Make no mistake; many of those who have lost a lot of money during “bad” markets like the great crash of 2000-2003 owned a lot of different things – there were a lot of “names” on their portfolio statements – and thought they were adequately diversified. In hindsight, it is clear they were not. Why? Putting too much of their wealth into too few companies or issues. But a lot of people who owned nothing but mutual funds – even many different mutual funds – also got whacked. And why is this? Again, concentration is the culprit.
You have probably heard the terms “asset class” and “asset allocation” before. Asset class means a type of investment whose overall market tends to move in different cycles from other classes. Take U.S. real estate and foreign stocks. While sometimes (due to mostly random forces) they move in the same cycle, it is reasonable to expect them not to go up and down together, since they are affected by significantly different forces. The technical term is correlation, and the prudent investor wants little pieces of lots of markets that are poorly correlated – that move in different cycles – to reduce the odds of everything in the portfolio going down at the same time. Those whose portfolios are predominantly invested in one or a few asset classes take a great risk of great losses if (or when) the only markets to which they are exposed crash before they get out.
This is why many investors with lots of different investments still lost big in 2000-03: high correlation among their portfolio components. If you owned ten funds that primarily invested in U.S. growth stocks (probably picked because their trailing performance numbers were the highest), then all ten funds got hammered when the market in which the funds swam tanked. The diversification was an illusion, since all ten funds invested in similar enough things that they were highly correlated. Many investors never realized that the very year NASDAQ crashed; U.S. utilities were up something like 40%. Those whose portfolios were asset-class diversified had nowhere near as painful a year.
Striving for portfolio efficiency – where we still want to maximize returns and minimize risk – takes us away from simple measures like the Sharpe Ratio, and into the nuts and bolts of Modern Portfolio Theory (MPT to save my fingertips). MPT’s basic premise is that if we have enough different pieces of poorly correlated asset classes in just the right percentage combinations, then we can get a lot of the risk to cancel out without sacrificing attractive target returns. MPT requires that we focus on the performance of the overall portfolio instead of the component pieces (though we still want to pick good pieces, and our friend Dr. Sharpe and his famous ratio can really help us here).
A MPT portfolio accepts the fact that some pieces will do poorly (relatively) in some periods, in exchange for probably having some pieces that will shine. Investors (and their advisors) who try to guess what the next “hot sector(s)” will be so they can put all their eggs there risk getting it wrong (and most usually do), and seeing the whole portfolio go down with the chosen sectors. Spreading it around makes it a lot likelier that you will already have of piece of those sectors when their time comes to be “hot,” and gives you a much higher statistical probability of overall portfolio success. By spreading your “bets” thinly, you’re a lot likelier to have at least a part of your “action” “hit” and at the same time less likely to lose “big” on those pieces that might otherwise be thickly “laid” on what might become poorly performing markets.
Let’s talk about “asset classes” in a bit more detail. Generally, the term is taken to mean different types of investments which trade in somewhat different markets in somewhat different cycles with respect to time. In the above example, utilities were soaring at the same time NASDAQ (and other major U.S. indexes) was crashing. Different asset classes emerge for stocks of different “size” companies (large cap, mid cap, small cap, micro cap), value vs. growth styles, bonds of different maturities and qualities, investments outside of the United States in established and emerging economies, real estate, commodities, securitized real estate, private equity (investments in companies not publicly traded), hedge funds, and so on.
MPT’s defining concept is the efficient frontier. In a given universe of asset classes, for each level of risk there will be one and only one combination of asset classes in a unique weighting of each that produces the highest return; sort of like the Sharp Ratio in that it measures investment efficiency. The line that connects all these “dots” from lowest risk to highest risk is called the efficient frontier. You can find examples of the efficient frontier to look at in any basic investments textbook or at countless sites on the Internet. There are two important points I would like to make about the efficient frontier. First, the curve tends to flatten out: beyond a certain point, more risk does not produce any higher return. Second, most portfolios lie under the frontier, meaning they are inefficient and do not produce the maximum return for the risk taken. And when I say most, I mean the overwhelming majority of different asset class combinations possible. This, I firmly believe, is why so many investors never do as well as they could. They take far more risk than was smart – probably because no one ever thought to measure the risk relationships – and predictably suffer for it. In fact, many portfolios have returns that can be expected to produce losses if held long enough!
So, how do you determine what kind of mix you should have for your ideal efficient portfolio? Actually cooking up your own may involve biting off more study and math than you want to, but those interested can get mean variance optimizer software, and play around with different scenarios. I caution you that this part is as much art as science, however, and big helpings of markets knowledge (and the resultant intuition) are usually needed to pick prudent asset class universes and data time periods – and even the big-name “pros” often come up with mediocre ones at best.
The specific methods and mixes that we at Camarda use for our ISIS® portfolios are considered trade secrets, so I can’t and won’t reveal them to you here. If you choose to talk to us, we will prescribe a specific mix (along with a customized Investment Policy Statement for your own situation) at no cost or obligation in an attempt to earn your business. Other places to look are the profusion of asset allocation models offered from everyone from mutual fund companies, insurance companies (to sell their insurance products like annuities and variable life) and brokerage firms, to those cooked up by individual financial planners and salespeople. As mentioned, quality varies widely, and you would be wise to look at the risk/return data of the different mixes over different time periods when making your decision. Be sure to get returns after all costs, fees, and expenses in order to make fair comparisons. Remember, quality varies quite widely from superb to abysmal. You can get basic information on Camarda’s ISIS®, if you wish, and view the historical performance of all models – net of all costs, and based on actual client money – at camarda.com, and judge for yourself. As we often say, Camarda’s performance “truly speaks for itself.”
Once you’ve decided on an asset class mix, there is the problem of deciding which particular investment is best suited to represent the asset class in the mix. The Sharpe Ratio is a good barometer here, and other factors such as returns, costs, asset class fidelity, and so on should be considered. Camarda’s ISIS® process, for example, examines fifteen factors each quarter to determine its “best” choice of fund to represent foreign equities.
Remember, the investments world is in constant flux, and you need to revisit investment selections regularly; what was once good can quickly change, go bad, or simply be eclipsed by a better contender. Keep constant tabs.
Finally, be sure to check the “balance” regularly, and reset the percentages to the model when appropriate. The current crop of “winners” will soon exceed the model percentages desired for them (since they will go up more than the others during a given period), and if you don’t reset you will wind up having more of your money than is prudent in them. This increases risk and reduces probable return: efficiency goes down. Resetting or “rebalancing” reminds you to sell high and buy low (since you can get more of the ones that did not do so well “cheap” when you bring them back up to their target allocations).