Comparative analysis of leading and lagging techniques in investment strategies
DOI:
https://doi.org/10.12928/optimum.v16i1.13641Keywords:
Leading Technique, Lagging Technique, Comparative Analysis, InvesmentAbstract
This research compares the accuracy and performance of leading and lagging indicators in predicting stock price movements on the IDXGrowth30 index over a three-year period from January 2021 to December 2023. Leading indicators provide signals before a trend forms, while lagging indicators confirm trends after price movements have occurred. The Relative Strength Index (RSI) is used as a representation of the leading indicator, while the Parabolic Stop and Reverse (Parabolic SAR) represents the lagging indicator. This study evaluates the effectiveness of technical indicators in supporting investment decisions based on signaling theory. The results show that RSI has an accuracy rate of 94%, higher than Parabolic SAR's 39%, and also generates a larger return (14.652 compared to 3.032). These findings indicate that RSI is more effective in providing fast and accurate signals for growth-oriented stocks. This research fills a gap in the literature regarding the comparison of technical indicators on the IDXGrowth30 index, although the generalization of results remains limited.
References
Abbas, A., Triani, N., Rayyani, W. O., & Muchran, M. (2023). Earnings growth, marketability and the role of Islamic financial literacy and inclusion in Indonesia. Journal of Islamic Accounting and Business Research, 14(7), 1088-1105. https://doi.org/10.1108/JIABR-12-2021-0322
Amraini, A., Muchran, M., & Mira, M. (2025). Optimizing invesment decisions: A comparative analysis of fundamental and technical approaches. Optimum: Jurnal Ekonomi dan Pembangunan, 15(2), 195-206. https://doi.org/10.12928/optimum.v15i2.12537
Anand, A., & Venkataraman, K. (2016). Market conditions, fragility, and the economics of market making. Journal of Financial Economics, 121(2), 327-349. https://doi.org/10.1016/j.jfineco.2016.03.006
Aveh, F. K., & Awunyo-Vitor, D. (2017). Firm-specific determinants of stock prices in an emerging capital market: Evidence from Ghana Stock Exchange. Cogent Economics & Finance, 5(1). https://doi.org/10.1080/23322039.2017.1339385
Ayunda, A. S., & Purnamasari, V. (2025). Analysis of stock liquidity in banking development: Evidence from Bank Jago. Optimum: Jurnal Ekonomi dan Pembangunan, 15(1), 79-87. https://doi.org/10.12928/optimum.v15i1.11367
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567. https://doi.org/10.1016/j.najef.2018.06.013
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. Jurnal Institutions and Economies, 16(2), 30-56. https://doi.org/10.22452/IJIE.vol16no2.2
Chong, T., Ng, W.-K., & Liew, V. (2014). Revisiting the performance of MACD and RSI oscillators. Journal of Risk and Financial Management, 7(1), 1-12. https://doi.org/10.3390/jrfm7010001
Chourmouziadis, K., & Chatzoglou, P. D. (2016). An intelligent short term stock trading fuzzy system for assisting investors in portfolio management. Expert Systems with Applications, 43, 298-311. https://doi.org/10.1016/j.eswa.2015.07.063
Chui, A. C. W., Subrahmanyam, A., & Titman, S. (2022). Momentum, reversals, and investor clientele. Review of Finance, 26(2), 217-255. https://doi.org/10.1093/rof/rfac010
Corbet, S., Eraslan, V., Lucey, B., & Sensoy, A. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32-37. https://doi.org/10.1016/j.frl.2019.04.027
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. CRC Press. https://doi.org/10.4324/9781315115719
Firmansyah, M., Yuli, S. B. C., Boedirochminarni, A., Flejterski, S., & Kurniawan, M. L. A. (2025). Determinants of portfolio invesment in ASEAN countries. Jurnal Ekonomi Bisnis dan Kewirausahaan, 14(2), 198-214. https://doi.org/10.26418/jebik.v14i2.91515
Gurrib, I., & Kamalov, F. (2019). The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies. Macroeconomics and Finance in Emerging Market Economies, 12(2), 105-123. https://doi.org/10.1080/17520843.2019.1574852
Hassani, H., Komendantova, N., Rovenskaya, E., & Yeganegi, M. R. (2023). Social trend mining: Lead or lag. Big Data and Cognitive Computing, 7(4), 171. https://doi.org/10.3390/bdcc7040171
Huang, Z., Heian, J. B., & Zhang, T. (2011). Differences of opinion, overconfidence, and the high-volume premium. Journal of Financial Research, 34(1), 1-25. https://doi.org/10.1111/j.1475-6803.2010.01283.x
Koegelenberg, D. J. C., & van Vuuren, J. H. (2024). A dynamic price jump exit and re-entry strategy for intraday trading algorithms based on market volatility. Expert Systems with Applications, 243, 122892. https://doi.org/10.1016/j.eswa.2023.122892
KSEI. (2024). Statistik Pasar Modal Indonesia. Publikasi PT Kustodian Sentral Efek Indonesia, 1-7. Jakarta.
