Analysis of Shanghai Composite Index Yield Based on Stock Volatility

DOI

10.2991/978-94-6463-102-9_151How to use a DOI?

Keywords

Volatility; Stock Return; Three-Factor Model; SPSS

Abstract

His paper selects the data of the Shanghai Composite Index from September 2011 to September 2019 in the Guotai Junan database, and divides the received data into four groups according to stock price volatility. The group with the highest share price volatility is used to represent the high volatility groupt. And the group with the lowest share price volatility is used to represent the low volatility group. The significance of the factor model at low and high volatility was analyzed by linear regression. And from the graphs provided by spss, we can draw the conclusion that in the months of low volatility, the three-factor model is significant, as a result that the stock returns can be analyzed by this kind of model. Although the information from spss shows that Carhart four-factor model is able to give a reliable explanation to the stock returns, the determinant factors are the same as three-factor model, so this model does not need to be used in the analysis. When analyzing the high volatility group, the factor models fail, and three-factor model cannot explain the stock return exactly. At this phenomenon, the stock return is mainly affected by the investment environment and the stock return has obvious uncertainty.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ziyu Xu
PY  - 2022
DA  - 2022/12/29
TI  - Analysis of Shanghai Composite Index Yield Based on Stock Volatility
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 1440
EP  - 1447
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_151
DO  - 10.2991/978-94-6463-102-9_151
ID  - Xu2022
ER  -