基于银行类指数及其成分股的分析和预测
The Analysis and Forecast of Bank Index and Its Constituent Stocks
摘要: 以银行类指数及成分股的K线收盘价为研究样本，对银行类指数及其成分股进行了分析。基于银行类指数和其成分股的关系，首先进行了正回归分析，并通过回归诊断筛查出银行类指数与各个成分股样本数据中的异常点。为了使建立的模型更具有实际意义，利用岭估计消除变量间的多重共线性，并基于绝对约束估计和弹性约束估计选择了变量的最优子集，最终给出了符合实际的银行类指数和其成分股之间的线性回归方程。
Abstract: Taking the k-line closing prices of bank indexes and constituent stocks as research samples, the bank index and its constituent stocks are analyzed. Based on the relationship between the bank index and its constituent stocks, a positive regression analysis was carried out, and the abnormal points in the sample data of the bank index and each constituent stock were screened out through regression diagnosis. In order to make the model more practical, ridge estimation is used to elim-inate multicollinearity among variables, and the optimal subsets of variables are selected based on absolute constraint estimation and elastic constraint estimation. Finally, the linear regression equation between the bank index and its constituent stocks is given.
文章引用：韩笑, 滕兴虎, 窦婷. 基于银行类指数及其成分股的分析和预测[J]. 统计学与应用, 2020, 9(4): 506-514. https://doi.org/10.12677/SA.2020.94054

参考文献

[1] 吕凯晨, 闫宏飞, 陈翀. 基于沪深300成分股的量化投资策略研究[J]. 广西师范大学学报(自然科学版), 2019, 37(1): 5-16.
[2] 曾攀. 基于R/S分析法的股市走势分析[J]. 数学理论与应用, 2009, 29(1): 15-18.
[3] 方开泰. 实用多元统计分析[M]. 上海: 华东师范大学出版社, 1989.
[4] 何晓群. 多元统计分析[M]. 北京: 中国人民大学出版社, 2012.
[5] 杨虎. 杨玥含. 金融大数据统计方法与实证[M]. 北京: 科学出版社, 2016.
[6] Liu, K. (1993) A New Class of Biased Estimate in Linear Regression. Communications in Statistics—Theory and Methods, 22, 392-402. [Google Scholar] [CrossRef] 
[7] Yang, H. and Chang, X.F. (2010) A New Two-Parameter Estimator in Linear Regression. Communications in Statistics—Theory and Methods, 39, 923-934. [Google Scholar] [CrossRef] 
[8] 薛毅, 陈立萍. 统计建模与R软件[M]. 北京: 清华大学出版社, 2007.
[9] Luo, R., Tsai, C.L. and Wang, H. (2008) On Mixture Regression Shrinkage and Selection via the MR-Lasso. International Journal of Pure and Applied Mathematics, 46, 403-414.
[10] Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58, 267-288. [Google Scholar] [CrossRef] 
[11] Tibshirani, R. (2011) Regression shrinkage and selection via the Lasso: A Retrospective. Journal of the Royal Statistical Society: Series B, 73, 273-282. [Google Scholar] [CrossRef] 
[12] Zou, H. and Hastie, T. (2005) Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67, 301-320. [Google Scholar] [CrossRef] 
[13] Shen, Y., Han, B. and Braverman, E. (2015) Stability of the Elastic Net Estimator. Journal of Complexity, 32, 20-39. [Google Scholar] [CrossRef] 
[14] 周浩, 唐琦, 杨雪. 基于大数据的股票多因子量化投资策略优化研究[J]. 商讯, 2019(24): 86-87.
[15] 贺保国. 资本市场的量化投资策略和风控措施[J]. 中国商论, 2019(8): 51-52.

为你推荐