A Study on Behavior Scorecard for Investors Risk Tolerance in Bank A - Compare with Different Training Set of the Prediction Model

碩士 === 輔仁大學 === 統計資訊學系應用統計碩士班 === 102 === After the financial crisis in 2008, government should protect the investors in the investment market. In order to enhance the quality of the bank's wealth management services and implement risk control. In the norms of the "Financial Consumer Prote...

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Bibliographic Details
Main Authors: Yih-Shyan Wu, 吳懿軒
Other Authors: Te-Hsin Liang
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/kxsfgg
Description
Summary:碩士 === 輔仁大學 === 統計資訊學系應用統計碩士班 === 102 === After the financial crisis in 2008, government should protect the investors in the investment market. In order to enhance the quality of the bank's wealth management services and implement risk control. In the norms of the "Financial Consumer Protection Act ", the bank needs to understand the situation of investors in the background and assess Investors’ Risk Tolerance. The banks should evaluate the Investors’ Risk Tolerance seriously, and then financial consultants can design the combination of financial products according to Investors’ risk tolerance. It can help to enhance bank’s customer loyalty and professional image. In this study, Data were collected from Bank A by using the survey of “Personal Investment Worksheet” and the customers have purchased new the product of risk. Important variables were selected by two steps. The first step selected the significant variables by using Chi-Square Independent Test and reduced the multicollinearity in the data set by combining independent variables. The second step used the modeling data set to select 80% of the entire the modeling dataset. Resampling was done 30 times to construct 30 multinomial logistic models. The final important variables would be select if the number of voting is more than or equal to 30 times from the 30 models and Cramer’s value is more than or equal to 0.1. This study uses the modeling dataset to sample the proportion of different and get 70 samples of sub-training set to construct 70 multinomial logistic models. This study compares with the models. Finally, the behavior scorecard will be build base on multinomial logistic models. It can provid bank financial consultants refer investors' score to recommended financial products and asset allocation.