sued, but only lasted a couple of weeks. This signal once again reconfirms an important point:the strength of a signal is not necessarily related to the level atwhich the signal occurred. As you can see, the put–call ratio (Figure4.56) was almost up to 200 when this buy signal occurred. However,the ensuing rally, while profitable, was only a small bounce in anoverall bear market.After that, the market began falling in earnest once again. InAugust 1994, another sell signal was issued; and while it was ratherlate in the overall downtrend, it did manage to capture about 10points of the decline, from 39 cents down to 29 cents, which is avery good trade. At that point, the put–call ratio stayed very low forseveral months, issuing repeated sell signals. These didn’t really pan
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Figure 4.55CONTINUOUS LIVE-HOG FUTURES
out, although the market didn’t rally a great deal during that time,either. It appears that a large number of call speculators were tryingto pick the bottom during the period from November 1994 to February 1995. And for a period they were right. However, the marketcollapse to new lows in April 1995 attracted massive put buying,eventually culminating in a buy signal in June 1995 (Figure 4.56).The market then rallied into a sell signal in early October, which wasa successful signal as well.Overall, it appears that most of the sell signals for live hogs arrivewith the put–call ratio in the 40-to-50 neighborhood, while the buysignals are in the 130-to-150 neighborhood, except for the one bigspike in June 1994.The fifth member of the group of “pretty good” put–call ratiosignals is natural gas. This is normally thought of as a contract thatis related to the oil and oil-product contracts. However, natural
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THE PREDICTIVE POWER OF OPTIONSFigure 4.5621-DAY LIVE-HOG PUT–CALL RATIO
gas options give much better signals than do the oil and oil-productoptions.Figures 4.57 and 4.58 are those of natural gas, both the continuous futures chart and the put–call ratio. One thing that immediatelystands out from the put–call ratio chart (Figure 4.58) is that the numbers are quite high. Apparently, there is a fair amount of put tradingin natural gas options at all times. This is just an observation, for withthe dynamic approach, we really don’t care at what absolute levelsthe signals occur. The first signal—a buy in December 1993—wasan excellent signal. Then, a local minimum (sell signal) occurred withthe ratio near 60 in March. It was also a good signal. The next signalwas also a sell—the 70 level in August 1994—and it was a tremendous one, as natural gas futures went into a prolonged decline.However, the next few signals were not as good, assuming youare just trading the extremes. A sell signal at the 80 level in January1995 and buy signals in October 1994 (at the 160 level) and in April1995 (at the 180 level) were either poor or just barely profitable.
Figure 4.57CONTINUOUS NATURAL GAS FUTURES
Figure 4.5821-DAY NATURAL GAS PUT–CALL RATIO
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However, the May 1995 sell signal, also from the 80 level, wasanother good one.So, natural gas options’ put–call ratio can be traded from theextremes. In the past, this has meant sells occurring in the 60-to-80range, and buys in the 150-to-180 range. However, don’t rely onthose static numbers—use a dynamic approach.The remaining charts in this section of futures have some goodsignals and some poor ones. However, since futures tend to trend sostrongly, I think it is worthwhile following these signals because therehave been some tremendous ones. Remember, it is best to see thesignal come from an extreme level and to use some technical analysis to support the put–call ratio signal. Also, the introduction ofweighted ratios has aided in the trading of futures and futures optionsvia the put–call ratios.Figures 4.59 and 4.60 concern the put–call ratios for the Britishpound (BP) currency. Figure 4.59 is the standard ratio and Figure
Ratio
Figure 4.5921-DAY BP STANDARD PUT–CALL RATIO200 BP Continuous190 Futures (unscaled)180170B1601501401301201101009080706050S40D JF M A M02 2003
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Figure 4.6021-DAY BP WEIGHTED PUT–CALL RATIO400350
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4.60 is the weighted ratio. First, consider the standard ratio. Thereare two excellent buy signals, both circled on the chart. You mighthave been prematurely stopped out of the first one, though, becausea sell signal followed almost immediately. Assuming you stayed withyour long position as long as the futures were trending upward (a logical approach), just trading those two signals alone would be quiteprofitable because losses would be limited in the other situations.However, now consider the weighted British pound chart inFigure 4.60. There are three buys and three sells. All three buys areprofitable, and certainly the first sell is. The other two sells might bemarginally profitable as well. It’s quite clear from comparing thesecharts that the weighted ratio does a better job of generating signalsfor the British pound futures than the standard ratio does. This is truefor many of the futures, especially the currencies.
