se of dimensionality, 465Cutler, D., 349
Daniel, Hirshleifer, andSubrahmanyam (DHS)hypothesis, 375-376
Darvas, Nicholas, 36-37
Data distribution of the population,206-207
Data distribution of the sample,206-207
Data mining, see also Data-miningbias; Rule data mining casestudy
confirmation bias and, 64
defined, 171, 255, 256, 264
as multiple comparisonprocedure, 264-265
soundness of premise of,309-311
as specification search, 265-267
Data-mining bias, see also Datamining; Rule data miningcase study
anecdotal examples of, 256-261causes of, 263-264, 278-287defined, 255-256experimental investigations of,291-320ATRs and, 309-311variable merit rules and,311-820factors determining magnitudeof, 287-291objective technical analysis and,267-272520
Data-mining bias (Continued)solutions for dealing with,320-330statistical inference and, 272-278Data-snooping bias, 390-391, 449Dawes, R.M., 468-469Declarative statements, beliefs andknowledge and, 2-5Deductive logic, 112-121categorical syllogisms, 112-115conditional syllogisms, 115-121Denial of the consequent, 112-121,170, 219, 221Descartes, Rene, 126Detrending, 19, 27-28in case study, 391-392proof of value of, 475-476Diaconis, Percy, 260Directional modes, 21Discernible-difference test,cognitive content and, 3-4Divergence rules, tested in casestudy, 430-440Drosnin, Michael, 259, 260Dysart, Paul, 411, 412-413
Efficient Markets Hypothesis
(EMH), 331, 334-355
assumptions of, 343-345
flaws in, 345-348
consequences of marketefficiency, 335-336
efficient markets defined,334-335
empirical challenges to, 349-355
evidence of, 337-342
false notions of, 337
falsification and, 141-143
information content ofhypotheses and, 137-138
nonrandom price motion and,378-385
paradoxes of, 342-343
INDEX
Ehlers, J.F., 399, 400, 452Einstein, Albert, 108Elder, John, 83Elfron, B., 235Elliott Wave Principle (EWP),60-61, 69-70, 137Endowment bias, 375-376Engle, R.F, 434, 436Enumeration, deduction by,122-124Equity market risk premium, 379Error-correction model, 434-435Errors, unbiased and systematic,272-274Euler circles, 114-115Evidence, vague and contradictory,64-69Evidence-based technical analysis(EBTA), see also Rule datamining case studyacademic findings and, 8-9defined, 1differs from technical analysis,6-8future and, 463-465, 471-473Expected performance, defined,255Extended Middle, Law of, 111Extreme values and transitions (Erules), tested in case study,420-430
Falsifiable forecast, 47, 58Falsificationism, 130-136information content and,136-139scientific responses to, 139-143Falsification of the consequent,219-221Fama, Eugene, 339, 354-355Feedback, behavioral financetheory and, 366-371Felsen, J., 464Index
Festinger, L., 63Finucase, M., 470Flip-flop(s), 20, 361Forgas, Joseph, 471Fosback, Norman, 411, 412-413,416, 436, 464Fosback Index, 436French, Kenneth, 354-355Frequency distribution, 179-181,190-191measuring central tendency,191variability (dispersion)measurements, 192-193Futures markets, 380-384
Galileo Galilei, 106-107Gambler's fallacy, 99-100, 362Gilovich, Thomas, 38, 68, 80, 85Goldberg, L.R., 467
Gould, Stephen Jay, 58Granger, C.W.J., 434, 436Granville, Joseph, 408Grossman, S.J., 348, 378Grove, W.M., 468
Hall, J., 3-4Hansen, Peter, 329Harlow, C.V., 413Hastie, R., 468-469Hayes, T., 21Head-and-shoulders pattern,objectification example,151-161Heiby, W.A., 403, 433Herd behavior, 362-369Heuristic bias, 41, 86-87availability heuristic, 87-88heuristic defined, 86representativeness heuristic,88-93illusory trends and patternsand. 93-101
521
Hindsight bias, 50-58Hong and Stein (HS) hypothesis,376-377Hsu, P.-H., 451, 455Hulbert Digest, 48Hume, David, 126-128Hussman, J., 430Hypotheses, see also Hypothesistestsalternative, 221-225, 394development of concept of,128-130falsifiability and, 130-143null, 139, 166-172, 221-225,393Hypothesis tests:computer-intensive methods ofsampling distributiongeneration, 234-243confidence intervals contrastedto, 250-252defined, 217-218informal inference contrasted,218-223mechanics of, 227-234rationale of, 223-227Hypothetico-deductive method:stages of, 144-147technical analysis example,145-146
