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Multiple time scales investor sentiment impact the stock market index fluctuation: From margin trading business perspective

综合指数 索引(排版) 股票市场指数 股票市场 计量经济学 库存(枪支) 资本化加权指数 经济 期限(时间) 金融经济学 计算机科学 物理 万维网 工程类 古生物学 生物 机械工程 量子力学 综合指标
作者
Xinxin Chen,Yanhong Guo,Yingying Song
出处
期刊:The North American Journal of Economics and Finance [Elsevier BV]
卷期号:69: 102027-102027 被引量:6
标识
DOI:10.1016/j.najef.2023.102027
摘要

The stock market is viewed as a complex dynamic system, and investor sentiment has an important impact on index fluctuation. This study constructs investor sentiment from margin trading business perspective and investigates its impact on the Chinese stock market index fluctuation in multiple time scales. First, we utilize 12 indicators and two-stage PCA to construct a composite investor sentiment index of the margin trading business(ISMT). Second, based on TEI@I complex system theory, we use the VMD algorithm to decompose and reconstruct the ISMT, Shanghai Securities Composite Index(SSEC), and Shenzhen Securities Component Index(SZI), and obtain multiple time scale measurement sequences that reflect short-term, medium-term, and long-term fluctuation of each index, respectively. We provide evidence that the ISMT has an asymmetric impact on stock market index fluctuations. Specifically, for long-term trend, the ISMT has a significant positive impact on the SSEC, and a significant negative impact on the SZI. For medium and short-term trends, the ISMT has a significant positive impact on both the fluctuations of SSEC and SZI, and the impact degree on SSEC is greater than SZI. We also find that the impact degree of ISMT on SSEC and SZI decreases from short to long-term trend. In addition, we measure the fluctuation periodicity of ISMT in multiple time scales based on Fast Fourier Transformation, investigate the impact result during the COVID-19 pandemic, discuss the impact of ISMT on the other nine indexes commonly used in the Chinese stock market, and evaluate the predictive power of ISMT for 11 stock market index returns. This paper takes a new perspective and technology to investor sentiment research, and the results enrich relevant financial theories. The findings are crucial for investor decision-making and financial department regulation.
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