. Fischoff, “Hindsight Is Not Equal to Foresight: The Effect of OutcomeKnowledge on Judgment under Uncertainty,” Journal of Experimental Psy-chology: Human Perception and Performance 1 (1975), 288-299; B. Fischoffand R. Beyth, “I Knew It Would Happen—Remembered Probabilities ofOnce-Future Things,” Organizational Behavior and Human Performance13 (1975); B. Fischoff, “For Those Condemned to Study the Past: Heuristicsand Biases in Hindsight,” in Judgment under Uncertainty: Heuristics & Bi-ases, D. Kahneman, P. Slovic, and A. Tversky (Eds.) (Cambridge, UK: Cam-bridge University Press, 1982), 201-208.
Gary L. Wells and Elizabeth F. Loftus, Fyewitness Testimony: PsychologicalPerspectives (Cambridge, MA: Harvard University Press, 1984), and EugeneWinograd, “What You Should Know about Eyewitness Testimony,” Conlem-porary Psychology 31, no. 5 (1986), 332-334.
Baruch Fischoff, “Debiasing in a Research Paper for the Office of Naval Re-search,” in Judgment under Uncertainty: Heuristics and Biases, D. Kahne-man, P. Slovic, and A. Tversky (Eds.) (Cambridge, UK: Cambridge UniversityPress, 1982), Chapter 31.
R.F. Pohl. and B. Gawlik, “Hindsight Bias and the Misinformation Effect: Sep-arating Blended Recollections from Other Recollection Types,” Memory 3,no.1 (March 1995), 21-55; D. Stahlberg and A. Maass, “Hindsight. Bias: Im-paired Memory or Biased Reconstruction?,” Ewropean Review of Social Psy-chology (1998).
R. Hastie and R.M. Dawes, Rational Choice in an Uncertain World: The Psy-chology of Judgment and Decision Making, Chapter 7, “Judging by Scenar-ios and Explanations” (Thousand Oaks, CA: Sage Publications, 2001); R.C.Shank and R. P. Abelson “Knowledge and Memory: The Real Story,” in Ad-vances in Social Cognition, vol. 8, R. Weyer (Ed.) (Hillsdale, NJ: LawrenceEarlbaum, 1995), 1-86.
R.E. Nisbett,E. Borgiada, R. Rich Crandall, and H. Reed, “Popular Induction:Information is Not Necessarily Informative,” Chapter 7 in Judgment underUncertainty: Heuristics and Biases, D. Kahneman, P. Slovic, and A. Tversky(Eds.) (Cambridge, UK: Cambridge University Press, 1982).
B. Russell, Philosophy (New York: Norton, 1927).Notes 485
59.60.61.62.63.
64.5. For a definitive explanation of Elliott wave theory, see R. Prechter, Elliott
66.
67.
68.
69.
70.
Gilovich, How We Know, 91.
Hastie and Dawes, Rational Choice, Chapter 7.
Ibid., 134.
Ibid., 135.
Ibid., 136, referring to N. Pennington and R. Hastie, “A Cognitive Theory ofJuror Decision Making: The Story Model,” Cardozo Law Review 13 (1991),519-557.
Gilovich, How We Know, 105.
Wave Principle: Key to Stock Market Profits (Gainesville, GA: New ClassicsLibrary, 1978).
M. Livo, The Golden Ratio: The Story of Phi, the World’s Most AstonishingNumber (New York: Broadway Books, 2002).
In a recent. book, Socionomics (New York: New Classics Library, 2003),Prechter describes an objective computerized version of EWP that has beenrealized as an expert system. Prechter asserts that the system has producedprofitable signals and should be able to distinguish real price histories frompseudo-price histories generated by a random process, though as of June2006, these results have not been made public.
This criticism would be nullified if an objective method of wave counting candemonstrate the ability to distinguish random data series from authenticprice data and predict better than an appropriate random benchmark. I re-cently learned that an objective EWP algorithm has been developed byPrechter’s firm and is currently being tested. This program, called EWAVES,which is discussed in Precther’s 1999 book The Wave Principle of HumanSocial Behavior and the New Science of Socionomics. Though EWAVES hasnot yet been tested as to its ability to discriminate real from random pricedata, if the program were able to do so, it would provide important confirma-tion that Elliott’s original insights about market behavior have merit. Thistest, however, would not establish the predictive power of the objective sig-nals. According to Prechter, a separate test of this assertion is also being car-ried out.
