Prediksi Risiko Floating Loss pada Pasar Saham Indonesia melalui Penerapan Metode Teorema Bayes
DOI:
https://doi.org/10.59966/bisma.v4i3.2569Keywords:
bayes theorem, floating loss, stock market, investment risk, probabilityAbstract
The stock market is an investment instrument that has high risk, one of which is floating losses or losses that have not been realized. This study aims to apply Bayes' Theorem through the Naive Bayes method in predicting the probability of floating losses on stocks on the Indonesia Stock Exchange and identifying the market conditions that are most related to these events. The data used are in the form of historical data on stocks, JCI, rupiah exchange rates, BI Rate, trading volume, RSI, and MA50 during the period from January 2022 to December 2023. Predictions are made using historical information as a prior probability and market conditions as a likelihood to obtain a posterior probability. The results showed that the Naive Bayes model achieved an accuracy of 74.3%, with a precision of 62.2%, a recall of 75.7%, and an F1-score of 68.2% in the test data. The condition of the JCI decline of more than 1% became the most dominant likelihood with a ratio of 6.78, followed by the RSI below 30 of 4.78 and the price position below MA50 of 3.65. The limitations of this study lie in the observation period and the predictive variables used as well as the use of probabilistic approaches which still depend on the assumption of independence between variables. Further research suggests expanding the period and scope of stocks, adding fundamental variables and market sentiment, and comparing Naive Bayes with other classification methods to improve the predictability of floating loss risk.
References
As-Sami, N. I. (2025). Perbandingan Kinerja Metode Naïve Bayes dan Random Forest untuk Klasifikasi Risiko Saham Perbankan. Skripsi. Institut Pertanian Bogor.
Barberis, N., & Thaler, R. (2003). A Survey of Behavioral Finance. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 1, pp. 1053–1128). Elsevier. https://doi.org/10.1016/S1574-0102(03)01027-6.
Bayes, T., & Price, R. (1763). An Essay Towards Solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London, 53, 370–418. https://doi.org/10.1098/rstl.1763.0053.
Bursa Efek Indonesia. (2023). Statistik Pasar Modal Indonesia 2023. Artikel. PT Bursa Efek Indonesia. https://www.idx.co.id/id/data-pasar/statistik-pasar/.
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.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. https://doi.org/10.1201/b16018.
Gunawan, R., Maharani, C. P., Fitriyana, G., & Maharani, D. I. (2025). Sustainable Stock Screening Based on Fundamental and Technical Indicators Using Gaussian Naive Bayes Classifier. Jurnal Matematika, Statistika dan Komputasi. https://doi.org/10.20956/j.v22i3.49168.
Haryanto, A. M., & Riyatno. (2007). Pengaruh Suku Bunga Sertifikat Bank Indonesia dan Nilai Kurs terhadap Risiko Sistematik Saham Perusahaan di BEJ. Jurnal Keuangan dan Bisnis, 5(1), 24–40.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7.
Hull, J. C. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson Education.
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill.
Kustodian Sentral Efek Indonesia (KSEI). (2024). Laporan Tahunan dan Laporan Keberlanjutan 2023. PT Kustodian Sentral Efek Indonesia.
Lopez de Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons. https://doi.org/10.1002/9781119482284.
McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools (Revised ed.). Princeton University Press.
Pramesti, D., Fakhrurroja, H., & Karina, R. M. (2025). Public Sentiment and GoTo Stock Price Movement in Indonesia: A Null-Relationship Study Using Naïve Bayes and Non-Parametric Measures. Jurnal Teknik Informatika, 18(2), 257–269. https://doi.org/10.15408/jti.v18i2.46447.
Prawiranegara, T. R. (2024). Perbandingan Algoritma Naïve Bayes dengan K-Nearest Neighbor pada Prediksi Pergerakan Harga Saham. Institut Teknologi Sumatera.
Rachev, S. T., Hsu, J. S. J., Bagasheva, B. S., & Fabozzi, F. J. (2008). Bayesian Methods in Finance. John Wiley & Sons. https://doi.org/10.1002/9781118268896.
Rahmawati, I., & Triani, M. (2019). Penggunaan metode Naive Bayes dalam Prediksi Harga Saham di Bursa Efek Indonesia. Jurnal Informatika dan Sistem Informasi, 5(2), 112–121. https://doi.org/10.33557/juisi.v5i2.742.
Sitorus, H., & Tarihoran, Y. (2018). Analisis Harga Saham PT Astra Internasional Tbk Menggunakan Data dari Bursa Efek Indonesia dalam Jangka Waktu Pendek Menggunakan Metode Naïve Bayes dan Decision Tree-J48. TeIKa, 8(1), 21–33. https://doi.org/10.36342/teika.v8i1.2239.
Tandelilin, E. (2010). Portofolio dan Investasi: Teori dan Aplikasi (Edisi 1). Kanisius.
Trimono, Maruddani, D. A. I., & Ispriyanti, D. (2017). Pemodelan harga saham dengan geometric Brownian motion dan value at risk PT Ciputra Development Tbk. Jurnal Gaussian, 6(2), 261–270. https://doi.org/10.14710/j.gauss.6.2.261-270
Widarjono, A. (2021). Ekonometrika: Pengantar dan Aplikasinya Disertai Panduan EViews (Edisi 5). UPP STIM YKPN.
Zellner, A. (1996). An Introduction to Bayesian Inference in Econometrics. Wiley-Interscience. https://doi.org/10.1002/9781118165454.
Zhang, Y., Chu, G., & Shen, D. (2022). The Role of Investor Attention in Predicting Stock Prices: The Long Short-Term Memory Networks Perspective. Finance Research Letters, 49, 102947. https://doi.org/10.1016/j.frl.2022.102947.
