Trading Strategies Based on Trading Systems: Evidence from the Performance of Technical Indicators

محورهای موضوعی : Business Strategy

Sirous Keshavarz ¹ , Majid Vaziri Sereshk ² , Abdolmajid Abdolbaghi ³ , Mohamadhossein Arman ⁴

1 - Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Department of Industrial Engineering, Shahroud University of Technology, Shahroud, Iran
4 - Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran

تاریخ دریافت : 1400/05/25 تاریخ پذیرش : 1400/10/23 تاریخ انتشار : 1400/12/10

کلید واژه: Return, technical indicators, performance, Risk, Trading Strategies,

چکیده مقاله :

This study examines trading strategies based on trading systems by analyzing the performance of 11 technical indicators. The data used for analysis were financial data of all firms listed on the Teh-ran Stock Exchange in the period from 2010 to 2020. Excluding the firms whose data were not available for the period under study, 135 firms were selected as the research sample. The results showed that the signals containing three indicators of moving average, exponential moving average, and relative strength over a weekly up to six-month period to buy or sell stocks (as a strategy) could be used more confidently compared to other indicators to achieve higher returns and profitability. As a result, investors can use the signals that these three indicators in weekly (EMA) and monthly (MA, RSI) periods and the quarterly (MA) and six-month (RSI, EMA) periods to determine buying and selling strategies with the lowest investment risks. It is also recommended that investors use a combination of these three indicators to invest, and extend their investment period over a longer period of time to bear less risk, and more returns.

منابع و مأخذ:

Abbasi, E., Samavi, M.E., Koosha, E. (2020), Performance evaluation of the technical analysis indicators in comparison with the buy and hold strategy in tehran stock exchange indices, Advances in Mathematical Finance & Applications, 5(3), 285-301. DOI:10.22034/amfa.2020.1893194.1376.

Abdolbaghi A, A., Davoodi, Sayyed M.R., & Salimi Bani, M. (2019), The effectiveness of the automatic system of fuzzy logic-based technical patterns recognition: evidence from tehran stock exchange, Advances in mathematical finance & applications, 4(3), 107-125. DOI:10.22034/AMFA.2019.585179.1185.  

Ahmar, A. S. (2017). Sutte Indicator: A technical indicator in the stock market. International Journal of Economics and Financial Issues, 7(2), 223-226. http://dx.doi.org/10.17576/pengurusan-2016-48-01.

Alfonso, G., & Ramirez, D.R. (2020), A nonlinear technical indicator selection approach for stock markets. Application to the Chinese Stock Market, Mathematics, 8, 1301, 1-15.  https://doi.org/10.3390/math8081301.

Asha, T.E. (2014), A study on technical analysis and its usefulness in indian stock market. Mirror, 3(2), 159-165. dor: https://ssrn.com/abstract=3138554.

Bader, S., Alhashela, F. W., Almudhafa, J., Andrew, H. (2018), Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets, Pacific-Basin Finance Journal, 47 (1), P. 92-108. Doi:10.1016/j.pacfin.2017.12.005.

Bashir Khodaparasti, R., Jahangiri, K., & Boroumandzadeh, H. (2019), Comparison Of The Efficiency Of Technical Analysis Indicators In In The Capital Market Periods Of The Boom And Depression In The Active Manufacturing Companies At The Tehran Stock Exchange , Financial Knowledge of Securities Analysis(FINANCIAL STUDIES),  12(42), P. 161 -147. https://www.sid.ir/en/journal/ViewPaper.aspx?id=700742.

Dai, Zh., Zhu, H., & Kang, J. (2021), New technical indicators and stock returns predictability, International Review of Economics and Finance, 71, 127–142. DOI:10.1016/j.iref.2020.09.006.

Ebrahimi Sarve Olia, M.A., Babajani, J., Hanafizadeh, P., &Ebadpour, B. (2017), Assessment of the behavioral Determinants of individual investors in Tehran Stock Exchange based on structural equation modeling, Investment Knowledge, 6(22), P.145-131. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=599439

Eddie, C.M., Hui, Ka Kwan, K.C. (2014), 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.

Fama, E. F. (1970), Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486.

Izadikhah, M., Saen, RF., Ahmadi, K. (2017), How to assess the sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach, Asia-Pacific Journal of Operational Research, 34 (03), P.1740016, Doi:10.1142/S0217595917400164.

Jasdeep, S., Banga, B., & Wade, B. (2019), Profitability of alternative methods of combining the signals from technical trading systems,  Intelligent Systems in Accounting, Finance and Management. 26(1), 32–45. https://doi.org/10.1002/isaf.1442.

Kampouridis, M., & Otero, E.B.(2017), Evolving trading strategies using directional changes, Expert Systems With Applications, 73, p.145–160, doi:10.1016/j.eswa.2016.12.032.

Kenny, A. (2005), Advanced Technical Analysis, Publisher Abbas Kenny, 2005. ISBN-13: 9789640656006.

Kristjanpoller, W., Minutolo, M. (2018),  A hybrid volatility forecasting framework integrating GARCH, Artificial Neural network, Technical Analysis, and Principal Components Analysis, Expert Systems with Applications, 109(4), P.1-11. Doi:10.1016/j.eswa.2018.05.011.

Liu, L., Pan, Z. (2020), Forecasting stock market volatility: the role of technical variables, Economic Modelling, 84, p.55-65. DOI:10.1016/j.econmod.2019.03.007.

M’ng., JCP. (2018), Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets, Physica A: Statistical Mechanics and its Applications, 59(2), P. 336-345. DOI:10.1016/j.physa.2018.06.010.

Nti, IK., Adekoya, AF., Weyori, BA. (2019), A systematic review of fundamental and technical analysis of stock market predictions, Artificial Intelligence Review, 38(2), P. 36-49. Doi:10.1007/s10462-019-09754-z.

Ou, B., & Penman, t. (1983), Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11(4), 295-329. https://doi.org/10.1016/0165-4101(89)90017-7.

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.

Pramudya, R., & Ichsani, S. (2020), Efficiency of  technical analysis for the stock trading, International Journal of Finance & Banking Studies, 9(1), 58-67. https://doi.org/10.20525/ijfbs.v9i1.666.

Reilly, F., & Brown, K. (2011). Investment analysis and portfolio management: Cengage Learning. ISBN-13: 978-1305262997.

Roberts, M. C. (2005). Technical analysis and genetic programming: Constructing and testing a commodity portfolio. Journal of Futures Markets, 25(7), 643–660. https://doi.org/10.1002/fut.20161.

Salmani Danglani, S., Saeedi, P., Bahramzadeh, H. A., Pourshahabi, F. (2019), Representing the Pattern of Relationship between Personality Traits and Investment Patterns in the Stock Market, Journal of System Management, 5(1), p.79-114. Dor:20.1001.1.23222301.2019.5.1.5.6

Taheri, A., Shafiee, M., Evazzadeh Fath, F. (2019), Investigating the Role of Non-Financial Information Analysis and Risk- Return Analysis along with Financial Information in Increasing the Efficiency of the Stock Portfolio of Banks, Journal of System Management, 5(3), p. 123-138. Dor:20.1001.1.23222301.2019.5.3.8.3

Turner, T. (2007), A Beginner’s Guide to Day Trading Online, Adams Media, Second edition (January 19, 2007). ISBN-13:978-1593376864.

Vezeris, Dimitrios, Kyrgos, Themistoklis, Schinas, Christos, (2018), Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System, Journal of risk and financial management, 11, 56; doi:10.3390/jrfm11030056.