. He claims that intelligentand successful adults rarely change their most fundamental presupposi-tions.'’ This effect is most pronounced in people who have accumulatedsignificant knowledge in a particular area. This suggests that the most in-telligent and experienced subjective practitioners will be the least able toabandon a method in which they previously believed.72 =~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
ILLUSORY CORRELATIONS
 
The studies discussed up to this point have dealt with people’s erroneousbeliefs about themselves and their abilities. We now consider studies thatexamined people’s beliefs about the world at large, specifically whether apredictive relationship exists between a pair of variables.'"! These haveshown that people have a tendency to perceive illusory correlation. An il-lusory correlation is the false perception of a relationship between a pairof variables. It is a significant problem for subjective TA practitioners.
Subjective TA Methods Representedas Binary Variables
A pair of variables is said to be correlated when one, referred to as thepredictor, can be used to predict the other, referred to as the outcome ordependent variable. Although subjective TA methods are not usually de-scribed in these terms, all such methods can be viewed as asserting theexistence of a correlation between a pair of variables. The predictor is aTA pattern or signal such as the head-and-shoulders top pattern. The out-come is the post-pattern price movement the pattern is purported to pre-dict. A head-and-shoulders top pattern is supposed to predict a downtrendupon the pattern’s completion. In fact, the entire enterprise of TA, bothobjective and subjective, can be viewed as the search for such correla-tions. These are then used individually, though more often in combination,to forecast market trends.
It can also be said of subjective patterns, that both the predictor andthe outcome are binary variables. That is to say, they assume only twopossible values. With respect to the predictor variable, the pattern or sig-nal is either (1) currently present or (2) not present.' With respect to theoutcome variable, the future price movement the pattern or signal is pur-ported to predict, either (1) occurs or (2) does not occur.
For example, the head-and-shoulders top pattern is purported to becorrelated with (predictive of) a postpattern downtrend. At a given pointin time, a head-and-shoulder top pattern and neckline break signal is ei-ther present on the chart or it is not.' Following the signal the downtrendeither occurs or it does not. All subjective patterns and signals can be in-terpreted within this framework.
The central contention of this chapter is that subjective TA knowl-edge possesses only illusory validity. This section examines a specificillusion of validity—illusory correlation. An illusory correlation is theperception of a correlation between two variables that are not trulyrelated.The Illusory Validity of Subjective Technical Analysis 73
Why We Are Prone to Illusory Correlations
A substantial body of research indicates that people easily fall prey tothe illusion of correlation.'* These illusions seem to result from faultyinformation processing. The specific fault is paying too much attentionto instances that confirm the existence of the correlation while payingtoo little attention to cases that either do not confirm the presumed cor-relations or that outrightly contradict its existence. In TA, confirmatoryinstances are those cases in which the pattern/signal occurs and theoutcome that the pattern/signal is purported to predict also occurs. Forexample, the head-and-shoulders top pattern occurs and then a down-trend ensues. If you believe in the efficacy of a particular subjective pat-tern/signal, ask yourself if your belief is not entirely or primarilyfounded upon having seen instances in which the pattern has been suc-cessful (confirmatory examples). Now ask yourself if you are similarlyaware of examples where it failed (false positive or false negativesignals!).
However, confirmatory instances constitute only one type out of fourpossible types of outcomes that can occur between a pair of binary vari-ables. The other three types are (2) the pattern/signal does occur but themarket trend the pattern is supposed to predict does not occur, (3) thepatterr/signal does not occur but the outcome the pattern is supposed topredict occurs anyway, and (4) the pattern/signal does not occur and theoutcome the pattern/signal purports to predict does not occur either.Types one and four are correct predictions. Types two and three are pre-diction errors. Type two is called a false signal or false positive. Type threeis called a failure to signal or false negative.
The Allure of the Upper Left Cell
The four possible outcomes between a pair of binary variables can be il-lustrated with a two-by-two (2 x 2) contingency table (Figure 2.15). Thiscommon data analysis tool is composed of four cells, one for each of thefour possible outcome types. Into each cell goes a count of the number ofinstances of that type of outcome occurring within a sample of observa-tions. Such a table would be useful to represent subjective TA patternsand their outcomes were it not for the fact that their subjectivity pre-cludes obtaining the counts.
Studies show that people fall prey to illusory correlations becausethey pay excessive attention to the number of confirmatory instances,those falling into the upper-left cell of the table.’ At the same time, theyfail to take proper account of the number of cases falling into the other74 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Predicted PredictedOutcome OutcomeOccurs Does NOT Occur
CorrectPattern/Signal Prediction ErrorOccurs Confirmatory False SignalInstances
Pattern/Signal Error CorrectDoes NOT Failure to PredictionOccur Signal No opportunity
 
