ardonic humor, what themajority view was probably missing.I learned another lesson from reading all those Barron’s stories—Ilearned to be very cautious. Barron’s often revisits an investment after itfails or goes bankrupt to review why it failed. This allowed me to learnthe various ways investors can miss things: I came to see that when everyone agrees that a particular idea is valid, you should look very intently inthe other direction to identify the holes in their thinking.The major hurdle is that to apply the theory of contrary opionion youmust overcome the fundamental ideas that are making everyone so bullish or bearish. If the picture of the economy looks extremely good, noclouds are in sight, and everyone is bullish, the fact that everyone is bullish becomes more important than anything else. I came to have confidence that the world worked in this strange kind of way. My first book,written in late 1999, was really just an application of the basic ideas ofBABES and O’BUCS to the market in 1999.Understanding Why the Theoryof Contrary Opinion WorksAfter 30 years of study and application, the theory of contrary opinionstill mystifies me; I don’t completely understand why it works. No onehas ever explained it to my satisfaction. To me it is a little like the quantum theory—physicists use it and talk about it, but does anyone reallyunderstand it? Nothing I’ve read or heard completely explains the accuracy of the theory of contrary opinion. Nevertheless, the fact that topflight market analysts, such as Marty Zweig, Bernie Schaeffer, RobertFarrell, and others, have made major prognostications based solely onthis one theory shows how powerful many people consider it.For one thing, contrary opinion seems to explain why it’s so difficultto make money in the stock market. If markets simply went in the direction the majority thought they would, making money would be easy andwe’d all be rich. I think it was the observable fact that this isn’t the case,combined with the high correlation between extremes in sentiment withmarket tops and bottoms, that originally convinced me of the value ofthis theory.The usual explanation of why the theory of contrary opinion works isbased on this simple idea: If everyone is bullish, the majority of investors
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have already made their purchases, which means there is no one left tobuy and there is no means of driving prices higher. In the same vein, ifeveryone is bearish, most people have probably already sold their stocks,meaning that no one has any stocks left to sell.It is a good explanation, but it doesn’t account for everything. Forexample, I have seen markets that went through a small sideways correction after a big advance and ended with extremely bearish readings—readings equal to those you see at major bear market bottoms. Yet thenumber of possible sellers after such a small correction couldn’t possiblyhave been exhausted—not like at the end of a bear market. Until we geta better explanation for the theory of contrary opinion, however, thisone will suffice.
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A Brief History of the Theory
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Contrary opinion as a theory was first defined in 1954 by HumphreyNeill in his book, The Art of Contrary Thinking, but I’m sure it wasknown further back than that. The basic concept can be found in a number of earlier works.For example, in Edwin Lefevre’s book, Reminiscences of a Stock Operator, written in 1917, there is the statement, “Always buy when complete demoralization has set in.” In 1939, Charles Hardy did an extensivestudy called “Odd-Lot Trading on the New York Exchange.” It seems tobe the first effort to see how the small investor’s buying and selling compares to market performance. Later, Garfield A. Drew took that dataand, in New Methods for Profit in the Stock Market, pioneered what became known as the odd lot theory. According to this theory, the small investor, being the least sophisticated investor of the investor classes, endsup buying more at a market top and selling more at a market bottom.From these ideas emerged a number of other efforts based on tryingto measure what investors were doing. In the1940s, both the New YorkStock Exchange (NYSE) and the Securities and Exchange Commission(SEC) began publishing information about the buying, selling, and shortselling of other investor groups. These efforts gave rise to various technical indicators, such as the short interest ratio and the NYSE creditdebit balance. Then Humphrey Neill pulled all these efforts togetherunder one unifying theory called contrary opinion.As we study this evolution through the 1940s and 1950s, we startnoticing that two distinct but related ideas emerge. One is the idea of ap-
