Daily Daia 1/02/85 7/30/96 (Log Scale)

Value Line Composite (Geometric Average) - Percent Reversals

M J S DM J S ti M J S D M J SDMJSDM1 S D M I S D M J S D M J S DM I S D M J S D M 3

„1985. 1986 1987 1988 1989 1990 1991 J992 993 1994 1995 1996

Source: Ned Davis Research. Used by permission.

Trade-Signal Analysis

With these general concerns in mind, the various quantification methods can be put to use. The first, and perhaps most widely used, is the approach that relies on buy and sell signals, as shown in Figure 11.2. When the indicator meets the condition that it deems to be bullish for the market in question, it flashes a buy signal, and that signal remains in effect until the indicator meets the condition that it deems to be bearish. A sell signal is then generated and remains in effect until the next buy signal. Since a buy signal is always followed by a sell signal, and since a sell signal is always followed by a buy signal, the approach lends itself to quantification as though the indicator was a trading system, with a long position assumed on a buy signal and closed out on a sell signal, at which point a short position would be held until the next buy signal.

The methods greatest benefit is that it reveals the indicators accuracy rate, a statistic that is appealing for its simplicity: all else being equal, an indicator that had generated hypothetical profits on 30 of 40 trades would be more appealing than an indicator that had produced hypothetical profits on 15 of 40 trades. Also, the simulated trading system can be used for comparing other statistics, such as the hypothetical per

Figure 11.2 Value line composite showing buy and sell signals.

annum return that would have been produced by using the indicator. The per annum return can then be compared with the gain per annum of the benchmark index.

But the methods greatest benefit may also be its biggest drawback. No single indicator should ever be used as a mechanical trading system-individual indicators should instead be used as tools for interpreting market activity. Once the most reliable indicators are identified, they should be combined into models that can offer increased reliability for dictating trading actions.

The drawback is the tendency to make little or no distinction between the hypothetical and the actual. The signal-based method does not specify actual records of real-time trading performance. If they were, the results would have to account for the transaction costs per trade, with a negative effect on trading results. Figure 11.3

Figure 11.3 top half of chart shows how much profit is made trading on the signals in figure 11.2 when no commission costs were considered. The bottom half shows how just a Va% transaction cost can significantly reduce profits.

SUMMARY RESULTS FOR INDICATOR IN FIGURE 11.2 - NO TRANSACTION COSTS

VALUE LINE GEOMETRIC $627,079 1/24/72-7/30/96

I LAST I PROFIT NUMBERDAYS GAIN BATTING MODEL BUY/HOLD $10,000 j SIGNAL I CURRENTl OF PER PER AVERAGE GAIN PERGAIN PER INVESTMENT! j "Sell" I TRADE TRADESTRADETRADE ANNUM ANNUM I I I I I I I I I I

I 5/07/96 6.1% I 240 I 37 I 1.9% 50% 18.4% 4.3% $627,079

Maximum Drawdown: -4.68%

SUMMARY RESULTS FOR INDICATOR IN FIGURE 11.2

- INCLUDING TRANSACTION COSTS OF 1/4 PERCENT PER TRADE

VALUE LINE GEOMETRIC $189,425 1/24/72-7/30/96

I LAST I PROFIT NUMBERDAYS GAIN BATTTNG MODEL BUY/HOLD $10,000

I SIGNAL I CURRENTl OF PER PER AVERAGE GAIN PERGAIN PER INVESTMENTl

I "Sell" I TRADE TRADESTRADETRADE ANNUM ANNUM

I I I I I I I I I I

I 5/07/96 5.6% I 240 37 1.4% 45% 12.7% 4.3% $189,425

Maximum Drawdown: -4.68%

summarizes the indicators hypothetical trade results before and after the inclusion of a lA percent transaction cost, illustrating the impact that transaction costs can have on results. The more numerous the signals, the greater the impact.

Also, as noted in the results, another concern is the maximum drawdown, or the maximum loss between any consecutive signals. But again, as long as the single indicator is for perspective and not for dictating precise trading actions, indicators with trading signals can provide useful input when determining opportune periods for entering and exiting the market in question.

Several other testing methods can also be used to confirm a trading signal, supporting the case for market strength or weakness. A buy signal, for example, could be confirmed by a bullish-zone reading from an indicator that uses zone analysis.

Zone Analysis

In contrast to indicators based on trading signals, indicators based on zone analysis leave little room for doubt about their purpose-they do not even have buy and sell signals. Rather, zone analysis recognizes black, white, and one or more shades of gray. It quantifies the markets performance with the indicator in various zones, which can be given such labels as "bullish," "bearish," or "neutral" depending on the markets per annum performance during all the periods in each zone. Each period in a zone spans from the first time the indicator enters the zone to the next observation outside the zone. Unlike the signal-based approach, the indicator can move from a • bullish zone to a neutral zone and back to a bullish zone. An intervening move into a bearish zone is not required.

Zone analysis is therefore appealing for its ability to provide useful perspective without a simulated trading system. The results simply indicate how the market has done with the indicator in each zone. But this type of analysis has land mines of its own. In determining the appropriate levels, the most statistically preferable approach would be to identify the levels that would keep the indicator in each zone for roughly an equal amount of time. In many cases, however, the greatest gains and losses will occur in extreme zones visited for a small percentage of time, which can be problematic for several reasons:

1. If the time spent in the zone is less than a year, the per annum gain can present an inflated picture of performance.

2. If the small amount of time meant that the indicator made only one sortie into the zone, or even a few, the lack of observations would lend suspicion to the indicators future reliability.

3. The indicators usefulness must be questioned if it is neutral for the vast majority of time.

A good compromise between optimal hypothetical returns and statistical relevance would be an indicator that spends about 30 percent of its time in the high and