Forecasting Crude Oil Price Volatility With Analyst Commentary Sentiment: A Nonlinear Analysis Using Deep‐Learning Models

ABSTRACT This paper examines the role of analyst commentary sentiment (AS) in enhancing the forecasting of crude oil price volatility. Specifically, we first construct the AS index based on analyst commentaries and develop a volatility index using 5‐min high‐frequency crude oil price data. We then apply heterogeneous autoregressive (HAR) models and the state‐of‐the‐art deep‐learning models to analyze how analyst sentiment improves the forecasting of crude oil price volatility. The results show that the AS index captures significant information, improving forecasting accuracy of crude oil price volatility over medium‐term forecasting horizons, especially when deep‐learning models are employed. Additionally, deep‐learning models significantly improve the forecasting performance during periods of high volatility and negative analyst commentary sentiment, while traditional HAR models perform poorly during this period. Finally, from the perspective of asset allocation, the AS index helps crude oil futures investors to achieve considerable economic returns.