Bull flag pattern recognition: Evidence from the BOVESPA index
Abstract
This paper studies the predictive power of technical analysis using bull flag patterns based on a template matching technique. This method is applied to the stock market of Brazil (BOVESPA), for the period from February 1st 1995 to December 30th 2021. The empirical results demonstrate that bull flag trading rules can correctly predict the price movement direction of the index. Technical analysis achieved positive and significant annualized excess profits compared to the buy everyday approach, even when considering transaction costs. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance.
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Agrawal, S., Jindal, M., & Pillai, G. N. (2010). Momentum analysis based stock market prediction using adaptive neuro-fuzzy inference system (anfis). In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1).
Appel, G. (1979). The Moving Average Convergence-Divergence Method. Great Neck, NY: Signalert, 1647-1691.
Arévalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177-192.
Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F., & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119.
Bessembinder, H., & Chan, K. (1998). Market efficiency and the returns to technical analysis. Financial Management, 27(2), 5-17.
Bollinger, J. (1992). Using Bollinger bands. Stocks & Commodities, 10(2), 47-51.
Butler, M., & Kazakov, D. (2010). Particle swarm optimization of Bollinger bands. In International Conference on Swarm Intelligence (pp. 504-511). Springer, Berlin, Heidelberg.
Caginalp, G., & Balevonich, D. (2003). A theoretical foundation for technical analysis. Journal of Technical Analysis, 59(5-22).
Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015). Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert systems with Applications, 42(14), 5963-5975.
Chang, P. K., & Osler, C. L. (1995). Head and shoulders: not just a flaky pattern. Federal Reserve Bank of New York Staff Papers, (4).
Chen, T. L., & Chen, F. Y. (2016). An intelligent pattern recognition model for supporting investment decisions in stock market. Information Sciences, 346, 261-274.
Chong, T. T. L., & Ng, W. K. (2008). Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Applied Economics Letters, 15(14), 1111-1114.
Dawson, E. R., & Steeley, J. M. (2003). On the existence of visual technical patterns in the UK stock market. Journal of Business Finance & Accounting, 30(1‐2), 263-293.
de Souza, M. J. S., Ramos, D. G. F., Pena, M. G., Sobreiro, V. A., & Kimura, H. (2018). Examination of the profitability of technical analysis based on moving average strategies in BRICS. Financial Innovation, 4(1), 1-18.
Detzel, A., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2021). Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals. Financial Management, 50(1), 107-137.
Fama, E. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25(2), 383-417.
Friesen, G. C., Weller, P. A., & Dunham, L. M. (2009). Price trends and patterns in technical analysis: A theoretical and empirical examination. Journal of Banking & Finance, 33(6), 1089-1100.
Garcia, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy, 24(6), 2161-2178.
Gebka, B., Hudson, R. S., & Atanasova, C. V. (2015). The benefits of combining seasonal anomalies and technical trading rules. Finance Research Letters, 14, 36-44.
Leigh, W., Frohlich, C. J., Hornik, S., Purvis, R. L., & Roberts, T. L. (2008). Trading with a stock chart heuristic. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(1), 93-104.
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), 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), 155-159.
Leigh, W., Paz, N., & Purvis, R. (2002). Market timing: a test of a charting heuristic. Economics Letters, 77(1), 55-63.
Leigh, W., & Purvis, R. (2008). Implementation and validation of an opportunistic stock market timing heuristic: One-day share volume spike as buy signal. Expert Systems with Applications, 35(4), 1628-1637.
Leigh, W., Purvis, R., & Ragusa, J. M. (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), 361-377.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Ma, A., & Yu, T. (2021). Technical Analysis with Empirical Mode Decomposition: A Case in the Hong Kong Stock Market. The Journal of Wealth Management, 24(1), 41-48.
Marshall, B. R., Cahan, R. H., & Cahan, J. (2010). Technical analysis around the world. Working paper, Massey University New Zealand. Available at SSRN 1181367.
Masuku, K. L., & Gopane, T. J. (2022). Technical trading rules' profitability and dynamic risk premiums of cryptocurrency exchange rates. Journal of Capital Markets Studies, 6(1), 6-32.
Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance, 34(11), 2573-2586.
Nazário, R. T. F., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126.
Neftci, S. N. (1991). Naive trading rules in financial markets and wiener-kolmogorov prediction theory: A study of" technical analysis". Journal of Business, 64(4), 549-571.
Park, C. H., & Irwin, S. H. (2004). The profitability of technical analysis: A review. AgMAS Project Research Report, No. 2004-04.
Pring, M. J. (1991). Technical analysis explained. New York: McGraw-Hill Book Company, 2nd Edition.
Reed, G. F., Lynn, F., & Meade, B. D. (2002). Use of coefficient of variation in assessing variability of quantitative assays. Clinical and Vaccine Immunology, 9(6), 1235-1239.
Roberts, H. V. (1959). Stock-market "patterns" and financial analysis: methodological suggestions. The Journal of Finance, 14(1), 1-10.
Scholes, M., & Williams, J. (1977). Estimating betas from nonsynchronous data. Journal of Financial Economics, 5(3), 309-327.
Stanković, J., Marković, I., & Stojanović, M. (2015). Investment strategy optimization using technical analysis and predictive modeling in emerging markets. Procedia Economics and Finance, 19, 51-62.
Vidotto, R. S., Migliato, A. L. T., & Zambon, A. C. (2009). O Moving Average Convergence-Divergence como ferramenta para a decisão de investimentos no mercado de ações. Revista de Administração Contemporânea, 13(2), 291-309.
Wang, J. L., & Chan, S. H. (2007). Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(2), 304-315.
Wang, J. L., & Chan, S. H. (2009). Trading rule discovery in the US stock market: An empirical study. Expert Systems with Applications, 36(3), 5450-5455.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Zapranis, A. & Tsinaslanidis, P. E. (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), 6301-6308.
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Portuguese Journal of Finance, Management and Accounting
e-ISSN: 2183-3826
DOI: 10.54663/2183-3826
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