My training as a trader began more than thirty years ago. I was lucky enough then to learn from experienced and successful traders who had a deep understanding of the moment to moment struggle waged between buyers and sellers.
| I wish I was the fish, he thought, with everything he has against only my will and my intelligence, The Old Man and the Sea |
The approach of those teachers was shaped by their experience of the '29 crash and by the post-crash market of the thirties. The market of the early '30s was lively in both directions, and anyone who survived and succeeded during this turbulent period necessarily developed a keen appreciation of risk. For that reason, perhaps, the methods stressed by those early trader-teachers was both defensive and contrarian.
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Contrarian traders focus on detection of change in the trend of stocks and of the overall market, that is, on detection of accumulation and distribution. Contrarian techniques prompt traders to buy sold-out issues very near solid support, usually before upward trends are broadly recognized.
On the other hand, contrarians are ever-alert for signs that a stock or the market has entered a period of distribution. Early detection of strong-handed selling allows them to protect long profits and to prepare for important declines by putting out shorts in advance of markdown.
Early in my own career I studied the contrarian methods of my teachers diligently. However, my introduction to trading began during the go-go years of the '60s. The market at the time was trending upward strongly, and the market's leading issues were, nearly without exception, well above primary accumulation and showed no signs of distribution.
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The dominant feature of the market then was its continuing trend. But whatever edge I had depended on my ability to spot changes in the trend. I was soon completely befuddled. Stocks which lagged, and which appeared to be under accumulation, continued to languish, while strongly trending stocks rolled right on through preliminary indications of distribution.
A contrarian in a trending market, I was like an electrician who had been called out to fix the plumbing. The skills I had worked so hard to develop seemed irrelevant. I still had much to learn, and the market itself became my teacher.
What I will teach you in this series of lessons is, so far as I know, taught nowhere else. The main ideas are simple but of the highest strategic importance to anyone who wishes to trade successfully in all market environments.
Ja'nus: Early Roman god of gates and portals, represented by two opposing faces, probably suggesting the two sides of a door. Janus symbolizes the two-sided nature of things.
During bull markets, traders alternate between two modes. At times, traders favor relatively strong stocks. Laggards are sold, and the proceeds are used to finance the purchase of stronger stocks. A strength-following strategy works best during these periods.
At other times, the reverse is true. Profits are taken in stocks which have been strong, and the proceeds are redirected into laggard "bargains". During these periods, a contrarian approach is more profitable.
During strength-following markets traders express a preference for relatively strong stocks. As a result, stocks with a recent history of relative strength outpace the benchmark. At the same time, relatively weak stocks are sold or ignored, so they fall behind the benchmark. Capital flows from weakness toward strength
A strength-following market is a positive-feedback system. Higher relative strength attracts buyers, who in turn drive relative prices even higher. Lower relative strength brings out sellers, and weak issues continue relatively weak. As a result, a performance gap, or Spread, opens up between relative-strength leaders and relative-strength laggards, as illustrated in the graphic below:
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At some point, the Spread is wide enough that traders recognize increased risk in pursuing RS leaders further. At the same time, opportunities among laggard stocks become compelling.
As traders' preference switches to laggards, positions in strong stocks are sold and laggard bargains are purchased. As a result, RS leaders may sell-off or consolidate as laggards begin to break to the upside. Capital flows from strength toward weakness.
A contrarian market is a negative-feedback system. Low relative price attracts buyers, and their buying turns relative price higher. High relative price, on the other hand, brings out sellers, and the relative price of RS leaders begins to sag. As a result, the performance gap, the Spread, between RS leaders and RS laggards narrows, as illustrated in the next graphic:
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The Spread constantly expands, then contracts, as traders first push stocks to relative-strength extremes before pulling them back toward the benchmark. This process is repeated again and again. This dynamic is the power behind much of the market's fluctuations, and is as natural and necessary as breathing or the beat of the heart.
Traders who limit themselves to either a strength-following or a contrarian approach do well part of the time, but at other times their one-sided strategy produces inferior returns. Changing the focus of trading from relative strength leaders to relative strength laggards, and back again, in synch with the Spread improves overall results and reduces frustration.
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In some seasons, trend following is good; in others, reversing is good. The problem is how to differentiate the two seasons in advance. Victor Niederhoffer The Education of a Speculator |
The Spread is the difference in performance between relative-strength leaders and relative-strength laggards. As we saw in the previous lesson, this difference changes over time. During strength-following markets, the Spread expands. The opposite is true during contrarian markets, when the Spread contracts.