FIGURE 2.15 2 x 2 contingency table.
three cells. We make this mistake for two reasons. The first has to do withthe salience of the confirmatory instances, and the second has to do witha faulty intuitive notion of what constitutes sufficient evidence to con-clude that a correlation exists.
The salience of evidence refers to how noticeable, vivid, or out-standing it is. Salient evidence is attention grabbing. Confirmatory exam-ples are salient because they confirm a suspected hypothesis. In otherwords they are rewarding. When subjective technicians think they havediscovered a useful pattern or signal, they typically will examine histori-cal data to see if their initial supposition was correct. Thus, they are en-gaged in informal hypothesis testing. In this situation, confirmatoryinstances, examples in which the pattern occurred and it made a correctprediction, will be highly noticeable because they reward the analyst’sinitial conjecture. Consider what it might have been like for R. N. Elliottjust after formulating his initial hunch that all market movements con-form to the same fundamental pattern—a five-segment impulse wave fol-lowed by a three-segment corrective wave (5-3 pattern). At that point,his hypothesis had not yet attained the status of a strong belief. It wasmerely a suspicion. However, once that hypothesis was born in Elliott’smind, it motivated a search for additional confirming cases. In otherwords it was Elliott’s initial conjecture itself that made confirmatorycases salient.The Illusory Validity of Subjective Technical Analysis a3
Necessary versus Sufficient Evidence
Once the confirmatory instances take front stage, a faulty intuition that isquite common enters the picture. This intuition, which is responsible forproducing the illusion of a correlation, concerns the amount of evidencethat is thought to be sufficient to establish a valid correlation. It is com-monly thought that confirmatory evidence, by itself, is sufficient. This isincorrect!
It’s not that this intuition is wrong so much as it is incomplete. Intu-ition correctly tells us that confirmatory instances (events in the upper leftcell) are indeed necessary to establish the existence of a correlative rela-tionship. If the head-and-shoulders pattern, or any pattern or signal forthat matter, is a valid predictor, then there ought to be instances in whichthe pattern occurred and the expected outcome also occurred. However,it is a mistake to assume that a goodly number of confirmatory instancesare sufficient to establish a correlative relationship. In other words, al-though confirmatory instances are necessary, they are not by themselves,sufficient to establish the existence of a correlative relationship. In reality,the number of instances falling into all four cells of the contingency tablemust be taken into account to determine if valid correlation exists.
It is because we fail to realize that a given belief is not supported bysufficient evidence that it takes on an illusion of validity. Thus we come tosee it not as an opinion or an article of faith but as a reasonable inferencefrom objective evidence.!” Numerous studies'® have shown that, whenpeople rely on informal analysis to determine if there is a relationship be-tween two variables, they tend to make two kinds of errors. They fail todetect correlations that are, in fact, valid, even when the correlation is rel-atively strong!” (invisible correlations), and they falsely perceive correla-tions that are invalid (illusory correlations). Both errors are related to theobserver's expectations stemming from pre-existing beliefs.
First, let’s consider what happens when people are shown data fortwo variables!" that are in fact related but which are not expected to becorrelated. Experiments have shown that they are unable to detect therelationships unless the correlation exceeds approximately 0.70, on ascale of 0 to 1.0, where 1.0 represents a perfect correlation and zero rep-resents no correlation. A correlation of 0.70 is far stronger than anythinglikely to be encountered within the domain of TA."!! This finding suggeststhat subjective TA analysts relying on informal/intuitive data analysismethods will be prone to missing valid correlations that they do not ex-pect to exist.
What is even more relevant to the problem of illusory TA knowledgeis the tendency to perceive correlations that do not exist. When observershave a prior belief that a relationship exists between pattern and outcome,76 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
or there is a motivation to find one, the confirmation bias, discussed previ-ously, makes it likely that a correlative relationship will be detected evenif none exists. It is hard to imagine a TA analyst who is not impacted eitherby prior belief, motivation, or both.
Even if there is no prior belief or no motivation to believe, peoplestill display a pronounced tendency to perceive illusory correlations.Smedslund performed one of the earliest experiments showing this ten-dency.'”? Nurses were shown 100 cards that were supposed to representactual patients. Each card indicated if the patient manifested a particularsymptom or not (the binary predictor) and whether that patient was ulti-mately diagnosed with a specific disease (the binary outcome). Thenurses’ task was to determine if the symptom was truly correlated withthe disease. The data shown to the nurses is summarized in the contin-gency table in Figure 2.16.
Over 85 percent of the nurses said the symptom was correlated withthe disease. They were persuaded by the 37 salient confirmatory instancesfalling in the upper left cell (symptom present and disease present). How-ever, their conclusion was not supported by the data. A proper analysis ofthis data, which takes all cell counts into consideration, indicates that thesymptom is not correlated with the disease.
 