A falsifiable prediction is one that is sufficiently specific such that errors canbe clearly identified. For example, the success or failure of a signal issued byan objective rule can be determined. It either made or lost money. Forecastsof a continuous target variable, such as in a regression model, are typicallyevaluated by a loss function, such as squared or absolute error, where erroris equal to predicted minus actual.
Hulbert’s Financial Digest is an independent newsletter that evaluatesthe performance of other financial advisory newsletters. According to theJuly 2005 long-term performance ratings, the annualized returns earned bythe best known newsletter based on Elliott wave analysis, The ElliottWave Financial Forecast, using both its long and short signals is given as:+1.1 percent for 5 years +1.1 percent, —-28.2 percent for 10 years, —25.9 per-cent for 15 years, and -18.2 percent for the duration of the life of the ser-vice since 12/31/84. For the same time periods, Hulbert’s gives thefollowing annualized returns for the Dow Jones Wilshire 5000 index with
 486
71.
72.
73.
76.77.
78.
79.
80.
81.
82.83.
NOTES
dividends reinvested: -1.3 percent for 5 years, +10 percent for 10 years,and +10.7 percent for 15 years. It should be pointed out that a buy andhold in a market index may not be a suitable benchmark for a timing ser-vice that assumes both long and short positions. Per recommendations inChapter 1, a benchmark that represents the null hypothesis for testing thesignificance of a long/short signals returns is a random signal with thesame proportion of long/short positions.
Regarding the author's self-interest in book sales, making the message morecompelling serves this interest. Try as I might, I cannot completely eliminatebias from deciding what to say or not say. Ultimately, TA’s best protection isobjective evidence evaluated in an objective fashion. In Part Two of thisbook, evidence regarding the efficacy of 4,500 TA rules will be presented inas unvarnished and objective way I as know how.
David G. Myers, Intuition, Its Powers and Perils (New Haven: Yale Univer-sity Press, 2004), 116.
Research articles on the confirmation bias can be found in P.C. Wason, “Onthe Failure to Eliminate Hypotheses in a Conceptual Task,” Quarterly Jour-nal of Experimental Psychology 12 (1960), 129-140; K. Klayman and PC.Wason, “Reasoning about a Rule,” Quarterly Journal of Experimental Psy-chology 20 (1968), 273-281; H.J. Einhorn and R.M. Hogarth, “Confidence inJudgment: Persistence in the Illusion of Validity,” Psychological Review 85(1978), 395-416; J. Klayman and Y.-W. Ha, “Confirmation, Disconfirmation,and Information in Hypothesis Testing,” Psychological Review 94, no. 2(1987), 211-228.
. R. Park, Voodoo Science: The Road from Foolishness to Fraud (New York:
Oxford University Press, 2000).
. G.T. Wilson and D. Abrams, “Effects of Alcohol on Social Anxiety and Psy-
chological Arousal: Cognitive versus Pharmacological Processes,” CognitiveResearch and Therapy 1 (1975), 195-210.
Gilovich, How We Know, 50.
M. Jones and R. Sugden, “Positive Confirmation Bias in the Acquisition of In-formation,” Theory and Decision 50 (2001), 59-99.
L. Festinger, A Theory of Cognitive Dissonance (Palo Alto, CA: StanfordUniversity Press, 1957).
S. Plous, The Psychology of Judgment and Decision Making (New York: Mc-Graw-Hill, 1993), 23.
Practitioners who actually trade on their predictions do get clear feedbackon results, but as pointed out in this chapter this feedback can be diluted byother cognitive distortions (e.g., self-attribution bias).
A bull trap, or breakout failure, occurs when shortly after the breakout pene-tration occurs, prices repenetrate the breakout level in the opposite direc-tion. This has been referred to as a turtle soup signal because a contingent ofcommodity traders called the Turtles, who trade on the basis of breakouts,find themselves in hot water when breakout failures occur.
Gilovich, How We Know, 58.
Gilovich cites this from M. Gazzaniga, The Social Brain (New York: Harper& Row, 1985); R.E. Nisbett and T.D. Wilson, “Telling More Than We CanNotes 487
84.
