There are many reasons to use a buy and hold, strategic asset allocation investment philosophy. You may believe in the philosophical and mathematical rationale for using the strategy, which employs Nobel Prize-winning theories. Or, you may be ambivalent about the theory, but you don't believe active managers can consistently outperform passively invested portfolios. Third, you may believe that active, tactical money management is a difficult and impractical business model.

I believe, however, that all three reasons are flawed and planners should consider adding active portfolio management to their practices.

RANDOM IS NOT NORMAL

The mathematical equations that form the quantitative basis for MPT are based on 19th-century discoveries in physics and astronomy. They entered finance through the work of Louis Bachelier, who developed the first option pricing model in 1900. Bachelier relied on the notion that day-to-day price movements were like the flip of a coin, and so the measure of randomness he used in his equations was a normal distribution.

At the time, normal distributions and bell curves were considered to be the best measure of randomness for a series of observations that clustered around the mean. Without normal distributions there can be no measure of standard deviation, which most of us recognize from our efforts to memorize MPT equations for the CFP exam. Bachelier also borrowed the mathematics from early studies in the field of thermodynamics to assume that price movements are constant.

Today, these assumptions seem antiquated. In his book, The (Mis) Behavior of Markets, Benoit Mandelbrot has proposed a new, more elegant measure of randomness based on what he calls power laws. Instead of the odds of an event decreasing exponentially as you move away from the average, which is the case with normal distributions, under power laws the odds of an unexpected event are much higher.

The notion that price movements are constant throughout time has also been disproved by current mathematical methods. For example, GARCH models (generalized auto-regressive conditional heteroscedasticity, if you must know) measure the clustering of volatility in pricing models-something that would not occur if financial markets could be described by the same physics and mathematics as the natural world.

WHO'S YOUR DATA?

If antiquated math doesn't get your attention, perhaps the problem of what data to use to calculate an efficient frontier will. Harry Markowitz's elegant algorithm for calculating efficient portfolios depends on three asset class inputs: future mean returns, future standard deviation and future correlations. For the past four decades, the investment industry has been using average historical data for all three inputs. The rationale is that markets are efficient, so the average of the historical data should suffice.

Given a world of perfect economic homeostasis and perfectly rational investors, it would make sense to use historical averages as inputs to the MPT model. Today, however, competing theories better fit our own empirical observations, which point out that, in real life, investors are anything but perfectly rational news-discounting machines.

H. Woody Brock, chief investment strategist with SED Investments, and Mordecai Kurz, a highly regarded economics professor at Stanford University, offer an alternative to the efficient market pricing theory called Rational Beliefs Equilibrium Theory. This theory assumes that the economy is not stationary and that investors lack perfect economic foresight. Instead, the market is assumed to have different pricing regimes so the parameters of the data change over time. Investors make mistakes in their forecasts, which give rise to a different kind of risk called endogenous risk-the risk that prices will become untethered from historical averages due to the misbehavior of market participants.

Brock says there is a more dynamic way to use historical data in the MPT model that results in a more efficient asset allocation that changes over time. These changes in asset allocation do not require investors to assume structural changes in the financial markets; they simply require that the historical data be used more dynamically. The highest portfolio returns are earned when investors forecast structural changes that cannot be seen in the past market data. These subjective changes to data inputs create the highest possible efficient frontier. On this frontier, investors strive to make forecasts that are "less wrong" than the consensus. Brock tells us that this is where true alpha is found.

It is now clear that planners have misunderstood and distorted Markowitz's model. They persist in believing in efficient markets, therefore making assumptions about the future performance of asset classes that are dangerously simplistic-and almost certainly wrong when markets are fully priced.