, POBox 7530, Incline Village, NV 89452.
. The MTA Body of Knowledge on Technical Indicators attributes the accumu-
lation distribution indicator to Marc Chaiken, as does R.W. Colby, The Ency-clopedia of Technical Market Indicators, 2nd ed. (New York: McGraw-Hill,2003). No specific publication is referenced. The accumulation distributionindicator proposed by Chaiken is similar to an indicator developed by LarryWilliams, The Secrets of Selecting Stocks for Immediate and Substantial510
26.
27.28.
29.
31.32.33.34.
35.3. This formulation of CNHL is as presented in Technical Indicators Reference
37.
38.
39.
40.
NOTES
Gains (Carmel Valley, CA: Conceptual Management, 1971), later republishedby Windsor Books in 1986. Williams’s indicator compares the open price tothe close as the numerator of the range factor. Chaiken compares the closeprice to the midprice of the daily range.
As presented in Technical Indicators Reference Manual for AIQ TradingExport Pro, a software product of AIQ Systems, PO Box 7530, Incline Village,NV 89452.
Ibid.
Attribution of NVI to Norman Fosback can be found in the MTA web siteBody of Knowledge and in S.B. Achelis, Technical Analysis from A to Z(New York: McGraw-Hill, 2001), 214.
Paul Dysart is credited with the creation of both positive- and negative-volume indexes by C.V. Harlow, An Analysis of the Predictive Value of StockMarket “Breadth” Measurements (Larchmont, NY: Investors Intelligence,1968).
. According to Fosback’s study, the NVI identified the bullish phase of the pri-
mary trend with a probability of 0.96 over the time period 1941 to 1975. Thiscompares to a base rate bull-trend probability of 0.70 over that time period.N.G. Fosback, Stock Market Logic: A Sophisticated Approach to Profits onWall Street (Dearborn, MI: Dearborn Financial Publishing, 1993), 120.
Ibid.
Harlow, Analysis of the Predictive Value.
R.W. Colby, The Encyclopedia of Technical Market Indicators, 2nd ed. (NewYork: McGraw-Hill, 2003).
Fosback, Stock Market Logic, 76-80.
Manual for AIQ Trading Export Pro, a software product of AIQ Systems.C.R. Nelson, The Investor's Guide to Economic Indicators (New York: JohnWiley & Sons, 1987), 129. The author points out that a positive spread (i.e.,long-term rates higher than short-term rates) is often a bullish indicator forfuture changes in stock prices, especially for stock indexes that are not infla-tion adjusted.
Moody’s Investors Service is a widely utilized source for credit ratings, re-search, and risk analysis for corporate debt instruments. Information can befound at www.moodys.com.
Aaa is the designated definition for the highest quality of corporate bond. Se-curities carrying this rating are judged to have the smallest degree of invest-ment risk. The principal is secure and interest payments are protected by arobust margin. Although there is always a chance that various protective ele-ments can change, these changes are easy to visualize. The Aaa rating meansthat it is highly unlikely for any drastic changes to occur that would erodethe fundamentally strong positions on these securities. Definition provided:www.econoday.com/clientdemos/demoweekly/2004/Resource_Center/aboutbondmkt/morefixed.html.
Baa bonds are considered medium-grade obligations. They are neither highlyprotected nor poorly secured. Interest payments and security on the princi-pal appear adequate for the present, but there may be certain protective ele-Notes Sit
41.42.43.44,45.46.47.
48.
ments lacking. These securities have the possibility of being unreliable overany great length of time. These bonds lack outstanding investment character-istics, making them attractive to speculators at the same time. www.econo-day.convVclientdemos/demoweekly/2004/Resource_Center/aboutbondmkt/morefixed.html.
For a full discussion of alternative trend analysis methods and rules derivedfrom them see Kaufman, Chapters 5 through 8.
J. Hussman, “Time-Variation in Market Efficiency: A Mixture-of-DistributionsApproach,” available at www.hussman.net/pdf/mixdist.pdf.
