Optimizing Exchange-Traded Fund Predictions Using Autoregressive Recurrent Networks With Sentiment Scores and Temporal Keyword-Enhanced Integration

Exchange-traded funds (ETFs) have emerged as a favored investment vehicle due to their liquidity, affordability, and diversified exposure across asset classes. However, their price dynamics, influenced by macroeconomic trends, market sentiment, and real-time news, challenge traditional forecasting methods. This study advances ETF price prediction by integrating the probabilistic forecasting with autoregressive recurrent networks (DeepAR) with normalized sentiment scores (NSS) and a novel temporal keyword-enhanced sentiment framework. Temporal keyword-enhanced sentiment combines trending keywords from Google Trends with user-defined macroeconomic terms to enrich news sentiment capture, while NSS consolidates conflicting sentiments into a daily signal. Applied to eight U.S. ETFs spanning bonds, equities, and commodities, the enhanced DeepAR-NSS model consistently outperformed the baseline DeepAR, reducing mean absolute percentage error by 22-49.7% and root mean square error by up to 40.6%. Notable improvements were observed in bond ETFs (e.g., GOVT) and volatile assets (e.g., GLD), though high-risk ETFs like JNK exhibited increased root mean square error variability. These findings highlight the efficacy of sentiment integration in financial forecasting and underscore the need for asset-specific model refinement.