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plying the theory by measuring the percentage of bullishness or bearishness in a group of investors. The other idea is to measure how big thatgroup of investors is or how big a certain investor activity has become.The distinction here should be clearly understood.It is a little like observing a crowd assembling below you for a politicalrally, Democrats on one side and Republicans on the other. There are twothings you could measure. One is the comparative size of the two sides (forexample, twice as many Democrats as Republicans). The second is measuring the total size of the crowd, irrespective of affiliation, which wouldindicate the degree of interest in the subject matter of the rally.It is important to separate these two distinct but related concepts:measuring the percent of bullishness or bearishness and measuring thebreadth of investor interest. For example, in 1978, after 13 years of dismal stock returns, only 16% of the American public held stocks. Interestin stock investing was at an all-time low. During this period on September 29, 1978, however, investor sentiment gave a high bullish reading.The market had rallied for 6 months, and bullish sentiment, for thosewho were investing, was extremely intense. Therefore, sentiment wasvery bullish for those interested in stock investing although the level ofinterest in stocks was low. By 1999, the situation had changed. In January 1999, the same indicator of investor sentiment was giving the samereading as it did in 1978. However, now 60% of the American public,after a 17-year bull market, had been lured back into stocks, showinghigh public interest in stock investing.When analysts talk of sentiment, they are usually referring to thecomparative bullishness or bearishness of those investing. When theytalk about the intensity of public involvement in stocks or the fervor behind it, they usually call these speculation indices.It is important to distinguish these two concepts. All bull markets always end with the investing participants bullish, but all great bull marketsend with broad public interest in stock investing. It is a long-acceptedconcept that great bull markets end when everyone is playing the stockinvesting game, talking about it at cocktail parties and over back yardfences.Since its formative years, the theory of contrary opinion has changedlittle. What has changed, however, are the methods analysts use to determine what investors are feeling at the moment. Many former indicators that worked very well have been made useless by the new derivativeinvestments. Some still work well.
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Is Investor Sophistication a Factor?One of the oldest ideas about stock investing is that investors canbe classified into categories and ranked by their investment knowledge and sophistication. There are people in the know and those onthe outside (insiders versus outsiders). The concept is that sophisticated investors would be relatively right about the market, and theuninformed investor would be relatively wrong.The odd lot theory developed in the 1930s was an attempt todescribe the class of investor that is usually wrong at market turningpoints. There have been attempts to try to locate the other side—thegroups that show correct market activity at turning points. I havenever been satisfied with the results.Significantly, Humphrey Neill’s formulation of contrary opinionin 1954 doesn’t distinguish whose opinion we are measuring. It saysonly that at major market junctures the widely accepted opinionabout market direction will be wrong. After searching for years, Ihave come to the conclusion that almost any large body of individuals, no matter their level of sophistication, generally holds thewrong opinion at critical market junctures. In other words, there isno class of investors whose investment actions are usually right atmajor turning points.This seems to be true of mutual fund money managers as agroup. You would think that this would be a sophisticated group, yetaccording to statistics, they usually have the highest cash position inthe funds they manage at a market bottom (when they should befully invested) and the least amount at the top (when they shouldhave lots of cash). Some people attribute this to investor redemption, but a careful study disproves it.Similarly, it was commonly believed that the trading activity ofWall Street firms and floor specialists showed that they were indeedvery sophisticated and usually right with their investments. Statisticsof their trading activity seemed to confirm it. In my opinion, however, the calculation wasn’t measuring what everyone thought itwas. Other measurements often showed a different picture.With that said, the only group that did seem to be somewhatright with their investments were corporate insiders—the corporate
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officers and directors who have to register with the SEC when theywant to buy or sell their companies’ stocks. Their buying and sellinggenerally seemed to turn out pretty well, but outside of this smallgroup, I have found no others.
