|Name:||John E. Grable|
|Title:||Investor Risk Tolerance: Testing The Efficacy Of Demographics As Differentiating and Classifying Factors|
|Degree:||Doctor of Philosophy|
|Department:||Housing, Interior Design and Resource Management|
|Committee Chair:||Ruth H. Lytton|
|Committee Members:||Rebecca Lovingood|
|Keywords:||Survey of Consumer Finances, discriminant analysis|
|Date of defense:||October 20, 1997|
|Availability:||Release the entire work for Virginia Tech access only.
After one year release worldwide only with written permission of the student and the advisory committee chair.
This study was designed to determine whether the variables gender, age, marital status, occupation, self-employment, income, race, and education could be used individually or in combination to both differentiate among levels of investor risk tolerance and classify individuals into risk-tolerance categories. The Leimberg, Satinsky, LeClair, and Doyle (1993) financial management model was used as the theoretical basis for this study. The model explains the process of how investment managers effectively develop plans to allocate a client’s scarce investment resources to meet financial objectives. An empirical model for categorizing investors into risk-tolerance categories using demographic factors was developed and empirically tested using data from the 1992 Survey of Consumer Finances (SCF) (N = 2,626). The average respondent was affluent and best represented the profile of an investment management client. Based on findings from a multiple discriminant analysis test it was determined that respondent demographic characteristics were significant in differentiating among levels of risk tolerance at the p < .0001 level (i.e., gender, married, single but previously married, professional occupational status, self-employment status, income, White, Black, and Hispanic racial background, and educational level), while three demographic characteristics were found to be statistically insignificant (i.e., age, Asian racial background, and never married). Multiple discriminant analysis also revealed that the demographic variables examined in this study explained approximately 20% of the variance among the three levels of investor risk tolerance. Classification equations were generated. The classification procedure offered only a 20% improvement-over-chance, which was determined to be a low proportional reduction in error. The classification procedure also generated unacceptable levels of false positive classifications, which led to over classification of respondents into high and no risk-tolerance categories, while under classifying respondents into the average risk-tolerance category. Two demographic characteristics were determined to be the most effective in differentiating among and classifying respondents into risk-tolerance categories. Classes of risk tolerance differed most widely on respondents’ educational level and gender. Educational level of respondents was determined to be the most significant optimizing factor. It also was concluded that demographic characteristics provide only a starting point in assessing investor risk tolerance. Understanding risk tolerance is a complicated process that goes beyond the exclusive use of demographic characteristics. More research is needed to determine which additional factors can be used by investment managers to increase the explained variance in risk-tolerance differences.
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