necessary implication ofEMH.
Updating a belief in a probabilistically correct way implies that new in-formation impacts a prior belief in accord with Bayes’ theorem, wherebythe prior belief is altered in accord with the predictive value of the newinformation.
Correct prices induce the proper allocation of resources among differentpossible uses. For example, if the price of corn is too high, more of it thannecessary will be produced, flooding the economy with more corn than isneeded at the expense of something else such as wheat. Net result: too many502 NOTES
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
boxes of Corn Flakes and too few boxes of Wheaties to meet consumerdemand.
P. Samuelson, “Proof That Properly Anticipated Prices Fluctuate Randomly,”Industrial Management Review 6 (1965), 41-49.
B.G. Malkiel, A Random Walk Down Wall Street (New York: W.W. Norton &Company, 2003).
E. Fama, “Efficient Capital Markets: A Review of Theory and EmpiricalWork,” Journal of Finance 25 (1970), 383-417, as cited by A. Shleifer in Inef-Jicient Markets: An Introduction to Behavioral Finance (Oxford, UK: Ox-ford University Press, 2000), 1.
A. Shleifer, Inefficient Markets: An Introduction to Behavioral Finance (Ox-ford, UK: Oxford University Press, 2000), 1.
N.N. Taleb, Fooled by Randomness: The Hidden Role of Chance in the Mar-kets and in Life (New York: Texere, 2001).
A money manager with no skill whatsoever has a 0.5 probability of beatingthe market in any given time period (e.g., 1 year). In this experiment, Talebassumed a universe of money managers with a 0.50 probability of beating themarket in a given year.
Actually there are three forms of EMH—strong, semistrong, and weak. Thestrong form contends that no information, including inside information, canbe used to beat the market on a risk-adjusted basis. This form is impossibleto test because it cannot be reliably ascertained who knew what and whenthey knew it. The testimony of those who trade on inside information is no-toriously untrustworthy.
E. Fama, L. Fisher, M. Jensen, and R. Roll, “The Adjustment of Stock Pricesto New Information,” International Economic Review 10 (1969), 1-21.These results conflict with later studies in behavioral finance that found mar-kets did not properly discount surprising earnings announcements. My inter-pretation is that Fama’s studies did not consider the degree to whichearnings announcements differed from prior Street expectations. Later stud-ies done by V. Bernard showed that stock prices do experience trends afterearnings announcements, if the degree of surprise in the announcement islarge. V. Bernard, “Stock Price Reactions to Earnings Announcements,” Ad-vances in Behavioral Finance, R. Thaler (Ed.) (New York: Russell SageFoundation, 1992; V. Bernard and J.K. Thomas, “Post-Earnings Announce-ment Drift: Delayed Price Response or Risk Premium?,” Journal of Account-ing Research, Supplement 27 (1989), 1-36; V. Bernard and J.K. Thomas,“Evidence That Stock Prices Do Not Fully Reflect the Implications of Cur-rent Earnings for Future Earnings,” Journal of Accounting and Economics13 (1990), 305-341.
A noninformative event is one that carries no implications for the future cashflows of the asset or the future returns of the security.
The capital asset pricing model was proposed by William F. Sharpe in “Capi-tal Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,”Journal of Finance 19 (1964), 425-442.
APT was first proposed by S. Ross, “The Arbitrage Theory of Capital AssetPricing,” Journal of Economic Theory (December 1976). APT says a linearNotes 503
23.
24.
26.
27.
28.29.
30.31.32.33.
34.
35.
36.
37.
38.
model relates a stock’s returns to a number of factors, and this relation-ship is the result of arbitrageurs hunting for riskless, zero-investment op-portunities. APT specifies factors, including the rate of inflation, thespread between low and high quality bonds, the slope of the yield curve,and so on.
In other words, the current price change is correlated with the priorchange (i.e., a lag interval of 1), then with the change prior to that (lag in-terval = 2), and so forth. A correlation coefficient is computed for each ofthese lags. Typically, in a random data series, the correlations drop off veryquickly as a function of the lag interval. However, if the time series of pricechanges is nonrandom in a linear fashion, some of the autocorrelation co-efficients will differ significantly from the pattern of autocorrelations of arandom sequence.
