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Statistical approach to the optimisation of the technical analysis trading tools: trading bands strategies

Ryazanova Oleksiv, Marta (2008) Statistical approach to the optimisation of the technical analysis trading tools: trading bands strategies. PhD thesis Economie et Finance, CERNA- Centre d'économie industrielle, ENSMP p.261.

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Abstract

In this thesis we have proposed several approaches to improve and optimize one of the most popular technical analysis techniques - trading bands strategies. Parts I and II concentrate on the optimization of the components of trading bands: the middle line (in the form of the moving average) and bandlines. Part III is dedicated to the improving of the decision-making process. In Part I we proposed the use of kriging method, a geostatistical approach, for the optimization of the moving average weights. The kriging method allows obtaining optimal estimates that depend on the statistical characteristics of the data rather than on the historical data itself as in the case of the simulation studies. Unlike other linear methods usually used in finance, this method can be applied to both equally spaced data (in our context, traditional time series) and data sampled at unequal intervals of time or other axis variables. Part II proposes a method based on the transformation of the data into a normal variable, which enables the definition of the extreme values and, therefore, the bands' values, without constraining assumptions about the distribution function of the residuals. Finally, Part III presents the application of disjunctive kriging method, another geostatistical approach, for more informative decision making about the timing and the value of a position. Disjunctive kriging allows estimating the probability of certain thresholds being reached in the future. The results of the analysis prove that the proposed techniques can be incorporated into successful trading strategies.

Item Type:PhD Thesis (PhD)
PhD Supervisor:Galli, Alain
Date:03 December 2008
Board of examiners:Lautier, Delphine and Okhrin, Yarema and Schmitt, Michel and Philippe, Frédéric
Ecole Doctorale:ED 396 ECONOMIE, ORGANISATIONS, SOCIETE
Discipline:Economie et Finance
Collection (Fonds):Mines ParisTech (ENSMP)
Institution:ENSMP
Department:CERNA- Centre d'économie industrielle
Subjects:9. Sciences of Economy, Management and Society
Uncontrolled Keywords:Finance, Analyse statistique, Krigeage, Estimateur, méthode Bollinger, Moyenne mobile, Calcul théorie bandes, échantillon, Finance, Statistical analysis, Kriging, Estimator, Bollinger Method, Moving average, Band theory calculation, Sample
ID Code:5145
Deposited By:Claudine Abauzit
Deposited On:20 May 2009

References

1. Aan, P. “Weekly high/low moving average”, Stock & Commodities, V.7:12, pp.431-432,

Copyright © Technical Analysis Inc.

2. Acar, E., S.E. Satchell. 1997. “A theoretical analysis of trading rules: an application to the

moving average case with Markovian returns”, Applied Mathematical Finance, #4, pp.165-

180.

3. Achelis, S. B. “Technical analysis from A to Z”, McGraw-Hill Professional, 2000.

4. Ait-Sahalia, Y. 1996. “Non-parametric pricing of the interest rate derivative securities,

Econometrica, #64, pp.527-560.

5. Ait-Sahalia, Y. 1999. “Transition densities for interest rate and other non-linear

diffusions”, The Journal of Finance, Vol. LIV, #4.

6. Ait-Sahalia, Y. 2002. “Maximum likelihood estimation of the discretely sampled

diffusions: a closed form approximation approach”, Econometrica, Vol. 70, #1, pp.223-262.

7. Alexander, S.S. 1961. “Price movements in speculative markets: Trends or random

walks”, Industrial Management Review, #2, pp.7-26.

8. Alexander, S.S. 1964. “Price movements in speculative markets: Trends or random walks,

Number 2”, Industrial Management Review, Spring, pp.25-46.

9. Alexander, C. “Trade with moving averages”, Stock & Commodities, V.11:6, pp.257-260,

Copyright © Technical Analysis Inc.

10. Allen, F. and Karjalainen. 1999. “Using genetic algorithms to find technical trading rules”,

Journal of Financial Economics, #51, pp. 245-271.

11. Arms, R.W., Jr. “Volume-adjusted moving averages”, Stock & Commodities, V.8:3,

pp.109-111, Copyright © Technical Analysis Inc.

