prior to this point, therule would have failed to capture all the data’s valid patterns. Such a ruleis said to be underfitted. The phenomenon of underfitting and overfittingis illustrated in Figure 9.8. Note that increased complexity always pro-duces improved performance in the training set, which is shown by thecontinual growth in training set performance. Also note that the boundarybetween underfitting and overfitting is defined by the point at which thetest set performance peaks and begins to decline.
The concept of complexity search is abstract and may be new to somereaders. In the interest of clarification, consider following the specific ex-ample of a human guided rather than a machine guided complexity opti-mization. We begin with a basic two moving-average crossover reversalrule.” The rule is defined by two parameters, the short-term moving-averagelook-back span and the long-term moving-average look-back span. Ini-tially, all possible combinations of the parameters for the two averages460 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
Rule Under Fit Rule Over Fit
             
+— —>'«<—
       
| Too Simple Too Complex
High
Training Set
Performance
Test SetRule PerformancePerformance Jo 2 8 4 eee A oor eee , /OptimalComplexity(Proper degree of fit)
Low :
v
Low High
Rule Complexity
FIGURE 9.8 Optimizing rule complexity.
would be run on the training set. If each moving average were allowed 10possible parameter values, a brute-force search would involve 100 tests.!The parameter combination with the highest performance in the trainingset (i.e., the best rule) would then be run on the test data set. Assume thatthe best rule earned a 30 percent annualized return in the training dataand a 15 percent return in the test data. So far, this is nothing but ordinaryparameter optimization, and because the test data has been used only onetime, the 15 percent return is an unbiased estimate of the rule’s future per-formance.
Now, suppose that we are not satisfied with a 15 percent annualizedreturn, so we start to data mine a broader universe that includes complexrules that build on the simple two moving-average crossover rule.” We goback to the drawing board and propose a more complex rule by adding arule based on an Wilder's Relative Strength Index (RSD, an indicator thatmeasures the rate of price change. In this example, the addition of the RSIrule alters the original dual moving-average crossover rule from one thatalways reverses from long to short or short to long to a rule that can re-main out of the market altogether. One way to do this would be to use theRSI indicator as a filter: If the RSI indicator is greater than a threshold,rsi_max, at the time of a sell signal by the basic dual-moving-averagecrossover rule, a neutral position would be taken instead of a short posi-tion. Conversely, and if the RSI were less than the threshold, 7si_min, atthe time of a dual-moving-average buy signal, a neutral position instead ofCase Study Results and the Future of TA 461
a long position would be taken. This more complex rule has four parame-ters that can be optimized: short moving-average span, long moving-averagespan, 7si_max, and rsi_min. A large number of parameter combinationsare then run against the training data.
As expected, the increased complexity allows the four-parameter ruleto earn a higher return in the training data. Suppose it makes 50 percent inthe training data compared to 30 percent earned by its two-parameter pre-decessor. The increased performance in the training data was a predictableconsequence of the increased complexity (additional parameters). We are,however, pleasantly surprised to learn that the four-parameter rule alsodoes better in the testing set, earning 20 percent versus the 15 percentearned by the basic dual moving-average rule. When a more complex ruleperforms better in the test set, it is reasonable to assume that the addi-tional complexity successfully exploited additional systematic aspects ofmarket behavior.
Therefore, it’s back to the drawing board again with another attemptto improve the rule performance with yet additional complexity. This in-volves new conditions with additional parameters. The new conditionsmight be other filters, or they could be alternative entry signals. At eachincrement in complexity, all the possible combinations of the parametervalues are evaluated on the training set and the best performing combina-tion is then run against the test set. The process of adding complexity iscontinued until performance in the test set begins to decline. This is thesignal that the boundary between underfitting and overfitting has beencrossed; the additional complexity has begun to fit the noise in the train-ing data. The optimal rule is the rule of highest complexity that did thebest performance in the test set.
What then is the purpose of the third data segment, the validation set?Thus far, it has not been touched. The astute reader will have realized thatthe performance attained by the optimal complexity rule in the test dataset is positively biased. The repeated visits to the test set to find the rule ofoptimal complexity induced a positive bias—the data-mining bias. Hence,to obtain an unbiased estimate of the optimal complexity rule’s expectedperformance in the future, we must go to the validation set. Because thevalidation set was not utilized in the search for optimal parameter valuesor optimal complexity, it remains untainted.
THE FUTURE OF TECHNICAL ANALYSIS
 