HOW INVESTOR EXPECTATIONS ARE MEASUREDThe methods for measuring investor expectations fall into two categories.The first attempts to measure what investors are thinking by calculatingratios based on what they are doing—their transactions. The second category measures what investors are thinking by directly polling them toassess their opinions on the market.It’s important to note that no matter which method you use (measuring investment activity or polling), the data is applied in the sameway. In practice, you must have a fair amount of data on hand, enough toback-test the data through a number of severe bull and bear markets.You need long historical data to determine with any accuracy what levelsrepresent extreme investor sentiment for that indicator. For example, ifsome indicator or ratio goes to a reading of two at four separate bearmarket bottoms, you set two as an extreme reading of bearish sentimentfor that indicator.First Method: Investor ActivityInvestor activity can be measured by looking at a variety of indicators, including short-selling ratios, put/call ratios, the volatility index, and theRydex ratio.The Short-Selling RatiosAn investor usually sells stock short when he or she is expecting a decline. Thus, the amount of short selling in the market is usually a measure of bearish sentiment. In the past, analysts have measured shortselling in a variety of ways: odd-lot short selling, total public short selling,member short selling, specialist short selling, and short open interest.For example, Figure 5.1 shows the specialist short-selling ratio from
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FIGURE 5.1 This chart plots the famous specialist’s short-selling indicator,which is similar to the short-selling indicator Marty Zweig used in his 1972Barron’s article. Short-selling indicators used to be widely followed and were veryuseful, but because of derivative trading and hedging strategies, they are nolonger useful.130
Marty Zweig’sBarron’s Article
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1966 to 1976. It measures the percentage of total short selling attributedto floor specialists. These short-selling indicators were extremely usefulat signaling market tops and bottoms in the 1960s and 1970s, but theyhaven’t worked since 1984, which marked the advent of program trading.Program trading is the instantaneous buying and selling short of astock or index to lock in an abnormal difference in price between twosimilar or identical investments. This type of short selling is done solely
Short selling is selling borrowed shares of stock under the expectation of being able to buy them back at a lower price and returningthe shares you borrowed. This strategy makes money if prices declineand loses money if prices rise.
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to lock in an investment gain; it does not indicate any expectation thatprices are headed lower. Ultimately, this arbitrage short selling becameso large that it introduced too much noise into the ratios, which madethem useless. Although they are still followed, I do not recommend thatanyone use the short-selling indicators any longer as a means to measuremarket sentiment.The Put/Call RatioAnother method of measuring investor activity is the put/call ratio. Officially, a put is an option to sell a stock at a specified price for a specifiedtime. People buy puts if they expect the price of a stock to decline. A callis an option to buy a stock at a specified price for a specified time. People buy calls when they expect the price of a stock to advance. Becauseputs and calls can get very complicated, let’s just say that puts are betsthat stocks will decline, and calls are bets that they will advance. By calculating the ratio between the number of puts and calls, it is possible toget a good idea of what investors expect stock prices to do.In 1970, before the creation of the Chicago Board Option Exchange(CBOE), the only available data on puts and calls was an informal optionmarket put together by certain option dealers. It was Marty Zweig, in acouple of pioneering articles in Barron’s, who analyzed the use of optionsas a measure of contrary opinion. The data necessary to follow these options, however, was difficult to come by. When formal option marketswere established in the mid-1970s, it became much easier to track theseratios on a daily basis. I have been following them since the first yearCBOE opened.You can calculate two different ratios: the ratio of puts to calls for allindividual stocks and the ratio of puts to calls for the index options. First,let’s look at index options.I remember dreaming in the late 1970s, about five years before theirinception, that if index options were ever created it would be the perfectway to apply the theory of contrary opinion. When my dream was finallyrealized, I was disappointed; they never turned into the great indicator ofcontrary opinion I thought they would be. It was quite a disappointment.On average, index put buying exceeds index call buying by as muchas 2 : 1, but this ratio has been gradually rising over the years. For thisreason, it’s hard to establish the band that represents extremes in bullishor bearish sentiment. More importantly, the ratio has a tendency to go inthe opposite direction from what you would expect. Studies seem to indicate that more institutions are using index options as market hedges,
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thereby skewing the numbers the way program trading skewed the shortselling ratios. Therefore, I do not use index options as an indicator.
NOTEI know that the expansion in financial products over the last 20 yearsis good—I often trade the index options. However, from anotherpoint of view, I am disappointed to have lost first the old short-selling indicators and then the index options as good and reliable indicators for helping predict what the market is going to do. This isespecially true as we enter a trading range market, when these toolswould have been very useful.
The only indicator I use for studying the ratio of puts to calls is theratio on individual stocks. This is the same basic method that MartyZweig pioneered in 1970, and I believe it is still the best puts and calls indicator of investor sentiment. You can see this indicator in Figure 5.2. Itusually goes high at market bottoms and low at market tops, followingthe pattern that you would expect from contrary opinion.
