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Abstract
Risk tolerance will continue to be an important factor shaping consumer and household decision making. This study has added to the risk-tolerance literature by showing how, while generally stable, risk-tolerance can change over time. The overarching purposes of this study were to (a) identify the macroeconomic variables that influence individual risk tolerance, (b) clarify the relationship between biopsychosocial and environmental factors that influence risk tolerance, (c) and determine whether social support influences a persons risk tolerance. The final goal of the dissertation was to provide a substantial contribution to the risk-tolerance literature and offer insights into which, if any, variables influence an individuals change in risk tolerance over time. The sample, provided by FinaMetrica, contained approximately 4,983 individuals who completed at least two financial risk-tolerance assessments from the period between 2010 and 2014. The results of the regression analysis show strong similarities to previous studies examining the relationship between risk tolerance and biopsychosocial and environmental factors. Age, gender, education level, income, and net worth all showed significant relationships to financial risk-tolerance scores. Specifically, older respondents exhibited lower scores and females reported a lower risk tolerance than men. Income, household size, and net worth were all positively associated with an increase in risk-tolerance scores. The most interesting aspect of the macroeconomic analysis was the negative relationship between GDP and risk-tolerance score. GDP had a negative coefficient, indicating that a rise in GDP was associated with lower risk-tolerance scores. In addition, the results suggest that as spending on social support, as a percentage of a countrys gross domestic product, goes up, risk tolerance decreases. Finally, based on the results of the logistic regression, individuals who scored extremely low or high on risk assessments, older individuals, and those with lower education levels were most likely to exhibit change over a four-year window.