Predicting stock market crises using daily stock market valuation and investor sentiment indicators |
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| Affiliation: | 1. Department of Banking and Finance, Cheng Shiu University, Kaohsiung, Taiwan, ROC;2. Department of Finance, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC;3. Sales department, Paralink Networks, Inc., New Taipei City, Taiwan, ROC;1. Patrick E. Molony Professor, Department of Economics, Auburn University, 138 Miller Hall, Auburn, AL 36849, United States;2. Department of Economics, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, United States;1. Graduate School of Management, National Taiwan University of Science and Technology, Taiwan;2. Department of Risk Management and Insurance, National Chengchi University, Taiwan;3. School of Business, Central South University, China;4. Department of Financial Engineering and Actuarial Mathematics, Soochow University, Taiwan;1. School of Business and Economics, Department of Accounting and Finance, Thompson Rivers University, 900 McGill Road, Kamloops, BC V2C 5N3, Canada;2. College of Business Administration, Department of Finance, Kent State University, P.O. Box 5190, Kent, OH 44242-0001, USA;3. School of Business and Economics, Department of Economics and Finance, Lynchburg College, 1501 Lakeside Drive, Lynchburg, VA 24551, USA |
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| 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. |
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| Keywords: | Daily early warning system Stock market crises Investor sentiment Ensemble Empirical Mode Decomposition Logit model |
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