M.J. Pring, Technical Analysis Explained, 4th ed. (New York: McGraw-Hill,2002).
Kaufman, 394-401.
Heiby, Stock Market Profits.
C. Alexander, Market Models: A Guide to Financial Data Analysis (NewYork: John Wiley & Sons, 2001), Chapter 12.
R.F. Engle and C.W.J. Granger, “Co-integration and Error-Correction: Repre-sentation, Estimation and Testing,” Econometrica 55 (1987), 251-276.Alexander, Market Models, 324-328, 353-361. One type of test used to estab-lish the fact that a time series is stationary, called the unit-root test, is de-scribed.
. The indicator is a time series of the errors (residuals) of the linear relation-
ship between the two series.Fosback, Stock Market Logic.
. In 1983 the author, in association with Raden Research Group, developed a
time series operator called YY that was similar in concept to cointegrationanalysis with the exception that YY values were normalized by the stan-dard error of the regression and no explicit test was made for residual sta-tionarity. YY’s development was inspired by an article by Arthur Merrill,“DFE: Deviation from Expected (Relative Strength Corrected for Beta),”Market Technician Journal (August 1982), 21-28. The YY-based indicatorswere used as candidate inputs for a variety of predictive modeling projects.One example was a sentiment indicator based on advisory sentiment on T-bonds. The predictive power of the raw consensus information was im-proved by a YY transformation that used past market changes as theregressor variable.
CHAPTER 9 Case Study Results and
1.
the Future of TA
This is an extreme rule, Type 12, based on input series 28 (10-day moving av-erage of the up/down volume ratio), using threshold offset of 10 (upper = 60,lower = 40) and a channel normalization look-back of 30 days. Type 12 isshort although the indicator is above the upper threshold, long at all othertimes.
The bias-compensated p-values were this bad (worse than p = 0.5) in part be-cause half of the 6,402 rules were inverse rules. This guaranteed that half ofSS)
10.
ll.12.
13.14.
NOTES
the competing rules would have a negative return, and, hence, have nochance of being a true competitor. In effect the test had twice as many rulesbeing tested as were truly in competition. This makes your test overly con-servative. That is to say, the chance of a Type-! error (false reject of the null)was lower than 0.10. It also means that the power of the test (ability to cor-rectly reject the null) was less than it would have been had the inverse rulesbeen left out of the competition. However, as explained in Chapter 1, the in-verse rules were included because it was not known if the traditional TA in-terpretation was correct.
J.P. Romano and M. Wolf, “Stepwise Multiple Testing as Formalized DataSnooping,” Econometrica 73, no. 4 (July 2005), 1237-1282.
H. White, “A Reality Check for Data Snooping,” Econometrica 68, no 5 (Sep-tember 2000).
W.R. Ashby, Introduction to Cybernetics (New York: John Wiley & Sons,1963). In fact, Ashby was referring to the problem of system control ratherthan system prediction, though there are obvious similarities between thetwo problems.
P-H Hsu and C-M Kuan, “Reexamining the Profitability of Techni:with Data Snooping Checks,” Journal of Financial Econometrics 3,(2005), 606-628. Note the authors are using the term data snooping as I usethe term data mining. They are checking and making adjustment for data-mining bias using a version of White's reality check, without the enhance-ments suggested by Wolf and Romano (see Chapter 6).
Ibid.
Eui Jung Chang, Eduardo José Aratjo Lima, and Benjamin Miranda Tabak,“Testing for Predictability in Emerging Equity Markets,” Emerging MarketsReview 5, issue 3 (September 2004), 295-316; Malay K. Dey, “Turnover andReturn in Global Stock Markets,” Emerging Markets Review 6, issue 1 (April2005), 45-67; Wing-Keung Wong, Meher Manzur, and Boon-Kiat Chew, “HowRewarding Is Technical Analysis? Evidence from Singapore Stock Market,”Applied Financial Economics 13, issue 7 (July 2003), 543; Kalok Chan andAllaudeen Hameed, “Profitability of Momentum Strategies in the Interna-tional Equity Markets,” Journal of Financial & Quantitative Analysis 35,issue 2 (June 2000), 153.