FIGURE 5.2 The Chicago Board Options Exchange puts-to-calls ratio from1990 through 2000. A high ratio is 0.65; a low reading is 0.35.
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Newer IndicatorsThe VIX index and the Rydex mutual fund ratio of bull and bear fundmoney flows are two newer indicators of investor sentiment that haveproved to have value. Unfortunately, they don’t have a long track recordto establish extreme readings through a number of bull and bear marketcycles. What we have seen of them shows that they are very useful.The Volatility Index (VIX). The VIX index requires a bit of explanation.It is based on the theory of the price of options.For many years no one knew how to price options. Then, about 30years ago, three finance professors named Fisher Black, Myron Scholes,and Robert Merton came up with similar formulas. The formulas contained the strike price, an interest rate, the time remaining before expiration, and the volatility of the underlying stock. The last number,volatility, was one of the more important parameters in the equation. Ifa stock is extremely volatile, the price of the option must allow for that.In general, if one stock is twice as volatile as another, given everythingelse the same, an option on it should be twice as large.How is this parameter (volatility) measured? Theoretically, it is measured by going back many months and calculating how much a stockmoves around. The mathematical procedures for doing this are well established. For example, it is possible to calculate the volatility of the S&P500 by going back over the last 12 months and calculating how much theS&P 500 moved above and below a certain average price. By doing this,we might calculate that it fluctuated by about 15% per year.In theory, what investors are supposed to do is put this volatilitynumber for the S&P 500 into the option equation along with the strikeprice and other factors and calculate the price they should pay for an option on the S&P 500. In practice, though, investors don’t heed these theoretical calculations much. They have a tendency, especially when theyget a strong opinion about the direction of the market, to buy options atalmost any price. Because of this market technicians decided to turn thesituation around and work the equations the other way. They take thecurrent market price for the options and work backwards, calculating(using the option formulas) the implied volatility. In fact they do this witheight options to take out any anomaly, average the implied volatility offour close-to-expiration calls and four close-to-expiration puts.This is the VIX: It is the implied volatility of the S&P index, but it isn’tthe S&P 500; it is the S&P 100, which is the original index used
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to trade index options. Originally, the S&P Corporation didn’t want to riskthe good name of the S&P 500 index by using it as a base for option trading. So they invested another index that would act just like it and used itinstead; this was the S&P 100. Later, as option trading became more accepted and computer power increased, that all changed. Now almostevery major index has options available, including the S&P 500. The VIXstill measures, however, the implied volatility of the original S&P 100.Figure 5.3 shows the VIX index plotted against the OEX. It is easy tosee that local stock market bottoms occurs when speculators are pricingup the options and increasing the implied volatility. When prices havereached a peak, the implied volatility is low. The VIX has been calculatedonly back to 1993, so we don’t have a long history of this index to determine what is high and what is low. So far, 20 is considered low and morethan 35 is considered high. I do believe that, as long-term investor activity changes with the market, these extremes will change significantly.Only time will tell.FIGURE 5.3 This graph plots the OEX index (the S&P 100) versus the impliedvolatility (the VIX) of the closest OEX options to expiration. We don’t yet haveenough history to identify extreme readings. What we do know so far is thatwhen the VIX goes under 20, markets have made recent tops. Similarly, whenthe VIX has gone to a high of 35, that has signaled a local bottom. Myexperience tells me, however, that these high and low readings will change inthe future as long-term investor activity increases or decreases and as the realunderlying volatility of the S&P 100 also changes.
