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Understanding Risk Tolerance & Capacity!

Calculating a client's risk capacity and risk tolerance are foundation to a financial advice and investing framework, we go deeper into the academic understanding of these two main elements of risk.

Risk Tolerance

Individuals have preferences regarding tolerance for risk, and this is generally consistent across different contexts, including investments. Historically, personal preferences for risk (or risk tolerance) has driven most portfolio allocation within financial advice. While considerations of risk capacity may be equally important, it still is valuable to ascertain and apply individual preferences for risk tolerance to investment portfolios, as it is the indicator of ensuring the client is able to 'sleep at night' with their investments.


Historically, demographics alone were used to determine tolerance for risk, and thus asset allocation. However, older people, and women, are not always less risk tolerant than others (Kannadhasan, 2015). These types of generalisations can result in clients becoming disempowered from the advice process.

Households that are younger male-led, more educated, who hold shares, with higher net wealth, are more likely to take financial risks (Huang, Xu, & Chiang, 2016). However, this is likely more a result of human capital and financial capital

(covered in the risk capacity section), than actual demographic trends.

It is these structural inequalities which often compound the impact of individual preference for risk on decreasing net wealth relative to others.

Risk Tolerance Components

The measurement of risk tolerance has existed in the psychological literature for decades, and covered a range of personal contexts.

While economists have often bundled risk tolerance with time preferences (Hanna et al., 2011; Claudia R. Sahm, 2012), psychologists see tolerance for risk as a personal and individual preference which may be impacted by context.

Components of risk tolerance calculations include the ranking of goals, emergency savings, emotions elicited during decision making, preferred risk-return trade-off, betting odds, and required rate of return (Cordell, 2002). The challenge is that people are not rational, and so each of the above components is different for everyone. The irrationality of investors has been studied extensively by authors who have found that we place excessive weight on small losses and highly value avoiding uncertainty (Tversky & Kahneman, 1992). We can also get addicted to risk taking behaviour, resulting in a positive feedback loop (Buckley, 2015). Our tolerance for risk also changes with our perception of economic conditions

(Claudia R Sahm, 2012).

Risk Tolerance Questionnaires

Investment risk tolerance questionnaires apply psychometric survey design principles to reliably and validly predict individual risk tolerance preferences (Hanna et al., 2011).

Authors have found that the responses to the surveys correlate strongly with investors perceptions of their own risk tolerance (Hallahan, Faff, & McKenzie, 2003). This means it is likely there is methodological error introduced into historical methods to ascertain risk tolerance, as it is not actual risk tolerance that is measured, rather, beliefs about one's own risk tolerance. The most accurate measure of assessing risk tolerance is of course to assess actual behaviour.

Historical Actions

What we actually do is a more accurate indicator of our beliefs than what we say we believe. This is also the case in assessing risk tolerance. It is possible to assess the responses to the risk tolerance survey with actual historical behaviours, and authors have found consistency in this analysis (Roszkowski & Grable, 2005; Yook & Everett, 2003).

Tolerance for Risk Studies

Individual preferences for risk has previously been studied in the Psychology literature as 'sensation seeking', which relates explicitly to risk taking behaviour

(Arnett, 1994; Zuckerman, Kolin, Price, & Zoob, 1964; Zuckerman & Neeb, 1980). Since this ground-breaking research was done there have been repeated tests and re-tests of tolerance for risk using empirical methodology.

There are a number of explicit behaviours which are associated with a high tolerance for risk such as smoking, driving fast (Zuckerman & Neeb, 1980), investing in stocks (Keller & Siegrist, 2006) and adventure tourism (Gilchrist, Povey, Dickinson, & Povey, 1995). However, authors have found that it is the responses to actual lotteries (with the client's own money) that elicit the best predictors of actual market responses (Pennings & Smidts, 2000).

Below is an example of using 'risk capacity' and 'risk tolerance' within a goals­based investing framework.

Risk Capacity

Some authors consider risk capacity to be the most important component of a client's tolerance for financial risk (Davies, 2017). This is despite the fact that risk capacity is an objective calculation determined by a client's actual financial situation, rather than their ability to 'sleep at night' with their investment portfolio risk-return trade-off.

Risk capacity is the amount of risk that a client can take in their investments, depending on their situation. The critical components of risk capacity calculations are: liquidity, time horizon, net wealth, debt, net wealth calculation, and potential considerations for the risk capacity calculation.