E. Fama, “The Behavior of Stock Market Prices,” Journal of Business 38(1965), 34-106.
. E.E. Peters, Chaos and Order in the Capital Markets: A New View of Cycles,
  
Prices and Market Volatility (New York: John Wiley & Sons, 1991); E.E. Pe-ters, Fractal Market Analysis: Applying Chaos Theory to Investment andEconomics (New York: John Wiley & Sons, 1994).
A.W. Lo and A.C. MacKinlay, A Non-Random Walk Down Wall Street (Prince-ton, NJ: Princeton University Press, 1999).
S.J. Grossman and J.E. Stiglitz, “On the Impossibility of Informationally Effi-cient Markets,” American Economic Review 70, no 3 (1980), 393-408.Shleifer, Inefficient Markets, 2.
V. Singal, Beyond the Random Walk: A Guide to Stock Market Anomaliesand Low Risk Investing (New York: Oxford University Press, 2004), 5.
F. Black, “Noise,” Journal of Finance 41 (1986), 529-543.
Shleifer, Inefficient Markets, 10.
D. Kahneman and A. Tversky, “Prospect Theory: An Analysis of DecisionMaking under Risk,” Econometrica 47 (1979), 263-291.
R. Shiller, “Stock Prices and Social Dynamics,” Brookings Papers on Eco-nomic Activity 2, (1984), 457-498.
Cited by Shleifer, Inefficient Markets; J.B. De Long, A. Shleifer, L. Summers,and R. Waldmann, “Noise Trader Risk in Financial Markets,” Jowrnal of Po-litical Economy 98 (1990), 703-738.
R. Merton and P. Samuelson, “Fallacy of the Log-Normal Approximation toOptimal Portfolio Decision-Making over Many Periods,” Journal of Finan-cial Economics 1 (1974), 67-94.
The expectation of a game is (probability of a win X amount won) — (proba-bility of a loss X amount lost). Thus, per dollar bet, the expectation is +50cents. In investment-strategy terms, this would be called a profit factor of2.0. Very few investment strategies have such a favorable edge.
R. Shiller, “Do Stock Prices Move Too Much to Be Justified by SubsequentChanges in Dividends?,” American Economic Review 71 (1981), 421-436.Cited by Shleifer, Inefficient Markets, 20; D. Cutler, J. Poterba, and L. Sum-mers, “Speculative Dynamics,” Review of Economic Studies 58 (1991),529-546.504 NOTES
39. Cited by Shleifer, Inefficient Markets; R. Roll, “Orange Juice and Weather,”American Economic Review 74 (1984), 861-880; R. Roll, “R-squared,” Jowr-nal of Finance 43, (1988), 541-566.
40. Singal, Beyond the Random Walk, ix.
41. Relative performance refers to the stock’s performance relative to the per-formance of the average stock, or a benchmark. Relative performance is alsoknown as excess return.
42. Stocks can also be ranked on the basis of a prediction model that employs amultitude of indicators, typically referred to as factors.
43. When multiperiod horizons are used, the number of independent observa-tions are also reduced, so a one-month horizon, based on monthly data, is of-ten used.
44. The strong form of EMH is broader yet in claiming that not even inside infor-mation is useful in generating superior returns, but this version is nottestable.
45. R. Banz, “The Relation between Market Return and Market Value for Com-mon Stocks,” Journal of Financial Economics 9 (1981), 3-18.
46. E. Fama and K. French, “The Cross-Section of Expected Stock Returns,”Journal of Finance 47 (1992), 427-465.
47. Cited by Shiller, Irrational Exuberance; S. Basu, “The Investment Perfor-mance of Common Stocks Relative to Their Price-Earnings Ratios: A Test ofthe Efficient Markets,” Journal of Finance 32, no. 3 (1977), 663-682.
48. Cited by Shiller, Irrational Exuberance; E. Fama and K. French, “The CrossSection of Expected Stock Returns,” Journal of Finance 47 (1992), 427-466.
