Performance Evaluation of the Technical Analysis Indicators in Comparison with the Buy and Hold Strategy in Tehran Stock Exchange Indices
الموضوعات :Ebrahim Abbasi 1 , Mohammad Ebrahim Samavi 2 , Emad Koosha 3
1 - Associate Professor of Finance at Alzahra University, Tehran, Iran
2 - Department of Finance, College of Management and Economics, Financial Engineering, Science and Research Branch,
Islamic Azad University
3 - Department of Finance, Financial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Algorithmic Trading, Intelligent Trading Systems, Buy and hold Strategy, Technical Analysis, Technical Analysis Indicators,
ملخص المقالة :
Technical analysis is one of the financial market analysis tools. Technical analysis is a method of anticipating prices and markets through studying historical market data. Based on the factors studied in this type of analysis, indicators are designed and presented to facilitate decision-making on buy and sell stress and then buy and sell action in financial markets. This research evaluates performances and returns of 10 conventional technical analysis indicators based on the strategies set on the total stock exchange index, the total index of OTC market and 8 other (non-correlated) industry indices by using Meta Trader software from 2008 to 2018. Also, the significance of the difference between the returns of the indicators is tested using the buy and hold strategy. The results show a significant difference between the returns using some of the technical analysis indicators in some indices and buy and hold strategy. The effectiveness of technical analysis strategies varies across industries and EMA and SMA with respectively 6 and 5 repetitions, are the best strategies and BB with just one repetition has the least repetition. The investment industry index with the most repetition is the industry in which the strategies used in this study have been able to provide an acceptable return.
[1] Abbasi, E., Akefi, H., Adib Mehr, S., Multi-objective particle group optimization and fuzzy neural adaptive inference system, Journal of Investment Knowledge, 2015, 4(15), P.111-134 [In Persian].
[2] Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M., Mahmoodi, A., Jam, P., New Efficient Hybrid Candlestick Technical Analysis Model for Stock Market Timing on the Basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic, Expert Systems with Applications, 2017, 94(4), P. 21-31. Doi: 10.1016/j.eswa.2017.10.023.
[3] Bader, S., Alhashela, F. W., Almudhafa, J., Andrew, H., Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets, Pacific Basin Finance Journal, 2018, 47 (1), P. 92-108. Doi: 10.1016/j.pacfin.2017.12.005
[4] Barzideh, F., Allah Gholi, S., Relationship between Bollinger Bond Indicator returns and Relative Strength with Stock Exchange returns, Journal of Accounting Studies, 2009, 21(2), P. 84-107 [In Persian].
[5] Benington, G. A., Michael C. J.,Random Walks and Technical Theories: Some Additional Evidence In Security Evaluation and Portfolio, Journal of Finance, 1970, 25(2), P. 469-482. Doi: 10.2307/2325495
[6] Bessembinder, H., Chan, K., The profitability of technical trading rules in the asian stock markets, Pacific Basin Finance, 1995, 3(2), P. 257–284. Doi: 10.1016/0927-538X(95)00002-3
[7] Bhamini, G., Technical Analysis Indicators: pathway towards Rewording Journey, International Journal of Management and Social Sciences Research, 2014, 3(10), P. 87-93. Doi: 10.1016/j.chb.2019.04.023
[8] M’ng, JCP., Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets, Physica A: Statistical Mechanics and its Applications, 2018, 59(2), P. 336-345. Doi: 10.1016/j.physa.2018.06.010.
[9] Eddie, C.M., Hui, Ka Kwan, K.C., Can we still beat “buy-and-hold” for individual stocks?, Statistical Mechanics and its Applications, 2014, 410(3), P.513-534. Doi: 410.10.1016/j.physa.2014.05.061.
[10] Emami, A., Razmi, J., Jowlai, F., A Bootstrap Approach to Comparing the Profitability of Technical Analysis Indicators in Tehran Stock Exchange, Economic Research Magazine, 2007, 42(4), P. 88-110 [In Persian].
[11] Fathi, S., Parvizi, N., Integrating oscillators with moving average laws, Journal of Financial Engineering and Securities Management, 2016, 7(3), P. 41-53 [In Persian].
[12] Heibati, F., Rahnamay Roodposhti; f., The relationship between the two stock pricing approaches in Tehran stock exchange, Journal of Financial Studies, 2010, 5(2), P.136-115 [In Persian].
[13] Ito, A., Profits on technical trading rules and time-varying expected returns: evidence from Pacific-basin equity markets, Pacific Basin Finance, 1999, 7(3), P. 283–330. Doi: 10.1016/S0927-538X(99)000086
[14] Izadikhah, M., Saen, RF., Ahmadi, K., How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach, Asia-Pacific Journal of Operational Research, 2017, 34 (03), P.1740016, Doi: 10.1142/S0217595917400164
[15] Murphy, J., Technical analysis of financial Markets, publication New York Institute of Finance, 1999, P.13-15.
