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

Rangan Gupta, Jacobus Nel and Christian Pierdzioch
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202118

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().