Illusory correlations, 72-82asymmetric binary variables and78-80behavioral psychology and,81-82binary variables and, 72hidden or missing data and, 80possible outcomes of binaryvariables and faultyintuition, 73-78Illusory knowledge, 41-42, 49-50Imitative behavior. 362-369522
Immediate practical future:in case study, 393defined, 186-187Indicators, in case study,405-417interest rate spreads, 417market breadth indicators,413-416price and volume functions,406-413prices-of-debt instruments frominterest rates, 416-417Indicator scripting language (ISL),403-405Inductive logic, 121-124. See alsoStatistical inferenceIndurkhya, N., 472Information:biased interpretation of private,375-376biased interpretation of public,372-374, 376cascades of, 364-365discovery premium, 380stale, 340, 349, 351-354In-sample data, defined, 256Interest rate spreads, in case study,417Interval estimates, see ConfidenceintervalInverse rules, 21-22
Jacobs, B.I., 464
Jegadeesh, N., 352-353
Jensen, D.D., 282
Judgment heuristics, see Heuristicbias
Kahn, Ronald, 258
Kahneman, Daniel, 41, 86, 88,91-92, 345
Kelly Criterion, 348
Kestner. Lars. 383
INDEX
Knowledge:contrasted to beliefs, 1-5erroneous beliefs and, 33-38illusory, 41-42, 49-50scientific, 108-110
Kuan, C.-M., 451, 455
Kuhn, Thomas, 150
Lane, George, 403Law of Large Numbers, 96-99, 179,194-195, 209Law of Noncontradiction, 111-112Law of the Excluded Middle,111Leinweber, David J., 260-261Levy, K.N., 464Liquidity premium, 379-380,384-385Lo, Andrew, 341, 378Log-based market returns, 28-29Logic:consistency and, 111-112deductive, 112-121inductive, 121-124propositions and arguments, 112Long-short position bias, trendsand, 23-27Look-ahead bias, 29-30Lowenstein, G.F., 470Lowry, Lyman, 414
MacGregor, D., 470
MacKinlay, A. Craig, 341, 378
Magee, John, 333
Malkiel, Burton, 87
Market breadth indicators, in casestudy, 413-416
Market net trend, position biasand, 23-27
Markowitz, H.M., 323
Markowitz/Xu data miningcorrection factor, 321,323-324Index
Masters, Timothy, 238-239, 329,447, 472Meehl, Paul, 466, 468Mencken, H.L., 59Metcalfe, Janet, 45Mill, John Stuart, 132Momentum, predictability studiesand, 352-353Monte Carlo permutation method(MCP), 215, 238-240. Seealso Artificial trading rules;Rule data mining case studyapplied to back test of singlerule, 242-243contrasted to bootstrap method,235data-mining bias solutions and,320-330not used to generate confidenceintervals, 248, 250Mosteller, Frederick, 260Moving-average operator (MA), 18,398-401Moving average price-volumefunctions, 407-413Mt. Lucas Management Index(MLM), 382-384Multiple comparison procedure(MCP):data mining as, 264-265randomness and, 281-285Multiple threshold rules, 19-21Murphy, John, 333, 391
Narratives, see Second-handinformation biasNofsinger, John R., 50, 151,470Noise traders, 343, 345, 347Noncontradiction, Law of, 111-112Nonrandom price motion theories:behavioral finance theory and,355-378
523
foundations of, 356-357psychological factors, 357-362scientific hypotheses of,371-378self-organizing Ponzi schemesand, 370-371social factors, 362-369in context of efficient markets,378-385Efficient Markets Hypothesisand, 331, 334-355assumption flaws, 345-348assumptions of, 343-345consequences of marketefficiency, 335-336efficient markets defined,334-335empirical challenges to,349-355evidence of, 337-342false notions of, 337information content ofhypotheses and, 137-138nonrandom price motion and,378-385paradoxes of, 342-343importance of scientific theory,331-334Nonstationary statistical problems,174, 188Nonuniversal generalizations,122Noreen, Eric, 235Normal distribution, 211-213Null hypothesis, 139vs. alternative hypothesis,221-225in case study, 393in statistical analysis, 166-172
Objective binary signaling rules,see Binary rulesObjective reality, 107-108524
Objective technical analysis, 5-8confirmation bias and, 64data mining and, 267-272erroneous knowledge and,
261-264faulty inferences and, 36subsets of, 161-163Objectivity, of scientificknowledge, 108-109Observed performance, defined,256
Occam’s Razor, 107-108, 225-227
Optimism bias, 48, 361
Oscillators, 19
Osler, Carol, 151-161
Out-of-sample data, defined, 256
Out-of-sample performance
deterioration, 261-264
Out-of-sample testing, as data-
mining bias solution, 320,321-323
