Vol. 27 No. 48 (2014), Artículos
Vol. 27 No. 48 (2014)

Stock market investment strategy and chart pattern reconnaissance: An intraday Dow Jones Index data application

Artículos
Published 2014-06-30
Roberto Cervelló-Royo
Facultad de Administración y Dirección de Empresas Universidad Politécnica de Valencia
Francisco Guijarro Martínez
Profesor Titular en el Depto. de Economía y Ciencias Sociales y Vicedecano de Alumnado, Relaciones con Empresa e Investigación en la Facultad de Administración y Dirección de Empresas de la Universidad Politécnica de Valencia (UPV)
Karolina Michniuk
Phd Candidate por la Hamburg University of Applied Sciences (HAW) y la Universidad Politécnica de Valencia (UPV)
PDF (Spanish)

Supplementary Files

Cuadros y Gráficos (Spanish)
Cuadros y Gráficos Versión 2 (Spanish)
Figuras en formato original (Spanish)

Keywords

data snooping
índice Dow Jones
reconocimiento de patrones
análisis técnico
regla de trading

How to Cite

Cervelló-Royo, R., Guijarro Martínez, F., & Michniuk, K. (2014). Stock market investment strategy and chart pattern reconnaissance: An intraday Dow Jones Index data application. Cuadernos De Administración, 27(48), 119-152. https://doi.org/10.11144/Javeriana.cao27-48.eibr
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Abstract

This work introduces a new approximation of the flag price pattern recognition. A trading rule which provides positive risk-adjusted returns for intraday data of the Dow Jones Industrial Average Index is developed. In order to mitigate the ata snooping problem we use a data set of more than 90,000 observations, results are reported over 96 different configurations of the trading rule parameters. Results gathered from the whole sample confirm that the trading rule provides a positive return, even after considering the risk. Moreover, it beats the benchmark in the mean variance sense.

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Agudelo Rueda, D.A.; Uribe Estrada, J.H. (2009). ¿Realidad o Sofisma? Poniendo a prueba el análisis técnico en las acciones colombianas. Cuadernos de Administración, 22(38), 189-217.

Alexander, S. (1961). Price movement in speculative markets: trends or random walks. In P. Cootner (Ed.), The random character of stock market prices (pp. 199–218). Cambridge, Mass: MIT Press.

Allen, F. and Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245-271.

Blume, L.; Easley, D. and O’Hara, M. (1994). Market statistics and Technical Analysis: The role of volume. Journal of Finance, 49(1), 153.181.

Brown, D. y Jennings, R. (1989). On Technical Analysis. Review of Financial Studies, 2(4), 527-551.

Bollinger, J. (2002). Bollinger on Bollinger Bands. New York: McGraw Hill.

Brock, W., Lakonishok, J., LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47(5), pp. 1731–1764.

Campbell, J., Lo, A and MacKinlay, A. (1997). The Econometric of financial markets. Princeton: Princeton University Press.

Chang, K. and Osler,C. (1994). Evaluating chart based technical analysis: The head and shoulders pattern in foreign exchange markets. New York: Federal Reserve Bank.

Chang, E.J.; Araújo, E.J. and Miranda, B. (2004). Testing for predictability in emerging equity markets. Emerging Market Review, 5(3), 295-316.

Downes, J. and Goodman, J. (1998). Dictionary of finance and investment terms, (5th ed.). New York: Barron’s Educational Series, Inc.

Elder, A. (2002). Come Into My Trading Room: A Complete Guide to Trading. New York: John Wiley & Sons Inc.

Fama, E.F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance,25(2), 383-417.

Fama, E.F. (1991). Efficient capital markets:II. Journal of Finance, 46(5), 1575-1617.

Fama, E.F. and Blume, M.E. (1966) Filter rules and stock market trading. Journal of

Business, 39, 226–241

Hansen, P. (2005). A test for superior predictive ability. Journal of Business and Economic Statistics, 23(4): pp. 365–380.

Jegadeesh, N. and Titman, S. (1993), Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.

Jensen, M., Bennington, G. (1970). Random walks and technical theories: some additional evidences. Journal of Finance, 25(2), 469–482.

