A Hybrid FinTech-Driven Framework for Volatility Forecasting: The Role of Digital Attention and Technical Indicators in the Dubai Financial Market

Research Purpose: This study investigates the role of digital investor behavior, meas-ured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Weekly data from 2020 to 2025 were collected, covering both cri-sis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frame-works, with Max Drawdown employed as a complementary risk metric to capture ex-treme market movements. Findings: The results reveal that digital investor attention significantly contributes to volatility forecasting, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predic-tive performance compared to traditional approaches. The EGARCH model high-lighted the asymmetric impact of negative shocks, while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress. Scientific value: This study positions digital attention as a leading indicator in volatil-ity modeling, moving beyond conventional approaches that treat behavioral signals as supplementary. By integrating Google Trends with technical analysis, the research in-troduces a hybrid forecasting framework that can be adapted to other emerging mar-kets. Practical Implications: The findings offer practical value for policymakers and inves-tors. Regulators can use digital attention measures as early-warning signals to antici-pate instability, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools.