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Many researchers have applied technical trading methods to observe the likelihood of profitability from trading stocks in different stock markets around the world. However, the performance of the technical trading indicators for outperforming the buy and hold strategy is still in doubt. Different factors such as different stock markets and different technical trading indicators play a crucial role in different results for profitability. This paper investigates the profitability of the moving average, commodity channel index (CCI), and Bollinger bands indicators performing on 19 stocks, which were listed consistently from 2007 to 2017 in the Thailand SET50 Index with different asset conditions. Sixteen sets of asset conditions are constructed from the volume and volatility of stocks and trading period. According to our study, Bollinger band bottom reversal and CCI trend reversal trading strategies outperform the buy and hold strategy for all asset conditions. However, moving average trading rules perform better than the buy and hold strategy for the following asset conditions: high volatility stock, low or high volatility of trading period, low volume of stock, and low volume trading period. This study helps to show why some traders use technical trading strategies to trade stocks on Thailand SET50 index.
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Bessembinder, H., & Chan, K. (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3(2–3), 257–284. https://doi.org/10.1016/0927-538X(95)00002-3
Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange. Expert Systems with Applications, 37(12), 7908–7912. https://doi.org/10.1016/j.eswa.2010.04.045
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731–1764.
Caporale, G. M., Rault, C., Sova, A. D., & Sova, R. (2015). Financial development and economic growth: Evidence from 10 new European Union members. International Journal of Finance & Economics, 20(1), 48–60. https://doi.org/10.1002/ijfe.1498
Chang, Y. H., Jong, C. C., & Wang, S. C. (2017). Size, trading volume, and the profitability of technical trading. International Journal of Managerial Finance, 13(4), 475–494. https://doi.org/10.1108/IJMF-09-2016-0179
Chaysiri, R., Boontaricponpun, S., Sujjavanich, P., & Ua-ampon, K. (2019). The profitability of moving average trading strategies in the Thailand SET50 index: Past and future. Thammasat Review, 22(2), 150-167.
Chen, C. P., & Metghalchi, M. (2012). Weak-form market efficiency: Evidence from the Brazilian stock market. International Journal of Economics and Finance, 4(7), 22-32.
Chong, T. T., & Ng, W. (2008). Technical analysis and the London stock exchange: Testing the MACD and RSI rules using the FT30. Applied Economics Letters, 15(14), 1111–1114. https://doi.org/10.1080/13504850600993598
Costa, T. R. C. C., Nazário, R. T., Bergo, G. S. Z., Sobreiro, V. A., & Kimura, H. (2015). Trading system based on the use of technical analysis: A computational experiment. Journal of Behavioral and Experimental Finance, 6, 42–55. https://doi.org/10.1016/j.jbef.2015.03.003
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://www.jstor.org/stable/2325486
Fang, J., Jacobsen, B., & Qin, Y. (2014). Predictability of the simple technical trading rules: An out-of-sample test. Review of Financial Economics, 23(1), 30–45. https://doi.org/10.1016/j.rfe.2013.05.004
Gerritsen, D. F. (2016). Are chartists artists? The determinants and profitability of recommendations based on technical analysis. International Review of Financial Analysis, 47, 179-196.
Gunasekarage, A., & Power, D. M. (2001). The profitability of moving average trading rules in South Asian stock markets. Emerging Markets Review, 2(1), 17–33. https://doi.org/10.1016/S1566-0141(00)00017-0
Hayes, R. L., Wu, J., Chaysiri, R., Bae, J., Beling, P. A., & Scherer, W. T. (2016). Effects of time horizon and asset condition on the profitability of technical trading rules. Journal of Economics and Finance, 40(1), 41–59. https://doi.org/10.1007/s12197-014-9291-5
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183–201. https://doi.org/10.1016/j.jfds.2018.04.003
Hudson, R., Dempsey, M., & Keasey, K. (1996). A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935 to 1994. Journal of Banking & Finance, 20(6), 1121–1132. https://doi.org/10.1016/0378-4266(95)00043-7
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange. Expert Systems with Applications, 38(5), 5311–5319. https://doi.org/10.1016/j.eswa.2010.10.027
Lambert, D. R. (1983). Commodity channel index: Tool for trading cyclic trends. Technical Analysis Stocks & Commodities, 1, 120–122.