Technical analysis stands at a fork in the road. Its future depends on thepath practitioners take. The traditional path continues the nonscientificsubjectivist tradition of naive theories built on untestable propositions,462 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
anecdotal evidence, and intuitive analysis. The other path is the scientificapproach, which I refer to as evidence-based technical analysis (EBTA). Itis confined to testable methods and objective evidence, rigorously andskeptically examined with the appropriate tools of statistical inference.Thus, EBTA charts a course between foolish gullibility and relentlessskepticism.
I now exercise literary license for literary effect and make the follow-ing unfalsifiable, forecast: Technical analysis will be marginalized to theextent it does not modernize. There are ample precedents in the historyof science to substantiate this statement. Astrology, which gave birth tothe science of astronomy, now languishes on the periphery while its off-spring thrives as the mainstream discipline.
Will TA remain on its traditional unscientific path, thus fating it to becast aside with other ancient practices, or will it adopt EBTA and remainvital and relevant? The historical precedents are not favorable. The his-tory of science suggests that few practitioners who are committed to agiven paradigm ever abandon it. This is consistent with evidence pre-sented in Chapter 2, which established that beliefs are extremely resilient.Studies have shown that a belief can survive a complete discrediting ofthe evidence that originally gave rise to the belief. If TA were to remain onits traditional path, it would indeed be unfortunate. I believe that this willinevitably lead to TA relinquishing territory it originally staked to morerigorous disciplines, such as empirical and behavioral finance.
This is not to suggest that traditional TA will lose all its fans. Therewill always be consumers for the services of soothsayers and seers. Divin-ing the future is, after all, the world’s second oldest profession.’ Some au-dience is assured because of a deep psychic need to reduce anxietyprovoked by the uncertainty of the future. According to Scott Armstrong,an expert in forecasting theory, the brisk demand for the seers’ services ismost certainly not attributable to forecast accuracy. In his paper, “TheSeer-Sucker Theory: The Value of Experts in Forecasting,”** Armstrong ar-gues that people pay heavily for the experts’ views, despite their lack ofaccuracy. What customers are really buying is an illusory reduction in un-certainty and an off-loading of responsibility onto the expert. Armstrongrefers to these consumers as suckers because of the barrage of studiescited in his paper showing that experts’ forecasts are minimally betterthan those of nonexperts. This lack of accuracy has been found to holdtrue across a range of disciplines, including financial predictions.
One seminal study cited by Armstrong is by Alfred Cowles,”> whostudied the track record of the market-guru of the early 1930s, Hamilton,the editor of the Wall Street Journal. Despite Hamilton’s reputationamong his readers, Cowles’s study showed that, over the period 1902 to1929, 50 percent of the guru’s forecasts of directional change were wrong.Case Study Results and the Future of TA 463
Cowles also reviewed the predictive performance of 20 insurance compa-nies, 16 financial services, and 24 financial publications, all of which pro-duced unimpressive records. The same conclusion can be found in thestatistics provided by Hulbert’s financial digest, which currently followsthe performance of over 500 investment portfolios recommended bynewsletters. In one Hulbert study, 57 newsletters were tracked for the 10-year period from August 1987 through August 1998. During that time, lessthan 10 percent of the newsletters beat the Wilshire 5000 Index’s com-pound rate of return.
Armstrong also contends that expertise, beyond a minimal level, addslittle in the way of predictive accuracy. Thus, consumers would be better offbuying the least expensive predictions, which are likely to be as accurate asthe most expensive, or investing the modest effort required to achieve a levelof accuracy that would be comparable to the most expensive experts.
Recently, there have been signs that sophisticated consumers of WallStreet advice are unwilling to pay for traditional TA. During 2005, two ofWall Street’s largest brokerage firms shut down traditional TA research de-partments.”° Whether this is a random blip or the start of a trend remainsto be seen.
Evidence-Based Technical Analysis:The Scientific Path
Adopting a scientific approach will have immediate benefits for technicalanalysis. The elimination of subjective TA, either by its being reformulatedinto objective testable methods or by jettisoning it altogether, wouldtransform TA into a legitimate scientific activity. Only ideas that aretestable would be entertained, and only those that prove themselves withobjective evidence would be admitted to its body of knowledge.
This is not to suggest that, if EBTA were to be adopted, all answerswill be clear-cut and all debate will end—far from it. Debate is part andparcel of the scientific process. Even in the hard sciences, important ques-tions seek resolution, but the questions are about meaningful hypothesesthat spin off testable consequences.