12. Armstrong, M. “Basic linear geostatistics”, Springer, 2004.

13. Armstrong, M. and P. Delfiner. 1980. “Towards a more robust variogram: A case study

on coal”, Technical Report, #671, Centre de Géostatistique, Fontainebleau, France.

14. Armstrong, M. and R. Jabin. 1981. “Variogram models must be positive-definite”, Journal

of the International Association for Mathematical Geology, #13(5), pp.455-459.

15. Arrington, G.R. “The basics of moving averages” Stock & Commodities, V.10:6, pp.275-

278, Copyright © Technical Analysis Inc.

16. Arrington, G.R. “Building a variable length moving average” Stock & Commodities,

V.9:6, pp.219-223, Copyright © Technical Analysis Inc.

17. Ausloos, M. 2000. “Statistical physics in foreign exchange currency and stock markets”,

Physica A, #285, pp.48-65.

18. Balsara, N., K. Carlson and N. V. Rao. 1996. “Unsystematic futures profits with technical

trading rules: A case for flexibility”, Journal of Financial and Strategic Decision, Vol.9, #1

(Spring, 1996), pp.57-66.

19. Bennett B.,K. 2001. “Using a moving average to determine cotton futures market entry

dates”, The Journal of Cotton Science, #5, pp.218-223.

20. Black, F. and M. Scholes. 1973. “The pricing of options and corporate liabilities, Journal of

Political Economy, #81, pp.637-654.

21. Box, G. E. P, G. M. Jenkins and G.C. Reinsel. “Time series analysis: forecasting and

control”, Wiley Series in Probability and Statistics, 2008.

22. Blanchet-Scalliet, C., A. Diop, R. Gibson, D. Talay, E. Tanré, K. Kaminski.

2005. “Technical analysis compared to mathematical models based under

misspecification”, Working paper No.253, National Centre of Competence in Research Financial

Valuation and Risk Management.

23. Blume L., D. Easley and M. O’Hara. 1994. “Market statistics and technical analysis: The

role of volume”, The Journal of Finance, Vol.49, #1 (March, 1994), pp.153-181.

24. Bollinger, J. “Bollinger on Bollinger bands”, McGraw-Hill, 2002.

25. Brock, W., J. Laconishok and B. Lebaron. 1992. “Simple technical trading rules and the

stochastic properties of stock returns”, The Journal of Finance, Vol.47, #5 (December,

1992), pp.1731-1764.

26. Brown. D.P. and R.H. Jennings. 1989. “On technical analysis”, The Review of Financial

Studies, Vol. 2, #4, pp.527-551.

27. Carroll, R. J. Ruppert, D. “Transformation and weighting in regression”, CRC Press, 1988.

28. Chande, T. S. “Adapting moving averages to market volatility”, Stock & Commodities,

V.10:3, pp.428-433, Copyright © Technical Analysis Inc.

29. Chande, T. S. “Beyond technical analysis: How to develop and implement a winning

trading system”, John Wiley and Sons, 2001.

30. Chilès, J.-P. 1977. “Géostatistique des phénomènes non stationnaires”, Doctoral Thesis,

Université de Nancy-I, France.

31. Chilès, J.-P. 1979a. “La dérive à la dérive”, Technical Report, #591, Centre de

Géostatistique, Fontainebleau, France.

32. Chilès, J.-P. 1979b. “Le variogramme généralisé”, Technical Report, #612, Centre de

Géostatistique, Fontainebleau, France.

33. Chilès, J.-P. and P. Delfiner. “Geostatistics. Modelling spatial uncertainty”, A-Wiley-

Interscience publication, John Wiley & Sons, Inc., 1999.

34. Cressie N. A. C. «Statistics for Spatial Data», A-Wiley-Interscience publication, John Wiley &

Sons, Inc., 1991.

35. Cox, D. R., D. Oakes. “Analysis of Survival Data”, Chapman & Hall, London, 1984.

36. Dai, L. H. Wei, and L. Wang. 2007. “Spatial distribution and risk assessment of

radionuclides in soils around a coal-fired power plant : A case study from the city of

Baoji, China”, Environmental Research, Vol. 104, # 2, pp. 201-208.