49. Singal, Beyond the Random Walk.
50. Although the weak form of EMH typically only talks about past prices andpast price changes, I believe advocates of this position would also claimthat any other data relied upon by TA would be useless for predicting fu-ture returns.
51. N. Jegadeesh, and S. Titman, “Returns to Buying Winners and Selling Losers:Implications for Market Efficiency,” Journal of Finance 48, (1993), 65-91.
52. E. Fama, “Efficient Capital Markets: II,” Journal of Finance 46 (1991),1575-1617.
53. W. De Bondt and R. H. Thaler, “Does the Stock Market Overreact?,” Journalof Finance 40, no. 3 (1985), 793-805.
54. In this case, risk is as defined by CAPM, volatility relative to the market.
55. T.J. George and C.-Y. Hwang, “The 52-Week High and Momentum Investing,”Journal of Finance 59, no. 5 (October 2004), 2145.
56. C.M.C. Lee and B. Swaminathan, “Price Momentum and Trading Volume,”Journal of Finance 55, no. 5 (October 2000), 2017-2067; S. E. Stickel andR.E. Verecchia, “Evidence That Trading Volume Sustains Stock PriceChanges,” Financial Analysts Journal 50, no. 6 (November—December1994), 57-67. It should be noted that when the forward-looking horizon is 3to 12 months, high volume increases the price momentum’'s effect. However,when the return is looked at 3 years out, high-momentum stocks with lowvolume are more persistent on the upside, whereas negative-momentumstocks with high volume persist on the downside.Notes 505
57.
65.
66.
67.68.69.70.
71.
72.73.
74.
E. Fama and K. French, “1996 Common Risk Factors in the Returns onBonds and Stocks,” Journal of Financial Economics 33 (1993), 3-56; E.Fama and K. French, “Multifactor Explanations of Asset Pricing Anomalies,”Journal of Finance 51 (1996), 55-84.
W. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Con-ditions of Risk,” Journal of Finance 19 (1964), 435-442; also see J. Lintner,“The Valuation of Risk Assets and the Selection of Risky Investments inStock Portfolios and Capital Budgets,” Review of Economics and Statistics47 (1965), 13-37.Shleifer, Inefficient Markets, 19.
. J. Lakonishok, A. Shleifer, and R. Vishny, “Contrarian Investment, Extrapola-tion, and Risk,” Journal of Finance 49 (1994), 1541-1578.
. Shleifer, Inefficient Markets, 24.
. Ibid.
. Ibid.W. Edwards, “Conservatism in Human Information Processing,” in Formal
Representation of Human Judgment, B. Kleinmutz (Ed.) (New York: JohnWiley & Sons, 1968) 17-52.
D.G. Myers, Intuition, Its Powers and Perils (New Haven: Yale UniversityPress, 2004), 157.
A. Tversky and D. Kahneman, Judgment under Uncertainty Heuristics andBiases, in Judgment under Uncertainty: Heuristics and Biases, D. Kahne-man, P. Slovic, and A. Tversky (Eds.) (New York: Cambridge UniversityPress, 1982), 14.
George and Hwang, “52-Week High.”
Shiller, Irrational Exuberance,138.
Ibid., 139.
E. Shafir, I. Simonson, and A. Tversky, “Reason-Based Choice,” Cognition 49(1993), 11-36.
Systematic process can exhibit real trends, which are streaks that are longerin duration than those found in random walks, or they can exhibit true mean-reversion behavior, which means that streaks will be shorter in duration (i.e.,more frequent flip-flops between positive and negative outcomes) than is truefor random walks. In both truly trended processes and true mean-revertingprocess, prior observations contain useful predictive information. In true ran-dom-walk processes, prior observations contain no predictive information.Shiller, Irrational Exuberance, 148-169.
A. Asch, Social Psychology (Englewood Cliffs, NJ: Prentice-Hall, 1952),450-501.
M. Deutsch and H.B. Gerard, “A Study of Normative and Informational SocialInfluence upon Individual Judgment,” Journal of Abnormal and Social Psy-chology 51 (1955), 629-636.
