中国科学技术大学学报 ›› 2017, Vol. 47 ›› Issue (3): 231-235.DOI: 10.3969/j.issn.0253-2778.2017.03.005

• 研究论文:管理科学与工程 • 上一篇    下一篇

基于Logit模型的理财产品募集达标率研究

鲁炜,刘玲   

  1. 中国科学技术大学管理学院,安徽合肥 230026
  • 收稿日期:2015-05-03 修回日期:2015-09-10 接受日期:2015-09-10 出版日期:2023-03-27 发布日期:2015-09-10
  • 通讯作者: 鲁炜
  • 作者简介:鲁炜(通讯作者),男,1957年生,博士/副教授.研究方向:会计与金融.E-mail: weilu@ustc.edu.cn
  • 基金资助:
    国家自然科学基金(71121061)资助.

Research on raise compliance rate of bank financial products based on Logit

LU Wei, LIU Ling   

  1. School of Management, University of Science and Technology of China, Hefei 230026, China
  • Received:2015-05-03 Revised:2015-09-10 Accepted:2015-09-10 Online:2023-03-27 Published:2015-09-10

摘要: 选取2011年12月至2013年12月两年间的13667个理财产品的数据作为样本,并将样本分为训练样本和预测样本两部分,从银行的角度研究了理财产品募集达标(实际募集规模达到计划募集下限)的影响因素,通过Logit模型得出达标率与理财产品各种基本要素之间的关系,并用预测样本验证了模型的有效性.拟合结果显示:发行银行的信用评级越高、产品的销售范围越广、预期收益率上限越高、计划募集下限越低,理财产品的募集达标率越高,即越能实现募集达标;同时在影响理财产品募集达标的这4个因素中,银行的信用评级的影响最大,依次是销售范围、预期收益率上限、计划募集下限.

关键词: 银行理财产品, 达标率, 二元Logit 回归, 训练样本, 预测样本

Abstract: The factors that influence whether financial products would raise enough amounts to reach the standard (the actual amounts of products raised reach prescribed minimum of planned amounts) were studied. A sample of 13667 bank financial products issued in two years from 2011 December to 2013 December was used, which contains two parts(a training sample and a prediction sample), and Logit model was employed to analyze the relationship between the basic elements of bank financial products and raise compliance rate. In addition, the validity of the model was verified with a prediction sample. The results indicate that higher credit ratings of the issuing banks, wider sales area, higher expected rates of return, and lower prescribed minimum of planned amounts of producted raised will create a higher raise compliance rate. Furthermore, among the four factors, bank’s credit ratings made the largest influence, followed by sales area, expected rates of return ceiling and prescribed minimum of planned amounts of producted raised.

Key words: bank financial products, compliance rate, binary Logit, training samples, prediction samples

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