EBTA has limits. The major one is the inability to conduct controlledexperiments, the gold standard of scientific investigation. In a controlledexperiment, the effect of one variable can be isolated and studied by hold-ing all other variables constant. TA is inevitably an observational sciencethat must look to history for new knowledge.
Technical analysis is not alone in this regard. Archeology, paleontol-ogy, and geology also rely on historical data. Nevertheless, they are scien-tific disciplines because they deal in testable questions and look toobjective evidence to separate useful from useless ideas.464 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
There are statistical procedures that can partly ameliorate the lackof experimental controls. For example, when Fosback”’ quantified theoptimism/pessimism of mutual-fund managers, he removed the con-founding effect of short-term interest rates on cash reserves with regres-sion analysis. By nullifying the powerful influence of short-term interestrates on mutual fund cash reserves, he was able to derive an uncontami-nated measure of fund manager psychology called the Fosback Index. Ina like manner, Jacobs and Levy” used multiple regression analysis tominimize the confounding effects of multiple variables. This allowedthem to derive purified indicators for the prediction of relative stockperformance. For example, they were able to measure the pure return-reversal” effect, thus revealing it to be one of the most powerful andconsistent predictors of relative stock performance. In other words,they found a TA effect that works, namely, stocks that have been weakover the past month tend to have positive relative performance over thefollowing month.
Another disadvantage of not being able to conduct controlled exper-iments is the inability to produce new samples of data. Technical analy-sis researchers have one body of market history that gets used over andover again. This contributes to the data-mining-bias problem. The morerules one tests in a given body of data, the more likely a rule will fit thedata by accident. The inability to produce new sets of observations,which can be done in the experimental sciences, underscores the impor-tance of using statistical inference methods that take the data-miningbias into account.
The Expert’s Role inaHuman-Computer Partnership
I assert that the future of TA would be best served by a partnership be-tween TA experts and computers. This is not a new prescription. I wasfirst exposed to it in the writings of Felsen* over 30 years ago, and leadingedge TA practitioners have been taking advantage of advanced data mod-eling and machine learning for almost as long.*! Within the past decade,numerous articles in peer-reviewed journals have shown the value of ap-plying advanced data-mining methods to technical and fundamental indi-cators. Valid objective patterns have been found.*”
This partnership exploits a synergy between human experts and com-puters. The synergy exists because the information processing abilities ofthese two entities are complimentary—computers are strong where hu-mans are weak and visa versa. I am referring specifically to computersarmed with data mining software and humans possessing expertise in aparticular domain: TA, cytology, oil field geology, etc.Case Study Results and the Future of TA 465
Though it is an oversimplification, people are inventive but comput-ers are not. People can pose questions and hypothesize explanations thatorganize disparate facts into a pattern. However, this uniquely humanability has a dark side; it makes us gullible. Our gullibility is a conse-quence of a mind that evolved to be more prone to a state of belief thanan attitude of skepticism. Therefore, although we are good at proposingideas, like new indicators or new rules to test, we are ineffectual at theequally important task of disposing of poor ideas. Moreover, our intellectnever evolved to interpret highly complex or random phenomena. Thisshows up in our limited abilities to engage in configural thinking, the in-tellectual task required when combining a multitude of variables into aprediction or judgment.
Though computers cannot invent, data-mining software allows themto dispose of irrelevant indicators and to synthesize complex models thatcan combine numerous variables into predictions. In other words, com-puters can engage in configural analysis. A computer's ability to synthe-size complex high-dimensional models is only limited by the amount ofdata available. This constraint on predictive modeling, known as the curseof dimensionality, was named by mathematician Richard Bellman. Inessence it says that as the number of indicators comprising a multidimen-sional space is increased, where each indicator is represented by a dimen-sion, the observations populating the space become less dense at anexponential rate. A certain level of data density is required to allow data-mining software to distinguish valid patterns from background random-ness. Maintaining a given level of data density as dimensions are increasedthus requires an exponentially increasing number of observations. If 100observations meet data density requirements at the two-dimensional level,1,000 would be required at three dimensions and 10,000 at four dimensions.Despite this very real limitation on data mining, modern data-mining soft-ware is infinitely better at configural reasoning and complex pattern detec-tion than the smartest humans.
The bottom line is this: Computers can do what people can’t and peo-ple can do what computers can’t. It is the perfect marriage.
