Peruvian evidence of the efficiency of technical analysis on the Lima Stock Exchange
-
DOIhttp://dx.doi.org/10.21511/imfi.21(4).2024.24
-
Article InfoVolume 21 2024, Issue #4, pp. 301-313
- 81 Views
-
23 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
In emerging markets such as the Lima Stock Exchange, characterized by lower liquidity and market depth, investors face unique challenges in maximizing returns and mitigating risks. This study aims to evaluate the efficiency of technical analysis on the Lima Stock Exchange, focusing on 14 stocks from the S&P/BVL Peru General portfolio selected for their broad spectrum of economic sectors, high trading frequency, and consistency in the index, totaling 9,802 quotations during the period from June 21, 2021 to June 21, 2024. The results, excluding transaction costs, show that technical tools such as Momentum, Moving Averages, Stochastic Oscillator, and Williams Oscillator offer superior returns compared to the buy-and-hold strategy, especially in short periods of 5, 10, and 15 days, with average excess returns of 24.28%. However, when including transaction costs, only eight of the 14 stocks achieve excess returns, with the Relative Strength Index (RSI), Bollinger Bands, and Moving Averages standing out as the most efficient tools, especially over longer periods. These findings underscore the importance of careful management of transaction costs to optimize the benefits of technical analysis in emerging markets.
- Keywords
-
JEL Classification (Paper profile tab)G11, G14, G15
-
References41
-
Tables11
-
Figures0
-
- Table 1. Stocks from the S&P LSE Peru General Index that make up the sample
- Table 2. Moving averages (MA): Average annual return per stock, excluding transaction costs
- Table 3. Moving averages (MA): Average annual return per stock, including transaction costs
- Table 4. Momentum: Average annual return per stock, exclusion of transaction costs
- Table 5. Momentum: Average annual return per stock, including transaction costs
- Table 6. Relative Strength Index (RSI): Average annual return per stock
- Table 7. Stochastic oscillator (%K): Average annual return per stock
- Table 8. Williams %R Oscillator: Average annual return per stock
- Table 9. Bollinger bands: Average annual return per stock
- Table 10. Tools generating the best average annual returns per stock excluding transaction costs
- Table 11. Tools generating the best average annual returns per stock, including transaction costs
-
- Alhashel, B., Almudhaf, F., & Hansz, J. A. (2018). Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets. Pacific-Basin Finance Journal, 47, 92-108.
- Ali, A., Shah, A., Khan, A. H., Sharif, M. U., Zahid, Z. U., Shahid, R., Jan, T., & Zafar, M. H. (2024). Speed vs. efficiency: A framework for high-frequency trading algorithms on FPGA using Zynq SoC platform. Alexandria Engineering Journal.
- Arora, M. (2016). The Technical Analysis – A Logical Effort of A Psychological Mind. Paripex Indian Journal of Research, 4(5).
- Byrareddy, V. M., Islam, M. A., Nguyen-Huy, T., & Slaughter, G. (2023). A systematic rview of emerging environmental markets: Potential pathways to creating shared value for communities. Heliyon.
- Chakrabarty, A., Majumdar, A., & Chatterjee, M. (2024). Quantifying the Volatility of Stock Price Changes in the Indian Market Using the Moving Average Envelope and Bollinger Bands.
- Chong, F., Ling, H., Ng, D., & Yat, C. (2014). An Empirical Re-Investigation on the “Buy-and-hold Strategy” in Four Asian Markets: A 20 Years’ Study.
- Chou, H.-C., & Chen, D.-H. (2019). The use of technical analysis in sale-and-purchase transactions of secondhand ships. Maritime Economics and Logistics, 21(2), 223-240.
- Chovancova, B., & Árendáš, P. (2015). Long Term Passive Investment Strategies as a Part of Pension Systems. Economics & Sociology, 8(3), 55-67.
- Chuang, O.-C., Chuang, H.-C., Wang, Z., & Xu, J. (2024). Profitability of technical trading rules in the Chinese stock market. Pacific-Basin Finance Journal.
- Demirel, M., Demirel, M., & Unal, G. (2020). Applying multivariate-fractionally integrated volatility analysis on emerging market bond portfolios. Financial Innovation, 6(1), 1-29.
- Detzel, A. L., 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.
- Diaw, A. (2021). Corporate cash holdings in emerging markets. Borsa Istanbul Review, 21(2), 139-148.
- Dierkes, M. (2022). Isolating momentum crashes. Journal of Empirical Finance, 66, 1-22.
- Dockery, E., & Todorov, I. (2023). Further evidence on the returns to technical trading rules: Insights from fourteen currencies. Journal of Multinational Financial Management.
- Escobar, M., Ferrando, S., & Rubtsov, A. (2018). Dynamic derivative strategies with stochastic interest rates and model uncertainty. Journal of Economic Dynamics and Control, 86, 49-71.
