Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning

Rangan Gupta, Jacobus Nel and Christian Pierdzioch

Journal of Behavioral Finance, 2023, vol. 24, issue 1, 111-122

Abstract: Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its “good” and “bad” variants. Our results have important implications for investors and policymakers.

Date: 2023
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Working Paper: Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning (2021)
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DOI: 10.1080/15427560.2021.1949719

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