Overconfidence bias, 45-58
behavioral finance theory and,361
control illusion, 50
hindsight bias, 50-58
knowledge illusion, 49-50
manifestations of, 47
optimism bias, 48
self-attribution bias, 48-49
Parameter estimation:defined, 217-218interval estimates, 218, 243,245-253point estimates, 218, 243-245Parameter optimization, 266Pattern renaming, 64-65Perkins, David, 71Peters, Edgar, 341, 470Philosophy of science, 124-143distinguishing science frompseudoscience, 134-136
INDEX
history of, 125-136hypotheses and, 136-143scientific method and, 124-125Plato, 125Point estimates, 218, 243-245Ponzi schemes, self-organizing,370-371Popper, Karl, 130-136, 143Population mean, 191Population/population parameter:in case study, 393in statistical inference problem,186-188Practical significance, in casestudy, 394Prechter, Robert, 60, 61Predictions, 3confirmatory evidence and,218-219in hypothesis testing context,133-134immediate practical future and,186-187Predictive rules, scientific methodand, 110Price and volume functions, as casestudy indicator, 406-413Price predictability, EMH and,349-353Prices-of-debt instruments frominterest rates, as case studyindicator, 416-417Price volatility, Efficient MarketsHypothesis and, 349Principle of bounded rationality, 42Principle of simplicity, 107-108,225-227Pring, M.J., 431Probability, 193. See also Samplinginductive logic and, 121-122Law of Large Numbers, 194-195probability density function,167-172Index
probability distribution, 197-202relative frequency distribution,181-186, 197-202theoretical versus empirical, 196Programmability criterion,objective technical analysisand, 16Propositions, defined, 112Prospect Theory, 345Psychological factors, ofbehavioral finance theory,357-362P-value, 231-234Pyle, D., 472
Quantification, of scientificknowledge, 109Quants, 104
Randomization methods, seeBootstrap sampling; MonteCarlo permutation method
Randomness, data-mining bias and,263-264, 278-287. See alsoNonrandom price motiontheories
Random variables, defined, 175
Random walks, see EfficientMarkets Hypothesis
Rational investor assumption, ofEfficient MarketsHypothesis, 343-346
Rationality, limits of, 356-357
Reasoning by representativeness,87-88
Reinforcement schedule, illusorycorrelations and, 81-82
Relative frequency distribution,181-186, 197-202
Relative Strength Index (RSD,460
Representativeness heuristic,88-93, 93-101
525
Reversal rules, defined, 17Risk, defining and quantifying,340-341Risk transfer premiums, 378-385Roberts, Henry, 83Roll, R., 349Romano, J.P., 330Rule data mining case study:critique of, 448-451indicators used in, 405-417interest rate spreads, 417market breadth indicators,413-416price and volume functions,406-413prices-of-debt instrumentsfrom interest rates, 416-417parameters of, 389-392possible extensions of, 451-461raw time series used in, 405-406,417-418results of, 441-448rules tested in:divergence, 430-440extremes and transitions,420-430trend rules, 419-420in statistical terms, 392-394time-series operators in, 396-405channel breakout operator,397-398channel-normalizationoperator, 401-403indicator scripting language,403-405moving-average operator,398-401transforming data series intomarket positions, 394-396Rules, see Binary rules; Rule datamining case studyRussell, Bertrand, 59, 166Russo, J.E., 467526
S&P 500, see Rule data mining casestudySagan, Carl, 40-41Sample mean, 191, 243-245Sample size neglect, 361-362,372-374Sampling, see also Samplingdistributionbeads in a box example of,172-186frequency distribution and,179-181relative frequency distribution,181-186sample statistics, 175-177,188-189, 202, 393sampling variability, 177-179Sampling distribution, 201-202classical derivation approach,209-215computer-intensive methods ofgenerating, 215, 234-243,464-465, 471-473confidence intervals and,247-248data mining and, 276-278defined, 203mechanics of hypothesis testingand, 227-234sampling distribution of themean, 209-213trading performance and, 206uncertainty qualified by, 203-206Samuelson, Paul, 335-336Sawyer, J., 466Schoemaker, P.J.H., 467Scientific method:defined, 103, 332history of, 103-108hypothetic-deductive method,144-147key aspects of, 147-148logic and, 111-124
INDEX
