABC-optimized TFSI registers sharper increases than the equally weighted index during the 2000–2001 twin crisis, the 2008 global financial crisis, and the COVID-19 crisis. These results suggest that the ABC-based index provides a more effective framework for identifying systemic shocks compared to traditional weighting methods.