the mind is only able to handle amaximum of three factors when they must be evaluated in a configuralThe Illusory Validity of Subjective Technical Analysis 43
fashion.” Medical diagnosis is typically a configural-thinking problem. Aset of symptoms and lab results, if considered conjointly, can distinguishone disease from another, but taken in isolation they may be nothing morethan a set of disconnected uninformative facts.
Configural thinking is a more demanding mode of thought than se-quential or linear thinking. In a sequential/linear problem, the relevantvariables can be analyzed independently, so even though there may be amultitude of variables, the message conveyed by each variable is unaf-fected by what others variables are saying. Therefore, once each variablehas been interpreted on its own, the set of individual messages can becombined in a linear fashion (i.e., added algebraically”’), to derive theircollective meaning. Suppose, for example that a sequential problem in-volves seven variables, each of which can assume a value of either +1 or—l. Further, assume that five of the variables have the value +1 and twohave the value -1. The linear or additive combination! would be equal to+3 or [(+5) + (-2)]. Research has shown that, when experts make multi-factored decisions in a subjective fashion, they primarily rely on a linearcombining rule,” though they do it less effectively than formal linear re-gression models.” These studies have shown that human experts are lesseffective than linear regression models because they fail to combine theinformation in the consistent manner of a formal mathematical model.
The combining of information in a linear fashion that satisfies the re-quirements of a sequential-thinking problem falls short when the problemdemands a configural solution. In a configural problem, the relevant infor-mation is contained in the web of relationships (interactions) between thevariables. This means that the variables cannot be evaluated in isolationas they can in a sequential/linear problem. To clarify what is meant by in-teractions, imagine a configural problem involving only three variables, A,B, and C. Further suppose that each variable can assume only two read-ings, high or low (i.e., the variables are binary). In a configural problem, ahigh reading on factor A may mean one thing when B is low and C is high,whereas a high reading on A can mean something entirely different whenB is high and C is low. In a sequential/linear problem, a high reading onfactor A carries the same message irrespective of the readings on B and C.
The difference between the two configurations—Configuration 1 (A-high, B-low, C-high) and Configuration 2 (A-high, B-high, C-low)—are illus-trated in the Figures 2.4 and 2.5. The eight cells of the three-dimensionalspace show that there are eight distinctly different possible configurationsof three binary variables. However, a linear combination of three binaryvariables can only assume four” distinct values.
The type of thinking problem faced by a subjective market analyst at-tempting to make a forecast by combining the readings on five indicators(variables), a relatively modest number, is likely to be configural rather44 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
A—High, B—Low, C—High
 
 
FIGURE 2.4 Configuration 1.
than sequential.”* Simply determining which of the five indicators shouldbe combined (some may be redundant or irrelevant) to produce aninformative prediction is a huge configural problem in itself. With only fiveindicators, the predictive power of 26 possible combinations must be eval-uated (10 pairs, 10 triplets, 5 quadruplets, and 1 quintuplet). Then, once agood combination has been identified, applying the multi-indicator rulewould also entail configural thinking. As seen earlier, even if the indica-tors are binary, the simplest possible, 3 binary variables can have eightdistinct configurations. Four binaries have 16 possible distinct configura-tions and 5 binary variables have 32. Subjective analysts who believe they
A—High, B—High, C—Low
 
 
 
High ?Beepc i— aLow WV HighLow BLow HighA
FIGURE 2.5 Configuration 2.The Illusory Validity of Subjective Technical Analysis 45
can perform such a feat are too confident—way too confident! This is notsurprising. It is merely one example of the overconfidence bias.
TOO DANG CERTAIN: THE OVERCONFIDENCE BIAS
 