88.89.90.
91.
92.
93.
94.
95.96.97.
98.
Know: Verbal Reports on Mental Process,” Psychological Review 84 (1977),231-259.
T. Plummer, Forecasting Financial Markets: The Psychological Dynamics ofSuccessful Investing, 3rd ed. (London: Kogan Page Limited, 1998). Plummerdescribes waves patterns similar to EWP but sometimes there are threewaves and sometimes five.
. Gilovich, How We Know, 53.86.87.
Ibid., 54.
C.G. Lord, L. Ross, and M.R. Lepper, “Biased Assimilation and AttitudePolarization: The Effects of Prior Theories on Subsequently ConsideredEvidence,” Journal of Personality and Social Psychology 22 (1979),228-241.
Gilovich, How We Know, 54.
Ibid., 54.
T. Gilovich, “Biased Evaluation and Persistence in Gambling,” Journal ofPersonality and Social Psychology 44 (1983), 1110-1126; T. Gilovich and C.Douglas, “Biased Evaluation of Randomly Determined Gambling Outcomes,”Journal of Experimental Social Psychology 22 (1986), 228-241.
This refers to the situation where a subjective method or some aspect of ithas been objectified. For example, a study of the head-and-shoulders patternis cited in Chapter 3 that provides an example of a subjective pattern that hasbeen made testable, at least in one form. The evidence from that study con-tradicts the efficacy of the head-and-shoulders pattern.
L. Ross, M.R. Lepper, and M. Hubbard, “Perseverance in Self Perceptionand Social Perception: Biased Attributional Processes in the DebriefingParadigm,” Journal of Personality and Social Psychology 32 (1975),880-892.
D.L. Jennings, M.R. Lepper, and L. Ross, “Persistence of Impressions of Per-sonal Persuasiveness: Perseverance of Erroneous Self Assessments Outsidethe Debriefing Paradigm,” unpublished manuscript, Stanford University,1980; M.R. Lepper, L. Ross, and R. Lau, “Persistence of Inaccurate and Dis-credited Personal Impressions: A Field Demonstration of Attributional Per-severance,” unpublished manuscript, Stanford University, 1979.
Failure would be if artificially created random charts cannot be discrimi-nated from authentic price charts and if EWP signals do not generate profitssuperior to a random signals. Even if EWP signals prove to be useless ontheir own, they have proven to have value in a multivariate model due to syn-ergies with other indicators.
Prechter, Socionomics, Chapter 4.
Gilovich, How We Know, 22.
L. Ross, M. Lepper, F. Strack, and J.L. Steinmetz, “Social Explanation and So-cial Expectation: The Effects of Real and Hypothetical Explanations uponSubjective Likelihood,” Journal of Personality and Social Psychology 35(1977), 817-829.
I should point out that in writing this book I have made myself vulnerable tothis very problem. Explaining my position may make me resistant to evi-dence that invalidates said positions.488
99.
100.
101.
102.
103.104.
106.
107.
108.
NOTES
This study was quoted by Michael Shermer in Why People Believe WeirdThings. Shermer also discusses the phenomenon of ideological immunity asdefined by social scientist Jay Stuart Snelson.
J.S. Snelson, “The Ideological Immune System,” Skeptic Magazine 1, no. 4(1993), 44-55.
C.A. Anderson, M.R. Lepper, and L. Ross, “The Perseverance of Social The-ories: The Role of Explanation in the Persistence of Discredited Informa-tion,” Journal of Personality and Social Psychology 39, no. 6 (1980),1037-1049.
Note, for purposes of this discussion the patterns, signals, and their ex-pected outcomes are those employed by subjective analysts. Because we aredealing with the problem of faulty perceptions, what matters is the analyst'ssubjective perceptions of illusory correlations.
According to the subjective interpretation of the analyst.
