Structural drivers and forecasting dynamics of the BIST100 index: a hybrid ML/DL approach integrating liquidity, sentiment and macroeconomic factors

Purpose This study examines the daily closing level of the Borsa Istanbul 100 (BIST100) Index using a unified framework that combines machine-learning (ML) and deep-learning (DL) methods with macro-financial, behavioral and market-based indicators. Design/methodology/approach The empirical model analyzes a dataset consisting of 3,222 trading days spanning January 2010 to October 2022. The explanatory variable set includes foreign ownership, the policy rates of the Central Bank of the Republic of Türkiye and the US Federal Reserve, the consumer price index, the USD/TRY exchange rate, the Amihud illiquidity measure, and an investor-sentiment index derived from securities investment-trust discounts. Forecasting performance is evaluated within a common model-comparison framework. Findings The results show that both ML and DL models provide strong forecasting performance for the BIST100 Index. Across specifications, macro-financial variables contain the strongest predictive information, while liquidity and sentiment contribute complementary explanatory content. Overall, the findings suggest that BIST100 dynamics are shaped by the joint influence of economic fundamentals, market sentiment and liquidity conditions. The study contributes to the BIST100 and emerging-market forecasting literature by evaluating these determinants within an integrated data-driven framework. Originality/value This study pioneers a unified forecasting framework that simultaneously evaluates macroeconomic fundamentals, market illiquidity and behavioral sentiment, moving beyond the isolated approaches common in the literature. Furthermore, it bridges the gap between high predictive accuracy and economic interpretability, employing rigorous lagged-return validations to capture genuine predictive alpha without look-ahead bias.