In general people are too confident. The overconfidence bias is the docu-mented” tendency for people’s self-assessments to err on the high side ofthe truth. They tend to view their personal attributes and abilities as bet-ter than objective evidence would indicate. People view themselves asabove average in many ways, including how much they know and how ac-curately they know it. In other words people tend to be arrogant abouttheir knowledge.
The tendency is so pervasive that psychologists think overconfidenceis inborn, and they are quite confident about that. Biologists have specu-lated that high confidence is a feature of successful mating strategies, sowe are most likely the offspring of swaggering parents. High confidenceconfers benefits on humanity as a whole, even if the daring individual try-ing something new and dangerous dies. After enough attempts, someonesucceeds, and we all benefit. In addition, overconfidence is reinforced byyet other cognitive biases that distort what is learned from experience, in-cluding the self-attribution bias, the confirmation bias, and the hindsightbias, which are discussed later.
Columbia University psychologist Janet Metcalfe summed up re-search on human overconfidence with these humbling words:”
People think they will be able to solve problems when they won't;they are highly confident that they are on the verge of producing thecorrect answer when they are, in fact, about to produce a mistake;they think they have solved problems when they haven't; they thinkthey know answers to information questions when they don’t; theyhave the answer on the tip of their tongue when there is no answer;they think they produced the correct answer when they didn’t, andJurthermore, they say they knew it all along; they believe they havemastered learning material when they haven't; they think they haveunderstood, even though demonstrably they are still in the dark.’”*!
Some specific findings about overconfidence:
Overconfidence is most extreme for difficult or impossible tasks.” Stud-ies have shown extreme overconfidence in tasks such as predicting theoutcome of horse races,® distinguishing European from American46 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
handwriting, and predicting rising versus falling stock prices. In thedomain of TA, the analogous task would be that of combining a multi-tude of indicator readings into a market forecast by subjective config-ural reasoning.
¢ Overconfidence is related to an inability to properly assess the diffi-culty of various tasks and is more pronounced the more difficult thetask. TA analysts may not be aware of the configural reasoning de-manded by subjective data analysis.
¢ Confidence increases as the number of pieces of information are in-creased, though prediction accuracy does not.” Increased confidencewould only be justified if the individual bits of information werenonredundant, were useful, and could be successfully integrated.
¢ Confidence increases as the level of agreement among multiple inputsincreases. When inputs are independent (uncorrelated) and have pre-dictive information, increased confidence may be justified. When in-puts are redundant, it is not. Many TA indicators that appear to bedistinct because of naming conventions and specific calculations ac-tually measure similar market attributes. This can encourage unwar-ranted confidence.
¢ Doctors who were 88 percent confident they had correctly diagnosedpneumonia were correct in only 20 percent of the cases.» Physicianswere similarly overconfident in diagnosing skull fractures.”°
e Many other professionals were overconfident about their specificareas of expertise. For example, when managers of a chemical com-pany were 90 percent confident about company facts (i.e., they ex-pected to be wrong only 10 percent of the time) they were rightonly 50 percent of the time.*’ Executives of a computer companywere 95 percent confident about general business facts but werecorrect 80 percent of the time. With respect to specifics about theirown company, they were 95 percent confident but correct only 58percent of the time.”
¢ The overconfidence of Wall Street’s analysts with respect to their abil-ity to forecast quarterly earnings is attested to by the frequency ofearnings surprises. According to one study, average forecast errorwas 44 percent. This statistic was based on over 500,000 individualearnings predictions.” People are surprised when outcomes fall out-side of predicted ranges, and setting ranges too narrowly is a sign ofoverconfidence.
¢ Individual investors are overconfident in their ability to predict short-term market trends and pick stocks that will do better than the mar-ket, as evidenced by the level of their trading activity.”
e Wall Street strategists exhibit overconfidence in the forecasts for thegeneral stock market and are frequently surprised by actual out-The Illusory Validity of Subjective Technical Analysis 47
comes. However, they maintain their confidence in spite of these mis-takes."' If strategists were indeed learning to curb their confidence,they would make subsequent predictions with wider bands of uncer-tainty, and outcomes would be less likely to fall outside of thosebands. They do not appear to do this.
Given the pervasiveness of overconfidence, it is likely that practition-ers of subjective TA would be afflicted with it as well. It seems likely thattheir overconfidence would manifest with respect to three areas; (1) thepredictive power of specific methods, (2) the efficacy of subjective dataanalysis and informal inference as a means of knowledge discovery, and(3) the ability to perform configural reasoning as in synthesizing a marketforecast from a multitude of indicator reading and patterns. Both the com-plexity of the financial markets and the known limits of unaided humanintelligence suggest that high confidence with respect to any of theseareas is unwarranted.
