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

Rangan Gupta (), Jacobus Nel and Christian Pierdzioch
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Jacobus Nel: Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa

No 202118, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Investor Confidence; Realized Volatility; Macroeconomic and Financial Predictors; Forecasting; Machine Learning (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 G17 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
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