C. Alexander, Market Models: A Guide to Financial Data Analysis (NewYork: John Wiley & Sons, 2001), 347-387.
J.F. Ehlers, Cybernetic Analysis for Stocks and Futures: Cutting-Edge Tech-nology to Improve Your Trading (Hoboken, NJ: John Wiley & Sons, 2004),1-10.
Hsu and Kuan, “Reexamining the Profitability.”
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Sta ‘al Learn-ing: Data Mining, Inference and Prediction (New York: Springer, 2001),347-370.
Ibid., 283-290.
J.R. Wolberg, Expert Trading Systems: Modeling Financial Markets withKernel Regression (New York: John Wiley & Sons, 2000).
  
 Notes 513
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
J.F. Elder IV and D.E. Brown, “Induction and Polynomial Networks,” in Net-work Models for Control and Processing, M. D. Fraser (Ed.) (Bristol, UK: In-tellect Ltd, 2000).
W. Banzhaf, P. Nordin, R.E. Keller, and F.W. Francone, Genetic Program-ming: An Introduction; On the Automatic Evolution of Computer Pro-grams and Its Applications (San Francisco: Morgan Kaufmann, 1998).
N. Cristianini and J. Taylor-Shawe, An Introduction to Support Vector Ma-chines (New York: Cambridge University Press, 2000).
Complexity can mean different things in data mining. In the case of data min-ing for TA rules, it refers to the number of separate parameters and condi-tional statements required to define the rule. Higher complexity rules havemore parameters and conditions. A dual moving-average crossover rule hasone condition and two parameter values. The condition is: If short-term mov-ing average > than long-term moving average, then hold long position, elsehold short position. The two parameters are look-back spans for the short-term and long-term moving averages.
N. Gershenfeld, The Nature of Mathematical Modeling (New York: Cam-bridge University Press, 1999), 147.
If shorter-term moving average > longer-term moving average, then hold longposition, else hold short position.
There are more intelligent and efficient search methods than testing everypossible combination. These include genetic algorithm searching, simulatedannealing, guided searching based on gradient descent, and so forth. Two ex-cellent treatments of these topics can be found in Katz and McCormick, andPardo. See earlier footnote references.
In this example, the simple two moving-average crossover rule is a subset ofamore complex set of rules of greater complexity.
W.A. Sherden, The Fortune Sellers: The Big Business of Buying and SellingPredictions (New York: John Wiley & Sons, 1998), 1.
J.S. Armstrong, “The Seer-Sucker Theory: The Value of Experts in Forecast-ing,” Technology Review (June/July 1980), 16-24.
A. Cowles, “Can Stock Market Forecasters Forecast?” Econometrica 1(1933), 309-324.
In February 2005, Citigroup terminated its technical research department.Prudential Securities disbanded its technical research department later inthe year.
N.G. Fosback, Stock Market Logic: A Sophisticated Approach to Profits onWall Street (Dearborn, MI: Dearborn Financial Publishing, 1993), 80.
B.I. Jacobs and K.N. Levy, Equity Management: Quantitative Analysis forStock Selection (New York: McGraw-Hill, 2000), 27-37.
The return reversal effect is similar to the residual reversal effect, which isbased on cointegration analysis.
J. Felsen, Cybernetic Approach to Stock Market Analysis versus EfficientMarket Theory (New York: Exposition Press, 1975); J. Felsen, Decision Mak-ing under Uncertainty: An Artificial Intelligence Approach (New York:CDS Publishing Company, 1976).314
31.
32.
33.
34.
35.
36.37.
38.
39.
40.
NOTES
Two firms with which I had contact in the late 1970s that were using statisti-cal pattern recognition and adaptive learning networks were Braxton Corpo-ration in Boston, Massachusetts, and AMTEC Inc. in Ogden, Utah. I startedRaden Research Group, Inc. in 1982.