The Futility of the Subjective Forecaster
Despite the potential of a human-computer synergy many traditional prac-titioners insist on using subjective methods to derive a forecast or signalfrom a multitude of indicators. They do so in the face of an overwhelmingbody of evidence, that has been accumulating over the last 50 years thatdemonstrates the futility of such an approach. These studies show that, forrecurrent prediction problems, across a variety of fields, subjective pre-dictions are rarely superior to predictions based on even simple statistical466 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
rules (models). A recurrent prediction problem is one in which the sametype of prediction must be made repeatedly on the basis of a similar set ofinformation. Examples of recurring prediction problems include technicalanalysis, predicting violence of prisoners being released, predicting if aborrower is likely to default on a loan, and so on. The information set thatis available to the decision maker is a set of readings on a multitude ofvariables. In TA, this would be a set of technical indicator readings. Incredit evaluation, it would be a set of financial ratios. The task is to deter-mine if there exists a stable functional relationship between the indicatorsand the outcome that is to be predicted, to ascertain what the nature ofthat relationship is, and then to combine the variables according to thatfunctional relationship. When the relationship is complex and involvesmore than just a few factors, the decision maker is faced with a configuralthinking problem that exceeds human intellectual limits. The only choicefor the subjective expert is to fall back on intuition.
The seminal study comparing the accuracy of subjective predictions(expert intuition) to predictions based on statistical rules (models) wasdone by Paul Meehl* in 1954. It was a review of prior studies, known as ameta-analysis, which examined 20 studies that had compared the subjec-tive diagnoses of psychologists and psychiatrists with those produced bylinear statistical models. The studies covered the prediction of academicsuccess, the likelihood of criminal recidivism, and predicting the out-comes of electrical shock therapy. In each case, the experts rendered ajudgment by evaluating a multitude of variables in a subjective manner.“In all studies, the statistical model provided more accurate predictions orthe two methods tied.”?4 A subsequent study by Sawyer" was a metaanalysis of 45 studies. “Again, there was not a single study in which clini-cal global judgment was superior to the statistical prediction (termed ‘me-chanical combination’ by Sawyer).”*° Sawyer’s investigation is noteworthybecause he considered studies in which the human expert was allowed ac-cess to information that was not considered by the statistical model, andyet the model was still superior. For example, the academic performanceof 37,500 sailors in naval training school was more accurately predictedby a model based on test scores than by judges who had access to testscores and who had conducted personal interviews. The judges appar-ently gave too much weight to the nuanced information they thought theyhad obtained in the personal interviews and gave too little to objectivemeasurable facts.
An article by Camerer®” compared the accuracy of experts’ subjectivepredictions with two objective methods: the predictions of multivariatelinear models derived with regression analysis and a second methodbased on a mathematical model of how the experts rendered their judg-ments. The least accurate predictions were the subjective judgmentsCase Study Results and the Future of TA 467
made by experts. The most accurate predictions were made by the linearregression model. Figure 9.9 was prepared from data presented by Russoand Schoemaker® summarizing Camerer’s results. The figure of meritused to compare the forecasting methods was the correlation coefficientbetween predicted outcome and actual outcome. The correlation coeffi-cient can range from 0, signifying the predictions were worthless, to 1.0signifying perfect predictive accuracy. The prediction problems spannednine different fields: (1) academic performance of graduate students, (2)life-expectancy of cancer patients, (3) changes in stock prices, (4) mentalillness using personality tests, (5) grades and attitudes in a psychologycourse, (6) business failures using financial ratios, (7) students’ ratings ofteaching effectiveness, (8) performance of life insurance sales personnel,and (9) IQ scores using Rorschach Tests. Note that the average correla-tion of the statistical model was 0.64 versus the expert average of 0.33. Interms of information content, which is measured by the correlation coeffi-cient squared or r-squared, the model's predictions were on average 3.76times as informative as the experts’.
Numerous additional studies comparing expert judgment to statisticalmodels (rules) have confirmed these findings, forcing the conclusion thatpeople do poorly when attempting to combine a multitude of variables tomake predictions or judgments. In 1968, Goldberg®® showed that a linearprediction model utilizing personality test scores as inputs could discrimi-nate neurotic from psychotic patients better than experienced clinical di-agnosticians. The model was 70 percent accurate compared with expertaccuracy that ranged from 52 percent to 67 percent. A 1983 study showed
 