As reported in T. Gilovich, L. Allen, and H.M. Jenkins, “The Judgment ofContingency and the Nature of Response Alternatives,“ Canadian Journalof Psychology 34 (1980), 1-11; R. Beyth-Marom, “Perception of CorrelationReexamined,” Memory & Cognition 10 (1982), 511-519; J. Crocker, “Bi-ased Questions in Judgment of Covariation Studies,” Personality and So-cial Psychology Bulletin (1982), 214-220; H.M. Jenkins and W.C. Ward,“Judgments of Contingency between Responses and Outcomes,” Psycho-logical Monographs: General and Applied 79, no. 594 (1965), entire publi-cation; W.D. Ward and H.M. Jenkins, “The Display of Information and theJudgment of Contingency,” Canadian Journal of Psychology 19 (1965),231-241.
. A false positive signal occurs when the signal occurs but the expected out-
come does not occur. A false negative signal (failure to signal) occurs whenthe patterr/signal does not occur but the outcome the signal/pattern is pur-ported to predict occurs anyway.
In fact some studies show that people also consider the instances falling in thelower-right cell (correct identification of nonevents or nonopportunities), butby and large most attention is paid to those cases falling in the upper-left cell.Gilovich,. How We Know, 30. Here, Gilovich makes reference to an earlier ar-ticle: H.J. Einhorn and R.M. Hogarth, “Confidence in Judgment: Persistenceof the Illusion of Validity,” Psychological Review 85 (1977), 395-416.
The following studies discuss aspects of illusory correlation: R. Beyth-Marom, “Perception of Correlation Reexamined,” Memory & Cognition 10(1982), 511-519; J. Crocker, “Judgment of Covariation by Social Perceivers,”Psychological Bulletin 90 (1981), 272-292; J. Crocker, "Biased Questions inJudgment of Covariation Studies,” Personality and Social Psychology Bul-letin 8 (1982), 214-220; H.M. Jenkins and W.C. Ward, “Judgements of Contin-gency between Responses and Outcomes,” Psychological Monographs:General and Applied 79, no. 594 (1965), entire issue; J. Smedslund, “TheConcept of Correlation in Adults,” Scandinavian Journal of Psychology 4(1963), 165-173; W.D. Ward and H. H. Jenkins, “The Display of Informationand the Judgment of Contingency,” Canadian Journal of Psychology 19(1965), 231-241.Notes 489
109.
110.
111.112.113.
114.115.
116.117.
118.
119.120.
121.
122.
D.L. Jennings, T.M. Amabile, and L. Ross, “Informal Covariation Assessment:Data-Based versus Theory-Based Judgments,” Chapter 15 in Judgment un-der Uncertainty: Heuristics and Biases, D. Kahneman, P. Slovic, and A.Tversky (Eds.) (Cambridge, UK: Cambridge University Press, 1982), 220-222.The variables in these experiments were continuous, meaning that they canassume any value within a range, like temperature. Thus far, I have been re-ferring to binary variables that assume only two possible values: pattern pre-sent or pattern not present.
Jennings, Amabile, and Ross, “Informal Covariation Assessment.”Smedslund, “Concept of Correlation in Adults.”
J. Baron, Thinking and Deciding, 3rd ed. (Cambridge, UK: Cambridge Uni-versity Press, 2000), 177. Baron cites the following studies as supportive: H.Shaklee and D. Tucker, “A rule analysis of judgments of covariation betweenevents,” Memory and Cognition 8 (1980), 459-467; M.W. Schustak and R.J.Sternberg, “Evaluation of Evidence in Causal Inference,” Journal of Experi-mental Psychology; General 110 (1981); H. Shaklee and M. Mims, “Sourcesof Error in Judging Event Covariations,” Journal of Experimental Psychol-ogy: Learning, Memory and Cognition 8 (1982), 208-224; H.R. Arkes andA.R. Harkness, “Estimates of Contingency between Two Dichotomous Vari-ables,” Journal of Experimental Psychology: General 112 (1983), 117-135.For a discussion of the chi-square test, see R.S. Witte and J.S. Witte, Statis-tics, 7th ed. (Hoboken, NJ: John Wiley & Sons, 2004),. 469-492.
Significant in a statistical sense is a term that will be defined in Chapter 5.Gilovich, How We Know, 32.
