In July 2008, the giant government mortgage agencies Fannie Mae and Freddie Mac failed, the US Treasury bailed them out, and their directors were rewarded with bonuses. Earlier that year, Treasury forced JP Morgan Chase to buy Bear Stearns, and later Washington Mutual. JP Morgan was also rewarded, with billion of dollars in fines. Later in 2008, Auction Rate Securities markets froze and money market funds were in jeopardy.
While the causes for 2008’s financial meltdown are suppressed by state-run media, its effects were borne by everyone not in the good graces of government. The banking crisis that began in 2007 led to a collapse in stock prices, home values, interest rates, and crippled the retirement dreams of millions of Americans. While almost everything was losing value, corporate retirement plan managers claimed to be helpless to do anything about it.
After all, the meltdown could not have been anticipated, and its effects were not controllable, right?
As the dust cleared in late 2008, Black Swan Theory became a popular theme in investment policy meetings and newsletters. The mythical Black Swan is a metaphor for extreme outlier events that are unpredictable, have major consequences, and are rationalized after the fact. One rationale was that portfolio diversification did not work because everything dropped sharply at the same time.
But was 2008’s perfect storm impossible to anticipate? Did diversification really fail?
Black Swan Hunting
In April 2007, professor and statistician Nassim Taleb published his influential book titled The Black Swan: The Impact Of The Highly Improbable. And his message is clear: authoritarian macroeconomics experts are not experts, and they don’t know it. Their tendency is to assign simplistic explanations for outlier events. Serious analysis of the problem is what Taleb described as “stemming from the use of degenerate metaprobability.” In plain English, he explains it as “What happens, happens.”
In 1999, another statistician and investment manager built a system that anticipates the possibility of Black Swan events (it does not predict them). And it calculates their possible effects.
David Loeper’s Monte Carlo engine is based on the capital market assumptions (CMAs) of Modern Portfolio Theory, and he uses them to calculate the likelihood of extreme market events, both positive and negative. His overriding premise is that the future is uncertain. As Loeper explains about the nature of capital markets,
Being excessively conservative in assumptions will needlessly sacrifice an investor’s life. The data exists to discover this. If you have bad assumptions, the allocation will not behave as anticipated. Well reasoned assumptions should have modeled extreme ‘asymmetric correlation’ markets.
In other words, the possibility of something like 2008 should have been anticipated. Loeper did, and the personal sacrifices of individual investors could have been mitigated.
Asymmetric Correlation Ate My Homework
In plain English, what investment managers call asymmetric correlation really means, ‘Hey, at least we sound smart!’ Their diversifying asset classes such as hedge funds, real estate, emerging markets, and managed futures were expensive and unproven. Or as Loeper explains “they had insufficient data to draw valid conclusions.”
In Modern Portfolio Theory, each asset class has two potential assumption errors – median return and risk. And not only were the assumptions for the diversifiers unreliable, the addition of each one multiplies the potential for errors. This is because their correlation to each other must also be considered. What happened in 2008 was that the diversifier asset classes became positively correlated with the risk assets they were intended to hedge, meaning they all lost value at the same time. As a result, retirement plan investors were exposed to a great deal of needless sacrifice and risk.
For example, in 2008, stocks of US companies lost 37%. A portfolio consisting of just four asset classes (domestic stocks, foreign stocks, US government bonds, and cash), with 60% of that in stocks, lost about 18%. Yet many “more diversified portfolios,” also with 60% in stocks, were down more than twice as much. Besides all of these additional assumption errors, there was another big one – investment managers had an aversion to US government bonds.
Of Strategists and Optimizers
According to Loeper, “The purpose of fixed income in an asset allocation is to lower the overall risk of equity markets, particularly in shock environments.”
Only US government bonds do this well, and there is a vast historical data set behind their CMAs to prove it. Newer bond asset classes like foreign, high yield, emerging market, and TIPs have no such reliability. However, traditional investment managers do not use US government bonds to diversify risk assets because of perceived interest rate risk, or their economic forecasts.
And neither do many mathematical models known as mean variance optimizers (MVOs).
The similarity between traditional managers and optimizers is that they are predicting the future. This adds potential assumption errors. While strategists admit they couldn’t anticipate the events of 2008, MVOs are a different story. Their analysts merely adjust the data inputs of the new, diversifiying asset classes because those data sets were insufficient anyway. The problem is the optimizer might select a portfolio dominated by these new, alternative asset classes. Ask Yale University’s endowment how that worked out.
“I Don’t Want My Money to Work Hard. I Want it to Relax.”
Jerry Seinfeld is on to something here. It took a lot of work to get things so wrong, and it was expensive. Those who religiously follow government policy couldn’t foresee the disastrous consequences of government policy. Those who place their faith in unreliable data sets built on self-fulfilling prophecies were also blindsided.
Fortunately there is a proven alternative to the stress of beating the market – Moneyball. Billy Bean, GM of the Oakland A’s, showed the baseball world how to win the division while minimizing the cost per win. He replaced the unforced errors of talent scouts with reliable data. For retirement plan investors, it’s about winning the future while minimizing risk, and replacing forecasters with your own vision for the future.
Let the price mechanism of free markets do the work, anticipate the possiblility of extreme events, avoid unforced errors, and let the retirement nest egg relax. The uncertainty of complex systems like capital markets and its price information can be reckoned with. After all, “the data exists to discover this if you examine it closely.”