Dynamic Portfolio Theory and Management

One of our approaches is to vary (or switch) stock allocations in accordance with P/E10. Some have referred to this as dynamic portfolio allocation. It is not. I have read Dynamic Portfolio Theory and Management by Richard Oberuc. He describes the real thing.

The book has very little in the way of mathematics. It may have just enough mathematical notation to discourage some. It helps those readers who have at least a superficial understanding of the mathematical concepts involved.

Dynamic Portfolio Theory and Management surveys several modeling approaches. Instead of the measured, slow changes in allocations that we might consider, the writer’s timeframe was usually one month and one year at the longest. This is the kind of hyperactive trading activity that has a bad reputation.

As it turns out, the author includes an excellent review both of modeling and market timing. He defines market timing as being 100% in a risky asset or 100% in a low risk asset such as cash. This definition differentiates market timing from dynamic asset allocation, which allows for partial allocations among any number of investments.

The academic case against market timing has been critically dependent upon high transaction costs.

Sharpe’s 1975 study assumed a 2% transaction fee. Jeffrey (1984) assumed a 1% transaction cost. Those investigations showed that an exceedingly high prediction accuracy would have been required. (Sharpe’s numbers were 83% for matching the return of buy-and-hold and 74% to match the standard deviation. Jeffrey’s number was 72%.) Later, Sy (1990) showed that Sharpe’s selection of years had an undue influence on his results. For example, adding 1929 to 1933 to the sample would have reduced the break-even point from 83% to 65%. Other investigators have looked at very short-term trading and low fees. Lam and Li (2002) showed that the break-even point is around 68% with monthly revisions for a transaction fee of 0.4%.

The writer presents the results of several studies that show the full return potential when assuming a perfect forecasting ability. Typical results with 0.5% transaction costs were 14% to 16% for annual revisions and 25% to 35% with monthly revisions. Adding a third investment choice brings the potential to 43% with monthly timing (with all allocations restricted to 0% or 100% at any given time). A fourth choice brings the potential to 59% with monthly timing.

As soon as prediction models are introduced, the advantage over long-term buy-and-hold drops to 2% to 3%.

In terms of money managers who actually engage in timing, their advantage varies between 0% and 3% depending upon the time interval examined. Roughly 50% to 70% actually show significant market timing skill. The remaining 30% to 50% do not.

With dynamic asset allocation (which includes leverage and partial allocations), the greatest realistic advantage over long-term buy-and-hold increases to 4% (with stocks and T-bills) or 5% (with multiple asset classes).

Looking Beyond the Numbers Themselves

I often state that we need to look beyond numbers by themselves. I am more interested in the logic behind the calculations than in the mathematical details. I look for hidden flaws.

I think that John Mauldin has identified an important flaw in this approach. In an email newsletter article, he posits that there is only a finite amount of money that skill can extract from a market. With a large number of people following the same general approach with similar goals, the advantage gained by any one individual shrinks to a very low amount.

I believe that there is an even more important flaw. It is what John Bogle has mentioned regarding the size of mutual funds. Money managers pay much higher the transaction costs than those in studies. The costs are hidden. But they are real. Because money managers control significant amounts of money, their trading activity works against them. They cannot reallocate significant sums of money without changing prices to their own disadvantage.

Conclusions

I think that there is a potential advantage for small investors who time the market. It is small. It carries the risk of unforeseen factors and events.

Perspective

The case against market timing has been based upon very short-term (one year or less), frequent trading with high fees. It breaks down when the fees are reduced.

I fault those who have applied the term Market Timing loosely just to gain a debating point.

We don’t do anything like what the book describes. We change allocations gradually over long periods of time. We do not assume that we will generate exact numbers going forward. We include sensitivity studies.

I liked the book. It clarifies what market timing and dynamic asset allocation actually mean.

Have fun.

John Walter Russell
I reviewed this book originally on July 2, 2004.