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.

摘要

我们使用一种称为随机森林的机器学习技术，分析了投资者信心在预测美国 (US) 月度已实现股市总体波动中的作用，以及一系列广泛的宏观经济和金融变量。我们根据 2001 年至 2020 年期间的数据估计随机森林，并通过计算递归和滚动估计窗口的预测来研究长达一年的视野。我们发现，投资者信心，尤其是投资者信心的不确定性，对于总体已实现波动率及其“好”和“坏”变体具有样本外预测价值。我们的结果对投资者和政策制定者具有重要意义。