A.M. Safer, “A Comparison of Two Data Mining Techniques to Predict Abnor-mal Stock Market Returns, /ntelligent Data Analysis 7, no. 1 (2003), 3-14; G.Armano, A. Murru, and F. Roli, “Stock Market Prediction by a Mixture of Ge-netic-Neural Experts,” International Journal of Pattern Recognition & Ar-tificial Intelligence 16, no. 5 (August 2002), 501-528; G. Armano, M.Marchesi, and A. Murru, “A Hybrid Genetic-Neural Architecture for Stock In-dexes Forecasting,” Information Sciences 170, no. 1 (February 2005), 3-33;T. Chenoweth, Z.O. Sauchi, and S. Lee, “Embedding Technical Analysis intoNeural Network Based Trading Systems,” Applied Artificial Intelligence 10,no. 6 (December 1996), 523-542; S. Thawornwong, D. Enke, and C. Dagli,“Neural Networks as a Decision Maker for Stock Trading: A Technical Analy-sis Approach,” International Journal of Smart Engineering System Design5, no. 4 (October/December 2003), 313-325; A.M. Safer, “The Application ofNeural-Networks to Predict Abnormal Stock Returns Using Insider TradingData,” Applied Stochastic Models in Business & Industry 18, no. 4 (October2002), 380-390; J. Yao, C.L. Tan, and H-L Pho, “Neural Networks for Techni-cal Analysis: A Study on KLCI,” International Journal of Theoretical & Ap-plied Finance 2, no. 2 (April 1999), 221-242; J. Korczak and P. Rogers, “StockTiming Using Genetic Algorithms,” Applied Stochastic Models in Business& Industry 18, no. 2 (April 2002), 121-135; Z. Xu-Shen and M. Dong, “CanFuzzy Logic Make Technical Analysis 20/20?” Financial Analysts Journal60, no. 4 (July/August 2004), 54-75; J.M. Gorriz, C.G. Puntonet, M. Salmeron,and J.J. De la Rosa, “A New Model for Time-Series Forecasting Using RadialBasis Functions and Exogenous Data,” Neural Computing & Applications13, no. 2 (2004), 100-111.
PE. Meehl, Clinical versus Statical Prediction: A Theoretical Analysis anda Review of the Evidence (Minneapolis: University of Minnesota Press,1954).
R. Hastie and R.M. Dawes, Rational Choice in an Uncertain World: The Psy-chology of Judgment and Decision Making (Thousand Oaks, CA: Sage Pub-lications, 2001), 55.
J. Sawyer, “Measurement and Prediction, Clinical and Statistical,” Psycholog-ical Bulletin 66 (1966), 178-200.
Hastie and Dawes, Rational Choice, 55.
C. Camerer, “General Conditions for the Success of Bootstrapping Models,”Organizational Behavior and Human Performance 27 (1981), 411-422.
J.E. Russo and P.J.H. Schoemaker, Decision Traps: The Ten Barriers to Bril-liant Decision-Making and How to Avoid Them (New York: Doubleday/Cur-rency, 1989).
L.R. Goldberg, “Simple Models or Simple Processes? Some Research on Clin-ical Judgments,” American Psychologist 23 (1968), 483-496.
R.M. Dawes, “The Ethics of Using or Not Using Statistical Prediction Rules,”an unpublished paper written at Carnegie Mellon University.Notes 515
Al.
42.
43.44.45.
46.
47.48.
49.
52.53.
55.
56.
PE. Meehl, “Causes and Effects of My Disturbing Little Book,” Journal ofPersonality Assessment 50 (1986), 370-375.
W.M. Grove and PE. Meehl, “Comparitive Efficiency of Informal (Subjective,Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Proce-dures: The Clinical-Statistical Controversy,” Psychology, Public Policy, andLaw 2 (1996), 293-323.
Dawes, “Ethics of Statistical Prediction Rules.”
Hastie and Dawes, Rational Choice, 54.