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THE PREDICTIVE POWER OF OPTIONSFigure 4.6121-DAY JY WEIGHTED PUT–CALL RATIO
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The Japanese yen (JY) continuous futures chart and the weightedput–call chart are shown in Figure 4.61. These signals are not asprofitable as the ones for the British pound were, although the buysignal (circled) in August 2003 was a good one. Actually, if the continuous chart were expanded, you could see that some of the othersignals were marginally profitable; but overall the yen futures is notone of the best contracts in responding well to put–call ratio signals.Continuing with the theme of currency futures, we next examinethe potential of the put–call ratios in the Swiss franc (SF) futures. Thesignals are actually quite good here (Figure 4.62). There are nine signals marked on Figure 4.62, and all but two of them are circled. Thecircled ones could easily have been traded profitably—particularly thebuy signals. This was much better performance than was availablefrom the standard ratio (chart not shown).
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Figure 4.6221-DAY SF WEIGHTED PUT–CALL RATIO450
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The final currency chart is shown in Figure 4.63. It is that of theEuro currency (EC). Its signals are quite accurate, too. About half thesignals were accurate, and they caught all of the major moves. Thatis the object—especially with futures—to be on the right side of amajor, trending move.What causes the weighted ratio to be so much better in the currencies? It’s not completely clear, but it certainly seems that activityin the out-of-the-money options is what’s causing the problem forthe standard ratio signals. Generally, such activity—as least in considerable volume—comes from institutional traders. There could beanother explanation, though. The listed currency options trade onthe Chicago Mercantile Exchange (Merc), but there is a much biggermarket in currency futures, forwards, and options over the counter.Those traders and market makers could be using the Merc options to
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THE PREDICTIVE POWER OF OPTIONSFigure 4.6321-DAY EC WEIGHTED PUT–CALL RATIO
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hedge or to lay off risk; and that activity would occasionally cause asignal to be generated when, in fact, there was not a contrary opinion to be had. In fact, this laying off of risk in the listed futures market happens in other futures and commodities as well. Yet, it doesn’tseem to seriously affect the worth of the signals.We’ll finish the examples in this chapter with silver (SI) futures.The standard ratio has a poor track record for silver, and so we hadthought that the put–call ratios didn’t really work too well. But, whenwe applied the weighted ratio, we found some excellent signals. Figure 4.64 shows the continuous silver futures and the weightedput–call ratio. Three of the four successful signals were buy signals,which probably doesn’t mean anything significant. The best featureof this graph is that the buy signal in October 2003 caught the startof the big, bullish move that followed.
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Figure 4.6421-DAY SI WEIGHTED PUT–CALL RATIO
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Finally, the following contracts are the ones that seem to havethe best track records for put–call signals:British poundCocoaCoffeeCottonCrude oilDeutsche markEuro currencyEurodollarGoldJapanese yenLean hogsLive cattle
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Natural gasOrange juiceS&P 500SilverSugarSwiss francT-bonds10-year notesIn addition, the grains are sometimes worth watching as well—atleast corn, soybeans, and wheat. I would recommend using only theweighted put–call ratios for the grains.
FUTURES OPTIONS VOLUMESince we are discussing futures options in some detail, this may be anappropriate place to insert a brief item about the liquidity of futuresoptions. This liquidity is not a predictive quality, like the features wehave discussed previously in this chapter. Rather, this is more forinformation purposes. The various futures options are divided intofour broad categories.Very LiquidCrude oilEurodollarS&P 500T-bondsEuro currency10-year notes
LiquidCornCottonGoldCoffeeSoybeansSilverBritish poundWheatJapanese yen
Somewhat IlliquidHeating oilUnleaded gasLive cattleNatural gasSugarSwiss franc
Very IlliquidCocoaDollar indexCopperOrange juiceCRB index
As time goes by, some of the futures options in this list maychange positions. In general, I would expect them to become moreliquid. It is unlikely that any of the “very liquid” contracts would losetheir status. They have been very liquid for a long time and representa true hedging market as well as a speculative market; so continued
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volume is likely in Eurodollars, T-bonds, crude oil, and S&P 500options.
