Market Timing Synergy: Improving The Odds By Combining Indicators
Many years ago while conducting market timing research, I took time out to go to a seminar. One of the speakers was Lucia de Garcia, a Colombian woman who told a charming story about the “airport” in the village where she grew up.
Her village was in a valley high up in the mountains, and the long dirt road that served as the airport runway was barely visible by day, let alone night–and treacherously close to mountain peaks on every side. Yet planes often made nighttime landings at this village airport.
When a plane was approaching, the village church bell rang four times, sending all the villagers who had cars into immediate action. Along the dirt runway were 50 parking places. Each villager with a car quickly drove to his designated spot and turned his headlights on, aimed in the appropriate direction. The entire setup seemed ridiculous to outsiders because it was impossible for anyone high up to see a single headlight from a car.
But the point Lucia de Garcia was trying to make was, “When you bring many lights together, they illuminate more.” By combining the headlights of the 50 cars in the appropriate fashion, the villagers were able to light the dirt road of a runway and signal to the plane where to land. Whereas the light from a single set of headlights could not do the job, and even the lights of many cars was ineffective when not blended correctly, the proper combination of the lights did the trick. Over the years, that little village hosted hundreds of “emergency” landings, without a single accident, thanks to the village volunteers and their car headlights.
When I first heard that story, I realized that it was a very good analogy for what a successful timing model tries to do. As traders, the first thing we do is try to find as many independent variables as possible that can improve our market investing performance in and of themselves (or perhaps with just one technical filter). These variables are analogous to the individual car headlights that we need to orchestrate in a way that allows us to safely and profitably manage the investment markets.
Next, to improve our odds and risk-reward even more, we try to assemble and combine variables from totally different vantage points that work in and of themselves. In general, the more diverse the group of independent variables we are combining, the more “synergy” we get–meaning the more reliable the results and the better the risk-reward characteristics.
Another point I believe will be increasingly important in the future is that the variables we use help us locate short selling opportunities as well as long trades. In my opinion, far too many market “timing” systems just locate long trades. These systems, which are generally long the market or in either cash or T-bills, have done very well in the current major bull market, but often have substantially lower returns when the market turns down. Certainly, it is much better to spend a year in 5% T-bills than in a market that drops 30% in a year, but some shorting in this environment would provide even greater profit potential.
Combining Headlights
Let’s look at an example of the concept of combining independent variables to create a better synergistic system.
Model #1 I revealed the following system in my latest book, The Hedge Fund Edge (1998, John Wiley & Sons, New York). It is a pure momentum model that simply goes long the Nasdaq index whenever that index closes up 7.9% or more from a trough close value. Similarly, it simply goes short whenever the Nasdaq closes down 7.9% or more from a peak value close.
Thus, if we start the index at 100 we’ll go long on the first close over 107.9 or go short on the first close below 92.1, whichever happens first. If we go short first, then we’ll exit and reverse to a long trade on any close that is up 7.9% or more from the lowest close since we initiated our short position. If we go long first, then we’ll exit and go short on any close that is down 7.9% or more from the highest close since we initiated our long position. It’s a very simple stop-and-reverse system, based solely on momentum–a totally independent variable.
| System | Maximum drawdown | Compound Annual Return (CAR) | Annual profitability |
|---|---|---|---|
| Nasdaq % model | 22.4% | 18.2% | 91% |
| Buy-and-hold | 59.5% | 11.8% | 70% |
Table 1 shows clearly that despite its simplicity, the 7.9% model adds value to investing in the Nasdaq, both in terms of increased profits and decreased drawdowns, when compared to buying and holding the Nasdaq over the long term. Buying and holding the Nasdaq from 1963 through the end of 1997 resulted in a compound annual return of 11.8%, a 59.5% maximum drawdown (1972-74), and an annual profitability (percentage of profitable years) of 70%.
Yet simply by buying on 7.9% reversals up and shorting on 7.9% reversals down, you could have increased profits to an 18.2% compound annual rate, cut the drawdown to 22.4% and increased the number of profitable years from 70% to 91% (with 61% of all trades being profitable, combining longs and shorts). Clearly, the concept of momentum is a valuable independent variable that can help investors better time the markets!
Model #2 But let’s not stop there. Successful investors should constantly be on the lookout for variables they can use to improve their market timing. As investors who have read my trading courses and book know well, one of my favorite independent variables involves what I call the “Austrian Liquidity Cycle.”
The logic of this theory is that monetary, liquidity and interest rate variables are very significant in helping investors time equity, bond and commodity investment decisions. Let’s take one of the simplest monetary models and see if we find another good independent variable that will help us in our stock market long/short timing.
| System | Percent of days invested | Annual rate of return |
|---|---|---|
| ROC > -1.5% | 52% | 17.4% |
| ROC <= -1.5% | 40% | -0.3% |
Table 2 shows that whenever bond prices (measured by the Dow Jones 20 Bond Index) are not eroding fairly rapidly (that is, either rising or dropping at less than 1.5% over the latest year), stocks tend to move up at a much higher than average rate, and with a very high degree of reliability.
Conversely, when bonds dropped at a 1.5% or higher annual rate, stocks actually fell on average. We won’t go into the details of the Austrian Liquidity Cycle Theory, but if you want an explanation of why this bond-stock relationship exists, I suggest you read Chapter 2 of the book, The Hedge Fund Edge.
The bottom line, though, should be apparent: When bond prices are flat, rising or not falling too swiftly, stocks perform much better than average; when bonds are falling at a 1.5% or higher annual rate, stocks on average go down. Certainly the results in the above table show that bond rate movements are another very good independent variable for investors to watch to gain insight into how to best time long-short positions in stocks.
Now we have two “headlights,” if you will–two independent variables coming from totally different data and theories, but each helping investors in timing investment decisions. Let’s see what happens when we combine them.
Table 3 shows the results of combining the 7.9% reversal momentum system with the -1.5% DJ20 bond index monthly close annual return system we just discussed. In other words, we’re only going to buy the index when both systems would be long, and we’re only going to short the Nasdaq index when both systems would be short. When the systems disagree, we’re going to park in T-bill money market funds. This is a very straightforward combination of two simple models to determine whether to be
long, short or in cash.
| System | Maximum drawdown | Compound Annual Return (CAR) | Annual profitability | % profitable trades |
|---|---|---|---|---|
| Buy-and-hold | 59.5% | 11.8% | 70% | – |
| Nasdaq % model | 22.4% | 18.2% | 91% | 61% |
| Combination (Nasdaq % w/ bond) | 13.25% | 17.28% | 94% | 75% |
As you can see, the “synergy” between these two systems has been substantial. The initial 7.9% reversal system was profitable in 61% of trades, while the new combination system is profitable in over 75% of trades. Moreover, the maximum drawdown for the combination model is much lower–dropping from over 22.4% to 13.25%, which is also substantially below the 59.5% of buying and holding the Nasdaq index during this period. The combination model also had the highest annual profitability.
As in most systems, the key is the annual return compared to the maximum drawdown–this is the critical number we should be striving to optimize in our timing and trading. Here too, we see significant improvement versus buy and hold (11.8% compound annual return and a 59.5% maximum drawdown = 0.198) versus the 7.9% reversal system alone (18.2% CAR and 22.4% maximum drawdown = 0.8125) with our combined system (17.28% CAR and 13.25% max drawdown = 1.3).
We’ve skipped over some of the important processes we used to get to these results in order to get to the bottom-line system first and illustrate our point about mixing independent variables.
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