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 factor...
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Autor*in:	Huang, Jian [verfasserIn] Liu, Huazhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	Enthalten in: Journal of risk and financial management - Basel : MDPI, 2008, 12(2019), 2/91 vom: Juni, Seite 1-30
Übergeordnetes Werk:	volume:12 ; year:2019 ; number:2/91 ; month:06 ; pages:1-30

Links:	Volltext Volltext Link aufrufen Terms of use

DOI / URN:	10.3390/jrfm12020091

Katalog-ID:	1668147858

Internformat


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allfields	10.3390/jrfm12020091 doi 10419/238966 hdl (DE-627)1668147858 (DE-599)KXP1668147858 DE-627 ger DE-627 rda eng Huang, Jian verfasserin (DE-588)1189861275 (DE-627)1668620057 aut Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Liu, Huazhang verfasserin (DE-588)1189861305 (DE-627)1668620170 aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 12(2019), 2/91 vom: Juni, Seite 1-30 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:12 year:2019 number:2/91 month:06 pages:1-30 https://doi.org/10.3390/jrfm12020091 Resolving-System kostenfrei Volltext https://www.mdpi.com/1911-8074/12/2/91/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/238966 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 12 2019 2/91 6 1-30 26 01 0206 3490327659 x1k 01-07-19 2403 01 DE-LFER 3596086981 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/jrfm12020091 2403 01 DE-LFER https://www.mdpi.com/1911-8074/12/2/91/pdf 26 00 DE-206 56 multi-factor model 26 00 DE-206 56 risk factors 26 00 DE-206 56 OLS and ridge regression model 26 00 DE-206 56 python 26 00 DE-206 56 chi-square test
spelling	10.3390/jrfm12020091 doi 10419/238966 hdl (DE-627)1668147858 (DE-599)KXP1668147858 DE-627 ger DE-627 rda eng Huang, Jian verfasserin (DE-588)1189861275 (DE-627)1668620057 aut Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Liu, Huazhang verfasserin (DE-588)1189861305 (DE-627)1668620170 aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 12(2019), 2/91 vom: Juni, Seite 1-30 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:12 year:2019 number:2/91 month:06 pages:1-30 https://doi.org/10.3390/jrfm12020091 Resolving-System kostenfrei Volltext https://www.mdpi.com/1911-8074/12/2/91/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/238966 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 12 2019 2/91 6 1-30 26 01 0206 3490327659 x1k 01-07-19 2403 01 DE-LFER 3596086981 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/jrfm12020091 2403 01 DE-LFER https://www.mdpi.com/1911-8074/12/2/91/pdf 26 00 DE-206 56 multi-factor model 26 00 DE-206 56 risk factors 26 00 DE-206 56 OLS and ridge regression model 26 00 DE-206 56 python 26 00 DE-206 56 chi-square test
allfields_unstemmed	10.3390/jrfm12020091 doi 10419/238966 hdl (DE-627)1668147858 (DE-599)KXP1668147858 DE-627 ger DE-627 rda eng Huang, Jian verfasserin (DE-588)1189861275 (DE-627)1668620057 aut Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Liu, Huazhang verfasserin (DE-588)1189861305 (DE-627)1668620170 aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 12(2019), 2/91 vom: Juni, Seite 1-30 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:12 year:2019 number:2/91 month:06 pages:1-30 https://doi.org/10.3390/jrfm12020091 Resolving-System kostenfrei Volltext https://www.mdpi.com/1911-8074/12/2/91/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/238966 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 12 2019 2/91 6 1-30 26 01 0206 3490327659 x1k 01-07-19 2403 01 DE-LFER 3596086981 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/jrfm12020091 2403 01 DE-LFER https://www.mdpi.com/1911-8074/12/2/91/pdf 26 00 DE-206 56 multi-factor model 26 00 DE-206 56 risk factors 26 00 DE-206 56 OLS and ridge regression model 26 00 DE-206 56 python 26 00 DE-206 56 chi-square test
allfieldsGer	10.3390/jrfm12020091 doi 10419/238966 hdl (DE-627)1668147858 (DE-599)KXP1668147858 DE-627 ger DE-627 rda eng Huang, Jian verfasserin (DE-588)1189861275 (DE-627)1668620057 aut Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Liu, Huazhang verfasserin (DE-588)1189861305 (DE-627)1668620170 aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 12(2019), 2/91 vom: Juni, Seite 1-30 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:12 year:2019 number:2/91 month:06 pages:1-30 https://doi.org/10.3390/jrfm12020091 Resolving-System kostenfrei Volltext https://www.mdpi.com/1911-8074/12/2/91/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/238966 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 12 2019 2/91 6 1-30 26 01 0206 3490327659 x1k 01-07-19 2403 01 DE-LFER 3596086981 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/jrfm12020091 2403 01 DE-LFER https://www.mdpi.com/1911-8074/12/2/91/pdf 26 00 DE-206 56 multi-factor model 26 00 DE-206 56 risk factors 26 00 DE-206 56 OLS and ridge regression model 26 00 DE-206 56 python 26 00 DE-206 56 chi-square test
allfieldsSound	10.3390/jrfm12020091 doi 10419/238966 hdl (DE-627)1668147858 (DE-599)KXP1668147858 DE-627 ger DE-627 rda eng Huang, Jian verfasserin (DE-588)1189861275 (DE-627)1668620057 aut Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Liu, Huazhang verfasserin (DE-588)1189861305 (DE-627)1668620170 aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 12(2019), 2/91 vom: Juni, Seite 1-30 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:12 year:2019 number:2/91 month:06 pages:1-30 https://doi.org/10.3390/jrfm12020091 Resolving-System kostenfrei Volltext https://www.mdpi.com/1911-8074/12/2/91/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/238966 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 12 2019 2/91 6 1-30 26 01 0206 3490327659 x1k 01-07-19 2403 01 DE-LFER 3596086981 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/jrfm12020091 2403 01 DE-LFER https://www.mdpi.com/1911-8074/12/2/91/pdf 26 00 DE-206 56 multi-factor model 26 00 DE-206 56 risk factors 26 00 DE-206 56 OLS and ridge regression model 26 00 DE-206 56 python 26 00 DE-206 56 chi-square test
language	English
source	Enthalten in Journal of risk and financial management 12(2019), 2/91 vom: Juni, Seite 1-30 volume:12 year:2019 number:2/91 month:06 pages:1-30
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publishDateDaySort_date	2019-06-01T00:00:00Z
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title	Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market
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title_full	Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market Jian Huang and Huazhang Liu
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title_sort	examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of chinese stock market
title_auth	Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market
url	https://doi.org/10.3390/jrfm12020091 https://www.mdpi.com/1911-8074/12/2/91/pdf http://hdl.handle.net/10419/238966 http://creativecommons.org/licenses/by/4.0/
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Examination and modification of multi-factor model in explaining stock excess return with hybrid approach in empirical study of Chinese stock market

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