Note: This article appears on the ETFtrends.com Strategist Channel
By Giralda Advisors
In our earlier installments in this series (see archive here), we described the Risk-Managed Investing (RMI) approach to equities as embedding volatility dampening and/or downside risk mitigation directly within the equity investment itself. Previously, we also outlined (RMI and Investing in a Rising Rate Environment and RMI and the Liquid Alternatives Landscape) how RMI could be part of a solution to the portfolio problem caused by the disappointing prospects for non-equity asset classes, particularly fixed income and liquid alternatives. We described (RMI, Volatility Drag, and Sequence Risk and Alpha Generation through Risk-Managed Investing) how RMI could address such financial planning challenges as volatility drag and sequence risk, and add long-term alpha over complete market cycles. We calibrated how low the cost of risk management needs to be in order to be worthwhile (The Tolerable Cost of Risk-Managed Investing) and explored a range of RMI solutions in the marketplace (RMI Applications: Sector Rotation Using ETFs, RMI Applications: Tail Risk Hedging, and Marketplace Review of Risk-Managed Investments).
Over our final three installments on the subject of RMI, we will attempt to tie things together in a portfolio construction context. In this current piece, we discuss portfolio optimization once RMI solutions are introduced into the picture. The following installment will examine the implications for portfolio performance benchmarking and scorekeeping, and the capstone piece will explore the question “Is 80/20 the new 60/40?”.
A Brief Review of Portfolio Optimization
Modern Portfolio Theory (MPT) was born in the early 1950s with the published works of Harry Markowitz. MPT expressed the goal of portfolio construction as finding the “efficient frontier” — the set of portfolios that formed a theoretical boundary beyond which it was not possible to find a higher-return portfolio at the same or lower levels of risk or, equivalently, a less risky portfolio at the same or higher levels of return. MPT led to a shift in focus from evaluating individual assets in isolation to considering the way in which various investments behaved in concert and how their interaction affected the risk/return profile of the overall portfolio.
To find one’s way to the efficient frontier, an investor needed to estimate the expected return and volatility of each asset that was a candidate for inclusion in the portfolio. Additionally, and this was the key breakthrough of MPT, the investor needed to estimate how the behavior of one asset was correlated to the behavior of each of the other assets. All of this was mathematically derivable from prior history. Improvements, particularly in the estimation of volatility and cross-asset interrelationships, have come along over the years — see, for example, “Next Generation Investment Risk Management: Putting the ‘Modern’ Back in Modern Portfolio Theory” (Miccolis and Goodman, Journal of Financial Planning, January 2012). In one way or another, MPT-inspired “optimal portfolios” have been in the mainstream of professional investment management for more than 60 years now.
Diversification as a Risk-Management Device
While the practice of “not putting all of one’s eggs in a single basket” predated MPT, the theory did add mathematical rigor to the notion that diversification added value to the portfolio. Specifically, with the right mix of poorly correlated assets, one could construct a portfolio that had an expected return near the average expected return of its component assets but well below the average volatility. Thus, the value of diversification could be clearly seen as reducing risk, and the risk-management benefit of diversification could actually be quantified.
Accordingly, one of the major upshots of MPT has been the rise in popularity of “diversifying assets” such as liquid alternatives. These diversifying assets could afford to have only fair return prospects of their own if their correlation with high-return (and presumably high-risk) assets was very low. In other words, the power of diversification at the portfolio level was seen to be so great that it made attractive certain middling-return assets that might not be so appealing on their own.
Another Perspective on Diversification
The above discussion suggests another way to view diversification and portfolio optimization. In this view, the investor constructs a portfolio by starting with its most essential component. We believe most would agree that, for the preponderance of investors, that essential component is equities. Equities represent the growth engine of the portfolio, the component most likely to keep the portfolio ahead of inflation over the long run and make its owner’s financial plan work.
So, why invest in anything else? Well, equities carry with them the unfortunate tendency to substantially decline in value at unpredictable times. Diversifying into asset classes sufficiently different from equities is a way to buffer equity downside risk.
However, the risk mitigation thus offered by diversification is indirect and far from guaranteed. Indeed, equity drawdowns of significant degree have been known to drag many other asset classes down with them. Would it not be better, more direct, and less uncertain if you could find a way to embed downside risk mitigation explicitly within the equity investment itself? This, of course, is what RMI is all about.
We have presented this view earlier in this series, but now we are ready to examine it more rigorously — in the context of MPT’s efficient frontier.
Putting RMI to the MPT Test
To insert an RMI investment into an MPT optimization exercise, it is first necessary to estimate its expected return, volatility, and correlation with other asset classes. The actual historical performance of the RMI investment could be used to estimate those parameters in the same way they are estimated for the more traditional asset classes. If prior live history is not long enough (ideally, several full bull/bear market cycles should be represented), there are alternative methods. One method, if the RMI strategy is completely rules-based, is to simply model its prior performance mathematically. Another method is to express the RMI strategy’s performance in “deductible/copay/cost” terms as described in our earlier piece, The Tolerable Cost of Risk-Managed Investing, and estimate the parameters relative to the corresponding parameters of a standard index such as the S&P 500 Index.
We applied the latter method using an RMI strategy with a “deductible” of -10%, a “copay” of 50%, and a “cost” of 410 basis points per year — that is, the downside protection kicks in when the S&P 500 Index suffers a drawdown of -10% or worse, the protection mitigates 50% of any subsequent decline net of the cost of the protection, and the performance drag relative to the S&P 500 during periods when the protection is not needed is 410 basis points per annum. In the chart below we plot, in blue, the efficient frontier of a two-asset stock/bond portfolio, using the S&P 500 Total Return Index and the BarCap Aggregate Bond Total Return Index to represent the two assets. Twenty years of daily return data (December 30, 1994 through December 31, 2014) was used, and rebalancing was ignored for simplicity. On the same chart we plot, in red, the efficient frontier obtained by substituting for the S&P 500 the RMI investment we just described. The red, RMI-enhanced, frontier is seen to be elevated above the blue.
Importantly, this type of improvement in the risk/return profile of the portfolio can be expected whenever a cost-effective RMI strategy is introduced.
The implications of this phenomenon are profound. We will examine the implications for portfolio performance measurement and benchmarking in our next installment. And, in the final piece of this series, we will explore the potentially paradigm-shifting implications for the future of investing.
This article was written by Jerry Miccolis, Gladys Chow and Rohith Eggidi of Giralda Advisors, a participant in the ETF Strategist Channel.