37. Di Lorenzo, R. and V. Sciarretta. 1996. “Statistical evidence on a new method of trading

the financial markets”, published in AF journal, #24 (December, 1996).

38. Ehlers J. “Signal analysis concepts”, http://www.mesasoftware.com/technicalpapers.htm,

http://www.jamesgoulding.com/Research_II/Ehlers/Ehlers%20(Signal%20Analysis%20Concepts).doc,

http://moving-averages.technicalanalysis.org.uk/Ehle.pdf.

39. Emery, X. 2006. “A disjunctive kriging program for assessing point-support conditional

distributions”, Computers & Geosciences, Vol. 32, # 7, pp. 965-983.

40. Fama, E.F. and M.E.Blume. 1966. “Filter rules and stock market trading”, Journal of

Business, #39, pp.226-241.

41. Fang, Y. and D. Xu. 2002. “The predictability of asset returns: an approach combining

technical analysis and time series forecasts”, International Journal of Forecasting I.

42. Fernandez-Rodriguez, F., S. Sosvilla-Rivero and J. Andrada-Felix. 1999. “Technical

analysis in the Madrid stock exchange”, FEDEA Working paper (Documento de trabajo), #99-

05 (April, 1999).

43. Fernandez-Rodriguez, F., S. Sosvilla-Rivero and J. Andrada-Felix. 2000. “Technical

analysis in foreign exchange markets: Linear versus nonlinear trading rules”, Documentos de

Economia y Finanzas Internationalez, DEFI00/02 (September, 2000),

http://www.fedea.es/hojas/publicaciones.html.

44. Focardi, S. and F. J. Fabozzi. “The mathematics of financial modelling and investment

management”, John Wiley and Sons, 2004.

45. Gençay, R., Selçuk, F. and B. Whitcher. “An introduction to wavelets and other filtering

Methods in Finance and Economics”, Academic Press an Imprint of Elsevier, 2002.

46. Gray, A. and P. Thomson. 1997. “Design of moving average trend filters using fidelity,

smoothness and minimum revisions criteria”, Bureau of the Census Statistical Research

Division, Statistical Research Report Series, #RR96/01.

47. Greene W.H. “Econometric analysis”, Prentice Hall, 2007.

48. Hansen, L.P. and J.A. Scheinkman. 1995. “Back to the future: Generating moment

implications for continous time Marokv processes, Econometica, #63, pp.767-804.

49. Hartle, T. “Sidebar: Variable length moving average”, Stock & Commodities, V.13:10,

pp.418-423, Copyright © Technical Analysis Inc.

50. Harvey, A. “Forecasting, structural time series models and the Kalman filter”, Cambridge

University Press, 1991.

51. Harvey, A. Koopman, S.J., Shephard, N. “State space and unobserved component

models: Theory and applications”, Cambridge University Press, 2004.

52. Honoré, P. 1997. “Maximum-likelihood estimation of non-linear continuous-time term

structure models”, Working paper, Aarhus University.

53. Hu, L.Y. et Ch. Lantuejoul. 1988. “Recherche d’une fonction d’anamorphose pour la

mise en oeuve du krigeage disjonctif isofactoriel Gamma”, Etude Géostatistiques V –

Séminaire C.F.S.G. sur la Géostatistique 15-16 Juin 1987, Fontainebleau. Sci. De la Terre, Sér. Inf.,

Nancy,1988, #28, pp. 145-173.

54. Hudson, R., M. Dempsey and K .Keasey. 1996. “A note on the weak form efficiency of

capital markets: The application of simple technical trading rules to UK stock prices –

1935-1994”, Journal of Banking and Finanace, #20, pp.1121-1132.

55. Hutchinson, T. and P.G. Zhang. “Weighted moving averages”, Stock & Commodities,

V.11:12, pp.500-505, Copyright © Technical Analysis Inc.

56. James, F.E.Jr. 1968. “Monthly moving avearages – an effective investment tool?”, Journal

of Financial and Quantitative Analysis, (September, 1968), pp.315-326.