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The Rydex Ratios. The Rydex mutual fund family has several fundstied to the S&P 500 index including the Rydex Nova fund, which is leveraged to provide 150% exposure to the index, and the Rydex Ursa Fund,which goes up when the market goes down and provides the inverse performance of the index. This gives investors in the Rydex Family a big advantage—to invest and make money through either a bull market or bearmarket—as long as they can anticipate which fund to be in. Their shareholders are allowed to do unlimited exchanges between these funds. CarlSwenlin of Decisionpoint.com publishes a number of indicators based onRydex fund information. One such indicator divides the dollars investedin the “bear” fund, Ursa, by dollars invested in Nova, the bullish fund. Sofar it acts like a very good contrary opinion indicator. When investors arepouring too much money in the bear fund and not much in the bull fundthat is usually a sign of a bottom. Likewise, when they get too over invested in the bull fund that is a sign of a top.Figure 5.4 shows the correlation between the S&P 500 and thisbear/bull ratio. Since the data hasn’t been available for more than a fewyears, there is not yet sufficient history to know what extreme readings inFIGURE 5.4 The ratio of the amount of investor money that is in the Rydex UrsaFund divided by the amount in the Rydex Nova Fund. Although there isn’tenough history to formally establish what is a high or a low ratio, so far a ratioof 0.3 has occurred at a few major tops, while a ratio of 3.0 has occurred at afew major bottoms.
Rydex Data
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this index are. So far it seems that when the ratio is 0.3 that has indicatedenough bullish sentiment to signal a top, while a ratio of 3.0 representsenough bearish sentiment to signal a major bottom. Again we’ll have tosee how this indicator develops to gain more confidence in it. But so farit seems to work pretty well. (The historical range has been 0.23 to 3.48.)Second Method: Polling ServicesThe second method of measuring investor sentiment involves actuallypolling analysts and investors. Investor polling is being done by a numberof services.Futures Polling Services
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Two or three services poll futures and commodity traders (people whobuy and sell the stock market via index futures). The results of these pollsshow that futures traders are notoriously oriented to the short term. Thenumbers often go from a bullish extreme to a bearish extreme in just afew weeks. This fact highlights the importance of always knowing thetime intention of the trade, as mentioned in Chapter 1. In my experience, futures polling information is useful only when you take long-termmoving averages of the reading of each service and add them together.By combining the data in this way, you can produce a broader statisticalfoundation on which to base conclusions about investor sentiment. Ingeneral, unless I’m particularly interested in shorter-term movements, Iavoid futures polling services because their data can be misleading.The Guru IndexThe oldest and most reliable of the polling services is called Investor’s Intelligence, which was founded in 1963, to measure and compile the opinions of people who write newsletters about the stock market. Becausethe newsletter writers are often considered the gurus of the stock market, I call it the guru index.Each week, Investor’s Intelligence compiles and presents the percentage of newsletter writers who are bullish versus the percentage whoare bearish (a third category of writers are those expecting just a correction, but I have never found this number to be useful). The bullish andbearish numbers have become a wonderful contrary opinion indicatorbecause time has shown that newsletter writers often hold incorrectopinions at critical market junctures. Although it had one major failure in
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FIGURE 5.5 This chart plots a 10-week moving average of the Investor’sIntelligence percentage of newsletter writers who are bullish. The chart starts in1963, with the earliest data available. You can see that extreme bullish sentimentexceeds 60%, and extreme bearish sentiment is shown by readings less than30%. (Source: Investor’s Intelligence, 30 Church St., New Rochelle, NY 10801;phone (914) 632-0422.)
1974, when it failed to predict the last major wave down in the bear market of 1973 to 1974, the overall history of the guru index as a contraryopinion indicator is very good. In practice, I take 10-week moving averages of these percentages to help smooth out the readings. When I referto the guru indicator, I’m referring to a 10-week average of either thebullish readings or the bearish ones. Sometimes it is better to highlightone side or the other when presenting the data to people. Figure 5.5shows the 10-week moving average of the percentage of the bearishnewsletter writers charted back to 1970.Sometimes technicians use the Investor’s Intelligence bearish reading more than the bullish ones, or they take ratios of the two. However,I have found this to be an unnecessary finesse. The simple bullish orbearish reading works quite well, and it is also easier for most people tounderstand. However, to test the theory of contrary opinion explained inthe next section, I used the percentage of bearish newsletter writers.
