Classic Modern Portfolio Theory (MPT) is a passive portfolio management system where one selects a set of uncorrelated assets, sets allocation percentages for each, and periodically rebalances the portfolio’s assets to bring allocations back in line with original targets, from which we get the industry’s mantra of “diversify and rebalance.” In MPT it is believed the market is inherently an unpredictable random walk. Thus MPT advocates buy-and-hold diversification. According to the theory, it’s possible to construct an “efficient frontier” of optimal portfolios offering the maximum possible expected return for a given level of market risk. This theory was pioneered by Harry Markowitz in his paper “Portfolio Selection,” published in 1952 by the Journal of Finance.
Why Does MPT Come up Short?
MPT comes up short because MPT inherently discards valuable market data that is fundamental to understanding the markets. Modern Portfolio Theory fails to accurately describe market activity and also fails to account for time domain data.
Why is MPT Blind to Market Trends?
A commonly accepted definition of MPT is as follows: “MPT models an asset’s return as a normally distributed random variable, defines risk as the standard deviation of return, and models a portfolio as a weighted combination of assets so that the return of a portfolio is the weighted combination of the assets’ returns. By combining different assets whose returns are not correlated, MPT seeks to reduce the total variance of the portfolio. MPT also assumes that investors are rational and markets are efficient.”
So what’s missing?
The answer is that all of the mathematical functions listed above include only statistical functions — which means that there can be no time domain analysis results, and thus no trend analysis results. This is why an MPT practitioner can tell you which five things to buy and hold based on statistical performance in the past, but cannot tell you anything about which of them would be best to own next month. In fact, because MPT incorrectly models an asset’s return as a normally distributed random variable and incorrectly assumes that markets are efficient, it inherently dismisses the existence of trend information, and follows by applying a suite of mathematical analysis tools that destroys the time domain information and thus can’t possibly answer the question, “Which funds would be best to own next month?”
As an analogy, consider an Iowa farmer wanting to know if now is the time to plant his corn. He contacts the Modern Portfolio Theory Farm Adviser and is told (a) the average world temperature today was 62F, (b) the average temperature in his town for the last decade was 58F, (c) the temperature in his town is most well correlated with that of Hamburg, Germany, and most uncorrelated with that of Sydney, Australia and La Paz, Bolivia, and (d) the standard deviation of temperature from average is 3.5F within his state. After pondering all of this great information, the farmer still has no idea whether to plant corn now or wait another week. There is no temperature trend information whatsoever from which the farmer might be able to improve his chance of having a great crop this season.
What should a farmer do with the Modern Portfolio Farm Agent’s advice? It’s obvious — run out and buy a few other farms scattered around the world to abate the risk of any one of them doing poorly, but certainly not waste time trying to improve results by observing trends in the weather, pests, or environmental regulation.
- By discarding time domain information MPT is inherently unable to suggest what to buy or sell next month.
- MPT may be ideal for “buy and hold” investors, but “buy and sell” decisions require time domain data analysis.