This is the premiere blog posting on AdvisorOne by Geoff Davey, a pioneer in using psychometrics to assess clients’ risk profiles. A former advisor in his native Australia, Davey cofounded risk tolerance technology firm FinaMetrica in 1999; advisor users of his Web-based risk-profiling system are worldwide.

Many studies have shown that risk tolerance correlates positively with income and wealth. The correlations are not strong, usually around 0.3, but they seem to be universal.

There is a temptation to think that higher income and/or higher wealth lead to higher risk tolerance. However, there is always a danger in trying to read a cause and effect relationship into a correlation. To know for sure we would need to conduct a longitudinal study measuring risk tolerance, income and wealth as we went along.

Failing that, we can conduct a thought experiment. Suppose that Bill and Bob have different appetites for risk. Presented with a choice between taking a certain \$100 and a 50/50 gamble of winning \$0 or \$X, Bill will take the gamble when X is \$250 but Bob won't take the gamble until it reaches \$300. Looking at any single \$250 gamble choice, Bill has a 50% chance of being no worse off than Bill.  However, if Bill and Bob are presented with a series of such choices, the longer the series runs the more certain it is that Bill will finish up better off than Bob. With a series of 10, Bill has an 83% chance of being no worse off than Bob and by the time we get to a series of 100 that chance has increased to 98%.  Over 10 choices, Bill will finish with \$1,000 but Bob could expect to have \$1,250, though he may have nothing or \$2500.

Now suppose that Bill and Bob both started with a kitty of \$1,000 and that rather than the choices being framed from a base of \$100, they were framed from a base of 10% of the kitty at the time. For 10 choices, Bob’s kitty grows to \$2,593 but Bill’s grows to an expected average of \$3,260 and 62% of the time will be greater than \$2,590. At worst Bill will have \$1,000 and at best \$9,300.

Overall, by taking more risk Bill can expect to be significantly better off.

So how does this relate to real life? Clearly, life’s choices are rarely as simple as in our example and rather than

a series of identical choices we face a series of mainly different choices where there are usually more than two alternatives—and those alternatives will often include the possibility of losses. Further, the range of outcomes is often not clear and they must be estimated rather than calculated. Finally, we may make cognitive errors in assessing the situation and in identifying and evaluating the alternatives.

As we know from experience, risky choices take many forms and occur in different contexts including employment, borrowing, insurance and investment. For the riskier alternatives to be considered there would be a commensurately greater expected reward, but this will come with the possibility of an unfavorable outcome. The more risk tolerant amongst us will need less of an incentive to take the riskier alternatives. If we continue that pattern over time, all other things being equal, we should finish up better off.

So my hypothesis is that risk tolerance is a driver of financial success rather than the converse.