. Shiller, Irrational Exuberance, 148-169.3. RJ. Shiller cites D.D. Bikhchandani, D. Hirshleifer, and I. Welch, “A Theory
of Fashion, Social Custom and Cultural Change,” Journal of Political Econ-omy 81 (1992), 637-654; A.V. Nanerjee, “A Simple Model of Herd Behavior,”Quarterly Journal of Economics 107, no. 3 (1993), 797-817.506
77.78.79.80.81.
82.83.
84.
85.86.
87.
88.
89.
90.
91.92.93.
94.95.
NOTES
Shiller, Irrational Exuberance, 152.
Ibid., 164.
Ibid., 165.
Ibid., 166.
See Carl Anderson, “Notes on Feedback and Self-Organization,” atwww.duke.edu/~carl/pattern/characteristic_student_notes.htm.
Shiller, Irrational Exwberance, 67.
N. Barberis, A. Shleifer, and R. Vishny, “A Model of Investor Sentiment,”Journal of Financial Economics 49 (1998), 307-343.
This assumption is consistent with studies showing that people have diffi-culty distinguishing random series from series that display systematic behav-ior such as mean-reversion or trending behavior. This failure accounts forsports fans’ misperception of the hot hand and chartists’ inability to distin-guish real from random charts cited in chapter 2.
Shiller, Irrational Exuberance, 144.
R.J. Shiller cites Barberis, Shleifer, and Vishny, “Model of Investor Senti-ment,” and N. Barberis, M. Huang, and T. Santos, “Prospect Theory and AssetPrices,” Quarterly Journal of Economics 116, no. 1 (February 2001), 1-53; K.Daniel, D. Hirshleifer, and A. Subrahmanyam, “Investor Psychology and Se-curity Market Over and Underreaction,” Journal of Finance 53, no. 6 (1998),1839-1886; H. Hong and J. Stein, “A Unified Theory of Underreaction, Mo-mentum Trading, and Overreaction in Asset Markets,” Journal of Finance54, no. 6 (December 1999), 2143-2184.
Daniel, Hirshleifer, and Subrahmanyam, “Investor Psychology”; and K.Daniel, D. Hirshleifer, and A. Subrahmanyam, “Overconfidence, Arbitrage andEquilibrium Asset Pricing,” Journal of Finance 56 (2001), 921-965.
N. Barberis, “A Survey of Behavioral Finance,” in Handbook of the Econom-ics of Finance, Volume 1B, G.M. Constantinides, M. Harris, and R. Stulz(Eds.) (New York: Elsevier Science, B.V., 2003).
N. Chopra and R. Lakonishok, “Measuring Abnormal Performance: DoStocks Overreact?,” Journal of Financial Economics 31 (1992), 235-268; R.La Porta, R. Lakonishok, A. Shleifer, and R. Vishny, “Good News for ValueStocks: Further Evidence on Market Efficiency,” Journal of Finance 49(1997), 1541-1578.
J.B. De Long, A. Shleifer, L. Summers, and R. Waldmann, “Positive FeedbackInvestment Strategies and Destabilizing Rational Speculation,” Journal ofFinance 45, no. 2 (1990), 379-395; N. Barberis and A. Shleifer, “Style Invest-ing,” Journal of Financial Economics 68 (2003), 161-199.
Hong and Stein, “Unified Theory.”
S.F. LeRoy, “Risk Aversion and the Martingale Property of Stock Returns,”International Economic Review 14 (1973), 436-446; R.E. Lucas, “AssetPrices in an Exchange Economy,” Econometrica 46 (1978), 1429-1446.
A.W. Lo and A.C. MacKinlay, A Non-Random Walk Down Wall Street (Prince-ton, NJ: Princeton University Press, 1999), 5.
Ibid.
S. Grossman, “On the Efficiency of Competitive Stock Markets WhereTraders Have Diverse Information,” Journal of Finance 31 (1976), 573-585.;Notes 507
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
S. Grossman and J. Stiglitz, “On the Impossibility of Informationally EfficientMarkets,” American Economic Review 70 (1980), 393-408.
