The Impact of Investor Sentiment on Stock Returns
DOI:
https://doi.org/10.62051/s554wz06Keywords:
Investor Sentiment, Principal Component Analysis, CSI 300 Index, Single-Factor Regression, VAR Model.Abstract
This paper aims to explore the relationship between investor sentiment and stock returns. An investor sentiment index was constructed using principal component analysis, and its reliability was verified. The empirical research results indicate that the returns of the CSI 300 Index are significantly positively correlated with investor sentiment, meaning that when the sentiment index increases, returns also rise; conversely, when the sentiment index decreases, returns fall. Similarly, a significant positive correlation exists between investor sentiment and the returns of the SSE 50 Index, CSI 500 Index, Shanghai Composite Index, and Shenzhen Component Index. Further construction of a VAR model and impulse response analysis revealed the mutual influence between SENT (sentiment index) and stock returns. The study found that SENT has a more substantial impact on the indices in the short term, but this effect gradually weakens and converges to zero over time. This suggests that investors are more influenced by sentiment in the short term, but as market information increases, the impact of sentiment on the market diminishes. This research is of great significance for understanding the influence of investor sentiment on the stock market.
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[6] Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
[7] Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
[8] Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
[9] SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
[10] Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
[11] Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
[12] Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
[13] SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
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