Risk capacity is broadly comprised of human capital (our ability to generate wealth eg: our income or level of education), and financial capital (our current net wealth). While our human capital generally decreases as we age (as we have fewer earning years ahead of us), financial capital generally increases as we age.

Financial Capital


A critical component of risk capacity is net wealth, which when combined with liquidity etc, provides an indication of financial capital. While our current Western society is primarily concerned with financial wealth as an indicator of social status, thus net wealth is a critical indicator of risk capacity. This is because of the impacts on economic status and access to choices (Henretta & Campbell, 1978).

While the primary contributor to net wealth is traditionally the family home (Costa­Font, Frank, & Swartz, 2018; Pfeiffer, 2017), this is generally not converted to income in retirement (Poterba, Venti, & Wise, 1994), and thus should be excluded from new wealth calculations for the purposes of portfolio allocation.

Home ownership is on the decline, as a result of changing family socio­demographics, particularly those who are married (Drew, 2015; Mundra & Uwaifo Oyelere, 2018), as well as declines in taxation incentives (Rosen, Bank, Eckstein, Stern, & Tcheau, 2017), gender, race and education challenges (Bourassa & Shi, 2017), and access to credit (Bourassa & Shi, 2017; Spader, McCue, & Herbert, 2016).

Property investment, even for a family home, is a very risky asset class according to the academic literature (Costa-Font et al., 2018; Teye, Haan, Elsinga, Bondinuba, & Gbadegesin, 2017; Wei, Zhang, & Liu, 2017).

Net wealth needs to be considered alongside actual historical behaviour to fully account for risk capacity (Sarlija, Bensic, & Zekic-Susac, 2009).

The net wealth calculation needs to measure investible assets (versus Jetski's and plasma screen TV's) as a proportion of net wealth (Davies, 2017). This is because it ensures that in a financial shock, lifestyle is not jeopardised (Davies, 2017).


When a client holds more liquid assets, this contributes positively to risk capacity because they are able to utilise their assets if needed (Davies, 2017), for either risk management or taking advantage of opportunities (Green, Melzer, Parker, & Rojas, 2016). Both a qualitative (eg. the holiday home is a family heirloom) and quantitative (eg. cash is more liquid than property) aspects of liquidity need to be considered, although it's calculation may pose some challenges.

Time Horizon

Investment time horizon is unique for each client, and each goal the client holds. Hence, goal specific time-horizon is also unique for each client. It is intuitive that the investment time horizon for each goal would impact on the clients capacity to take financial risk (Roszkowski & Grable, 2005).

Historically, researchers considered which life phase a client was in to determine time horizon (Cordell, 2001), and indeed the default superannuation funds generally still follow this approach. We now follow a much more sophisticated approach which considers the unique time horizon for each goal.

The literature on investing according to the life-cycle is very thorough (Basu & Drew, 2007, 2009; Chai, 2009; Li & Yao, 2007; Shefrin & Thaler, 1988; Viceira, 2007).

The time horizon for each goal, combined with the importance of each goal to the client, will impact the risk capacity calculation. Generally, a longer goal time-horizon will result in a higher risk capacity for that goal.

Human Capital

Human capital is our ability to generate wealth through income. In societies with high education standards and consistent wages across occupations (or marginal taxation structures to generate this effect), most wealth is stored as human capital rather than financial capital (Hanna et al., 2011).

In general, calculations of human capital include income volatility, education, occupational level (eg. manager), and years to retirement (more years to retirement, means higher human capital).


In the West there is a global trend of increasing over-indebtedness (Amadi, 2012; Gathergood, 2012; Nottage, 2013; Ottaviani & Vandone, 2011). Debt is risky because the result of financial stress, such as job loss, can have significant drops in consumer consumption (Baker, 2018). The financial implications of debt are that it can result in higher exposure to external negative contexts such as increasing interest rates or falling property prices (Kim, 2016).

Risk Capacity Calculation

Risk capacity is a scale (Cordell, 2002), which takes into account the equal contributors of financial capital and human capital (Hanna et al., 2011). Desired financial returns and psychological risk tolerance components should not be included in risk capacity calculations, but rather be stand-alone considerations

(Davies, 2017).

Insurance needs are included in this calculation, as it is the wealth protection component of the clients financial situation (Cordell, 2001). As does the relative size of each goal as a proportion of existing wealth (Davies, 2017).


