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dc.contributor.authorRabbani, Abed Golam
dc.description.abstractThis study developed and empirically tested a model using risk tolerance and demographic data from the Rutgers New Jersey Agricultural Experiment Station Investor Risk Tolerance database. The purpose of this study was to develop a methodology to estimate affect (i.e., feelings), use affect to describe investors, and to determine the degree to which affect measure is associated with investor’s portfolio risk. A survey created by Grable and Lytton (1998) was used to estimate subjective evaluation (SE) and objective evaluation (OE). Two theories Risk-as-Feelings (RaF) hypothesis and Classical Test Theory (CTT) were utilized to guide the estimation of affective evaluation (AE) score and development of AE groups. There were two components in GL-FRT. One component was composed of nine cognitive assessment items that were the indicators of OE. One item was chosen as an indicator of SE. A differential prediction model demonstrated that respondents did exhibit AE as suggested by the RaF hypothesis. A series of statistical analyses using chi-square tests of homogeneity of demographic characteristics for each AE group, an ordinal regression analysis of demographic characteristics as a predictor of AE groups, and a cluster analysis using AE groups and demographic characteristics showed that demographic characteristics were not good descriptors of AE groups. Finally, the findings of an OLS regression analysis of AE groups and PR scores controlling for the demographic variables and reliance on professional advice showed that AE group was associated with PR scores. This study showed that the error associated with FRT estimation is an indicator of affect and that affect is measurable using AE. The findings from this study provide financial planners a tool for estimating affect (i.e., AE). This tool is also helpful for investors who are increasingly responsible for their own investment decisions. As financial planners are responsible for understanding individual attitudinal differences to determine the appropriate portfolio for their clients, they may use these findings to assist clients make decisions that will help in wealth generation and fulfilling their financial goals.
dc.subjectRisk as Feelings
dc.subjectSubjective evaluation
dc.subjectObjective evaluation
dc.subjectAffective evaluation
dc.subjectDifferential Prediction
dc.titleAnalysis of affect associated with financial risk tolerance
dc.description.departmentHousing and Consumer Economics
dc.description.majorHousing and Consumer Economics
dc.description.advisorJohn Grable
dc.description.committeeJohn Grable
dc.description.committeeJoseph Goetz
dc.description.committeeSwarn Chatterjee

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