- Gao, Y., Wu, J., Feng, Z., Hu, G., Chen, Y., & He, W. (2023). A new BRB model for technical analysis of the stock market. Intelligent Systems with Applications, 18, 200198.
- Ghose, S., Heiman, A., & Lowengart, O. (2017). Isolating strategy effectiveness of brands in an emerging market: A choice modeling approach. Journal of Brand Management, 24(2), 161-177.
- Guo, J. (2022). What Can Explain Momentum? Evidence from Decomposition. Management Science, 68(8), 6184-6218.
- Hameed, A., Ni, Z., & Tan, C. I. (2023). Momentum and individual investor trades: Evidence from Singapore. Pacific-Basin Finance Journal, 82, 102186.
- Hasan, S., Nurhasanah, S., & Santoso, W. P. (2024). Analisis Teknikal Menggunakan Moving Average (MA), Moving Average Convergence-Divergence (MACD), dan Relative Strength Index (RSI) Untuk Mengoptimalkan Dalam Pengambilan Keputusan Investasi Pada Saham Sektor Manufaktur Index LQ45 BEI Tahun 2022-2023.
- Hung, C., & Lai, H. N. (2022). Information asymmetry and the profitability of technical analysis. Journal of Banking and Finance, 134, 106347.
- Jegadeesh, N., & Titman, S. (2023). Momentum: Evidence and insights 30 years later. Pacific-Basin Finance Journal.
- Kouaissah, N., Kouaissah, N., Orlandini, D., Ortobelli, S., Ortobelli, S., & Tichý, T. (2020). Theoretical and practical motivations for the use of the moving average rule in the stock market. Ima Journal of Management Mathematics, 31(1), 117-138.
- Kubińska, E., Czupryna, M., Markiewicz, Ł., & Czekaj, J. (2016). Technical Analysis as a Rational Tool of Decision Making for Professional Traders. Emerging Markets Finance and Trade, 52(12), 2756-2771.
- Lee, M. C., Chang, J. W., Yeh, S.-C., Chia, T.-L., Liao, J., & Chen, X.-M. (2022). Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies. Neural Computing and Applications, 34(16), 13267-13279.
- Liu, Q., & Kung, K. P. (2023). Optimality of Buy-and-Hold Strategies. Eurasian Journal of Business and Management, 11(1), 32-45.
- Lobão, J., Fortuna, N., & Silva, F. (2020). Do psychological barriers exist in Latin American stock markets. Revista de análisis económico, 35(2), 29-56.
- M’ng, J. C. P., & Zainudin, R. (2016). Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies. PLOS ONE, 11(8), 1-19.
- O’Brien, T. J. (2020). Buy-and-Hold and Constant-Mix May Be Better Allocation Strategies Than You Think. The Journal of Portfolio Management, 46(6), 159-171.
- Putra, A. S., & Miharja, R. (2023). Technical analysis in companies listed on LQ45 on the Indonesia Stock Exchange 2011–2018. Jurnal Ilmu Manajemen.
- Rivera, L. E. S. (2022). Comparative Analysis of Efficiency in the Economic Sectors of Lima Stock Exchange. Europe.
- Romaniuk, J., & Nenycz-Thiel, M. (2016). Lapsed buyers’ durable brand consideration in emerging markets. Journal of Business Research, 69(9), 3645-3651.
- Shi, Y., Shi, Y., & Tingting, Y. (2023). Can technical indicators based on underlying assets help to predict implied volatility index. Journal of Futures Markets, 44(1), 57-74.
- Sundlöf, C., & Krantz, G. (2016). A comparative study of technical indicator performances by stock sector: RSI, MACD, and Larry Williams %R applied to the Information Technology, Utilities, and Consumer Staples sectors. Degree Project in Computer Science, KTH School of Computer Science and Communication, Stockholm, Sweden.
- Tao, Y. (2023). Trend-based forecast of cryptocurrency returns. Economic Modelling, 124, 106323.
- Tomtosov, A. (2023). Overlapping portfolio holdings and unique sources of emerging market risk. Borsa Istanbul Review.
- Wang, T., & Sun, Q. (2015). Why investors use technical analysis? Information discovery versus herding behavior. China Finance Review International, 5(1), 53-68.
- Wen, D., Liu, L., Wang, Y., & Zhang, Y. (2022). Forecasting crude oil market returns: Enhanced moving average technical indicators. Resources Policy, 76, 102570.
- Xiang, Y., Zhao, Y., & Deng, S. (2023). Asset-return momentum prediction through pattern recognition. Knowledge Based Systems, 268, 110443.
- Zatwarnicki, M., Zatwarnicki, K., & Stolarski, P. (2023). Effectiveness of the Relative Strength Index Signals in Timing the Cryptocurrency Market. Sensors, 23(3), 1664.
- Zhang, Y., Kappou, K., & Urquhart, A. (2024). Macroeconomic momentum and cross-sectional equity market indices. Journal of International Financial Markets, Institutions and Money.