What the heck is Post Modern Portfolio Theory anyway?
Constructing portfolios to maximize expected return based on a given level of market risk is widely understood and accepted as the basis for building a sound investment portfolio.
Harry Markowitz had the first foundational elements of MPT published in the March 1952 Journal of Finance. Rather than causing waves all over the financial world, the work languished on dusty library shelves for a decade before being rediscovered. His work didn’t achieve importance until the early 1970s, when stocks and bonds got slammed at the same time. This changed investors’ ideas of risk and return. It gave new life to his theories. The implications of MPT broke over Wall Street in a series of waves that created the door through which indexing and passive investing entered Wall Street.
The key to MPT is diversification and noncorrelation. The asset classes are just tools. There used to be many asset classes that did not correlate. Not so much anymore.
Investors’ devotion to modern portfolio theory has only strengthened over time, with the rise of passive investing and the struggles of active managers. MPT tells them to just buy the index.
The term, “Post-modern portfolio theory (PMPT)” was first used by The Pension Research Institute in an article in Pensions and Investments magazine, May 2, 1988. For over 25 years, advancements to the science of investing has too been gathering dust on the shelves as it applies to mainstream investment theory.
By the mid 1990’s Dr. Frank Sortino had developed tangible solutions for identifiable weaknesses in MPT. In his article titled “On The Use and Misuse of Downside Risk“, he demonstrates how common risk measurement techniques cause calculation errors. The most frequently used and least reliable procedure for calculating downside risk considers only those historical returns that fall below some minimal acceptable return.
Before we make an investment, we don’t know what the outcome will be. After the investment is made and we want to measure performance, all we do know is what the outcome was and not what it could have been. To cope with this uncertainty, we assume that a reasonable estimate of the range of possible returns as well as the probabilities associated with those returns can be estimated.
- Some of these uncertain returns are undesirable and therefore crucial to the calculation of downside risk.
- The other uncertain returns are associated with reward, and we want to be able to make some intelligent decisions about the tradeoffs between risk and reward for different investments.
It stands to reason that the more accurately we can describe this shape of uncertainty, the better will be our investment decisions. Standard Deviation looks at the dispersion of the mean inferring that risk on the downside is equivalent to risk on the upside, measured as volatility. Because the probability of an outcome is not captured by volatility it can’t help determine when and if the investment will achieve a positive or negative return. The Sortino Upside Potential Ratio captures the probability of whether the future return of any investment will be positive or negative.
Quantifying risk assumes an ability to obtain reasonable estimates of the location point of the distribution, which is usually the mean, and the dispersion about the mean, called variance. If the distributions are not symmetric, it would also help to have estimates of skewness and kurtosis in order to manage this uncertainty.
Bootstrapping is a statistical method capturing downside moves in annual returns that are consistently positive. Using substantially more data points not only indicates what did happen but what could happen as well. In the article, Dr. Sortino’s example of the Japanese market clearly identified why bootstrapping is so important. Even though the Japanese Market showed positive returns for several years in a row, underlying those returns were negative moves that weren’t being captured. Bootstrapping identified that negative returns could happen and they actually did.
The Upside Potential Ratio was developed to underscore the need for a statistic that is based on investor needs rather than preferences. Standard Deviation captures an investor’s preference with regard to volatility but does not identify the risk of failing to achieve the investment objective.
The difficulties Dr. Sortino and William Sharpe faced with their ratios after the dot.com bubble burst couldn’t be ignored. Again in the 2008 financial crisis, asset class correlations merged toward 1 as everything moved down together. It may just have caused the financial community to dust off some of the earlier work of these pioneers as we see the signs of their improvements starting to emerge in investment solutions.
These are the foundational concepts at the core of post modern portfolio theory and the science used to manage Salt Creek Investors Active Allocations. Please check out the article published in the winter 1996 issue of the Journal of Portfolio Management and is reproduced here with the permission of the publisher.
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