Gupta, RanganNel, JacobusPierdzioch, Christian2022-07-012022-07-012023Rangan Gupta, Jacobus Nel & Christian Pierdzioch (2023) Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning, Journal of Behavioral Finance, 24:1, 111-122, DOI: 10.1080/15427560.2021.1949719.1542-7560 (print)1542-7579 (online)10.1080/15427560.2021.1949719https://repository.up.ac.za/handle/2263/86026Using 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.en© 2021 Taylor and Francis. This is an submitted version of an article published in Journal of Behavioral Finances, vol. 24, no. 1, pp. 111-122, 2023, doi : 10.1080/15427560.2021.1949719. Journal of Behavioral Finance is available online at : https://www.tandfonline.com/loi/hbhf20.Investor confidenceRealized volatilityMacroeconomic and financial predictorsForecastingMachine learningPreprint Article