According to one study, there are two forecasting professions thathave managed to avoid overconfidence: meteorologists and horse-racehandicappers. Their estimates of their predictive abilities are well cali-brated because: “They face similar problems every day; they make explicit(falsifiable) probabilistic predictions; and they obtain swift and precisefeedback on outcomes. When these conditions are not satisfied, overconfi-dence should be expected for both experts and non-experts.””
It is impossible for subjective TA practitioners to obtain accuratefeedback in the context of historical research simply because theirmethods for generating forecasts and evaluating forecast accuracy arenot well defined. Feedback is only possible with objective methods.However, in the context of real-time forecasting, subjective practitionerscould obtain feedback if they were willing to make falsifiable forecasts.A falsifiable forecast is one that has cognitive content. That is to say, itsets up a clearly discernable difference between the outcomes thatwould indicate the forecast had been correct and outcomes that wouldindicate the forecast has been in error. Consider the following as an ex-ample of a falsifiable forecast:
The S&P 500 will be higher twelve months from today, and will notdecline more than 20 percent from current levels within that time.
If the market is not higher 12 months hence or if it drops more than 20percent from the current level within the next 12 months, it is clear thatthe forecast was in error. Unfortunately, falsifiable forecasts are rarelygiven by subjective practitioners, thereby preventing feedback for themand their customers. Overconfidence persists.48 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
It’s Going to Be Great: Optimism Bias
The optimism basis is overconfidence that extends to a generalized andunwarranted hopefulness about the future.*? As a consequence, most peo-ple think their lives will be sprinkled with more favorable and fewer unfa-vorable events than the lives of their peers.
The optimism bias suggests that TA analysts’ beliefs about their pre-dictive prowess will persist even if their prior predictions have notworked out well. This is borne out by data provided by the Hulbert Di-gest,“ a newsletter that tracks the performance of market forecasters bytranslating their recommendations into a portfolio whose value can betracked. Some newsletter writers have attempted to explain away theirpoor performance ratings by claiming that Hulbert’s evaluation methodsare flawed. According to Hulbert, these claims are leveled primarily at theassumptions he is required to make to turn a newsletter’s vague recom-mendations into specific recommendations whose performance can bemeasured. Many newsletters tracked by Hulbert give clear advice, makingsuch assumptions unnecessary.
Despite their dismal long-term performance, newsletter writers con-tinue to make confident predictions, and subscribers continue to believethey will be helpful. Both groups are optimistically biased. If subscriberswere realistic, they would cancel their subscriptions and the writerscould not continue in business. Obviously, something other than predic-tive success keeps faith alive. One possible explanation is that both sub-scribers and newsletter authors have bought into a story that explainswhy the underlying method should work, even if it does not work inpractice. As is explained later, a compelling story can overcome statisti-cal evidence because the mind has evolved a greater taste for storiesthan abstract facts. (See the secondhand information bias and the powerof narratives. )
Self-Attribution Bias: Rationalizing Failure
The tendency toward overconfidence is amplified by other biases that dis-tort what we learn from experience. The self-attribution bias refers to thetendency to interpret past successes and failure in a biased and self-serv-ing manner. “Numerous studies across a wide range of situations havefound that people attribute positive outcomes to their abilities while at-tributing failures to external circumstances.” As a result, we come awaywith a falsely optimistic assessment of our strategies and _ abilities.Whereas the feedback of past failures might motivate change and im-proved performance, self-serving interpretations short-circuit such learn-ing. This may explain how newsletter writers can maintain confidence inThe Illusory Validity of Subjective Technical Analysis 49
the face of performance that, by any objective standard, would be re-garded as negative.
In addition to making us feel good, self-serving explanations makeintuitive sense. When we exert ourselves and succeed, the causal chain-linking effort to a good outcome is easy to understand and has the ring oftruth. Attributing success to good luck or some other factor not under ourcontrol not only has less emotional appeal, it also seems less plausible. Weall prefer the plausible. However, a failure despite our best efforts seemsmore likely to be attributable to bad luck. Common sense tends to over-look the role of luck (randomness) in favorable outcomes. That’s the jobof the statistician.