57. Jensen, M. C. and G. Benington. 1970. “Random walks and technical theories: Some

additional evidence”, The Journal of Finance, Papers and Proceedings of the Twenty-Eight

Annual Meeting of the American Finance Association New-York, N.Y. December, 28-30,

1969, Vol.25, #2 (May, 1970), pp. 469-482.

58. Katz, J.O. and D. McCormick. “The encyclopedia of trading strategies”, McGraw-Hill

Professional, 2000.

59. Kavajecz K.A and E.R. Odders-White. 2004. “Technical analysis and liquidity provision”,

The Review of Financial Studies, Vol. 17, # 4 (Winter, 2004), pp.1043-1071.

60. Kennedy, P. “A guide to econometrics”, The MIT Press, 1998.

61. Lang Chao-Yi, “Kriging interpolation”, at www.nbb.cornell.edu.

62. Levich, R. and L. Thomas. 1993. “The significance of technical trading-rule profits in the

foreign exchange market: A bootstrap approach”, Journal of International Money and Finance,

#12, pp.451-474.

63. Li, P. 2005. “Box-Cox transformations: An overview”, presentation,

http://www.stat.uconn.edu/~studentjournal/index_files/pengfi_s05.pdf.

64. Lien, K. “Day trading the currency market: Technical and fundamental strategies to profit

from market swings”, John Wiley and Sons, 2006.

65. Lo, A. W. 1988. “Maximum likelihood estimation of generalized Ito processes with

discretely sampled data, Econometric Theory, #4, pp.231-247.

66. Lo, A. W. 2007. “Efficient market hypothesis” in “The New Palgrave: A Dictionary of

Economics” by Blume, L. and S. Durlauf, New York: Palgrave McMillan, 2007.

67. Lo, A. W., Mamaysky, H. and J.Wang. 2000. “Foundations of technical analysis:

Computational algorithms, statistical inference, and empirical implementation”, The

Journal of Finance, Vol. LV, #4 (August, 2000), pp.1705-1765.

68. Lo, A. and J.Wang. 2000. “Trading volume: Definitions, data analysis and implications of

portfolio theory”, The Review of Financial Studies, Vol 13, #2 (Summer, 2000), pp.257-300.

69. Martinez, W. L. and A. R. Martinez. “Computational statistics handbook with

MATLAB”, CRC Press, 2001.

70. Matheron G. “Osnovy prikladnoi geostatistiki (Treatise of Geostatistics), Mir, Moskow

1968.

71. Matheron G. 1969. “Le krigeage universel”, Cahiers du Centre de Morphologie Mathématique de

Fontainebleau, Fasc.1, Ecole des Mines de Paris.

72. Matheron G. “La théorie des variables régionalisées, et ses applications”, Les Cahiers du

Centre de Morphologie Mathématique de Fountainebleau, 1970.

73. Matheron, G. 1973. “Le krigeage disjonctive”, Technical Report, #360 Centre de

Géostatistique, Fontainebleau, France.

74. Matheron, G. 1976. “A simple substitute for conditional expectation: the disjunctive

kriging”, in “Advanced Geostatistics in Mining Industry”, 1976, Reidel Publishing Company

Dordrecht.

75. Matheron, G. 1977. “Peut-on imposer des conditions d’universalite au krigeage

disjonctif”, Technical Report, Centre de Géostatistique, Fontainebleau, France.

76. Matheron, G. 1986. “Sur la positivité des poids de krigeage”, Technical Report, #30/86/G,

Centre de Géostatistique, Fontainebleau, France.

77. Murphy, John J. “Technical analysis of the financial markets”, New York Institute of Finance,

1999.

78. Neely, C.J. 1997. “Technical analysis in the foreign exchange market: A Layman’s guide”,

Review, September/October, 1997, pp.23-38.

79. Neely, C.J., P. Weller and R. Dittmar. 1997. “Is technical analysis in the foreign exchange

market profitable? A genetic Programming Approach”, The Federal Reserve Bank of St. Louis

Working Paper Series, #96-006C, August, 1997.

