Comparison of banking stock price movements using KNN and Linier Regresi methods
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Published:
Nov 8, 2023
Keywords:
k-NN , Linier Regresi, MAE, MSE, RMSE
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Computer Science
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Abstract
Stock price movements are a reflection of various situations and factors such as economics, politics and markets. Influencing factors such as economic news both foreign and domestic such as monetary policy, changes in interest rates and political events such as general elections. Indonesian politics is currently holding a general election process, the Presidential election can influence stock movements. Causing investors to be more careful about investing in this case in banks in Indonesia that have state-owned status, such as BTN, BRI, BNI and Bank Mandiri. This research predicts stock price movements using the Linear Regression method compared to the k-NN method, to find the best evaluation results from the two methods in selecting banks that are more profitable and do not influence political process factors. The results obtained were that Bank Mandiri was safer and promised profits using the Linear Regression method which was better than k-NN with RMSE 281,012, MAE 97,909 and MSE 80348,873. Bank Mandiri is safer and promises profits
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Andriani, W., Gunawan and Anandianskha, S. . (2023) “Comparison of banking stock price movements using KNN and Linier Regresi methods ”, Jurnal Mantik, 7(3), pp. 1942-1950. doi: 10.35335/mantik.v7i3.4304.
References
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Tomsa, D. (2018). Regime resilience and presidential politics in Indonesia. Contemporary Politics, 24(3), 266–285. https://doi.org/10.1080/13569775.2017.1413502
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1). https://doi.org/10.1088/1757-899X/324/1/012049
WIREs Computational Stats - 2020 - Filzmoser - Robust linear regression for high?dimensional data An overview.pdf. (n.d.).
Yennimar, Kelvin, Suwandi, & Amir. (2022). Comparison Analysis of SVM Algorithm with Linear.
Almahdali, F., Natsir, S., Hasanuddin, B., & Husnah. (2021). The Effect of Servant Leadership, Emotional Intelligence, and Self-Efficacy on Organizational Citizenship Behavior. International Journal of Multicultural and Multireligious Understanding, 8(5), 682–694.
Engineering, G., Crossing, M., Engineering, G., & Crossing, M. (1810). The Assassing Impact of Cebtral Bank Digital. Spatio-Temporal Analysis of Seismic Anisotropy Associated with the Cook Strait and Kaik¯oura Earthquake Sequences in New Zealand Kenny, 1–10.
Fitriyaningsih, I., Tampubolon, A. R., Lumbanraja, H. L., Pasaribu, G. E., & Sitorus, P. S. A. (2019). Implementation of Artificial Neural Network to Predict S&P 500 Stock Closing Price. Journal of Physics: Conference Series, 1175(1). https://doi.org/10.1088/1742-6596/1175/1/012107
Hamzah, F. B., Hamzah, F. M., Razali, S. F. M., & Samad, H. (2021). A comparison of multiple imputation methods for recovering missing data in hydrological studies. Civil Engineering Journal (Iran), 7(9), 1608–1619. https://doi.org/10.28991/cej-2021-03091747
Hasan, M. B., Mahi, M., Sarker, T., & Amin, M. R. (2021). Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector. Journal of Risk and Financial Management, 14(5). https://doi.org/10.3390/jrfm14050200
Huang, W., Gao, T., Hao, Y., & Wang, X. (2023). Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices. Energy Economics, 127, 107106. https://doi.org/https://doi.org/10.1016/j.eneco.2023.107106
Kane, J. X., Connell, B., Yip, H., McAlister, J. C., Beckingsale, P., Snibson, G. R., & Chan, E. (2020). Accuracy of Intraocular Lens Power Formulas Modified for Patients with Keratoconus. Ophthalmology, 127(8), 1037–1042. https://doi.org/10.1016/j.ophtha.2020.02.008
Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, & Antika Zahrotul Kamalia. (2021). Stock Price Prediction using Time Series Model Comparing Linier Regression, LSTM and GRU Algoritm. Seminastika, 3(1), 39–46. https://doi.org/10.47002/seminastika.v3i1.275
Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 13(2), 139–149. https://doi.org/10.1007/s41060-021-00279-9
Li, Y., Yang, Y., Che, J., & Zhang, L. (2019). Predicting the number of nearest neighbor for kNN classifier. IAENG International Journal of Computer Science, 46(4), 1–8.
