May 25, 2012

When Bad Things Happen to Good Portfolios—and What to Do About It

Three responses arose from the crash of 2008. Can they somehow work together?

Scott Welch began his session at the “Best of IMCA” seminar series with a few stark reminders.

“Modern Portfolio Theory is just that, a theory; it’s not Modern Portfolio Fact,” Welch stressed Thursday afternoon in Denver. “Efficient Market Hypothesis is a hypothesis; it’s not Efficient Market Statement.”

Both of these investing staples have too many simplifying assumptions to address them otherwise, he added, but it’s something many people forgot, especially between 2002 and 2007.

“But 2008 brought this home in a big, big way,” Welch, senior managing director and member of the executive committee at alternative investment firm Fortigent, said.

He then launched into his presentation. Titled “When Bad Things Happen to Good Portfolios: Rethinking Risk and Diversification,” it began with an extensive examination of the three reactions advisors and investors have had since; the behavioral response, the “quant” response and the “rethinking the problem” response, three camps each with strong arguments and respected advocates.

The behavioral response wryly notes that “modern portfolio theory ain’t so modern anymore” and must be reevaluated. This reevaluation holds that investors act irrationally and do not have uniform risk tolerances or “utility maximizing” risk and return objectives. Rather, an investor’s risk tolerance is dependent on the starting point of wealth and changes over time in an “asymmetrical way.”

“MPT measure statistical portfolio qualities that do not capture non-statistical sources of risk within portfolios,” Welch noted. “And extreme market conditions like Black Swans cause complete model failure. Also, investors do not view upside and downside risk in the same way.”

“Is there a better way?” he asked. “In other words, can we bridge the divide between Eugene Fama and Richard Thaler?”

So called “Post-Modern Portfolio Theory” might be this “ecumenical bridge.” It measures downside risk more accurately through the Sortino ratio, semi-variance efficient frontiers, lower partial moment, etc. to find how investors actually think about their money. The upside is also more accurately captured through minimum acceptable returns (MAR) and upside potential of an objectives-based investment strategy.

“The verdict to date, based on tons of analysis, is that it is a better way,” Welch said. “However, there is little practitioner acceptance of implementation, probably because there are 25 years of investor ‘education’ to undo.”

He then moved to a discussion of the quantitative responses since 2008 (or the theory that it can all be solved with math), among them market “turbulence” optimization.

“This holds that markets pass through periods of ‘quiescence’ and ‘turbulence,’” he said. “These periods are both persistent and forecastable. The key is to build and manage portfolios to incorporate these different turbulent regimes, rather than assuming static volatility and correlation conditions, which results in more ‘true’ diversification, less tail risk and more consistent portfolio performance.”

Another quant response was Extreme Value Theory (EVT), which is the statistical analysis of results that deviate wildly from the norm, that is, the study of market behavior in the “fat tail” portion of the return distribution.

“Applying EVT to portfolio management supposedly leads to a better understanding of both the likelihood and the outcome of extreme market events, and thus drives more intelligent portfolio construction and risk management.”

Value at Risk (VaR) and Conditional Value at Risk (CVar) are “somewhat related to EVT,” he said. “VaR is a statistical metric that measures the potential risk of loss within a portfolio over a specified time period. CVaR measures extreme risk or the risk beyond VaR.”

“What do all these quant responses have in common?” he asked. “Standard deviation and correlation analysis are backward looking, unstable over time and represent an insufficient measure of actual portfolio risk. Also, assuming normal ‘bell curve’ risk can be very dangerous, meaning traditional MPT metrics severely underestimate actual market risk, the empirical evidence indicates that the chance of a market ‘blow up’ is far greater that what is indicated by normal distribution assumptions.”

The last response, the “rethink the problem response,” began with a discussion of Black Swan events.

“The response to Black Swans was to note that they are utterly unpredictable, are far more likely to occur and will have a far worse impact than statistics suggest. Once they occur, they are rationalized in hindsight. The practical implication for portfolio management is that ‘catastrophe insurance’ or conversely ‘opportunity exploiters’ should be standard components of any portfolio asset allocation.”

He then noted the “Unified Field Theory” response is a “very interesting avenue of exploration with vast potential, but we are still very early in the game.”

He claimed the theory is a completely different way to think about the problem, one that integrates MPT, behavioral science and evolutionary neurobiology.  

“Dr. Andrew Lo suggest in his book Adaptive Market Hypothesis that we should examine the financial markets as a complex adaptive system, meaning it’s a system made up of myriad and multiple interconnected networks of relationships that ‘learn from experience’ and evolve accordingly over time.”

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