Studies of the self-attribution bias among gamblers are revealing.“ Byevaluating past losses in a biased manner, they are able to maintain a faithin their abilities in the face of growing losses. Surprisingly, past losses arenot ignored. Actually the gamblers devote a great deal of cognitive energyto failures thereby recasting them in a more favorable light. Losses aretreated as near wins or attributed to bad luck. Wins, on the other hand, arecredited to genuine betting skill.” In the end, both wins and losses windup feeding the gambler’s sense of competence.
As a trader I frequently heard self-serving rationalizations. I was guiltyof it myself until I started keeping a trading log early in my trading careerwith predefined exit points where I was forced to admit my earlier fore-cast was in error. Even though my forecasts were based on subjective TA,my evaluation criteria were objectively defined in advance. I did not startto question my abilities until a considerable amount of negative feedbackjarred my faith.
Sure, I Know That!: The Knowledge Illusion
The knowledge illusion is a false confidence in what we know—both interms of quantity and quality. It is based on the false premise that more in-formation should translate into more knowledge.** When horse-race hand-icappers were given more items of information, they grew more confidentin their forecasts, but their actual accuracy did not improve.*’ The addi-tional facts created the illusion of more knowledge.
The knowledge illusion is relevant to TA because it is most likely tooccur in situations characterized by large amounts of data. Not only do fi-nancial markets generate a torrent of individual time series, but each onecan be transformed into a far greater number of derivative time seriescalled technical indicators. This provides ample opportunity for TA practi-tioners to see themselves as more knowledgeable than they really are.
The knowledge illusion stems in part from an overconfidence inthe mind’s ability to perform configural reasoning, the simultaneous50 ~~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
interpretation of a multitude of variables. In fact the unaided mind is ableto deal with only two or three variables in a configural fashion (see PartTwo). By failing to appreciate these limits, people easily assume that con-sidering more variables (indicators) will lead to greater knowledge and amore informed opinion.
I Can Handle That: The Illusion of Control
The illusion of control is an unwarranted belief in our ability to controloutcomes. People who feel in control are happier and more relaxed thanpeople who do not.” This cognitive distortion is fed by the self-attributionbias and it, in turn, feeds the overoptimism bias.
According to Nofsinger, activities that are most likely to induce the il-lusion of control have the five following characteristics”:
1. A high level of personal involvement2. A large range of choice
3. A great amount of information available to consider (KnowledgeIllusion)
4. A high level of familiarity with the task
. Early success in the activity
oO
The first four clearly apply to subjective TA. A high level of personalinvolvement is engendered by the frequent analysis of market data, thecreation of new indicators and new ways to interpret them, the drawingand redrawing of trend lines, the counting and then recounting of Elliottwaves, and so forth. There is also a large degree of choice: which marketsto follow, which indicators to use, where to place a trend line, and so on.The various methods used become familiar as they are studied and usedregularly. The fifth factor, early success, is a matter of chance. Some willexperience initial success and because of the self-attribution bias arelikely to attribute it to their expertise and the efficacy of the TA methodsrather than chance. All of these factors can induce and maintain an unjus-tified sense of control and an ability to earn market-beating returns.
The Hindsight Bias: I Knew Things Would TurnOut That Way
The hindsight bias creates the illusion that the prediction of an uncertainevent is easier than it really is when the event is viewed in retrospect, af-ter its outcome is known. Once we learn the upshot of an uncertain situa-tion, such as which team won a football game or in which direction pricesThe Illusory Validity of Subjective Technical Analysis 51
moved, subsequent to a TA pattern, we tend to forget how uncertain wereally were prior to knowing the outcome. See Figure 2.6.
This cognitive distortion can be understood in terms of the way mem-ories are stored in the brain. Past events are stored in categories of similarevents (e.g., sports events) rather than as a sequential temporal record.For this reason similar types of events that occurred at different times be-come intermixed. As a result, after the outcome of a football game be-comes known, the pregame state of knowledge (uncertainty) becomesintermixed with definitive postgame knowledge. Our state of knowledgeprior to the game was uncertain because of the ambiguous nature of thepregame evidence. One could have made the case for either team winning.The intermixing of pregame knowledge with postgame knowledge hap-pens immediately and unconsciously, leaving us unable to recall what weknew before the game or our state of doubt. Consequently, the pregameevidence, which was in fact quite uncertain, seems less uncertain after thewinner became known. This creates a false sense of confidence in ourability to make predictions. It is because of this very pitfall that scientistsare especially careful about defining procedures for making predictionsand evaluating their accuracy.
Subjective TA is especially prone to the hindsight bias because itlacks clearly defined rules for pattern identification, forecast generation,
| wonder if!
should bet onthe Jets?
The other team
is pretty good.
NY Jets Win
Football Game [7
             