True Diagnosis
 
 
 
Disease DiseasePresent NOT PresentCell A Cell BSymptomPresent 37 17instances instancesCell DSymptom
NOT Present
33 13
instances instances
 
FIGURE 2.16 Data evaluated by nurses—Smedslund study (1963).The Illusory Validity of Subjective Technical Analysis 77
Giving All Cells Their Due
A formal statistical test that considers all cell counts would be the rigor-ous way to determine if the nurses’ data was indicative of a valid correla-tion. However, one need not even resort to a formal test to see that thereis no correlation between the symptom and the disease. All cells can betaken into consideration by computing some simple ratios of the cellcounts. For example, the proportion of patients that manifested the symp-tom and who ultimately proved to have the disease was approximately0.69 (cell A divided by [cell A + cell B], 37/54 = 0.685). However, the pro-portion of patients that did not have the symptom that ultimately were di-agnosed with the disease was almost the same or 0.72 (cell C divided by[cell C + Cell D], 33/46 = 0.717). This quick-and-dirty calculation showsthat likelihood of having the disease was about the same (.69 and .72) re-gardless of whether the symptom was present. Had the symptom reallybeen correlated with the disease, the two proportions would have beenquite different, such as 0.90 of those with the symptom had the diseasewhereas only 0.40 of those without the symptom had the disease. Thiswould not necessarily suggest that the symptom was a cause of the dis-ease but only that it was a valid predictor of it.
In light of the fact that even simple ratios showed no correlation be-tween symptom and disease, it is surprising that 85 percent of the nursesconcluded that the symptom was a reliable predictor of the disease. Thenurses were presented with other data sets as well. Overall, the factor thatbest explained whether the nurses perceived a correlation was simply thenumber of confirmatory instances (cell A: symptom and disease both oc-cur). Their judgments were unaffected by the relative proportions thatwent into the calculation done earlier. In other words, the nurses didnot pay attention to the number of cases in the other three cells. As previ-ously noted, many psychological studies have supported Smedslund’sfindings.'"
The statistically correct way to detect a correlation between two bi-nary variables is the Chi-square test.''! The test determines whether thenumber of instances falling into each cell differs significantly from thenumber that would be expected in each cell if there were no correlationbetween the variables. In other words, the test determines if cell countsdepart significantly from a random sprinkling of the instances among thefour cells. When cell counts do depart significantly'® from a random pat-tern, the Chi-square statistic takes on a large value. This is legitimate evi-dence of a correlation.
In Figure 2.17 the data from the nurses’ study have been altered to il-lustrate how cell counts might appear for a symptom that is correlatedwith the disease. Each cell also contains the number of instances that78% ~~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
 
Final Diagnosis
 
 
 
Disease DiseasePresent NOT Present
SymptomPresent
Expected = 37.8 | Expected = 16.2
SymptomNOT Present
Expected = 32.2 | Expected = 13.8
 