Put–Call Ratio Trading SummaryPut–call ratio charts can be a very good measure of contrarian sentiment. However, they should not be used in a vacuum. They are bestused when the put–call ratio signal corresponds well with a technicalformation in the underlying instrument. For example, if a buy signalis generated by the put–call ratio, then the underlying should bebreaking out over resistance or at least should be trading up above ashort-term moving average—making higher highs and higher lows.Once positions are established, stay with the trend, using a trailingstop to lock in profits and perhaps taking partial profits. If theput–call ratio should reverse signals, that could be a reason to stopyourself out, but not if the underlying has already established a goodtrend; stay with the trend, using the trailing stop.Finally, from the previous charts, you can see that the weightedcharts are more useful, in general, than standard charts—althoughboth have their successes. Hence, it is necessary to have put–callcharts and data in order to view the signals; but a history (at least oneyear) of such put–call signals is needed in order to determine howsuccessful the past signals have been. We provide a large amount ofdata on our web site, located at www.optionstrategist.com, in thesubscriber area called The Strategy Zone. In that section, the put–callratio charts—both standard and weighted—are presented every dayfor every underlying instrument that was identified in this chapter ashaving the best history of put–call ratio signals. In addition, any stockwhose options average considerable volume on a daily basis areincluded. This criterion keeps the active stock options on the list. Afree one-week trial to The Strategy Zone is available to anyone whosigns up for it.
ON MOVING AVERAGESThis section ideally belongs in a chapter on technical analysis, butsince I don’t have one of those in this book, this seems the best place
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for it. You have noticed that our use of the put–call ratio necessitateslocating the local maxima and local minima of a moving average inorder to identify buy and sell signals. There are many other technicalindicators in use that require similar identifications. So, just how doyou go about deciding whether a local maximum or minimum hasbeen reached? It’s easy to look at a historical chart, such as thoseshown throughout the second part of this chapter, and see where thetops and the bottoms were in retrospect. But can they be identified ina timely manner while they are being formed? The answer to thatquestion is a qualified “yes,” and we demonstrate why in this section.You may have judged by now that I am not a fan of usingabsolute levels to identify trading opportunities with moving averages. The whole discussion of the index put–call ratio pointed outthat the market is dynamic, and it is often the direction of a movingaverage, and not its actual value, that is important. This analysis canbe applied to many other moving averages. For example, I havefound that it applies quite well to the TRIN, or Arms Index, also.Advocates of the Arms Index use a 10-day, 50-day, or other movingaverage to judge whether the market is overbought or oversold.The Arms Index is computed daily as follows:Number of advancing issuesNumber of declining issues
×
Volume of declining issuesVolume of advancing issues
In theory, the number would be 1.00 in a balanced market.What really happens is that volume is generally dominant; so smallnumbers occur on bullish days (the smallest I recall is about 0.25),and large numbers occur on bearish days (large selloffs can generatenumbers higher than 3.00). If the numbers are too high for toolong, as identified by a moving average of the daily numbers, themarket is getting oversold and we look to buy the market. If themoving average gets too low, the market is overbought, and we lookfor a sell signal.I have found that absolute levels might work okay for the shortterm averages (1.20 is oversold for the 10-day Arms Index, while0.80 is overbought). However, the longer-term averages don’tadhere to such rigid interpretation. Buy and sell signals come at various absolute levels. But when the longer-term Arms Index moving
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averages peak, a market buy signal is at hand; when they bottom out,a market sell signal is generated. So, the following discussion isapplicable to those moving averages as well; and, in fact, it was theobservation of the Arms Index signals that led me to explore the following concepts originally, back in 1977.Any trader who uses moving averages to generate buy and sellsignals knows that he can tell what level is needed on the next trading day in order to generate the given signal. A typical moving average trading system, employed especially by commodity technicaltraders, is the moving average crossover system. You keep twomoving averages, with differing number of days in each moving average; and when they cross each other, you have a buy or a sell signal.Typically, the shorter-term average is used as the directional signal.Thus, if we were keeping a 10-day moving average and a 20-daymoving average, we would have a buy signal when the 10-day average crossed the 20-day one from below to above. Conversely, wewould have a sell signal when the 10-day average crossed the 20-dayone from above to below. This would generally be identified as atrend-following system. It would generate big profits when a longtrend emerged but would do poorly in a trading range market thatoscillated back and forth.Example: Any trader using a crossover system should know at what levelshe will get a signal on the following day. Suppose we know the followinginformation:Closing price 20 days ago: 80Closing price 10 days ago: 60Current 10-day moving average: 62.0Current 20-day moving average: 63.5At the current time, the 10-day average is lower than the 20-day average; so we must assume that we are in the midst of a sell signal. However,we also want to be alert for a possible buy signal, since the two averages arerelatively near each other.The pertinent question is, “What closing price today would cause the 10day average to cross over the 20-day average and generate a buy signal?”This is an easy matter to figure out. We merely need to compute the sum of
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the intervening closes and see what close today would cause the desiredresult.Sum of 10 closes in current 10-day moving average: 620Sum of 20 closes in current 20-day moving average: 1,270The 10-day sum is merely the moving average, 62.0, times 10. The20-day sum is that moving average, 63.5, times 20.Now we know for sure that the closing price of 10 days ago will not bepart of the 10-day moving average after today’s close. So the 10-day sumafter today’s trading will be the current sum, minus the close from 10 daysago, plus today’s close:New 10-day sum = 620 – 60 + today’s close = 560 + today’s closeSimilarly, we can compute the new 20-day sum:New 20-day sum = 1,270 – 80 + today’s close= 1,190 + today’s closeSo now we have a simple arithmetic equation to solve to see what levelfor today’s close would make the 10-day moving average be greater thanthe 20-day moving average. Let t stand for “today’s close,” so that the formulas are shorter. When will the 10-day average be greater than the 20-daymoving average?