L. Jaeger, Managing Risk in Alternative Investment Strategies: SuccessfulInvesting in Hedge Funds and Managed Futures (London: FinancialTimes-Prentice Hall, 2002), 27.
Gains from a true inefficiency are not a free lunch, because it is costly toidentify them, but they need not entail the risk of additional volatility in re-turns. Thus true inefficiencies can generate investment performance with ahigh relative Sharpe ratio.
H.D. Platt, Counterintuitive Investing: Profiting from Bad News on WallStreet (Mason, OH: Thomson Higher Education, 2005).
Equity and bond markets provide a quick way for owners of equities to selltheir position to other investors. The liquidity of stocks and bonds thusmakes them attractive to the initial investors, thereby making it easier forcompanies to raise debt and equity financing.
Grains (corn, soybeans, soybean meal, soybean oil, and wheat); financials (5-year T-notes, 10-year T-notes, long-term treasury bonds); currencies (Aus-tralian, British, Canadian, German, Swiss, and Japanese); energy (heating oil,natural gas, crude oil, and unleaded gas); cattle; metals (gold, copper, and sil-ver); and soft/tropical (coffee, cotton, and sugar).
G.R. Jensen, R.R. Johnson, and J.M. Mercer, “Tactical Asset Allocation andCommodity Futures,” Journal of Portfolio Management 28, no. 4 (Summer2002).
An asset-class benchmark measures the returns earned and risks incurred byinvesting in a specific asset class, with no management skill.
Lars Kestner, Quantitative Trading Strategies: Harnessing the Power ofQuantitative Techniques to Create a Winning Trading Program (NewYork: McGraw-Hill, 2003), 129-180.
The eight market sectors tested were foreign exchange, interest rates, stockindex, metals, energy, grains, meats, and softs.
The nine industry sectors were energy, basic materials, consumer discre-tionary, consumer staples, health care, financials and information technol-ogy, telecom. The three stock indexes were S&P 500, NASDAQ 100, andRussell 2000.
The five trend-following systems were channel breakout, dual moving-aver-age crossover, two version of momentum, and MACD versus its signal line.For more description see Kestner’s Quantitative Trading Strategies.
M. Cooper, “Filter Rules Based on Price and Volume in Individual SecurityOverreaction,” Review of Financial Studies 12, no. 4 (Special 1999),901-935.
CHAPTER 8 Case Study of Rule Data Mining for
1.
the S&P 500
J.P. Romano and M. Wolf, “Stepwise Multiple Testing as Formalized DataSnooping,” Econometrica 73, no. 4 (July 2005), 1237-1282.508 NOTES
2. J.J. Murphy, Intermarket Technical Analysis: Trading Strategies for theGlobal Stock, Bond, Commodity and Currency Markets (New York: JohnWiley & Sons, 1991). I was first exposed to the idea of using relationships be-tween markets for indicator development in 1979 by Michael Hammond andNorman Craig of Advance Market Technologies (AMTEC), Ogden, Utah.AMTEC was one of the first firms to apply artificial-intelligence methods tomarket prediction. AMTEC’s modeling approach was based on the work ofRoger Baron on adaptive learning networks, now known as polynomial net-works, at Adaptronics Inc, in the 1970s. I routinely used intermarket indica-tors in work for clients of Raden Research Group, including Cyber-TechPartners (1983), Tudor Investment Corporation (1987), and Manufacturer'sHanover Trust (1988).
3. P-H Hsu and C-M Kuan, “Reexamining the Profitability of Technical Analysiswith Data Snooping Checks,” Journal of Financial Economics 3, no. 4(2005), 606-628.
4. Many of the automated methods are described in T. Hastie, R. Tibshirani,
and J. Friedman, The Elements of Statistical Learning: Data Mining, Infer-
ence and Prediction (New York: Springer, 2001).
Asegment of the future over which the rule’s profitability persists that is long
enough to compensate the effort required to discover the rule but not of un-
limited duration.