| knew the Jet’swould win.
It was so obvious!!
Why didn’t | bet?
 
 
 
 
 
| | | .| October | ! October 2 ! October 3
Time
FIGURE 2.6 > The hindsight bias.52 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
and prediction evaluation. In real time, the practitioner of subjective TAfaces a task of overwhelming ambiguity. A single chart contains a mind-boggling number of possible predictive cues that range from the ex-tremely bearish to the extremely bullish. However, when the same chart isviewed in retrospect, the ambiguities that had faced the analyst trying tomake a prediction fade away because outcomes are known. This creates afalse credibility for subjective chart analysis.
Let’s consider a hypothetical example illustrating how outcomeknowledge might minimize the true ambiguities in a chart, thus overstat-ing the predictive power of subjective chart analysis. Consider Figure 2.7and what was knowable as of time A. The pattern could be interpreted asbullish: a prior uptrend with a bullish flag consolidation temporarily inter-rupting the uptrend.
Or the same chart could be interpreted as bearish: a head-and-shoulder pattern, with a neckline breach and then a return rally provid-ing an ideal time for a short sale. This interpretation is illustrated inFigure 2.8. Note that both charts are identical up to point A, except forthe price swings that have been highlighted and the forecast madebased on the pattern.
 
FIGURE 2.7 Bullish conjecture.The Illusory Validity of Subjective Technical Analysis 53
 
FIGURE 2.8 Bearish conjecture.
In reality, at point A the chart is an ambiguous mixture of technicalfeatures that could support either a bullish or bearish forecast. The trueuncertainty, which is depicted in Figure 2.9, explains why chartists shownthe same history will often express a variety of predictions
Now let’s alter the situation by imagining how chartists might look atthe same chart at a later time, point B. We will look at two different out-comes paths subsequent to point A. This is intended to illustrate how thehindsight bias can obscure the ambiguity that existed at time A, therebycreating an illusion of validity for subjective chart analysis, irrespective ofwhich path prices actually took.
First consider an analyst’s impression looking at the chart for the firsttime at time B in Figure 2.10. This is the same chart shown previously withan uptrend tacked on after point A. This observer knows an uptrend tookplace between A and B. It is my contention that the hindsight bias wouldencourage the subjective analysts to notice the bullish flag pattern ratherthan the bearish head-and-shoulder pattern. In other words, the posses-sion of outcome knowledge tends to make patterns that gave incorrectpredictions less noticeable while making patterns that predicted accu-rately more noticeable. What was in fact an ambiguous pattern to an ob-server only possessing knowledge up to point A appears as an obviously54 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
 
FIGURE 2.9 The true uncertainty of foresight.
Price
 
Bullish FlagObvious in Hindsight
FIGURE 2.10 The false certainty of hindsight.
 The Illusory Validity of Subjective Technical Analysis 55
bullish bull flag to an observer looking back from point B. Similarly, theoutcome knowledge possessed by the observer at point B renders thebearish head-and-shoulders pattern less noticeable. The observer at pointB comes away with the impression that the evidence on the chart sup-ports the validity of chart analysis.
Alternatively, if the price path from time A to time B had been a down-trend, as depicted in Figure 2.11, I contend that the hindsight bias wouldcause an observer at point B to notice a head-and-shoulders top patternthat predicted successfully, but the observer would not notice the bullishflag whose forecast proved erroneous.
These illustrations were contrived to make a point. Whatever the out-come, it is always possible to selectively notice chart features that seem-ingly predicted correctly, amongst what is truly an ambiguous mixture ofbullish and bearish cues. The vague manner in which subjective chartpatterns are defined, the lack of objective evaluation criteria, and the op-eration of the hindsight bias create an illusion of validity for subjectivechart analysis.
Who among us, upon first learning of the head-and-shoulder pattern,
     
