Modelling financial risk tolerance of female South African investors
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The general consensus is that investors display inconsistent financial risk-taking behaviour based on their gender. Literature suggest that female investors are less risk-tolerant than their male counterparts. The investor’s ability to take on risk stems from the knowledge of their degree of financial risk tolerance. Risk tolerance refers to the degree of uncertainty an investor is willing to bear in terms of the investments they make. Risk tolerance can be influenced by both demographical factors as well as cognitive/ emotional factors. Demographical factors typically include the investor’s age, race, marital status etcetera; whereas, cognitive factors typically refer to the investor’s personality traits. The primary objective of this study was to develop a model based on individual risk tolerance, for female South African investors, in order for investment firms to measure, more accurately, their investors’ risk profiles. Six empirical objectives were formulated, where the first three objectives focussed on the entire sample. Thereafter, the remaining three objectives focussed only on the female portion of the sample, in order to conduct the structural equation model for female investors. A comprehensive literature review was conducted in order to support the empirical analysis of this study. The literature review covered risk and its inherent elements including risk tolerance and the factors affecting risk tolerance. A comprehensive review of personality traits and the factors influencing investors’ personality traits was also conducted. The literature review was followed by a methodological chapter highlighting the methodological underpinnings of this study. This study followed the views of the positivist research paradigm, where a Thereafter, a secondary data analysis (SDA) technique was implemented. The target population for this study was investors who held formal investments at an investment firm in South Africa. The research instrument constituted a self-administered questionnaire which was electronically distributed to 4 800 of the investment firm’s clientele. The investment firm implemented a purposive sampling technique in order to ensure an unbiased sample. The sample size constituted 1 065 investors of which 469 were male and 596 were female investors. In terms of the investors’ demographic variables, results obtained indicated that investors’ demographic variables differ significantly based on their gender. With regard to investors’ personality traits, investors were more inclined to be more extraverted and open to new experiences. Furthermore, investors indicated that they were less inclined to be agreeable and emotional. Additionally, investors were more concerned with being risk averse than considering the time horizons of the investments they make. Moreover, investors also displayed average levels of financial risk tolerance. Results obtained suggests that investors’ gender significantly influenced their level of risk tolerance. Results indicated that the investor’s level of education also significantly influenced their level of risk tolerance. Investors with lower levels of education indicated higher levels of risk tolerance; whereas, investors with higher levels of education indicated lower levels of risk tolerance. Additionally, the results obtained were utilised to develop a model to aid in investment firms’ efforts to profile their female investors more accurately. The model constituted the investor’s personality traits, level of risk tolerance and level of education. By making use of this model, investment firms can suggest or even create suitable investment vehicles tailored to the needs of their female clientele. Like most other research studies, this study was faced with limitations of its own. Future researchers should consider expanding the sampling frame and sampling size, in order to obtain a more holistic sample. Furthermore, as investors tend to display irrational investment behaviour, researchers should consider developing a model to curb such behaviour.