Published: 2024-12-01
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions
DOI: 10.35870/ijsecs.v4i3.2606
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Abstract
This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.
Keywords
Stock Portfolio Analysis ; Machine Learning Algorithms ; Investment Decisions ; Business Management ; Stock Market
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2025)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i3.2606
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Munawir
Digital Business Study Program, Institut Teknologi dan Bisnis Muhammadiyah Bali, Jembrana Regency, Bali Province, Indonesia
Upik Sri Sulistyawati
Entrepreneurship Study Program, Institut Teknologi dan Bisnis Muhammadiyah Bali, Jembrana Regency, Bali Province, Indonesia
-
Retnoningsih, E., & Pramudita, R. (2020). Mengenal machine learning dengan teknik supervised dan unsupervised learning menggunakan Python. Journal of Informatics, 7(2), 156. https://doi.org/10.51211/biict.v7i2.1422
-
Fadilah, W. R. U., Agfiannisa, D., & Azhar, Y. (2020). Analisis prediksi harga saham PT. Telekomunikasi Indonesia menggunakan metode support vector machine. Finance Journal, 5(2), 45–51. https://doi.org/10.21111/fij.v5i2.4449
-
Patriya, E. (2020). Implementasi support vector machine pada prediksi harga saham gabungan (IHSG). Jurnal Ilmiah Teknologi dan Rekayasa. https://doi.org/10.35760/tr.2020.25i1.2571
-
Arsi, P., & Waluyo, R. (2021). Analisis sentimen wacana pemindahan ibu kota Indonesia menggunakan algoritma support vector machine (SVM). Journal of Technology, 8(1), 147–156. https://doi.org/10.25126/jtiik.0813944
-
Apriliah, W., Kurniawan, I., Baydhowi, M., & Haryati, T. (2021). Prediksi kemungkinan diabetes pada tahap awal menggunakan algoritma klasifikasi random forest. SISTEMASI, 10(1), 1129. https://doi.org/10.32520/stmsi.v10i1.1129
-
Windarto, A. P., et al. (2017). Predicting growth of retail treasury bonds using artificial neural networks. Economic Growth Journal, 4(2), 184–197. https://doi.org/10.20527/klik.v4i2.90
-
Ainurrachma, M., & Mawardi, I. (2022). Market reaction to the merger of state-owned Islamic banks. Journal of Islamic Economics Theory and Application, 9(1), 92–105. https://doi.org/10.20473/vol9iss20221pp92-105
-
Halimi, I., Marthasari, G. I., & Azhar, Y. (2019). Predicting gold prices using univariate convolutional neural network. Jurnal Repositor, 1(2), 105–116. https://doi.org/10.22219/repositor.v1i2.612
-
Diasmara, A. D., Mahastama, A. W., & Chrismanto, A. R. (2021). Sistem cerdas permainan papan "The Battle of Honor" dengan decision making dan machine learning. Jurnal Buana Informatika. https://doi.org/10.24002/jbi.v12i2.4905
-
Fitriana, D., Andini, R., & Oemar, A. (2016). Pengaruh likuiditas, solvabilitas, profitabilitas, aktivitas dan kebijakan dividen terhadap return saham perusahaan pertambangan yang terdaftar pada BEI periode 2007–2013. Journal of Accounting, 2(2).
-
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
-
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
-
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education.
-
Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business students (7th ed.). Pearson Education.
-
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge. https://doi.org/10.4324/9780203774441
-
Fitriana, T., Nugroho, S., & Putri, H. (2016). The impact of macroeconomic variables on investment decisions. Economic Policy Review, 15(3), 90–108.
-
Foglia, M., Recchioni, M., & Polinesi, G. (2021). Smart beta allocation and macroeconomic variables: The impact of COVID-19. Risks, 9(2), 34. https://doi.org/10.3390/risks9020034
-
Fauzie, S., Soeparno, W., & Tampubolon, E. (2019). The influence of macroeconomic on optimal portfolio returns from banking shares. https://doi.org/10.4108/eai.8-10-2018.2288736
-
Zainuri, Z. (2021). Effectiveness of controlling foreign portfolio investment inflows using macroeconomic policy instruments and institutional strengthening (case study in Indonesia and Thailand). Jurnal Penelitian Ekonomi dan Bisnis, 6(2), 121–134. https://doi.org/10.33633/jpeb.v6i2.4446
-
Hong, L., Lamberson, P., & Page, S. (2021). Hybrid predictive ensembles: Synergies between human and computational forecasts. Journal of Social Computing, 2(2), 89–102. https://doi.org/10.23919/jsc.2021.0009.
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