Gilovich cites a series of studies that discuss problem of illusory correlationsin connection with asymmetric binary variables: R.H. Fazio, S.J. Sherman,and PM. Herr, “The Feature-Positive Effect in the Self-Perception Process:Does Not Doing Matter as Much as Doing?,” Journal of Personality and So-cial Psychology 42 (1982), 404-411; H.M. Jenkins and R.S. Sainsbury, “Dis-crimination Learning with the Distinctive Feature on Positive or NegativeTrials,” in Attention: Contemporary Theory and Analysis, D. Mostofsky(Ed.) (New York: Appleton-Century-Crofts, 1970); J. Newman, W.T. Wolff,and E. Hearst, “The Feature Positive-Effect in Adult Human Subjects,” Jour-nal of Experimental Psychology: Human Learning; and Memory 6 (1980),630-650; R. Nisbet and L. Ross, Human Inference: Strategies and Shortcom-ings of Social Judgment (Englewood Cliffs, NJ:: Prentice-Hall, 1980); P.C.Wason and P.N. Johnson-Laird, Psychology of Reasoning: Structure andContent (London: Batsford, 1965).
In fact vague pattern/signal definitions and lack of objective evaluation crite-ria facilitate the discovery of seemingly confirmatory instances.
Gilovich, How We Know, 21.
A. Tversky and I. Gati, “Studies in Similarity,” in Cognition and Categoriza-tion, E. Rosch and B. Loyd (Eds.) (Hillsdale, NJ: Lawrence Earlbaum, 1978).D. Bernstein, E.J. Clarke-Stewart, A. Roy, and C.D. Wickens, Psychology, 4thed. (Boston: Houghton Mifflin, 1997), 208.
Reference to this study can be found at www.azwestern.edu/psy/dgershaw/lol/ReinforceRandom.html.490 NOTES.
123. Bernstein, Clarke-Steward, Roy, and Wickens, Psychology, 208.
124. Gilovich, How We Know, 10.
125. www.datamininglab.con/.
126. N. Jegadeesh and S. Titman, “Returns to Buying Winners and Selling Losers:Implications for Stock Market Efficiency,” Journal of Finance 48 (1993),65-91; N. Jegadeesh and S. Titman, “Profitability of Momentum Strategies:An Evaluation of Alternative Explanations,” Journal of Finance 56 (2001),699-720.
127. FD. Arditti, “Can Analysts Distinguish between Real and Randomly Gener-ated Stock Prices?” Financial Analysts Journal 34, no. 6 (November/De-cember 1978), 70.
128. H.V. Roberts, “Stock Market ‘Patterns’ and Financial Analysis: Methodologi-cal Suggestions,” The Journal of Finance 14, no. 1 (March 1959), 1-10.
129. Ibid.
130. Roberts’ examples did not include volume data, which some claim is neces-sary for pattern identification.
131. Arditti, “Can Analysts Distinguish.”
132. J. Siegel, Stocks for the Long Run, 3rd ed. (New York: McGraw-Hill, 2002).
133. Ibid., 286-288.
134. There are specific methods in statistics such as the runs test that detect de-partures from random behavior.
135. Gilovich, How We Know.
136. Ibid., 14-15.
137. Hastie and Dawes, Rational Choice, 4.
138. D.G. Myers, Intuition: Its Powers and Perils (New Haven: Yale UniversityPress, 2004), 1.
139. Ibid., 4.
140. For example, if we always predict our grade on an exam to be higher than itturns out to be. An unbiased judgment can also be wrong, but its errors arenot always on the same side of the true answer—for example, if you pre-dicted a grade that was sometimes above and sometime below the grade youfinally received.
141. Uncertain situations are those whose outcomes are difficult or impossible topredict.
142. B. Malkiel, A Random Walk Down Wall Street (New York: W.W. Norton,1973).
143. There is a school of thought within probability theory that defines probabil-ity as the prior relative frequency, where relative frequency is the number ofpast events divided by the total number of instances in which the event couldhave taken place. We take up this topic in greater detail in Chapters 4 and 5.
144. This is true if events are independent. However, if events are dependent,then the occurrence of an event makes another more likely. For example, anearthquake is typically followed by a series of smaller quakes or aftershocks.Plane crashes are independent events—the occurrence of one has no bear-ing on the occurrence of another.
145. A. Tversky and D. Kahneman, “Judgement under Uncertainty: Heuristics andBiases,” Science 185 (1974), 1124-1131.Notes 491
146.
147.148.