FIGURE 2.17 Evidence of valid correlation.
would be expected if there were no predictive relation between symptomand disease (the random pattern). For example, in cell B, there were only7 occurrences of disease with the symptom present (a false signal). If theinstances conformed to a random pattern, one would expect about 16false signals. Thus, the relative proportion of disease, if symptom is pre-sent, is 0.87 versus 0.50 when the symptom is not present. The differencein the proportions shows that the symptom is correlated with the disease.
The Role of Asymmetric Binary Variablesin Illusory Correlation
Illusory correlations are especially likely to emerge when the variablesinvolved are binary variables of a particular type—asymmetric binaryvariables.!'° Binary variables can be either symmetric or asymmetric. Abinary variable is said to be of the symmetric type if each of its possiblevalues is associated with the presence of an attribute (red/blue or Re-publican/Democrat) or the occurrence of an event (rain/snow orThe Illusory Validity of Subjective Technical Analysis 79
flood/fire). However, in the case of an asymmetric binary variable, one ofthe variable’s two values depicts the absence of an attribute or thenonoccurrence of an event. For example, (attribute present/attribute notpresent) or (event occurred/event did not occur). In other words, one ofthe variable’s states represents something not happening or a feature notbeing present.
Research!” shows that when both the predictor and outcome vari-ables are of the binary asymmetric type, the confirmatory instances (pat-tern occurs and expected outcome occurs) are extraordinarily salient andespecially likely to consume the attention of the informal data analyst.This attentional shift to the upper left cell strongly encourages the percep-tion of illusory correlations. Recall that when the variables are asymmet-ric binaries, the other three cells of the contingency table containinstances in which one or both of the variables are registering the absenceof the pattern, the nonoccurrence of the expected outcome or both pat-tern absence and outcome nonoccurrence.
The nonoccurrence of an event, or the absence of an attribute, is eas-ily overlooked, because people have difficulty conceptualizing and evalu-ating this kind of evidence. The research just alluded to has shown it takesmore cognitive effort to evaluate instances where something does not oc-cur than instances where something does. To illustrate the extra cognitiveeffort required to process a statement involving the nonoccurrence of anevent or the absence of an attribute, consider the following two state-ments. Although they both convey the same information, the meaning ofthe first, which is given in an affirmative form, is intuitively obvious. How-ever, the meaning of the second, which is given in the negative form, re-quires some thought.
Statement 1: All humans are mortal.Statement 2: All nonmortals are nonhumans.
Many of the propositions of subjective TA assert the existence of acorrelation between a pair of asymmetric binary variables: a signal/pat-tern that is either present on the chart or not and an outcome that the sig-nal/pattern is supposed to predict, which either occurs or does not. Thusthe psychological research described earlier suggests that subjective TApractitioners will be especially likely to fall prey to illusory correlations.They will tend to be overly impressed with confirmatory instances, inwhich the pattern/signal did occur and the expected outcome also oc-curred, and give too little attention and weight to the three other types ofinstances. Some examples of subjective patterns along with their ex-pected outcomes are illustrated Table 2.1.8O = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
TABLE 2.1 Subjective Patterns and Their Expected Outcomes
 
 
 
Pattern/Signal Expected OutcomeHead-and-shoulders top Downtrend
Completed 5th Elliott wave Corrective wave
Strong uptrend, then flag Continuation of uptrendExtremely bullish sentiment readings Bear market
 
