Their study includes some neat charts that further validate the mechanics of this approach. For example, the success of this approach is based on gambling theory which describes optimal bet sizing. If we assume expected return is either constant or unknowable, but positive, then it is optimal to always bet the same amount. However, markets force most investors to increase and decrease their bet sizes, sometimes dramatically, based on changing volatility. An investor's exposure to risk is much larger when the market's volatility is 30% than when it is 15%, but most investors fail to adjust portfolios for this higher risk.
The charts below illustrate how this plays out in markets. The charts at the top prove that returns in one month have little relationship to returns in the next month (left), but the volatility in the current month does a good job of forecasting the volatility in the subsequent month (right). So adjusting one's exposure based on the most recent month's volatility makes sense.
Source: Russell Investments
Some might argue that higher volatility should equate to higher expected returns, but the chart below invalidates that assumption. There is no relationship between volatility and expected return.
Source: Russell Investments
If higher volatility does not improve expected returns, then we should concern ourselves with optimal bet sizing. By doing so, we substantially improve investment results, especially after accounting for risk.
Source: Butler|Philbrick|Gordillo & Associates, 2012. Results are pro-forma and for illustrative purposes only.
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