(560 + t) > (1,190 + t)10
20
Solving, we get t > 70. Thus, if the stock (or commodity, or whatever)closes above 70 today, we will get a buy signal. Futures traders, whose brokers take such stop orders, could enter an order such as “buy at 701⁄4, stopclose only.” Then they could go fishing or play golf.
This simple method can be used to decide what level of trading isneeded on the next trading day in order to make the moving averagebe at a specific level—presumably a level that would generate somesort of signal for whatever trading system you are using.
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Now, let’s look at a put–call ratio signal. Suppose, for example,that we define local maximum for a put–call ratio moving average asoccurring when “the average forms a top that is not exceeded for 10trading days.” If that happens, we will call the point a local maximum.Using the method from the preceding example, we could always tell,after 9 days had passed, what closing price for the 10th day’s tradingwould keep the moving average below that peak level of 9 days ago,thus ensuring that 10 days had passed since the peak was seen.Hence, we would have our local maximum, by the 10-day definition.That is interesting, but hardly useful enough for our purposes. Itwould only alert us to a buy or a sell signal after 9 days had passed.We could miss a lot of market movement in that time period. Whatwe’d like to know, if possible, is if a local maximum or local minimumis forming on the first or second day of the 10-day period. Now thatwould be something truly useful. In the context of the previous example, that would be tantamount to predicting where the sums will be in10 days. We know what numbers are coming “off” the moving average in that time (the closes for the previous 10 days), but we have noidea what the closing prices will be for the next 10 days, in order toadd back onto the sums. Thus, it seems as though we have nochance of predicting where a moving average might be 10 tradingdays from now.Unfortunately, unless you’re clairvoyant, you can’t predict withcertainty what tomorrow’s trading will bring—much less the next 10days—but you can often come up with a good guess, based on theprobability of what’s happened in the past. This is especially true ifyou’re dealing with items that are confined to a range, such as thedaily Arms Index readings or the put–call ratio (rather than stock)index, or futures prices themselves.The first thing that you must do, if you hope to predict where amoving average will be some days hence, is to define the distributionof the closing values that will be added to the moving average duringthat time. The equity put–call ratio is defined as the number ofequity puts that trade on a given day, divided by the number of equitycalls that traded on that same day. Typically, this number is between0.30 and 0.50. Occasionally, it falls outside of that range; but if wesummarized the daily numbers for a long period of time, we wouldfind that almost all of them fall within this range.
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For simplicity’s sake, let’s assume that on any given day theequity put–call ratio can only be equal to 0.30, 0.35, 0.40, 0.45, or0.50. Furthermore, let’s assume that these five results all occur randomly, with equal probability. Now, these assumptions are not completely correct, but they are reasonable and are simple enough toallow us to demonstrate the technique needed to predict a movingaverage some days hence.Once this step—defining the distribution—is accomplished, thetechnician is well on his way to achieving the desired result of beingable to predict the moving average forward in time. Let’s start realsimple, and see how a two-day prediction would be accomplished.
