Optimization of MACD Indicator Parameters on the Movement of the Indonesia Sharia Stock Index (ISSI): A Technical Approach Based on Historical Data

Teuku Syifa Fadrizha Nanda; Ana Fitria

doi:10.22373/5tz46p44

Authors

Teuku Syifa Fadrizha Nanda Universitas Islam Negeri Ar-Raniry Banda Aceh
Ana Fitria Universitas Islam Negeri Ar-Raniry Banda Aceh

DOI:

https://doi.org/10.22373/5tz46p44

Keywords:

MACD, ISSI, technical strategy, parameter optimization, Islamic stock market

Abstract

The Indonesia Sharia Stock Index (ISSI) has shown steady growth, reflecting rising interest in Sharia-compliant investments. However, technical tools like MACD are still underutilized. This study aims to identify the optimal MACD parameter configuration (i.e., the combination of N1, N2, N3) for ISSI movements in order to generate the highest return with measurable risk. A quantitative-exploratory approach was applied using daily closing data of ISSI from 2013 to 2023. A total of 1,152 MACD parameter combinations were systematically tested using Python simulations and subsequently validated through backtesting with Pine Script on the TradingView platform. The performance of each configuration was evaluated based on cumulative return and Sharpe ratio. The findings show that the MACD configuration of (5, 21, 8) produced a cumulative return of 234% over the ten-year period, significantly outperforming the standard configuration, which yielded only 152%. Moreover, the optimal setup achieved a Sharpe ratio of 1.75, indicating superior investment performance in terms of risk-adjusted returns. Interestingly, most of the top-performing MACD configurations consisted of odd numbers. This may be associated with the five-day weekly trading cycle, in which odd-numbered periods such as 5, 7, or 9 days tend to be more responsive to short-term trend shifts. This pattern appears to align with the psychological rhythm of the relatively stable and orderly Sharia market. The study concludes that adjusting MACD parameters to suit specific market characteristics can significantly enhance the effectiveness of technical analysis and investment strategies.

Downloads

Download data is not yet available.

References

Apple, Gerald. (2005). Technical Analysis: Power Tools For Active Investors. New York: FT Press.

Barberis, N., Shleifer, A., Vishny, R. (1998). A Model of Investor Sentiment. Journal of Financial Economics, 49 (3), 307-343. https://doi.org/10.1016/S0304-405X(98)00027-0

Basrowi, B., Fauzi, F., & Utami, P. (2020). Apakah Memilih Saham Daftar Efek Syariah Indonesia dengan Analisis Teknikal Akan Menguntungkan? Al-Infaq: Jurnal Ekonomi Islam, 11(1), 39–50. https://doi.org/10.32507/ajei.v11i1.555

Borowski, K., & Pruchnicka-Grabias, I. (2019). Optimal lengths of moving averages for the MACD oscillator for companies listed on the Warsaw Stock Exchange. Argumenta Oeconomica, 43(2), 73–97.

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

Chen, W., & Zhu, Z. (2025). Wavelet-transformed MACD with Genetic Optimization for Enhanced Trading Performance. arXiv preprint arXiv:2501.10808.

Ehlers, John F. (1991). MACD Indicators Revisited. Technical Analysis of Stock and Commodities, Volume 9:10 (391-395).

Erić, D., Andjelić, G. B., & Redžepagić, S. (2009). Application of MACD and RVI indicators as functions of investment strategy optimization. Journal of Economics and Business, 27(1), 89–103.

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

Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46 (5), 1575-1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x

Hasan, M., Dridi, J. (2010). The Effects of the Global Crisis on Islamic and Conventional Banks: A Comparative Study. IMF Working Paper, 10 (201).

Joshi, D. L. (2022). Evaluating the Effectiveness of MACD in Predicting Stock Prices and Investment Decisions. International Journal of Finance & Economics, 27(4), 5598–5614.

Kang, B.-K. (2023). Parameter Optimization of the MACD Indicator for Financial Time Series. Journal of Risk and Financial Management, 16(12), 508. https://doi.org/10.3390/jrfm16120508

Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311–5319.

Malkiel, B. G. (2003). The Efiicient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17 (1), 59-82. DOI: 10.1257/089533003321164958

Methalchi, M., Marcucci, J., & Chang, Y. (2012). Are moving average trading rules profitable? Evidence from the European stock markets. Applied Economics, 44(12), 1539–1559.

Mukmin, A., & Firmansyah, F. (2020). Analisis perbandingan volatilitas indeks harga saham IHSG dan JII. Jurnal Ekonomi dan Perbankan Syariah, 3(1), 69–84.

https://doi.org/10.46899/jeps.v3i1.155

Murphy, J.J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New Jersey: Prentice Hall Press.

Nanda, T. S. F. (2022). Penerapan konsep divergence pada indikator momentum MACD. Jurnal Riset Akuntansi & Komputerisasi Akuntansi (JRAK), 13(2), 109–120.

Nanda, T. S. F. (2024). Menguji konsep hidden divergence pada indikator momentum Relative Strength Index. Jurnal Riset Akuntansi & Komputerisasi Akuntansi (JRAK), 15(1), 26–38.

Nanda, T. S. F., et al. (2024). Perbandingan kinerja indikator teknikal MACD dan RSI terhadap pergerakan ISSI. Jurnal Organisasi dan Strategi Ekonomi (JOSE), 6(1), 45–58.

Prasetyo, A. S., Vibriyanto, N., & Wulandari, R. (2024). Comparison of volatility of Jakarta Islamic Index with LQ45 stock in Indonesia. Jurnal Manajemen dan Organisasi, 15(3), 281–292. https://journal.ipb.ac.id/index.php/jmo/article/view/56464

Rhea, R. (1932). The Dow Theory: An Explanation of Its Development and an Attempt to Define Its Usefulness as an Aid in Speculation. New York: Barron’s.

Shiller, R. J. (2000). Irrational Exuberance. New Jersey: Princeton University Press.

Songwe, V., Stern, N., Bhattacharya, A. (2022). Finance for climate action: Scaling up investment for climate and development. London: Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science.

Sullivan, R., Timmermann, A., White., H. (2002). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. The Journal of Finance, 54 (5). DOI: 10:1111/0022-1082.00163.

Tharavanij, P., Siraprapasiri, V. & Rajchamaha, K. (2015). Performance of technical trading rules: evidence from Southeast Asian stock markets. SpringerPlus, 4 (552). https://doi.org/10.1186/s40064-015-1334-7.

Zoicaș-Ienciu, A. (2015). The sensitivity of moving average trading rules performance with respect to methodological assumptions. Procedia Economics and Finance, 32, 1353–1361.