 
 
 
 
Correlation 0.6Predicted 5versusActua 0.4 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.1 IExpertModel
 
 
 
 
 
 
 
 
 
1 928 Sr v4 Sh IG 7 eh 0)
FIGURE 9.9 Predictive performance of subjective expert versus objective linearmodel in nine fields.468 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
how poorly human experts do when the number of variables is increased.The task was to predict the propensity for violence among newly admittedmale psychiatric patients based on 19 inputs. The average accuracy of theexperts, as measured by the correlation coefficient between their predic-tion of violence and the actual manifestation of violence, was a poor 0.12.The single best expert had a score of 0.36. The predictions of a linear sta-tistical model, using the same set of 19 inputs, achieved a correlation of0.82. In this instance the model's predictions were nearly 50 times more in-formative than the experts’.
Meehl continued to expand his research of comparing experts andstatistical models and in 1986 concluded that “There is no controversy insocial science which shows such a large body of qualitatively diverse stud-ies coming out so uniformly in the same direction as this one. When youare pushing 90 investigations [currently greater than 150°] predictingeverything from the outcomes of football games to the diagnosis of liverdisease and when you can hardly come up with a half dozen studies show-ing even a weak tendency in favor of the clinician, it is time to draw apractical conclusion.”*!
The evidence continues to accumulate, yet few experts pay heed. Themost comprehensive study comparing the accuracy of experts’ predic-tions with those produced by linear statistical prediction models wasdone by Grove and Meehl in 1996, which covers 136 studies. In 96 per-cent of these studies, the rules either outperformed or equaled the ex-perts. In a similar study in 2000, Swets, Monahan, and Dawes comparedthree methods of prediction in medicine and psychology: (1) expert sub-jective judgment, (2) statistical rules (models), and (3) a combination ofthe first two where the output of the statistical rule was modified by theexpert’s judgment. Their finding was consistent with prior studies, leadingto the conclusion that experts who continue to operate on the basis ofsubjective judgment, are not only in error in a predictive sense but per-haps in an ethical one as well.”
From the foregoing, Hastie and Dawes" draw the following conclusions.
1. Experts and novices make subjective judgments by relying on a rel-atively small number of inputs (3 to 5). Assessments of livestockand weather forecasting are exceptions in that a larger number ofinputs is typically used. The ability to use a larger set of informa-tion can be explained by the fact that immediate and precise feed-back is available, which enables the experts in these two domainsto learn. In other areas, like medical diagnosis, school admissions,and financial forecasting, feedback is often delayed or never avail-able. Hastie and Dawes’s conclusion suggests why subjective tech-nicians do not learn from their mistakes—precise feedback is notCase Study Results and the Future of TA 469
available because of the lack of objective pattern definitions andevaluation criteria.
2. For a wide range of judgment tasks, experts’ judgments can be ex-plained by an additive (linear) model of the inputs.
3. Few judges use nonlinear/configural thinking to combine inputs eventhough they are under the impression that they are doing so. How-ever, when they do use configural reasoning, they tend to do itpoorly.”
4. Experts do not understand how they arrive at their own judgments.This problem is most pronounced among highly experienced experts.As a result, their judgments are inconsistent when given the same setof inputs on different occasions.
. Inmany domains, judges do not agree with each other when given thesame set of information. The low level of interjudge agreement saysthat someone is wrong.
aw
6. When irrelevant information is added to the set of inputs, judges be-come more confident although accuracy does not improve. Clearly,they are unable to distinguish relevant from irrelevant variables.
7. There are very few judges who are demonstrably superior.
Hastie and Dawes’s conclusions have received additional supportfrom Tetlock,'* who evaluated predictions from experts in different fieldsand compares them to relatively simple statistical models that extrapo-late recent trends. Tetlock’s work, which appears to be the most rigorouslong-term study ever conducted of expert judgment in the political andeconomic domains, does suggest that certain subjective cognitive stylesof decision making are superior. He calls this style the fox. However, heshows that all human-based predictions, whatever their style, pale incomparison to formal model-based predictions. Using a two-dimensionalframework for evaluating forecast accuracy, discrimination, and calibra-tion, the accuracy of formal models, based on a method called general-ized autoregressive distributed lags, far exceeds the performance ofhuman experts."”
Why Are Experts’ Subjective Predictions So Poor?
Although there is general agreement among those who study expert judg-ment, that it is rarely superior to objective forecasts, there is less agree-ment about why. A number of explanations have been proposed: theintrusion of emotional factors, the lack of consistency in how factors areweighted, excessive weight given to vivid concrete features that have little470 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
predictive power, inadequate weight given to abstract statistics that pos-sess predictive power, the failure to use configural rules properly, fallingprey to illusory correlations, failing to notice valid correlations, and be-ing affected by the actions and statements of others. Many of these werecovered in Chapter 2, with respect to the birth and maintenance of erro-neous beliefs, and in Chapter 7, with respect to information cascadesand herd behavior, and I will not repeat them here. Here, I will considerthe intrusion of emotional factors on the subjective forecaster, whichhas not been discussed in prior chapters.
A number of investigators have considered the effect of emotions onsubjective judgments made under conditions of uncertainty. One model,proposed by Loewenstein et al.,“* suggests that a subjective forecast isimpacted by the decision maker’s anticipated emotional reaction to theprediction’s outcome. Even though the expert’s anxieties about thechance of being wrong have no valid role in a forecast, they appear toplay a role. Nofsinger® presents Lowenstein’s model in a diagram that Ihave reproduced in Figure 9.10. Note that when a person is making aprediction they are impacted by anticipating their emotions in responseto the possible outcomes. This affects both the cognitive evaluation ofinformation and the current emotional state of the decision maker.’
Slovic, Finucane, Peters, and MacGregor! explain the intrusion ofemotions on the subjective decision maker in terms of affect. Affect refersto the good or bad feelings associated with a stimulus. These feelings of-ten operate unconsciously and produce rapid reflex-like behaviors that
AnticipatedOutcomes
IncludingAnticipated ™,Emotions CognitionEvaluation we
 