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THE THEORY OF CONTRARY OPINION: THE PROOFAs I explained in Chapter 1, the stock market I subscribe to holds thatprices are random and unpredictable most of the time, except at certainmoments, when they can become predictable. In my experience, thebest way to discover these predictable moments is to use contrary opinion. I’ve learned this after many years in the stock market. However, it’sone thing to say this and another to prove it. So, I performed an intriguing study using the Investor’s Intelligence bearish readings. I believethis study proves the statement that the market is random most of thetime but predictable when sentiment is at extremes.The assumption in this study was this: If sentiment is useful at predicting the market, I would find that the market did well after extremelyhigh bearish readings and poorly after extremely low bearish readings.Readings between extremes should show no correlation. On the otherhand, if sentiment wasn’t useful at all and the market was completelyrandom, I would see no correlation of any kind. All I would see was average market performance, regardless of the sentiment reading.Gathering DataI began the data series in 1970, when Investor’s Intelligence startedweekly sentiment readings. I calculated the 10-week moving average ofthe percentage of advisors who were bearish for every week from 1970 to1998. I then calculated how the stock market performed over the 6months following each reading. For example, I’d calculate the 10-weekmoving average for January 1, and then calculate how the market didfrom January 1 to June 31. Then, I’d calculate the 10-week average forthe next week, January 7, and determine how the market did over thenext six months, from January 7 to July 7. Altogether, I surveyed a totalof 1,447 weeks in the 29 years of data, yielding 1,447 sentiment readingsand 1,447 corresponding 6-month performance numbers.I then divided the results into groups, with each group representinga tight bracketing of the sentiment reading. I did this in order to see howthe market performs after extreme readings, so I located all the times theguru index showed a certain low range of readings and then calculatedthe stock market’s performance for those readings. For example, the firstgrouping comprised all the times bearish sentiment was between zeroand 10% (historically, an extremely low level of bearish sentiment). Over28 years, such a low level of bearish sentiment occurred only 13 times.
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The average stock market return for these 13 times was –5%. Bearishreadings of 10% to 20% (again extremely low) occurred 200 times, andthe average performance for these 200 times was zero. Readings between 20% and 30% occurred 309 times, and the average 6-month performance was 7.7%. Readings between 30% and 40% occurred 482times, and the average return was 13.9%. Readings between 40% and50% occurred 310 times, and the result was 6.9%. Readings between50% and 60% (levels of extremely high bearish sentiment) occurred 118times, and the average 6-month result was 22.8%. There were only 15times when the reading went higher than 60%, and the average return 6months later was 25.3%. The average return from the Dow Jones industrials over the 28 years was 8.8%. (Note: These results do not take dividends into account, but their inclusion would not change the results ofthe study. They would lift all the total return numbers a little but wouldnot change the relative performance of each group with respect to theaverage Dow Jones return.)The study went into much greater detail than this, but the data I’vegiven here is enough to make my point. The chart in Figure 5.6 summarizes the results just presented. The chart plots the grouping of bearishsentiment (on the X-axis) against the average 6-month market gain forthat grouping. The 6-month returns have been annualized.Examining the ResultsAs Figure 5.6 shows, the performance of the stock market correlateswell with extremes in sentiment. Market performance after extremelylow bearish sentiment (newsletter writers expecting higher prices) wasmuch lower than average. Market performance after extremely highbearish sentiment (newsletter writers expecting low prices) was muchhigher than average. Sentiment readings between these extremesshowed little correlation with market performance.As you’ll recall, I explained in Chapter 4 the dangers of using technical indicators to predict markets beyond 6 to 9 months. Because sentiment is classified as a technical indicator, if this caution explains why Imeasured the market performance over six months rather than a year.Because it is a technical type of indicator, contrary opinion is to have predictive value, it should probably correlate best with movements lastingfrom 6 to 9 months.Even so, with contrary opinion it is sometimes acceptable to make anexception to the 6-month rule and use the sentiment readings to forecast
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FIGURE 5.6 The plotted points represent the average Dow Jones return for sixmonths (on average) for seven different sentiment groupings. This 28-year studyshows that the stock prices outperform when newsletter writers are extremelybearish (right two points) and under perform when there is an extreme lack ofbearishness (left two points). Sentiment readings that are not extreme (middlethree points) show little correlation with market moves as one would expect.
longer-term trends, which would occur when the indicators go to veryext