Hidden or Missing Data Compounds the Problemof Illusory Correlations
The discussion so far shows how people come to believe in bogus correla-tions because of an insufficient consideration of all relevant informationin a contingency table. However, the subjective technician’s plight doesnot end here. Further compounding the propensity to detect illusory cor-relations is the problem of missing data. The cell counts needed to fill inthe contingency table are not even readily available. The missing dataproblem is yet another consequence of subjective analysis.
Without objectively defined signals/patterns and without objectivelydefined standards for evaluating their outcomes, it is impossible obtainthe requisite counts that are needed to fill the cells of the contingencytable. Without these counts, the true efficacy of the signal/pattern cannotbe evaluated. This applies even to the confirmatory instances found in theupper left cell of the table, although subjective practitioners seem neverto run short of confirmatory examples.''® Objective definitions are theonly solution to the missing data problem. Until TA adopts objectivity, itwill continue to operate on the basis of magical thinking and myth.
Once an illusory correlation is accepted as fact, two previously men-tioned cognitive errors assure the misconception’s longevity. The mere be-lief in the specious relationship increases the likelihood that additionalsupportive evidence will be noticed (confirmation bias). Moreover, the al-leged correlation is easily assimilated into one’s system of beliefs. AsGilovich points out, once a phenomenon is suspected to exist, people areeasily able to come up with an explanation about why it exists and what itmeans. In other words humans are very good at inventing stories after thefact.'!® Studies show that when people are falsely informed that they areeither above average or below average at some task, they can readily in-vent a reason that explains why.!”° Part of the appeal of subjective TA isthe fascinating story offered about why it works. The story itself helpssustain belief.The Illusory Validity of Subjective Technical Analysis 81
A Behaviorist’s Explanation of [lusoryCorrelations
Thus far, the discussion has explained the birth and longevity of erro-neous beliefs in terms of cognitive psychology. A separate subdiscipline ofpsychology, behaviorism, offers a somewhat different account of whyerroneous beliefs as to the efficacy of subjective TA should be so durable.
Behavioral psychology is concerned with roles of reward (positive re-inforcement) and punishment (negative reinforcement) in the learning ofhabitual behaviors. If a pigeon is put in a cage and rewarded with a foodpellet each time it pecks at a button, it will eventually learn the habit ofpecking the button. Once this behavior has been acquired, the bird willcontinue pecking the button so long as the food pellets keep coming.However, if the reward is stopped, the bird eventually learns that the be-havior no longer pays, and the learned behavior is extinguished.
One area of interest within behavioral psychology is the relationshipbetween the strength of a habit, indicated by the degree to which it re-sists extinction, and the specific type of reinforcement schedule thatoriginally led to the habit’s formation. The question is which type of rein-forcement schedule produces the strongest habits. The simplest type ofreinforcement is rewarding every action—each peck earns a pellet. An-other is partial reinforcement, in which rewards come only at fixed timeintervals, such as every 60 seconds, or fixed behavior intervals, such asevery tenth peck. It has been discovered that partial reinforcementschedules produce slower learning (habit takes longer to form) butstronger habits.
Most relevant to the domain of TA is the fact that of all the partial re-inforcement schedules investigated, random reinforcement'?! produceshabits that are most resistant to extinction. Under random reinforcement,the organism’s behavior is not truly associated with the receipt of a re-ward. Behavior is rewarded by chance. In one experiment, a pigeonpecked at a colored button and received random reinforcement for a mere60 seconds. However, the pecking behavior continued for 3.5 hours afterthe reward was stopped. In other words, the pathetic bird engaged in afruitless activity for a period that was 210 times longer than the randomreinforcement period that produced the habit.!
Random reinforcement is precisely the type of reward received bysomeone who believes in an illusory TA correlation. Every so often, bychance, the signal/pattern is followed by the expected outcome, thus rein-forcing the belief. Behaviorist studies would predict that such a beliefwould be extremely resistant to extinction. Superstitions and compulsivegambling, two habits that rest on illusory correlations, are known to beextremely difficult to cure.!??82 ~~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
When I worked on the floor of the American Stock Exchange, I metseveral traders who displayed superstitious behavior. One fellow insistedon wearing the same tie every day, which contained an accurate historicalrecord of his tastes in soup. Another was adamant about entering the trad-ing floor through one particular door of the several available. Another re-fused to use a red pen. Each of these odd habits was probably initiated byan accidental pairing of the behavior with a desired or undesirable out-come and then perpetuated by random reinforcement.
MISPLACED FAITH IN CHART ANALYSIS
 
