PROFESSIONAL PORTFOLIO DESIGN (part two)

disclaimer

*** This is for educational purposes only and is not intended as investment advice. I don’t know you or your personal situation, so I can’t say what investments may or may not be appropriate for you. Please do your own research before investing. ***

where we left off

In the last post we used the Horizon Actuarial Survey risk-return data and Excel’s Solver function to build three portfolios.

P1: Max return (100% stocks)
P2: 50/50 Stock/Bond portfolio
P3: Minimum risk (10% stocks, 80% bonds, 10% real estate)

P2 was an arbitrary 50/50 mix to demonstrate portfolio risk/return math. Let’s see what some portfolios targeting risk levels between P1 & P3 might look like.

portfolios targeting different risk levels

Ok, we know P1 is the riskiest posture we can take, using our three asset classes. According to Horizon, stocks have an expected risk (standard dev.) of 16.6%. What if we want to dial back our risk slightly to, say, 13%? Let’s change our settings in Solver as follows and see what portfolio we get:

Open Solver by clicking the “data” menu at the top of the screen in Excel, and enter in the objective, variables and constraints using the asset class risk/return data from Horizon, and our calculations from part one .

Objective (Solver’s goal): maximize return (cell B9)
Variables (Solver will change these until the goal is met): % allocations to stocks, bonds, and real estate (cells H3:H5)
Constraints (rules solver can’t violate in seeking the goal):
- Allocation to each asset class (H3:H5) must be greater than or equal to zero (can’t be negative)
- Asset class allocations must sum to 100% (H7)
- Portfolio standard deviation (B13) must be less than or equal to 13%
Click “Solve” in the bottom-right of the gray box and let Solver work its magic.

The resulting portfolio (P4) looks like this:

Asset allocation: 73% stocks, 27% real estate (no bonds)
expected return = 6.65%
expected risk (std dev) = 13%

[sidenote: send me an email or DM on Twitter and I’ll send you the excel template I used]

We can repeat this exercise, decreasing the target portfolio risk by a few % and letting excel calculate asset allocations that meet the criteria.

In this fashion I created portfolios P5 & P6 targeting portfolio risk levels of 10% and 7%, respectively. We know our minimum risk portfolio has a standard dev. of 5.2%, so there’s no point trying to generate a less risky portfolio; it isn’t possible with these three asset classes.

Someone wanting to take less risk than the least risky portfolio could either hold a risk- free asset like cash (i.e. increase the value of their emergency savings) or combine different amounts of cash with the minimum risk portfolio until they reach an acceptable risk level.

building the “efficient frontier”

I created a scatterplot with our hypothetical portfolios on it. The horizontal x-axis is risk (standard dev), the vertical y-axis is expected return. The table below contains all the portfolio details (asset allocation %s, expected return and risk stats, etc.)

The green highlighted portfolios represent the maximum return achievable for their level of risk. In finance terms, these portfolios are “efficient”. The orange highlighted portfolios are inefficient, because we can build higher expected-return portfolios that have the same expected risk. To illustrate, I generated portfolios 7 & 8.

P7 (45% stocks/29% bond/26% RE) has the same expected risk as our basic 50/50 stock/bond mix (P2), but a higher expected return. How is this possible? First, because real estate has a higher expected return than bonds, reducing the bond allocation and increasing real estate increases the portfolio’s expected return. Real estate also has a higher standard deviation (risk) than bonds, but it isn’t perfectly correlated with stocks or bonds (its correlation to each is less than 1.0). So adding real estate to other asset classes it isn’t perfectly correlated with lowers the expected volatility of the portfolio, offsetting the higher volatility of real estate on its own.

Likewise, P8 (23% Stock/61% bond/16% RE) has the same expected risk as holding 100% bonds, but nearly 1% higher expected return. Adding two asset classes (stocks and real estate) that are imperfectly correlated gives us a double dose of risk reduction at the portfolio level. Even though both stocks and RE are more volatile on their own, when held alongside bonds in a portfolio, their imperfect co-movement reduces volatility of the overall mix. And since both have a higher expected return than bonds, the portfolio return goes up the more stocks and RE we add in.

If you were to draw a line connecting each efficient portfolio (the green dots), the resulting curve is referred to as an “efficient frontier”. It’s a visual representation of how much risk is involved in targeting a given level of return, and the corresponding shifts in asset allocation.

At the end of this exercise, it will be nice to have a few portfolios across the risk spectrum from lower to higher risk to choose from. But wait you say, don’t we just need one single portfolio? At any point in time, sure, one person probably needs one portfolio strategy. But as we go through life our goals and attitude toward risk will change. The portfolio we start with as young professionals may not serve us as retirees. So it helps to have portfolios of different risk postures from which to choose.

But we probably don’t need seven of them. The table below contains five of the portfolios we created, (more or less) evenly spaced across the efficient frontier (I removed portfolios 8 and 7). The portfolios are named based on their risk profile, conservative to aggressive.

that’s a wrap

Congratulations!

You just completed a strategic asset allocation from start to finish. Together we:

Defined asset classes
Gathered market data
Created asset class blends targeting specific risk/return levels (see list)

These asset allocations are templates we can use to build investable portfolios. To implement, we simply find investments in the market that represent each asset class, and purchase them at the designated percentages. We’ll do this in the next post!

THINGS TO TRY

You could use this process to

Include more asset classes – US small cap stocks and international stocks would be a couple to consider.
Experiment with risk/return estimates from other providers.
Repeat the exercise at regular intervals (i.e. annually) so the portfolios adjust to changing market views.

As the market environment changes, prospects for risk and return change, and the portfolios that result from this process will shift, reflecting the current views of investment professionals. It isn’t perfect, and the future will differ from these estimates. But SAA done well can provide a sound basis to help make investment decisions.