C.F. Camerer and E.J. Johnson, “The Process-Performance Paradox in Ex-pert Judgment: How Can Experts Know So Much and Predict So Badly?”Chapter 10 in Research on Judgment and Decision Making: Currents, Con-nections and Controversies, W.M. Goldstein and R.M. Hogarth (Eds.), Cam-bridge Series on Judgment and Decision Making (Cambridge, UK: CambridgeUniversity Press, 1997).
PE. Tetlock, Expert Political Judgment: How Good Is It? How Can WeKnow? (Princeton, NJ: Princeton University Press, 2005).
Ibid., 77.
G.F. Loewenstein, E.U. Weber, C.K. Hsee, and N. Welch, “Risk as Feelings,”Psychological Bulletin 127, no. 2 (2001), 267-287.
J.R. Nofsinger, "Social Mood and Financial Economics,” Journal of Behav-ioral Finance 6, no. 3 (2005), 144-160.
Ibid., 151.
. P. Slovic, M. Finucane, E. Peters, and D. MacGregor, “The Affect Heuristic,”
Chapter 23 in Heuristics and Biases: The Psychology of Intuitive Judg-ment, T. Gilovich, D. Griffin, and D. Kahneman (Eds.) (Cambridge, UK: Cam-bridge University Press, 2002), 397-420.
Ibid., 416.
J.P. Forgas, “Mood and Judgment: The Affect Infusion Model (AIM),” Psycho-logical Bulletin 117, no. 1 (1995), 39-66.
Nofsinger, “Social Mood,” 152.
Some books on the general topic of TA Indictors: S.B. Achelis, TechnicalAnalysis from A to Z, 2nd ed. (New York: McGraw-Hill, 2001); E.M. Azoff,Neural Network Time Series Forecasting of Financial Markets (New York:John Wiley & Sons, 1994); R.W. Colby, The Encyclopedia of Technical MarketIndicators, 2nd ed. (New York: McGraw-Hill, 2003); P.J. Kaufman, New Trad-ing Systems and Methods, 4th ed. (Hoboken, NJ: John Wiley & Sons, 2005);J.F. Ehlers, Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSPTechnology to Improve Your Trading (Hoboken, NJ: John Wiley & Sons,2004).
D. Pyle, Data Preparation for Data Mining (San Francisco: Morgan Kauf-mann, 1999); T. Masters, Neural, Novel & Hybrid Algorithms for Time Se-ries Prediction (New York: John Wiley & Sons, 1995); T. Masters, PracticalNeural Net Recipes in C++ (New York: Academic Press, 1993); LH. Wittenand E. Frank, Data Mining: Practical Machine Learning Tools and Tech-niques, 2nd ed. (San Francisco: Morgan Kaufmann, 2005); E.M. Azoff, NeuralNetwork Time Series Forecasting of Financial Markets (New York: JohnWiley & Sons, 1994).516 NOTES