[16] Kristjanpoller, W., Minutolo, M., A hybrid volatility forecasting framework integrating GARCH, Artificial Neural network, Technical Analysis and Principal Components Analysis, Expert Systems with Applications, 2018, 109(4), P. 1-11. Doi: 10.1016/j.eswa.2018.05.011.
[17] Ko, K.C., Value investing and technical analysis in Taiwan stock market, Pacific-Basin Finance Journal, 2014, 26(2), P. 14-36. Doi: 10.1016/j.pacfin.2013.10.004
[18] Qi, L., Technical Analysis and Stock Return Predictability: An Aligned Approach, Journal of Financial Markets, 2017, 38(5), P. 103-123. Doi: 10.1016/j.finmar.2017.09.003.
[19] Lin, S.K., Shin, Y. W., Pei-Ling, T., Application of hidden Markov switching moving average model in the stock markets: Theory and empirical evidence, International Review of Economics & Finance, 2009, 18(2), P. 306-317. Doi: 10.1016/j.iref.2008.06.010
[20] Lu, T.-H., The profitability of candlestick charting in the Taiwan stock market, Pacfic Basin Finance, 2014, 26(3), P. 65–78. Doi: 10.1016/j.pacfin.2013.10.006
[21] Masafumi, N., Akihiko, T., Soichiro, T., Bitcoin technical trading with artificial neural network, Physica A: Statistical Mechanics and its Applications, 2018, 5(10), P. 587–609. Doi: 10.1016/j.physa.2018.07.017
[22] Mohd, N., Prabhat, S., Profitability of Oscillators used in Technical analysis for Financial Market, Advances in Economics and Business Management, 2015,2(9), P. 925-931.
[23] Murphy, J., Technical analysis in capital market, Translated by Farahanifar, Kamyar and Ghasemian Langroodi, Reza. Tehran, Chalesh publition, 2017, Edition 13, P.10-12. [In Persian]
[24] Nti, IK., Adekoya, AF., Weyori, BA., A systematic review of fundamental and technical analysis of stock market predictions, Artificial Intelligence Review, 2019, 38(2), P. 36-49. Doi:10.1007/s10462-019-09754-z.
[25] Pourzamani, Z., Rezvani Aghdam, M., Comparison of the Effectiveness of Combined Strategies of Technical Analysis with buy and hold strategy for buying stocks in ascending and descending periods, Financial Analysis of Securities Analysis, 2017, 10(33), P.17-31[In Persian].
[26] Raei, R., Hosseini, S. F., Comparison of buy and sell returns based on technical indicators and fuzzy logic and the combined method of genetic algorithm fuzzy logic, Journal of Financial Engineering and Securities Management, 2015, 24(2), P.1-14 [In Persian].
[27] Nazário, RTF., Silva, JL. Sobreiro, VA., Kimura, H., A literature review of technical analysis on stock markets, The Quarterly Review of Economics and Finance, 2017,66(2), P.115-126. Doi: 10.1016/j.qref.2017.01.014.
[28] Nabavi, S. A., Chashemi, A. H., Investigation of MA index effectiveness in technical analysis in order to stock price forecasting, Journal of Financial Analysis of Securities, 2011, 10(2), P.83-106 [In Persian].
[29] Mohammadi, S., Technical analysis in Tehran stock exchange, Financial Research, 2004, 17(2), P.97-129 [In Persian].
[30] Sudheer, V., Trading through technical analysis: an empirical study from Indian stock market, International Journal of Development, 2015, 5(8), P. 5410-5416. Doi: 10.5539/ijef.v9n3p91
[31] Wang, Q., Xu, W., Zheng, H., Combining the Wisdom of Crowds and Technical Analysis for Financial Market Prediction using Deep Random Subspace Ensembles, Neurocomputing, 2018, 29(4), P. 51-61. Doi: 10.1016/j.neucom.2018.02.095.
[32] Yu, H., Nartea, G.V., Gan, C., Yao, L.J., Predictive ability and profitability of simple technical trading rules: recent evidence from southeast Asian stock markets, International Review of Economics & Finance, 2013, 25(3), P. 356–371. Doi: 10.1016/j.iref.2012.07.016
[33] Zhu, M., Atri, S., Yegen, E., Are candlestick trading strategies effective in certain stocks with distinct features?, Pacific Basin Finance, 2016, 37(2), P. 116–127. Doi: 10.1016/j.pacfin.2015.10.007