Lento, C., & Gradojevic, N. (2007). The profitability of technical trading rules: A combined signal approach. Journal of Applied Business Research, 23(1), 13–28. https://doi.org/10.19030/jabr.v23i1.1405
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.
Marshall, B. R., & Cahan, R. M. (2005). Is the 52-week high momentum strategy profitable outside the US? Applied Financial Economics, 15(18), 1259–1267. https://doi.org/10.1080/09603100500386008
Marshall, B. R., Qian, S., & Young, M. (2009). Is technical analysis profitable on US stocks with certain size, liquidity or industry characteristics? Applied Financial Economics, 19(15), 1213–1221. https://doi.org/10.1080/09603100802446591
Metghalchi, M., Glasure, Y., Garza-Gomez, X., & Chen, C. (2007). Profitable technical trading rules for the Austrian stock market. International Business & Economics Research Journal, 6(9), 49–58. https://doi.org/10.19030/iber.v6i9.3405
Mills, T. C. (1997). Technical analysis and the London stock exchange: Testing trading rules using the FT30. International Journal of Finance & Economics, 2(4), 319-331. https://doi.org/10.1002/(SICI)1099-1158(199710)2:4<319::AID-JFE53>3.0.CO;2-6
Nazário, R. T. F, 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. https://doi.org/10.1016/j.qref.2017.01.014
Oppenheimer, H. R., & Schlarbaum, G. G. (1981). Investing with Ben Graham: An ex ante test of the efficient markets hypothesis. Journal of Financial and Quantitative Analysis, 16(3), 341-360.
Parisi, F., & Vasquez, A. (2000). Simple technical trading rules of stock returns: Evidence from 1987 to 1998 in Chile. Emerging Markets Review, 1(2), 152–164. https://doi.org/10.1016/S1566-0141(00)00006-6
Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826. https://doi.org/10.1111/j.1467-6419.2007.00519.x
Ratner, M., & Leal, R. P. C. (1999). Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking & Finance, 23(12), 1887–1905. https://doi.org/10.1016/S0378-4266(99)00042-4
Risso, W. A. (2009). The informational efficiency: The emerging markets versus the developed markets. Applied Economics Letters, 16(5), 485–487. https://doi.org/10.1080/17446540802216219
Stübinger, J., Mangold, B., & Krauss, C. (2018). Statistical arbitrage with vine copulas. Quantitative Finance, 18(11), 1831–1849. https://doi.org/10.1080/14697688.2018.1438642
Tay, F. E. H., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309–317. https://doi.org/10.1016/S0305-0483(01)00026-3
Tharavanij, P., Siraprapasiri, V., & Rajchamaha, K. (2015). Performance of technical trading rules: Evidence from Southeast Asian stock markets. SpringerPlus, 4(1), 1-40. https://doi.org/10.1186/s40064-015-1334-7
Tian, G. G., Wan, G. H., & Guo, M. (2002). Market efficiency and the returns to simple technical trading rules: New evidence from U.S. equity market and Chinese equity markets. Asia-Pacific Financial Markets, 9(3-4), 241–258.
Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501–5506. https://doi.org/10.1016/j.eswa.2013.04.013
Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3), 6668–6680. https://doi.org/10.1016/j.eswa.2008.08.019
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. https://doi.org/10.1080/0960310022000020906
Yoon, Y., & Swales, G. (1991). Predicting stock price performance: A neural network approach. In Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences (pp.156–162). U.S.A: IEEE.
Yu, H., Nartea, G. V., Gan, C., & Yao, L. J. (2013). Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets. International Review of Economics & Finance, 25, 356–371. https://doi.org/10.1016/j.iref.2012.07.016
Zhang, G., B., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
Zhu, H., Jiang, Z. Q., Li, S. P., & Zhou, W. X. (2015). Profitability of simple technical trading rules of Chinese stock exchange indexes. Physica A: Statistical Mechanics and its Applications, 439, 75–84. https://doi.org/10.1016/j.physa.2015.07.032