Kavacejz, D. and Odders-White, E. (2004). Technical Analysis and liquidity provision. Review of Financial Studies, 17(4), 1043-1071.

Lee, K., Jo, G. (1999). Expert system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16(4), pp. 357–364.

Leigh, W., Modani, N., Hightower, R. (2004). A computational implementation of stock charting: abrupt volume increase as signal for movement in New York Stock Exchange Composite Index. Decision Support Systems, 37(4), pp. 515–530.

Leigh, W., Modani, N., Purvis, R., Roberts, T. (2002). Stock market trading rule discovery using technical charting heuristics. Expert Systems with Applications, 23(2), pp. 155–159.

Leigh, W., Paz, N., Purvis, R. (2002). Market timing: a test of a charting heuristic. Economics Letters 77(1), pp. 55–63.

Leigh, W., Purvis, R., Ragusa, J. (2002). Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems, 32(4), pp. 361–377.

Lo, A. and MacKinlay, C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41-66.

Lo, A. and MacKinlay, C. (1999). A non random walk down Wall Street. Princeton: Princeton University Press.

Lo, A., Mamaysky, H., Wang, J. (2000). Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55(4), pp. 1705–1770.

Malkiel, B. (1996). A random walk down Wall Street: Including a life-cycle guide to personal investing. New York: W.W. Norton.

Neely, C.; Weller, P. and Dittmar, R. (1997). In technical analysis in the foreign exchange market profitable?: a genetic programming approach. Journal of Financial and Quantitative Analysis, 32(4), 405-426.

Neftci, S. (1991). Naive trading rules in financial markets and Weiner–Kolmogorov prediction theory: a study of technical analysis. Journal of Business, 64(4), pp. 549–571.

Park, C. and Irwin, S. (2004). The profitability of technical analysis: a review. AgMAS Project Research Report No. 2004-04, http://ssrn.com/abstract=603481.

Park, C. and Irwin, S. (2007). What do we know about the profitability of technical analysis. Journal of Economic Surveys, 21(4), pp. 786–826.

Paulos, J. (2003). A mathematician plays the stock market. Cambridge: Basic books.

Pruitt, S. and White, R. (1988). The CRISMA trading system: Who says technical analysis can’t beat the market? Journal of Portfolio Management, 14(1), 55-58.

Ratner, M., Leal, R. (1999). Test of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking and Finance, 23(12), pp. 1887–1905.

Ready, M. (2002). Profits from technical trading rules. Financial Management, 31(3), 43-61.

Reitz, S. (2005). On the predictive content of technical analysis. North American Journal of Economics and Finance, 17(2), 121-137.

Sullivan, R., Timmermann, A., White, H. (1999). Data snooping, technical trading rule performance, and the bootstrap. Journal of Finance, 54(5), pp. 1647–1692.

Tabell, A. and Tabell, E. (1964). The case for technical analysis. Financial Analyst Journal, 20(1), 67-76.

Teixeira, L., de Oliveira, A. (2010). A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Systems with Applications, 37(10), pp. 6885–6890.

Timmermann, A., Granger, C. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), pp. 15–27.

Treynor, J. and Ferguson, R. (1985). The case for technical analysis. Financial Analyst Journal, 20(1), 67-76.

Van Horne, J.C. and Parker, G.G.C. (1967) The random-walk theory: an empirical test.

Financial Analysts Journal,23, 87–92.

Van Horne, J.C. and Parker, G.G.C. (1968) Technical trading rules: a comment. Financial

Analysts Journal, 24, 128–132.

Wang, J., Chan, S. (2007). Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(4), pp. 304–315.

Wang, J., Chan, S. (2009). Trading rule discovery in the US stock market: An empirical study. Expert Systems with Applications, 36(3), pp. 5450–5455.

White, H. (2000). A reality check for data snooping. Econometrica, 68(5), pp. 1097–1126.

Zapranis, A., Tsinaslanidis, P. (2012). A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the US stock market. Expert Systems with Applications, 39(7), pp. 6301–6308.

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