Predicting stock market crises using daily stock market valuation and investor sentiment indicators

Junhui Fu, Qingling Zhou, Yufang Liu and Xiang Wu

The North American Journal of Economics and Finance, 2020, vol. 51, issue C

Abstract: The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.

Keywords: Daily early warning system; Stock market crises; Investor sentiment; Ensemble Empirical Mode Decomposition; Logit model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940818304108

DOI: 10.1016/j.najef.2019.01.002

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