  
Bearish H&SObvious in Hindsight
 
FIGURE 2.11 > The false certainty of hindsight.56 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
did not peruse historical charts to find examples of the head-and-shoul-ders pattern to see if it worked as claimed? And who among us did notcome away with the impression, “This stuff actually works!” Were wemerely victims of the hindsight bias?
There is persuasive experimental evidence” that people are afflictedwith hindsight bias. In one typical study, students were asked to rate theprobability of various outcomes in advance of President Nixon’s trip toChina in 1972. For example, they were asked to predict if a meeting wouldtake place between President Nixon and chairman Mao Tse-tung and if theUnited States would establish a diplomatic mission in China but not grantthe diplomatic recognition that China wanted. Both events were uncertainprior to the trip. Two weeks after the trip, when the events that did takeplace were known, the students were asked to recall what they had previ-ously predicted. After a two-week interval, 67 percent of the students re-membered their predictions as being more accurate than they actuallywere. In other words, they were unable to recall their prior uncertainty.After several months, the percentage of students afflicted with hindsightbias jumped from 67 percent to 84 percent.
The hindsight bias has been found to operate powerfully in trial testi-mony. Witnesses believe they are giving accurate accounts, but their recallof the order of events and of specific details are altered by knowing howmatters actually turned out.
The hindsight bias infects historical accounts. Historians, having thebenefit of hindsight, will often point out that the rise of the Third Reichwas quite predictable. They claim that the seeds of Nazism were obviousin various writings that preceded the Third Reich. In actuality, the rise ofthe Third Reich was only one of numerous possible interpretations thatcould have been read into these accounts. The outcome, the rise of theThird Reich, which seems so inevitable to the historians, when looked atin retrospect, was merely one among an infinite number of possible pathsthat history could have taken.
Other experimental evidence shows that strategies aimed specificallyat reducing the hindsight bias are not effective.*t Even when people arewarned about hindsight bias and told to avoid it, it still occurs. It appearsto be beyond rational control. Not even professional expertise is helpful.In one study, a group of doctors were asked to evaluate the diagnostic er-rors made by other doctors. The doctors doing the evaluations werearmed with the knowledge of the disease that was ultimately confirmed bya pathology report. The evaluators were unable to understand how sucherrors could have been made by a trained physician. Again, outcomeknowledge makes the past appear as if it should have been more pre-dictable than it really was.
What cognitive processes are responsible for hindsight bias? ThoughThe Illusory Validity of Subjective Technical Analysis 57
the matter is not settled, it seems to go beyond a desire to see ourselves assmart and in control. One conjecture that has received some support sug-gests that it has to do with the way memory works, or, better said, fails towork.” In simplified terms, the recording of memories by the brain is not apassive process in which events are stored in proper temporal sequenceready for replay in that sequence. Rather, memory involves an active de-construction of events for storage and then an active reconstruction ofevents when they are recalled. As events are experienced, they are slicedand diced and stored by associative categories, rather than by time of oc-currence. An encounter with a dog last month at a friend’s house in thecountry is dissected and stored in distinct categories and neural locations;memories of dogs, memories of friends, and memories of trips to thecountry, all of which may have occurred at various times in the past. Whenwe attempt to recall the last month’s visit to the country, the mind recon-structs the memory by splicing together bits of information stored in theseseparate associative locations. Psychologists have speculated that ourbrains evolved this way because it is generally effective and efficient.However, because time of occurrence is not an important feature of thestorage system, it becomes difficult to recall the order of events. And it isthe order of events, such as when we received a bit of knowledge, that ismost critical when we are assessing our predictive abilities.
In the process of deconstructing events for storage, new experiencegets mixed in with old experience. Just as a drop of ink in a glass of clearwater becomes thoroughly and irreversibly intermixed, recently acquiredknowledge, such as the outcome of an uncertain event, becomes inextri-cably intermixed with what was known prior to the outcome. Post-outcomeknowledge becomes indistinguishable from pre-outcome knowledge andseems as if it were known all along. This explains why it is so difficult forpeople to reconstruct prior states of uncertainty.
Can subjective TA analysts overcome the hindsight bias? To answerthis question we must consider the analyst in the context of two differenttasks: (1) pattern research—the search for patterns with predictive powerin historical data, and (2) real-time forecasting—applying the patterns incurrent time to make new predictions. The task of pattern research is ex-emplified by the early investigations of Charles H. Dow, originator of theDow theory, or of Ralph N. Elliott, originator of the Elliott Wave Principle.In the process of formulating their ideas, they informally proposed andtested various hypotheses about which chart patterns had predictivepower. The task of making real-time predictions by applying those pat-terns in current time would be exemplified by Dow theorist Richard Rus-sell applying the Dow theory today or Robert Prechter, an Elliott-waveexpert, making a forecast today.
In the context of historical pattern research, I contend that hindsight58 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
bias is unavoidable because it is impossible to shield oneself from out-come knowledge. Simply knowing the path that prices took biases the an-alyst’s perception of the predictive power of whatever method is beingevaluated. Only objective TA methods offer the opportunity of avoidinghindsight bias because only information known at a given point in time isused to generate signals, and signals are evaluated in an objective manner.
In the context of current-time forecasting, subjective analysts couldprotect themselves from hindsight bias if they were willing to make falsifi-able forecasts. A forecast is falsifiable if, at the time a forecast is made,the analyst specifies (1) outcomes that would constitute a forecast error,or (2) the procedure that will be used to evaluate the forecast as well aswhen the procedure will be employed. For example:
The market will be higher six months from now, and within thattime frame the market will not decline more than 20 percent fromcurrent levels, or
The market will advance 20 percent from current levels before it de-clines 20 percent from current levels.
A buy signal has been given. Hold long position until a sell signal isgiven.
The first two forecasts