151.
156.157.158.
A valid class trait is one that is truly indicative of class membership. Oftenthe most obvious traits give valid signals of class membership, but notalways.
Hastie and Dawes, Rational Choice, 116.
For a description of Bayes’ law see S. Kachigan, Statistical Analysis (NewYork: Radius Press, 1986), 476-482, and Wikipedia, the online free encyclope-dia, see reference to Bayes’ theorem, 8 pages.
. A conjunction refers to the logical operator AND; for example, the entity is
an animal AND a horse.
. A. Tversky and D. Kahneman,” Judgments of and by Representativeness,” in
Judgment Under Uncertainty: Heuristics and Biases, D. Kahneman, P.Slovic, and A. Tversky (Eds.) (Cambridge, UK: Cambridge University Press,1982).
The probability of a series of characteristics connected by the AND operatoris equal to the product of the probability of each item in the list. Since theprobability of any item is less than one, a product of terms all of which have avalue less than 1.0 becomes smaller as the list grows larger. For example if X,Y, and Z are independent characteristics, and the probability of someone pos-sessing characteristic X = 0.5 , the probability of Y = 0.5 and the probability ofZ = 0.5, then the probability someone possesses both X and Y = 0.5 X 0.5 or0.25, and the probability of X and Y and Z = 0.5 X 0.5 X 0.5 is 0.125.
. This example comes from S. Plous, The Psychology of Judgment and Deci-
sion Making (New York: McGraw-Hill, 1993), 111.
. Nassim Nicholas Taleb, Fooled by Randomness: The Hidden Role of Chance
in the Markets and in Life, (New York: Texere, 2001).
. This is the two-standard-deviation confidence interval for the proportion of
heads in 100,000 coin tosses.
5. With regard to people’s faulty expectations about how a tossed coin should
behave, Gilovich refers to the following studies: R. Falk, “The Perception ofRandomness,” Proceedings, 5th International Conference for the Psychol-ogy of Mathematics Education (Grenoble, France: 1981); W.A. Wagenaar,“Generation of Random Sequences by Human Subjects: A Critical Survey ofLiteratures,” Psychological Bulletin 77 (1972), 65-2; D. Kahneman and A.Tversky, “Subjective Probability: A Judgment of Representativeness,” inJudgment under Uncertainty: Heuristics and Biases, D. Kahneman, P.Slovic, and A. Tversky (Eds.) (Cambridge, UK: Cambridge University Press,1982), Chapter 3.
The probability of either sequence is .05°, or 0.015626.
Gilovich, How We Know, 15.
Ibid., 20.
CHAPTER 3 The Scientific Method and
1.
Technical Analysis
M. Shermer, Why People Believe Weird Things: Pseudoscience, Supersti-tion, and Other Confusions of Our Time (New York: W.H. Freeman, 1997).492 NOTES
2. C. Van Doren, A History of Knowledge: Past, Present, and Future (NewYork: Ballantine Books, 1991).
3. S. Richards, Philosophy & Sociology of Science: An Introduction (Oxford,
UK: Basil Blackwell, 1983), 45.
Ibid.
. Ibid., 189.
Ibid., 201.
As quoted by Shermer, Why People Believe.
Van Doren, History of Knowledge, 189.
Ibid..
Richards, Philosophy & Sociology of Science, 14.
D.J. Bennett, Logic Made Easy: How to Know When Language Deceives You
(New York: W.W. Norton, 2004), 30.
Ibid., 31.
Ibid., 99 and 108; referring to P.C. Wason, “Self-Contradictions,” in Thinking:
Readings in Cognitive Science (Cambridge, UK: Cambridge University
Press, 1977), 114-128; S.S. Epp, “A Cognitive Approach to Teaching Logic,”
DIMACS Symposium, Teaching Logic and Reasoning in an Illogical World,
Rutgers, The State University of New Jersey, July 25-26, 1996, available at
www.cs.cornell.edu/Info/People/gries/symposium/symp.htm.
14. The fact that a given set of observations can be consistent with (explainedby) more than one theory or hypothesis and perhaps an infinite number of al-ternative theories is a problem known in philosophical and scientific circlesas the underdetermination of theories problem. Because a multitude of hy-potheses can be consistent with the same set of