57. Masters, Neural, Novel & Hybrid Algorithms.
58. Pyle, Data Preparation.
59. S.M. Weiss and N. Indurkhya, Predictive Data Mining—a Practical Guide(San Francisco: Morgan Kaufmann, 1998).
60. Pyle, Data Preparation, xviii.
61. Masters, Neural, Novel & Hybriad Algorithms, 2.
62. Weiss and Indurkhya, Predictive Data Mining, 21, 57.
63. R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology(New York: Penguin Group, 2005). 
Index
Affirming the consequent, 112-121,170, 219Alexander reversal filter, 18Alternative hypothesis:in case study, 394vs. null hypothesis, 221-225Anchoring, behavioral financetheory and, 358-361Arbitrage pricing theory (APT), 341limits of, 356-357rational pricing levels and, 343,344, 347-348Arditti, FD., 84Argument, defined, 112Aristotle, 104-105, 111, 122Armstrong, Scott, 462-463Artificial trading rules (ATRs):data-mining bias experimentsand, 291-293data mining’s soundness and,309-311differing expected returnsexperiment, 307-309equal merit experiment, 293-307Assumed execution prices, 29-30Asymmetric binary variables,78-80Availability heuristic, 87-88
Back testing, 15-16. See also Datamining; Data-mining biascomputer-intensive methods
applied to single rule,241-243data mining versus single-rule,268-271position bias and market trendcomponents, 23-27Bacon, Francis, 125-126Barberis, Shleifer, and Vishny (BSV)hypothesis, 372-374, 376Baseball statistics, data-miningbias and, 258Base rate fallacy, 91Bayes’ theorem, 90, 346Beads in a box sampling example,172-186sampling distribution and, 203statistical theory elements and,186-190Behavioral finance theory, 355-378foundations of, 356-357psychological factors, 357-362scientific hypotheses of, 371-378self-organizing Ponzi schemesand, 370-371social factors, 362-369Behaviorism, 81-82Beliefs:belief inertia, in behavioralfinance theory, 375contrasted to knowledge, 1-5erroneous, 33-38Bell curve, 211-2133518
Bellman, Richard, 465Benchmarks, 22-29detrending and, 27-28effect of position bias andmarket trend on, 23-27using logs instead ofpercentages, 28-29Best-performing rule:data-mining bias and, 263-264,278-287defined, 256Biases, see Subjective technicalanalysisBible Codes, data-mining bias and,258-260Binary rules, 15-31inputs, outputs, signals, 16,17look-ahead bias, 29-30subjective technical analysisand, 15-16, 72-78thresholds and:fixed, 16-19multiple, 19-21trading costs, 31traditional and inverse rules,21-22use of benchmarks in evaluation,22-29Black, Fischer, 345Bloom, Norman, 258Bootstrap sampling, 215, 235-238applied to back test of singlerule, 241-242confidence intervals and,248-250contrasted to Monte Carlomethod, 235data-mining bias solutions and,320-330Bostian, David, 409Bounded rationality, principle of,42
INDEX
Box Theory, 36-37Bulkowski, T.N., 161
Camerer, C., 466-467
Capital asset pricing model(CAPM), 340-341
Case study, see Rule data miningcase study
Categorical syllogisms, 112-115
Central Limit Theorem,211-213
Central tendency measurements,191
Chaiken, Marc, 409
Chang, Kevin, 151-161
Channel breakout operator (CBO),397-398, 419-420
Channel-normalization operator(CN), 401-403
Chart analysis, misplaced faith in,82-86
representativeness heuristic and
illusory trends and patterns,93-101
Clustering illusion, 99-100, 362
Cognitive content, of knowledgeand beliefs, 2-5
Cognitive psychology, seeSubjective technicalanalysis
Cohen, P.R., 282
Cointegration, 434-436
Commodity and currency hedgerisk transfer premium, 379,380-384
Complex rules, not in case study,392, 452-461
Computer-based samplingmethods, 215, 234-243
human interaction with, 464-465,
471-473
Conditional probability (p-value),231-233Index
Conditional syllogisms, 115-116invalid forms, 118-121valid forms, 117-118Confidence interval, 243, 245-247defined, 216generating with bootstrapmethod, 248-250hypothesis test contrasted to,250-252sampling distribution and,247-248for TT-4-91 rule, 252-253Configural-thinking problems,42-45Confirmation bias, 62-71behavioral finance theory and,358belief survival and, 69contradictory evidence and,67-69perception and motivation and,62-63questions and search and,63-64subjective methods and, 69-71vague evidence and, 66-67value evaluation criteria and,64-66Conjunction fallacy, 91-93Conservatism bias:behavioral finance theory and,357-358BVS hypothesis and, 372-374DHS hypothesis and, 375-376Consistency, rule of, 111-112Control illusion, 50Cooper, Michael, 353, 384Correlations, illusory, 72-82asymmetric binary variables and,78-80behavioral psychology and,81-82binary variables and, 72
519
hidden or missing data and, 80possible outcomes of binaryvariables and faulty
intuition, 73-78Cowles, Alfred, 462-463Cumulative sum price-volume
functions, 407-413Cur