nature of scientific knowledgeand, 108-110objectification of subjectivetechnical analysis, 148-151example, 151-161openness and skepticism in, 143,225philosophy of, 124-143search bias and, 64Secondhand information bias,58-61anchoring and, 360-361information diffusion and,365-366Self-attribution bias, 48-49DHS hypothesis and, 375-376Self-interest, secondhand accountsand, 61Shermer, Michael, 38Shiller, Robert, 333-334, 365,366Shleifer, Andre, 347Siegel, Jeremy, 84Signals, 16-18Simon, Barry, 259Simon, Herbert, 42Simplicity, principle of, 107-108,225-227Single-rule back-testing, versusdata mining, 268-271Skepticism, 143, 225Slope of yield curve, 417Slovic, Paul, 41, 470Snelson, Jay Stuart, 71Socioeconomics, 151Spatial clustering, 100-101Stale information, 340, 349,351-354Standard deviation, 192Standard error of the mean,213-215Statement about reliability ofinference, 190Index
Stationary statistical problems,174, 188Stationary time series, 19Statistical analysis:descriptive statistics tools:central tendencymeasurements, 191frequency distribution,190-191variability (dispersion)measurements, 192-193inferential statistics:elements of statisticalinference problem, 186-190sampling example, 172-186three distributions of, 206-207probability, 193Law of Large Numbers,194-195probability distribution,200-202probability distribution ofrandom variables, 197-199theoretical versus empirical,196sampling distribution and,201-206classical derivation approach,209-215computer-intensive approach,215used to counter uncertainty,165-172Statistical hypothesis, defined, 220Statistical inference:data mining and, 272-278defined, 189hypothesis tests:computer-intensive methodsof sampling distributiongeneration, 234-243confidence intervalscontrasted to, 250-252
defined, 217-218informal inference contrasted,218-223mechanics of, 227-234rationale of, 223-227parameter estimation:defined, 217-218interval estimates, 218, 243,245-253point estimates, 218,243-245Statistical significance, 23in case study, 394statistical significance ofobservation, 171statistical significance of test(p-value), 232-234Stiglitz, J.E., 343, 378Stochastics, 401-403Stories, see Secondhandinformation biasSubjective technical analysis, 5-8,15-16, 161-163adoption of scientific methodand, 148-151example, 151-161chart analysis and, 82-86confirmation bias and, 62-71erroneous beliefs and, 33-35futility of forecasting and,465471heuristic bias and, 86-93illusion trends and chartpatterns, 93-101human pattern finding andinformation processing,39-45illusory correlations and, 72-82overconfidence bias and, 45-58secondhand information biasand, 58-61as untestable and not legitimateknowledge, 35-38528
Syllogisms:categorical, 112-115conditional, 115-116invalid forms, 118-121valid forms, 117-118
Taleb, Nassim, 337Technical analysis (TA), 9-11. Seealso Evidence-basedtechnical analysis; Objectivetechnical analysis;Subjective technicalanalysisbeliefs and knowledge and, 1-5defined, 1future of, 461-473science and, 463-464Tetlock, P.E., 469Thoreau, Henry David, 62Threshold:binary rules and, 16-21defined, 17Time-series operators, in casestudy, 396-405channel breakout operator,397-398channel-normalization operator,401-403indicator scripting language,403-405moving-average operator,398-401Titman, S., 352-353Trading costs, 31Trend rules, tested in case study,419-420Trends, illusory, 83-86, 99Truth, in logic, 113TT-4-91 rule, 252-253Tversky, Amos, 41, 88, 91-92, 345
INDEX
Ulam, Stanislaw, 238Uncertainty:inability to recall prior, seeHindsight biasruling out, see Statistical analysisUncorrelated investor errors, inEfficient MarketsHypothesis, 343, 344,346-347Unfalsifiable propositions, 134-136Universe size, defined, 256
Validity:illusion of, 42in logic, 1138-115Variability (dispersion)measurements, 192-193Variable merit rules experiment,311-320
Walk-forward testing, 322
Ward, Artemus, 36
Weiss, S.M., 472
Wells, H.G., 165
Whewell, William, 128-130
White, Halbert, 236, 281,325
White's Reality Check (WRC),325-327, 329-330. See alsoRule data mining case study
Wilder, J. Welles, 460
Wilder’s Relative Strength Index(RSD, 460
Williams, Larry, 409
Wolf, M., 330
Xu, G.L., 323
Zero-centering adjustment, 237Zweig, Martin, 390<|end|>