Dr Katherine Hunt (BPsychSc, B Comm (Financial Planning), Honours in Finance

(University Medal). PhD}

Dr Katherine has been working with a.i. to develop a digital Risk Profile Questionnaire within the a.i. software platform, consisting of extensive academic research that enables advisers to have a robust foundation in determining risk for their clients, aligning their clients to the right asset allocation in line with their goals and life aspirations.

Dr Katherine has a Bachelor of Psychological Science, Bachelor of Commerce

(Financial Planning), and Bachelor of Finance (First Class Honours, University Medal).

You can also read one of the most extensive research reports carried out in Australia on Investment Risk Profiling: Lessons from Psychology by Dr Katherine, published in the Financial Planning Research Journal.

Research references as part of Dr Katherine's work:

Amadi, C. W. (2012). An Examination of the Adverse Effects of Consumer

Loans. International Journal of Business and Management, 7(3), 22-31.

Arnett, J. (1994). Sensation Seeking: A New Conceptualization and a New

Scale. Personality and individual differences, 16(2), 289-296.

Baker, S. R. (2018). Debt and the response to household income shocks: Validation and application of linked financial account data. Journal of Political Economy,

126(4), 1504-1557.

Basu, A., & Drew, M. (2007). Portfolio size and lifecycle asset allocation in pension funds. Paper presented at the The 15th Annual Conference on Pacific Basin Finance, Economics, Accounting and Management, Ho Chi Minh City, Vietnam.

Basu, A., & Drew, M. (2009). The case for gender-sensitive superannuation plan design. Australian Economic Review, 42(2), 177-189.

Bourassa, S. C., & Shi, S. (2017). Understanding New Zealand's decline in homeownership. Housing Studies, 32(5), 693-710.

Buckley, R. C. (2015). Adventure Thrills are Addictive. Frontiers in Psychology,


Chai, J., Horneff, W., Maurer, R., Mitchell, O.S. (2009). Extending Life Cycle Models of Optimal Portfolio Choice Integrating Felxible Work, Endogenous Retirement, and Investment Decisions with lifetime Payouts. NBER Working Paper Series, No. 15079.

Cordell, D. M. (2001). RiskPACK: How to evaluate risk tolerance. Journal of Financial Planning, 14(6), 36.

Cordell, D. M. (2002). Risk tolerance in two dimensions. Journal of Financial Planning, 15(5), 30.

Costa-Font, J., Frank, R. G., & Swartz, K. (2018). Access to long term care after a wealth shock: Evidence from the housing bubble and burst. The Journal of the Economics of Ageing.

Davies, G. (2017). New Vistas in Risk Profiling. CFA Research Foundation Briefs, 1-32.

Drew, R. B. (2015). Effect of changing demographics on young adult homeownership rates. Joint Center for Housing Studies Harvard University.

Gathergood J (2012) Self control financial literacy and consumer overindebtness Journal of Economic Psychology, 33(3), 590-602.

Gilchrist, H., Povey, R., Dickinson, A., & Povey, R. (1995). The sensation-seeking scale: Its use in a study of the characteristics of people choosing 'Adventure holidays'. Personality and individual differences, 19(4), 513-516.

Grable, J., Lytton, R., & O'Neill, B. (2004). Projection bias and financial risk tolerance. The Journal of Behavioral Finance, 5(3), 142-147.

Grable, J., & Lytton, R. H. (1999). Financial risk tolerance revisited: the development of a risk assessment instrument*. Financial services review, 8(3), 163-181.

Grable, J. E., & Lytton, R. H. (2003). The development of a risk assessment instrument: A follow-up study. Financial services review, 12(3), 257.

Green, D., Melzer, B. T., Parker, J. A., & Rojas, A. (2016). Accelerator or brake? cash for clunkers, household liquidity, and aggregate demand. Retrieved from Gilliam, J., Chatterjee, S., & Grable, J. (2010). Measuring the perception of financial risk tolerance: A tale of two measures. Journal of Financial Counseling and Planning, 21(2).

Hallahan, T., Faff, R., & McKenzie, M. (2003). An exploratory investigation of the relation between risk tolerance scores and demographic characteristics. Journal of Multinational Financial Management, 13(4-5), 483-502.

Hanna, S. D., Waller, W., & Finke, M. S. (2011). The concept of risk tolerance in personal financial planning.

Henretta, J. C., & Campbell, R. T. (1978). Net Worth as an Aspect of Status. American Journal of Sociology, 83(5), 1204-1223.