Example: We continue with the previous equity put–call ratio moving average example. However, in order to eliminate the use of decimal points asmuch as possible, we use 30, 35, 40, 45, and 50 as the possible daily closing values.Suppose that we know the following information:Current 10-day moving average: 40Daily ratio reading of 10 days ago: 50Daily ratio reading of 9 days ago: 45We also know that the sum of the 10-day moving average is 400 (10times 40).Given this information, we can now compute with some certaintywhere the moving average might be in two days. There are five possibledaily values for the first day—30, 35, 40, 45, and 50. And likewise thereare the same five values for the second day, for each of the possible valueson the first day. Thus, in all, there are 25 possible outcomes of two days’worth of equity put–call readings (five on day one times five on day two).We know that the current sum of the last 10 days’ worth of data is 400. Inaddition, we know what numbers are coming “off” the sum (50 and 45, asgiven earlier). Thus, the sum will be reduced by 95 over the next two days andthen will be increased by whatever actual daily numbers are added on. The topline's moving average of 36.5 is computed as follows: 400 – 95 + 30 + 30 =365; 365/10 = 36.5. Hence, we can then calculate all the possible values forthe moving average over the next two days (all the while remembering that wehave made the assumption that only five values are possible for any trading day).
ON MOVING AVERAGESDay 1PossibleValues
Day 2PossibleValues3035404550
10-DayMovingAverage36.537.037.538.038.5
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This list of 25 outcomes is the total spectrum of possible 10-day movingaverages after the next two trading days, under our assumptions.Now that we have calculated this list, here’s how to use it. Suppose thatwe want to know if the current 10-day moving average is going to be a localmaximum. From the list, we see that only 3 of the 25 outcomes result in amoving average that is equal to or greater than the current value, 40. Conversely stated, 22 of 25 possible outcomes result in a lower moving averageafter two days. Therefore, there is an 88 percent chance (22 divided by 25)that the current value of the moving average is going to be a local maximumfor two days. If we were using that as a trading signal, we would probably goahead and buy the market, based on the knowledge that there is such a high
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probability of the current value becoming an actual local maximum (a localmaximum in the put–call ratio identifies a buy point).
This technique is a very useful one. We can extrapolate it forlonger time periods. As stated earlier, I prefer to define a local maximum or minimum as a point, usually on the 21-day moving average,that is not exceeded for 10 trading days. Therefore, we could extendthe “trees” in the preceding example out to 10 nodes (using a computer, of course) and calculate all the possible 21-day moving averages after that time. Then, we can see how many of those possibleoutcomes exceed the current maximum, in order to predict the probability of that maximum actually holding for the entire 10-day period.The technique can be used for predicting moving averages in anysort of market as long as the daily values are numbers that generallylie within a range. It is therefore applicable to moving averages of theArms Index (where daily values normally lie between 0.60 and 1.40),or equity put–call ratios (values between 0.30 and 0.55), or indexput–call ratios (values between 0.90 and 1.50). There are many others that would fit the pattern as well—you can see from the put–callratio charts in this chapter that the daily ratios fall within very welldefined ranges. A lot of technical indicators are based on oscillatorsthat generate signals on crossovers or on exceeding a certain level.Any of those could be estimated with this system also.You must remember, of course, that real market conditions canbring unexpected results. For example, on the day of the crash of1987, the Arms Index was 14.07! You certainly wouldn’t allow forthat in your distribution of possible values when making predictions,but it did actually occur. Therefore, the result of your calculations candefine a highly probable maximum or minimum, or buy or sell, orwhatever; but there is always a chance that over the ensuing days,the actual values for the daily readings may be outliers. If that happens, it’s possible that you won’t get the signal you were anticipating; and if you had taken a trade in advance of the signal, you willprobably have to take a loss to extricate yourself.When I first completed the research for this system, I was using local maxima and minima in the 50-day Arms Index to predict intermediate-term buy
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(maxima) and sell (minima) points in the broad market. At the time (1977), Iwas working for Thomson McKinnon as the retail option strategist, and thehead of the department and his superior—an executive vice president—both scrutinized the system. They thought it looked workable, and wewaited for a signal to occur.At the time, I was using 90 percent probability as evidence of a signal. Ihave since tightened that to 95 percent. In any case, in August 1977, thesystem said that there was a 90 percent chance of a “market” buy signaloccurring. There were no index options trading in 1977, so we had to buystock options—or a set of several different stock options—in order to trade“the market.” We put together a package of three recommended options—IBM, Kodak, and General Motors—and called some of the firm’s largeroption brokers to give them this research.As it turned out, the signal did actually occur (that is, 10 days later, wehad a local maximum in the 50-day moving average of the Arms Index).However, the market was very dull, mostly trading sideways; and we eventually had to advise the brokers to sell out their positions at a