Kurniawan, M. L. A., Zakiyyah, N. A. A., & Juwita, A. H. (2025). Does money causes output? Evidence from Indonesia. International Journal of Monetery Economics and Finance, 18(6), 453-464. https://doi.org/10.1504/IJMEF.2025.150816
Lutey, M. (2022). Robust testing for bollinger band, moving average and relative strength index. Journal of Finance Issues, 20(1), 27-46. https://doi.org/10.58886/jfi.v20i1.3218
Luthfiya, K. A. S., & Darsono, S. N. A. C. (2025). Sustainable invesment challenges in emerging markets: Case of India's NIFTY100ESG indices. Optimum: Jurnal Ekonomi dan Pembangunan, 15(1), 139-153. https://doi.org/10.12928/optimum.v15i1.12706
Metghalchi, M., Kagochi, J., & Hayes, L. (2021). A technical approach to equity investing in South Africa: A tale of two indexes. Cogent Economics and Finance, 9(1), 1-20. https://doi.org/10.1080/23322039.2020.1869374
Muis, I. S., Prajawati, M. I., & S, B. (2021). Analisis teknikal return saham dengan indikator-indikator bollinger band, parabolic sar, dan stochastic oscillator. Jurnal Samudra Ekonomi Dan Bisnis, 12(2), 143-153. https://doi.org/10.33059/jseb.v12i2.2467
Ngo, T., & Le, T. (2019). Capital market development and bank efficiency: A cross-country analysis. International Journal of Managerial Finance, 15(4), 478-491. https://doi.org/10.1108/IJMF-02-2018-0048
Nidhi, N., Malik, N. S., & Singla, R. (2023). Efficiency of RSI investment strategy: A comparative study of Saudi Arabia, India, and China. Indian Journal of Finance, 17(11), 50-61. https://doi.org/10.17010/ijf/2023/v17i11/171931
Padhi, D. K., Padhy, N., Bhoi, A. K., Shafi, J., & Yesuf, S. H. (2022). An Intelligent fusion model with portfolio selection and machine learning for stock market prediction. Computational Intelligence and Neuroscience, 2022, 1-18. https://doi.org/10.1155/2022/7588303
Shalini, T., Pranav, S., & Utkarsh, S. (2019). Picking buy-sell signals: A practitioner's perspective on key technical indicators for selected indian firms. Studies in Business and Economics, 14(3), 205-219. https://doi.org/10.2478/sbe-2019-0054
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355-374. https://doi.org/10.2307/1882010
Wilder, J. W. (1978). New concepts in technical trading systems. Greensboro, N. C. : Trend Research.
Yao, C.-Z., & Li, H.-Y. (2020). Time-varying lead-lag structure between investor sentiment and stock market. The North American Journal of Economics and Finance, 52, 101148. https://doi.org/10.1016/j.najef.2020.101148
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Detri Heri Gemita, Muchriana Muchran, Mira Mira

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.






