Do Industries Predict Stock Market Volatility? A High Frequency Perspective Based on a Machine Learning Approach

In a novel take on the gradual information diffusion hypothesis of Hong et al. (2007), we examine the predictive role of industries over aggregate stock market volatility. Using high frequency data for U.S. industry indexes and various heterogeneous autoregressive (HAR) type and machine learning models, we show that most industries are informative for future aggregate market volatility in out-of-sample tests. Furthermore, we find that the predictive information captured by industries not only helps improve the volatility forecasts for the stock market, but can also be used to generate significant economic benefits for investors who use these volatility forecasts in their asset allocation strategies. While no evident patterns are observed in terms of the predictive role of industries for asymmetric volatility forecasts, interestingly, we find that the oil and gas industry plays a more dominant role for the component of aggregate market volatility that is associated with discount rate fluctuations whereas the consumer services industry is most informative over market volatility that is attributable to cash flow fluctuations. Our findings present novel insight to the predictive relationship between industries and the aggregate stock market from a volatility forecasting perspective with significant economic implications