6. H. White, “A Reality Check for Data Snooping,” Econometrica 68, no. 5 (Sep-tember 2000). Note that White uses the term data snooping to mean “data-mining bias,” as used in this book.
7. Mathematical operators include add, subtract, multiply, divide, powers,roots, and so forth.
8. Logical operators include and; 07; greater-than; less-than; if, then; else, andso forth.
9. Time-series operators include moving average, moving channel normaliza-tion (stochastics), breakout channels, moving slopes, and such (describedlater).
10. P.K. Kaufman, 249.
11. Ibid., 200.
12. Ibid., 202. Kaufman notes that a study done in 1970 by Dunn & Hargitt Finan-cial Services showed that a channel breakout using a four-week span, knownas Donchian’s four-week rule, was the best of several popular systems whentested on 16 years of data on seven commodities markets. Kaufman alsocompares the performance of channel breakout rules to four other trend-following methods (exponential smoothing, linear regression slope, swing-breakout, and point-and-figure) to Eurodollars for the 10-year period 1985through 1994. The channel breakout method had the highest risk-adjustedperformance over the 10-year period.
13. Asimple moving average is centered by plotting the moving-average value inthe center of the moving-data window. If N is the number of periods used tocompute the moving average, the average is plotted to correspond with avalue that is lagged by (n - 1)/2 data points.
onNotes 509
14.
15.16.17.
18.
19.
20.
2i.
22.
23.
24.
J.F. Ehlers, Rocket Science for Traders: Digital Signal Processing Applica-tions (New York: John Wiley & Sons, 2001); J.F. Ehlers, Cybernetic AnalysisSor Stocks and Futures: Cutting-Edge DSP Technology to Improve YourTrading (New York: John Wiley & Sons, 2004).
Kaufman, 256.
Ehlers, Rocket Science for Traders, 27.
W.A. Heiby, Stock Market Profits through Dynamic Synthesis (Chicago: In-stitute of Dynamic Synthesis, 1965).
Although almost all sources attribute the development of this time series op-erator to Dr. George Lane in 1972, no source mentions the use of the sameidea by Heiby at an earlier time. On this basis, I have attributed the develop-ment to Heiby. It is possible that Heiby based his work on yet an earlier au-thor.
For information about various back-testing software platforms see: forTradeStation www.TradeStationWorld.com; for MetaStock www.equis.com;for AIQ Expert Design Studio www.aigsystems.com; for Wealth-Labwww.wealth-lab.com; for eSignal www.esignal.com; for Neuroshell Traderwww.neuroshell.com; for AmiBroker www.amibroker.com; for Neoticker,www.tickquest.com; for Trading$olutions www.tradingsolutions.com; for Fi-nancial Data Calculator www.mathinvestdecisions.com.
Ultra Financial Systems, PO Box 3938, Breckenridge, CO 90424; phone 970-453-4956; web site www.ultrafs.com.
Market Timing Reports, PO Box 225, Tucson, AZ 85072; phone 520-795-9552;web site www.mktimingrpt.com.
Studies showing that trading volume contains useful information include thefollowing: L. Blume and M. Easley O’Hara, “Market Statistics and TechnicalAnalysis: The Role of Volume,” Journal of Finance 49, issue 1 (March 1994),153-181; M. Cooper, “Filter Rules Based on Price and Volume in IndividualSecurity Overreaction,” Review of Financial Studies 12 (1999), 901-935;C.M.C. Lee and B. Swaminathan, “Price Momentum and Trading Volume,”Journal of Finance 55, no. 5 (October 2000), 2017-2069; S.E. Stickel and R.E.Verecchia, “Evidence That Trading Volume Sustains Stock Price Changes,”Financial Analysts Journal 50 (November-December 1994), 57-67.
J.E. Granville, Granville’s New Strategy of Daily Stock Market Timing forMaximum Profit (Englewood Cliffs, NJ: Prentice-Hall, 1976). The attribu-tion to Woods and Vignolia can be found in the body of knowledge section onthe web site of the Market Technicians Association.
This formulation of OBV is as presented in Technical Indicators ReferenceManual for AIQ Trading Export Pro, a software product of AIQ Systems