Subjective technicians, irrespective of the specific method they practice,believe in the efficacy of visual chart analysis. Price charts are presumedto be a valid method for detecting exploitable order in financial marketdata. The pioneers of TA used charts to discover the discipline’s founda-tional principles, and today they remain the primary analysis tool. Thissection explains why confidence in chart analysis is misplaced.
This is not to say that charts have no role in TA. They can serve as asource of inspiration that leads to the formation of testable hypothesesabout market behavior. However, unless and until these conjectures arevigorously and objectively tested, they remain mere suppositions ratherthan trustworthy knowledge.
An Informal Search for Order Is Sure to Find It
Humans have both the need and the capacity to find order and meaning intheir experience. “It may have been bred into us through evolution be-cause of its general adaptiveness: We can capitalize on ordered phenom-ena in ways that we cannot on those that are random. The predispositionto detect patterns and make connections is what leads to discovery andadvance. The problem is that the tendency is so strong and so automaticthat we sometimes detect coherence even when it does not exist.”!4
A trait of intuitive, nonscientific thinking is the tendency to acceptwithout question surface impressions and obvious explanations. Unfor-tunately, what is obvious at first blush is not necessarily valid. To theancients it seemed obvious the sun orbited the earth. To the pioneers ofTA it seemed obvious that financial market prices formed patterns andtrends. Just as early observers of the heavens perceived mythical fig-ures like Leo the Lion and Orion the Hunter in the random arrange-ments of stars, the first technicians saw heads-and-shoulders, doubleand triple tops, and other patterns in the meandering movements ofThe Illusory Validity of Subjective Technical Analysis 83
stock and commodity prices. These patterns form the lexicon of subjec-tive chart analysis.
Our nervous system seems to be wired to see shapes regardless ofwhether they are real. Dr. John Elder,!”> a noted authority on predictivemodeling and data mining, calls this the “bunnies in the clouds” effect.There is, however, good reason to question the validity of chart analysis,and by extension, to question the validity of knowledge based on it. I con-tend that trends and patterns on charts that appear real to the eye may of-ten be illusions born of the mind’s voracious, though indiscriminate,appetite to perceive order.
This is not meant to imply that financial markets are devoid of authen-tic trends and patterns. In fact, there is sound empirical evidence that pat-terns exist which have predictive power.!”° It does mean, however, thatvisual inspection of charts is an inadequate means for discovering or veri-fying the authenticity of patterns. Studies'’’ have shown that even expertchartists cannot reliably distinguish between authentic stock charts andsimulated charts produced by a random process. A method of analysisthat cannot discriminate real charts from fakes cannot provide reliableanalysis of the real. A jeweler who can’t tell the difference between au-thentic diamonds and dime-store costume jewelry is in no position to ap-praise a diamond necklace.
Illusory Trends and Patterns in Financial Data
Statistician Harry Roberts said that technical analysts fall victim to the il-lusion of patterns and trends for two possible reasons. First, “the usualmethod of graphing stock prices gives a picture of successive (price) lev-els rather than of price changes and levels can give an artificial appear-ance of pattern or trend. Second, chance behavior itself producespatterns that invite spurious interpretations.”!°
Roberts showed that the same chart patterns to which TA attachesimportance” appear with great regularity in random walks. A randomwalk is, by definition, devoid of authentic trends, patterns, or exploitableorder of any kind. However, Roberts’ random-walk charts displayed head-and-shoulder tops and bottoms, triangle tops and bottoms, triple tops andbottoms, trend channels, and so forth.
You can create a random-walk chart from a sequence of coin flips bystarting with an arbitrary price, say $100, and adding one dollar for eachhead and subtracting one dollar for each tail. In other words, the face ofthe coin represents a price change and the chart shows a sequence of sim-ulated price levels. Each simulated price in the sequence is equal to thecumulative algebraic sum of all prior random changes added to the start-ing value. If you carry out this process for three hundred or so flips, you84 =~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
may be amazed at the patterns that appear. Many will be similar to the pat-terns found on actual charts of financial assets. Clearly the patterns ortrends that appear on a coin-flip chart cannot predict its future evolution.
This raises an important question: if chartist-like patterns can appearwith such regularity in random walks, and it is known that these patternscannot be predictive, how can the same patterns be considered predictivesimply because they happen to appear on a real chart?
Perhaps we should not trust the eye of Roberts. He was a statistician,not an expert in TA. If an expert chartist were able to spot the patternsoccurring in random-walk charts as fakes the way a competent jewelercan spot a counterfeit diamond, then Roberts’s random-walk experimentwould not prove anything other than his lack of chart expertise. Cer-tainly, expert chartists should claim that they can tell the difference be-tween patterns occurring in random walks and those occurring on realcharts. After all, if the patterns on actual markets charts form as a resultof changes in supply and demand and/or oscillations in market mood, asTA claims, they should look different from the accidental meanderings ona random-walk chart! !*°
Unfortunately expert chartists have been unable to tell the differencebetween real and fake charts. Arditti!! put them to the test. When pre-sented with an assortment of real charts mixed with random-walk charts,the chartists examined by Arditti were no more accurate at telling the realfrom the fake than a guess. This result was subsequently confirmed in aninformal study'” by Professor Jeremy Siegel of the Wharton BusinessSchool. Siegel presented the graphs shown in Figure 2.18 (four real andfour random) to a group of top Wall Street stockbrokers who consideredthemselves competent chart readers. Again, the experts were unable to re-liably distinguish the real from the random. I conducted a similar experi-ment using graduate business students who had just completed a coursein technical analysis. Their accuracy was consistent with guessing. Thereal charts are indicated by the X.
What can we conclude from these results? First, they do not implythat financial markets are random, and hence devoid of valid trends andpatterns. But they do call into question the efficacy of subjective/visualchart analysis as a means of knowledge acquisition and forecasting. Ifchart reading were a valid skill, it should at least be possible to distinguishan actual chart from a random fake.
Evidence of Illusory Trends in Sports
Financial markets are not the only arena in which observers are plaguedby the illusion of order. Many sports fans and athletes believe in perfor-mance trends, periods of hot and cold performance. What baseball fan orThe Illusory Validity of Subjective Technical Analysis 85
tne
vo | ens,
4Le x I faehammer | aedx “nlMac Ve Pen
FIGURE 2.18 Real and randomly generated stock charts.
Siegel, Jeremy J., Stocks for the Long Run, 3rd Edition, Copyright 2002, 1998,1994, McGraw-Hill; the material is reproduced with the permission of The McGraw-Hill Companies.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