 
 
 
 
Subjective OutcomesProbability Behavior —+| (IncludingAssessments Emotions)
 
 
 
 
 
 
 
 
 
 
 
 
Other Factors; FeelingsVividness aImmediacyBackgroundMood
 
 
 
FIGURE 9.10 Lowenstein’s model of judgment impacted by affect.Case Study Results and the Future of TA 471
are not under the decision maker’s conscious control. Slovic points outthat feelings and images can weigh heavily in judgments that would bebetter handled by a purely analytical and rational consideration of evi-dence. For example, a market analyst who has negative feelings associ-ated with a declining market is likely to remain more bullish than theevidence would warrant. However, not all judgments based on affect arebad. Some situations are accurately represented by the feelings they pro-voke and do demand the fast reflex-like decisions produced by this modeof thinking. Affective decision making was probably the dominant modeof judgment during the evolution of the human species. In other words,the speed of affective decision making had survival value. Fear of heightsor of large predators demanded faster choices than those that a consciousweighing of pros and cons would have been able to produce. “However,like other heuristics that provide generally adaptive responses but occa-sionally lead us astray, reliance on affect can also deceive us.” This oc-curs when the relevant features of a situation are not well represented bythe feelings they stimulate. Financial market prediction is such a situa-tion, yet the subjective forecaster remains vulnerable to the automaticand subconscious judgments of a heuristic based on affect.
The model offered by Joseph Forgas further illuminates the futility ofsubjective forecasting.” It explains that “the greater the complexity anduncertainty in a situation, the more emotions will influence a decision.”This characterizes the task faced by the subjective TA analyst who oper-ates against insurmountable obstacles when trying to evaluate a multitudeof indicators in a rational manner. I contend that subjective forecasting isno longer the analyst’s proper role.
The Expert's Proper and Crucial Rolein Human-Machine Partnership
The proper role of the TA practitioner in the envisioned human-computerpartnership is twofold: (1) proposing an information-rich set of candi-date inputs suitable for presentation to the data-mining software, (2)specifying the problem to be solved by data-mining software (i.e., thetarget variable. In other words, the TA expert’s proper role is to supplywhat the computer lacks, namely, expertise in the domain of TA and in-ventive talent.
It is somewhat ironic that in this high-tech era, the analyst’s role hasnow taken center stage. This is a result of the changed economics of datamining. Within the last decade, the price of computing power and power-ful data-mining software has declined sharply. Whereas these resourceshad been the exclusive domain of well-financed research organizations asrecently as a decade or two ago, today they are commodities accessible to472 CASE STUDY: SIGNAL RULES FOR THE S&P 500 INDEX
almost all market participants. These trends have democratized data min-ing. Most investors now have access to the same tools that once gave ahuge competitive advantage to the largest institutional investors. That ad-vantaged has been greatly diminished. Consequently, the competitive ad-vantage—the source of added value—has now shifted back to the humananalyst. The competitive advantage now lies with those TA experts pos-sessing the best insights with regard to proposing indicators and specify-ing target variables. The TA practitioner who embraces the EBTAphilosophy