Research on the investment strategy of new energy vehicle industry based on multi-factor stock selection
DOI:
https://doi.org/10.54691/bcpbm.v26i.2056Keywords:
component; Multi-factor stock selection model; New energy vehicle industry; Regression method.Abstract
With the development of the Chinese stock market and the continuous improvement of financial engineering technology, quantitative investment represented by the multi-factor stock selection strategy has been developing and growing in the Chinese stock market. This paper takes the quarterly data of the China Securities New Energy Vehicle Index from January 2017 to March 2021 as the research object, uses the Fama-Macbeth test to select factors, and constructs the regression equation based on the regression method. The regression equation is used to predict the stock return rate of the next quarter through the factor data of each quarter in 2020. The top 7 stocks with returns are selected to construct the investment portfolio in the way of equal-weighted capital allocation, and the investment portfolio is updated quarterly. Through the empirical test, this paper mainly draws the following three conclusions: First, the driving factors of stock return in the new energy vehicle industry mainly include the ratio of long-term debt to working capital (LTDWC), total profit growth rate (TPG), price/earnings to growth ratio (PEG), and equity turnover ratio (ET); secondly, the portfolio return based on the stock selection model in this paper is higher than the average level of the industry, and the investment return obtained is stable, suitable for investment targets; third, the new energy vehicle industry is in a period of rapid development, and the portfolio return rate is higher than the average level of A-share market. Therefore, it has huge potential investment value. This paper provides suggestions and solutions for investors' stock investment in the new energy vehicle industry.
Downloads
References
Fama Eugene F, French Kenneth R. Common risk factors in the returns on stocks and bonds[J]. North-Holland,1993,33(1):3-56.
John M. Griffin,Xiuqing Ji,J. Spencer Martin. Momentum Investing and Business Cycle Risk: Evidence from Pole to Pole[J]. The Journal of Finance,2003,58(6):2515-2547.
Eugene F. Fama,Kenneth R. French. A five-factor asset pricing model[J]. Journal of Financial Economics,2015,116(1):1-22.
Zhao Zhihui. Research of multi-factor model for stocks selection based on three-layer filtering method[D]. South China University of Technology, 2015.
Ouyang Zhigang, Li Fei. The applicability of the four-factor asset pricing model in Chinese stock market[J]. Journal of Finance and Economics,2016,31(02):84-96.
Liu Shuai. Quantitative investment: pricing model and investment strategy[D]. Shanghai University,2016.
Liu Yang, Xia Siyu, Hu Sirui. Research on quantitative stock selection of GARP and timing strategy of Markov chain[J]. Journal of Finance and Economics, 2016(5):66-71.
Liu Shujun. The stock investment value analysis on the basis of pe ratio and market value[D]. Southwestern University of Finance and Economics,2016.
Che Yang. The research of multi factor stock selection strategy based on machine learning method[D]. Tianjing University, 2017.
Shi Yue. The momentum alpha trading strategy with stock selection of big data factor[D]. Tianjing University, 2018.
Zhang Tiancheng. Analysis on the influencing factors of SSE constituent index stock return rate[D] Yunnan University of Finance and Economics,2019.
Zhang Weinan, Lu Tongyu, Sun Jianming. An SVM improvement prediction in multifactor model for stocks selection [J]. Application of Electronic Technique, 2019(9):22-27.
Zhang Hu, Shen Hanlei, Liu Yecheng. The study on multi-factor quantitative stock selection based on self-attention neural network[J].Journal of Applied Statistics and Management, 2020(3):556-570.
Zhang Ning, Shi Hongwei, Zheng Lang, Shan Zihao, Wu Haoxiang. PCANet-based multi-factor stock selection model for value growth[J]. Computer Science, 2020, 47(S2):74-77.






