Artikel
Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market
To search significant variables which can illustrate the abnormal return of stock price, this research is generally based on the Fama-French five-factor model to develop a multi-factor model. We evaluated the existing factors in the empirical study of Chinese stock market and examined for new factors to extend the model by OLS and ridge regression model. With data from 2007 to 2018, the regression analysis was conducted on 1097 stocks separately in the market with computer simulation based on Python. Moreover, we conducted research on factor cyclical pattern via chi-square test and developed a corresponding trading strategy with trend analysis. For the results, we found that except market risk premium, each industry corresponds differently to the rest of six risk factors. The factor cyclical pattern can be used to predict the direction of seven risk factors and a simple moving average approach based on the relationships between risk factors and each industry was conducted in back-test which suggested that SMB (size premium), CMA (investment growth premium), CRMHL (momentum premium), and AMLH (asset turnover premium) can gain positive return.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 12 ; Year: 2019 ; Issue: 2 ; Pages: 1-30 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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multi-factor model
risk factors
OLS and ridge regression model
python
chi-square test
- Event
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Geistige Schöpfung
- (who)
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Huang, Jian
Liu, Huazhang
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2019
- DOI
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doi:10.3390/jrfm12020091
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Huang, Jian
- Liu, Huazhang
- MDPI
Time of origin
- 2019
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