80. Neftci, S. N. 1991. “Naïve trading rules in financial markets and Wiener-Kolmogorov

predicition theory: A study of technical analysis”, Journal of Business, Volume 64, Issue 4

(October, 1991), pp. 549-571.

81. Nikifork, R. “Trends and moving averages”, Stock & Commodities, V.16:12, pp.583-587,

Copyright © Technical Analysis Inc.

82. Orfeuil, J.P. 1977. “Une approche statistique du probleme de l’alerte en pollution

atmospherique”, Note de Centre Géostatistique Fonteaineblueau, N-506.

83. Osler C.L. 2003. “Currency Orders and Exchange Rate Dynamics: An Explanation for

the Predictive Success of Technical Analysis”, The Journal of Finance, Vol. 58, #5 (October,

2003), pp. 1791-1819.

84. Osler, C. L. and P.H. Kevin Chang. 1995. “Head and shoulders: not just a flaky pattern”,

Federal Reserve Bank of New York Staff report, #4, pp.1-65.

85. Pardo, R. “The Evaluation and Optimization of Trading Strategies”, John Wiley and Sons,

2008.

86. Ratner, M. and R. Leal. 1999. “Tests of technical trading strategies in the emerging equity

markets of Latin America and Asia”, Journal of Banking and Finance, #23, pp.1887-1905.

87. Rode, D., Y. Friedman, S. Parikh and J. Kane. 1995. “An evolutionary approach to

technical trading and capital market efficiency”, The Wharton School University of

Pennsylvania, May 1, 1995.

88. Rivoirard, J. “Disjuncive kriging and non-linear geostatistics”, Clarendon Press, Oxford

University Press, 1994.

89. Saacke, P. 2002. “Technical analysis and the effectiveness of central bank intervention”,

Journal of International Money and Finance, #21, pp.459-479.

90. Santa-Clara, P. 1995. Simulated likelihood estimation of diffusions with an application to

the short term interest rate, Working paper, UCLA.

91. Scott, D. W. “Multivariate Density Estimation: Theory, Practice, and Visualization”,

Wiley-Interscience, 1992.

92. Shiryayev, I. “Probability”, New-York: Springer-Verlag, 1985.

93. Sullivan, R., A. Timmermann and H. White. 1999. “Data-snooping, technical trading rule

performance and the bootstrap”, The Journal of Finance, Vol.54, #5 (October, 1999), pp.

1647-1691.

94. Theoret Raymond, Rostan Pierre. “Les Bandes de Bollinger comme technique de

réduction de la variance des prix d'options sur obligations obtenus par la simulation de

Monte Carlo”, Les cahiers de la recherche, Research paper, Rouen School of

Management Research, # 50-2003/2004, www.esc-rouen.fr

95. Tilley, D.L. “Moving averages with resistance and support, Stock & Commodities, V.16:9,

pp.108-114, Copyright © Technical Analysis Inc.

96. Treynor, J.L. and R. Ferguson. 1985. “In defence of technical analysis”, The Journal of

Finance, Vol.40, #3 (July, 1985), Papers and Proceedings of the Forty-Third Annual

Meeting American Finance Association, Dallas, Texas, December 28-30 , 1984, pp.757-

773.

97. Triantafilis, J. I., O. A. Odeh, B. Warr and M. F. Ahmed. 2004. “Mapping of salinity risk

in the lower Namoi valley using non-linear kriging methods”, Agricultural Water

Management, Vol. 69, # 3, pp. 203-231.

98. Van Horne, J.C. and G.G.C. Parker. 1967. “The random walk theory: An empirical test”,

Financial Analysts Journal, #23, pp.87-92.

99. Vasicek, O. 1977. “An equilibrium characterization of the term structure, Journal of

Financial Economics, #5, pp.177-188

100. von Steiger, B., R. Webster, R. Schulin and R. Lehmann. 1996. “Mapping heavy

metals in polluted soil by disjunctive kriging”, Environmental Pollution, Vol. 94, # 2, pp.

205-215.

101. Wand, M. P. and M. C. Jones. “Kernel Smoothing”, CRC Press, 1995.

102. Williams, O.D. 2006. “Empirical optimization of Bollinger Bands for

profitability”, MA Thesis, Simon Fraser University

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