Efficiency and Predictive Power of Technical Trading Rules in Post-COVID-19 Era : A Study of BRICS Stock Market
DOI:
https://doi.org/10.17010/ijf/2025/v19i1/174695Keywords:
technical trading
, EMA, stock market, BRICS.JEL Classification Codes
, C58, G12, G14Paper Submission Date
, June 10, 2024, Paper sent back for Revision, November 22, Paper Acceptance Date, December 5, Paper Published Online, January 15, 2025Abstract
Purpose : This study was an attempt to test the efficiency and revalidate the technical trading strategies based on the exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) on stock returns in Brazil, Russia, India, China, and South Africa (BRICS) before and after the COVID-19 pandemic.
Methodology : The stock markets of BRICS were used from January 1, 2016, to February 29, 2024, to test the effectiveness of EMA, RSI, and MACD in a pre- and post-COVID sample.
Findings : The study did not suggest using EMA, RSI, and MACD indicators to generate buy and sell for a day trader because the technical rules under consideration failed to generate a positive alpha ratio ; hence, EMA, RSI, and MACD emerged as the low-performing technical trading rules for generating better stock returns than the “buy-hold†strategy in the BRICS stock market.
Practical Implications : Investors or traders in the stock markets of BRICS countries could clearly understand whether to use or not use the technical trading strategies studied in this research.
Originality : Unlike prior research on technical analysis, the current work provided a systemic way of back-testing technical trading strategies like EMA, RSI, and MACD.
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Aggarwal, S., & Khurana, S. (2018). Empirical examination of stock market volatility: An international comparison. Indian Journal of Finance, 12(1), 47–61. https://doi.org/10.17010/ijf/2018/v12i1/120741
Agrawal, M., Shukla, P. K., Nair, R., Nayyar, A., & Masud, M. (2022). Stock prediction based on technical indicators using deep learning model. Computers, Materials and Continua, 70(1), 287–304. https://doi.org/10.32604/cmc.2022.014637
Akhtar, F., Kumar, M., & Malik, N. S. (2021). A cointegration approach for selection of currency pairs. Indian Journal of Finance, 15(12), 22–38. https://doi.org/10.17010/ijf/2021/v15i12/160018
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. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x
Buchanan, B. G., English, P. C., & Gordon, R. (2011). Emerging market benefits, investability and the rule of law. Emerging Markets Review, 12(1), 47–60. https://doi.org/10.1016/j.ememar.2010.09.001
Chong, T., Cheng, S., & Wong, E. (2010). A comparison of stock market efficiency of the BRIC countries. Technology and Investment, 1(4), 235–238. https://doi.org/10.4236/ti.2010.14029
Chowdhury, M. I., Hasan, M., Bouri, E., & Tang, Y. (2024). Emotional spillovers in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 41, Article ID 100878. https://doi.org/10.1016/j.jbef.2023.100878
da Costa, T. R., Nazário, R. T., Bergo, G. S., 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
de Souza, M. J., Ramos, D. G., 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), Article no. 3. https://doi.org/10.1186/s40854-018-0087-z
Khuntia, S., & Pattanayak, J. K. (2017). Dynamics of Indian foreign exchange market efficiency: An adaptive market hypothesis approach. Indian Journal of Finance, 11(9), 39–52. https://doi.org/10.17010/ijf/2017/v11i9/118088
Mohammed, I., Gugong, B. K., & Ayuba, A. (2021). Capital structure, board size and financial performance of listed deposit money banks in Nigeria. NDA Journal of Management Sciences Research, 2(1), 151–165. https://www.researchgate.net/publication/362742901
Muruganandan, S. (2020). Testing the profitability of technical trading rules across market cycles: Evidence from India. Colombo Business Journal, 11(1), 24–46. https://doi.org/10.4038/cbj.v11i1.56
Nagendra, S., Kumar, S., & Jayashree. (2018). Implications and usefulness of fundamental and technical analysis in stock market decision making. Indian Journal of Finance, 12(5), 54–71. https://doi.org/10.17010/ijf/2018/v12i5/123702
Raghuram, G. (2017). Investigating the 'month of the year' effect in India. Indian Journal of Finance, 11(1), 11–28. https://doi.org/10.17010/ijf/2017/v11i1/108958
Reddy, C. V. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. https://doi.org/10.17010/ijf/2019/v13i8/146301
Sobreiro, V. A., da Costa, T. R., Nazário, R. T., Lima e Silva, J., Moreira, E. A., Filho, M. C., Kimura, H., & Zambrano, J. C. (2016). The profitability of moving average trading rules in BRICS and emerging stock markets. The North American Journal of Economics and Finance, 38, 86–101. https://doi.org/10.1016/j.najef.2016.08.003
Tadas, H., Nagarkar, J., Malik, S., Mishra, D. K., & Paul, D. (2023). The effectiveness of technical trading strategies: Evidence from Indian equity markets. Investment Management and Financial Innovations, 20(2), 26–40. https://doi.org/10.21511/imfi.20(2).2023.03
Vashishtha, V. M., & Kumar, P. (2013). 50 years of immunization in India: Progress and future. Indian Pediatrics, 50, 111–118. https://doi.org/10.1007/s13312-013-0025-0
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
