Subjective financial risk tolerance among students at selected South African universities : a comparative analysis of different fields of studies

MCom (Commerce in Risk Management) at the Vaal Triangle CampusCentral to the construction of investment portfolios and appropriate asset allocation is the level of financial risk tolerance (FRT) for each individual investor. In order to enhance the portfolio allocation process, individuals have to understand their financial ability and psychological willingness to tolerate financial risks. To carry this task out, the FRT level of individuals has to be quantified. The theoretical explanations behind FRT tend to stimulate contestations and discussions about what FRT is, how it can be measured and about the factors that can influence an individual’s tolerance level. The former has been adequately dealt with as researchers concurred that FRT can be explained as the willingness and ability to risk current financial resources in anticipation of higher future returns. FRT can be measured using either objective measures or subjective measures, however, evidence as to which of the two methods is superior is somewhat mixed given the strengths and weaknesses of each of the measures. Factors that influence FRT levels provide for a very important discussion that has seen a lot of demographic variables including age, gender, income, education, race and expenditure emerge. Interestingly, the debate as to how these demographic factors shape FRT levels is widespread and sometimes inconclusive, indicating the need for further analyses in this field, specifically in a South African context. The study reported in this document was designed to quantify the effect of age, gender, level of education (LOE), qualification and field of study (FOS) on FRT levels. The key empirical objective of this study was thus to determine the extent to which these demographic factors can be used to explain variations in FRT levels. Data was collected from selected South African universities using a questionnaire developed from reviewing a combination of previous questionnaires in this field, particularly the Grable and Lytton (1999a) questionnaire together with the Hanna and Lindamood (2004) questionnaire. A binary logistic regression model (BLRM) was adopted as the main econometric model used to analyse the effect demographic factors have on FRT levels. Ultimately, participants were classified as either risk tolerant (RT) or risk averse (RA) based on an empirical model of risk tolerance scoring adopted from the Grable and Lytton (1999a) study. Other tests such as the Mann-Whitney U test, median analysis, the Kruskal-Wallis test and correlations tests were conducted to establish and explain relationships between variables. Using correlation analysis, it was concurred that the sample was free from any inter-correlations among the independent variables. The Mann-Whitney U test concurred that there were significant differences in the FRT levels of males and females. On the other hand, the Kruskal-Wallis test concluded that FRT levels can be statistically significantly explained by LOE and FOS, while age and qualification could not statistically significantly explain FRT differences. Results from the BLRM showed that various demographic factors do in fact influence FRT levels. Specifically, gender, LOE and FOS were found to be statistically significant factors affecting an individual’s level of FRT. With regards to gender, it was concluded that females tolerate less financial risks compared to males. The results for LOE meant that higher levels of education increased FRT levels whilst the results for FOS meant that being in a finance related FOS increases FRT. Specifically, being in Humanities, Law, Education, Engineering & Information Technology (IT) decreased the likelihood of being FRT . On contrary, age and the qualification of participants were found to be statistically insignificant with regards to their influence on FRT. The findings of this study have provided new evidence from a larger and well representative South African sample which could be used to improve the understanding of FRT and its demographic determinants. It is important to also note that demographic factors are only a starting point with regards to assessing investor FRT. The understanding of FRT provides a complex process going beyond the exclusive use of demographic variables; hence more research is warranted to determine additional variables that can be used to improve the explained variance in FRT differences.Master