Huang, J.-T., Xu, X., & Chiang, T.-F. (2016). Household Expectations for Future Economy and Risk-Taking Attitudes. Journal of Financial Counselling and Planning, 27(1), 109-121.

Kannadhasan, M. (2015). Retail investors' financial risk tolerance and their risk­taking behaviour: The role of demographics as differentiating and classifying factors. I/MB Management Review, 27(3), 175-184.

Keller, C., & Siegrist, M. (2006). Investing in stocks: The influence of financial risk attitude and values-related money and stock market attitudes. Journal of Economic Psychology, 27(2), 285-303.

Kim, J. (2016). Why household debt held by Korean seniors is problematic: An international comparison. Economics Bulletin, 36(4), 2080-2093.

Kuzniak, S., Rabbani, A., Heo, W., Ruiz-Menivar, J., & Grable, J. (2015). The Grable and Lytton risk tolerance scale: A 15-year retrospective. Financial services review, 24, 177-192.

Li, W., & Yao, R. (2007). The Life-Cycle Effects of House Price Changes. Journal of Money, Credit and Banking, 39(6), 1375-1409.

Mundra, K., & Uwaifo Oyelere, R. (2018). Homeownership trends among the never married. Housing Studies, 1-26.

Nottage, L. (2013). Innovating for 'Safe Consumer Credit': Drawing on Product Safety Regulation to Protect Consumers of Credit. In T. Wilson (Ed.), International Responses to Issues of Credit and Over-indebtedness in the Wake of Crisis. Surrey, England: Ashgate.

Ottaviani, C., & Vandone, D. (2011). lmpulsivity and household indebtedness: Evidence from real life. Journal of Economic Psychology, 32(5), 754-761.

Pennings, J. M. E., & Smidts, A. (2000). Assessing the Construct Validity of Risk Attitude Management Science, 46(10), 1337-1348.

Pfeiffer, D. (2017). No Place Like Home: Wealth, Community & the Politics of Homeownership, by Brian J. McCabe. Journal of the American Planning Association, 83(2), 221-222.

Poterba, J. M., Venti, S. F., & Wise, D. A. (1994). Targeted Retirement Saving and the Net Worth of Elderly Americans. The American Economic Review, 84(2), 180-185.

Rosen, K. T., Bank, D., Eckstein, A., Stern, M., & Tcheau, M. (2017). Homeownership in Crisis: Where are We Now?

Roszkowski, M. J., & Grable, J. E. (2005). Estimating risk tolerance: The degree of accuracy and the paramorphic representations of the estimate. Journal of Financial Counseling and Planning, 16(2).

Sahm, C. R. (2012). How Much Does Risk Tolerance Change? Quarterly Journal of Finance, 02(04), 1250020.

Sahm, C. R. (2012). How much does risk tolerance change? The quarterly journal of finance, 2(04), 1250020.

Sarlija, N., Bensic, M., & Zekic-Susac, M. (2009). Comparison procedure of predicting the time to default in behavioural scoring. Expert Systems with Applications, 36(5), 8778-8788.

Shefrin, H. M., & Thaler, R. H. (1988). The behavioral life-cycle hypothesis. Quasi Rational Economics, 91-126.

Spader, J., McCue, D., & Herbert, C. (2016). Homeowner Households and the U.S. Homeownership Rate: Tenure Projections for 2015-2035. The Harvard Joint Center for Housing Studies, December 2016.

Teye, A. L., Haan, J. d., Elsinga, M. G., Bondinuba, F. K., & Gbadegesin, J. T. (2017). Risks in homeownership: a perspective on The Netherlands. International Journal of Housing Markets and Analysis, 10(4), 472-488.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.

Viceira, L. M. (2007). Life-cycle funds. Cambridge, MA, USA: Harvard University.

Wei, S.-J., Zhang, X., & Liu, Y. (2017). Home ownership as status competition: Some theory and evidence. Journal of Development Economics, 127, 169-186.

Yook, K. C., & Everett, R. (2003). Assessing risk tolerance: Questioning the questionnaire method. Journal of Financial Planning, 16(8), 48.

Zuckerman, M., Kolin, E. A., Price, L., & Zoob, I. (1964). Development of a sensation­seeking scale. Journal of Consulting Psychology, 28(6), 477-482.

Zuckerman, M., & Neeb, M. (1980). Demographic influences in sensation seeking and expressions of sensation seeking in religion, smoking and driving

habits. Personality and individual differences, 1(3), 197-206.

Dr Katherine Hunt

11 min read

May 23



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