Computing the Spread requires computation of the periodic relative strength of each target in a selected universe of stocks. Here are the steps:
A target universe should contain no less than 30 to 40 stocks. There is no maximum number of stocks. This universe should be well diversified, including stocks from a wide list of sectors and groups.
Ideally, stocks chosen for the purpose of computing the Spread will be a fair sample of the broad market. It is important to choose stocks on some basis not related specifically to relative strength or to price performance more generally. Selection based on price performance will not provide the cross-section of relative-strength performance essential to producing a useful Spread.
Caution: If the universe is not diversified, the Spread will tend to contract indefinitely. This is so because issues which are too closely related tend to behave similarly. If the universe included only, say, bank stocks, the Spread would tend to contract chronically because traders specializing in bank stocks arbitrage disparities by buying relatively weak and selling relatively strong issues which stray too far from the mean. Like sheep dogs, arbitrageurs keep similar stocks herded closely together. This sets up a chronic reversion to the mean and causes the Spread to contract indefinitely.
Any period may be used as a basis to construct The Spread. I have found that a 100-day computation picks up important shifts in the Spread while filtering out minor noise.
This is accomplished by averaging the daily changes of all stocks in the universe. Put another way, the daily percentage change of the benchmark is the average daily percentage change of the universe of stocks. For purposes of calculating the Spread, the benchmark is always an average of all stocks in the selected universe.
Leaders and laggards are determined by comparing the sum of each stock's 100 daily changes to the sum of the benchmark's 100 daily changes. To do this, divide the sum of the 100 daily changes for each stock, plus one, by the 100-day sum for the benchmark, plus one:
Sum of Target's 100 Daily Changes + 1/Sum of Benchmark's 100 Daily Changes + 1
Suppose the sum of 100 daily changes for the target is .269, and that the sum of benchmark's percentage changes over the same period is .212. To calculate the target's 100-day relative strength, substitute these values in the formula shown above:
.269 + 1/.212 + 1 = 1.047
In the last lesson, we learned to compute the periodic relative strength of stocks in a target universe. In this lesson, we will outline the remaining steps required to compute the Spread.
A diverse universe of stocks is sorted by relative strength into two sets, a) those which are stronger than or equal to the benchmark and b) those which are weaker than the benchmark. However, just knowing which stocks are currently stronger and which are weaker than the average stock (benchmark) by itself provides no information about the probable future performance of these two sets.
Strength-following traders take it as given that strong stocks will always perform better, while contrarians assume that weak stocks, because they are relative bargains, will perform better. We make neither assumption, but choose rather to inspect the forward performance of RS leaders and laggards to determine which group is in fact doing better. At times the trend of capital flow will favor strong stocks, while at other times, weak, or laggard, stocks.
Driving our method is this question: What is the predictive value of relative strength? Are strong stocks or weak stocks more likely to perform better in the future? Over, say, the next week?
To find out, we wait five trading days to see which set of stocks, those which are now stronger than the benchmark or those which are now weaker, perform better going forward.
Say we find that the average performance of strong stocks turns out to be superior to the average performance of weak stocks over that next week. Chalk one up for the strong stocks. But we are looking for a trend, and one week is hardly a trend.
To see a trend we repeat the process over time. Here's how to make those calculations:
1. Compute the 100-day relative strength of each stock in the universe using the method developed in the last lesson. This computation gives the periodic RS of each stock as of day 100.
2. Group stocks into RS leaders (RS > or =1.00) and RS laggards (RS < 1.00).
3. Average the percentage daily changes of the stocks in each group over the next five trading days, days 101 to 105.
Caution: The data set used to compute RS must be separate from the data used to compute forward performance. There are a couple of reasons for this. First, if we do not look ahead, then our results will have no predictive value. Second, if data used to compute RS are erroneously also used to compute forward performance, the results will not be predictive but self-fulfilling. You have made this mistake if the Spread always rises.
4. Repeat steps 1 - 3 for subsequent periods. For example, group stocks by RS as of day 101, then compute the average forward performance for each group for days 102 through 106, and so on. You should generate two sets of daily performance figures, one for RS leaders and one for RS laggards. If you have repeated steps 1 through 3 over, say, a three-month period, then the next step will generate a three-month history of the Spread.
6. Starting with a value of 0, cumulate daily forward performance figures for the group of RS leaders. This is accomplished by adding each day's figure to the previous sum. Repeat this process for RS laggards. Two series result from these calculations. One, shown in blue in the chart below, is the cumulative forward performance (CFP) of RS leaders. The other, in red, is the CFP of RS laggards.
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7. To compute the Spread, subtract the CFP of RS laggards from the CFP of RS leaders. The Spread (below) is the arithmetic difference between the CFPs of leading and lagging groups.
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