This paper presents empirical evidence on the financial characteristics of Chinese entrepreneurial SMEs, both state-owned and private enterprises, listed in the Chinese stock markets during 2006-2009. Building on exta...This paper presents empirical evidence on the financial characteristics of Chinese entrepreneurial SMEs, both state-owned and private enterprises, listed in the Chinese stock markets during 2006-2009. Building on extant literature and using a parametric approach on 359 sample SMEs for 2006-2009 period, the study examines the financial characteristics that are embedded in the financially healthy and unhealthy Chinese SMEs. The findings of the study suggest that financially healthy Chinese SMEs have strength in terms of liquidity, profitability, and leverage ratios as compared to financially unhealthy SMEs with the exception of a few cases in leverage and profitability. These findings are worth noting to understand the uniqueness of financial characteristics of the Chinese SMEs and useful for policy makers to deal with the issues related to financially distressed SMEs in China.展开更多
Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective ...Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.展开更多
文摘This paper presents empirical evidence on the financial characteristics of Chinese entrepreneurial SMEs, both state-owned and private enterprises, listed in the Chinese stock markets during 2006-2009. Building on extant literature and using a parametric approach on 359 sample SMEs for 2006-2009 period, the study examines the financial characteristics that are embedded in the financially healthy and unhealthy Chinese SMEs. The findings of the study suggest that financially healthy Chinese SMEs have strength in terms of liquidity, profitability, and leverage ratios as compared to financially unhealthy SMEs with the exception of a few cases in leverage and profitability. These findings are worth noting to understand the uniqueness of financial characteristics of the Chinese SMEs and useful for policy makers to deal with the issues related to financially distressed SMEs in China.
文摘Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.