Lubis, A. R., & Lubis, M. (2020). Optimization of distance formula in K-Nearest Neighbor method. Bulletin of Electrical Engineering and Informatics, 9(1), 326–338.
Lyu, Z., Yu, Y., Samali, B., Rashidi, M., Mohammadi, M., Nguyen, T. N., & Nguyen, A. (2022). Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam. Materials, 15(4). https://doi.org/10.3390/ma15041477
Mayer, D. G., & Butler, D. G. (1993). Statistical validation. Ecological Modelling, 68(1–2), 21–32. https://doi.org/10.1016/0304-3800(93)90105-2
Nazaritehrani, A., & Mashali, B. (2020). Development of E-banking channels and market share in developing countries. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-0171-z
Nuzula Agustin, I. (2019). the Approach of Box Jenkins Time Series Analysis for Predicting Stock Price on Lq45 Stock Index. Profit, 13(01), 18–25. https://doi.org/10.21776/ub.profit.2019.013.01.2
Prasetyo, F. A., Nugroho, S., & Setiawan, A. (2023). Prediction of stock closing price PT. Telkom Indonesia with LSTM method. AIP Conference Proceedings, 2706(1).
Prayudani, S., Hizriadi, A., Lase, Y. Y., Fatmi, Y., & Al-Khowarizmi. (2019). Analysis Accuracy of Forecasting Measurement Technique on Random K-Nearest Neighbor (RKNN) Using MAPE and MSE. Journal of Physics: Conference Series, 1361(1). https://doi.org/10.1088/1742-6596/1361/1/012089
Puzyrev, V. (2019). Deep learning electromagnetic inversion with convolutional neural networks. Geophysical Journal International, 218(2), 817–832. https://doi.org/10.1093/gji/ggz204
Ray, S. (2019). A quick review of machine learning algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35–39.
Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. (2020). COVID-19 Future Forecasting Using Supervised Machine Learning Models. IEEE Access, 8, 101489–101499. https://doi.org/10.1109/ACCESS.2020.2997311
Saputra, J., Astuti, D., & Pertiwi, D. (2021). Effect of Financial Literacy, Risk Attitude and Saving Motives To Disposition Bias on Mutual Funds Investors. International Journal of Financial and Investment Studies (IJFIS), 2(1), 27–33. https://doi.org/10.9744/ijfis.2.1.27-33
Sewdien, V. N., Preece, R., Torres, J. L. R., Rakhshani, E., & van der Meijden, M. (2020). Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting. Renewable Energy, 161, 878–892. https://doi.org/10.1016/j.renene.2020.07.117
Shen, J. (2009). Linear Regression. Encyclopedia of Database Systems, 1622–1622. https://doi.org/10.1007/978-0-387-39940-9_542
Soebroto, A., Cholissodin, I., Pratiwi, D., & Putra, G. (2022). Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture. Habitat, 33(1), 86–92. https://doi.org/10.21776/ub.habitat.2022.033.1.9
Su, C. W., Yuan, X., Umar, M., & Chang, T. (2023). Is presidential popularity a threat or encouragement for investors? Economic Research-Ekonomska Istrazivanja , 36(2). https://doi.org/10.1080/1331677X.2022.2129409
Syofyan, R., Putra, D. G., & Aprayuda, R. (2020). Influence of Company Value Information, Dividend Policy, and Capital Structure on Stock Price. SAR (Soedirman Accounting Review)?: Journal of Accounting and Business, 5(2), 152. https://doi.org/10.20884/1.sar.2020.5.2.3554
Tomsa, D. (2018). Regime resilience and presidential politics in Indonesia. Contemporary Politics, 24(3), 266–285. https://doi.org/10.1080/13569775.2017.1413502
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1). https://doi.org/10.1088/1757-899X/324/1/012049
WIREs Computational Stats - 2020 - Filzmoser - Robust linear regression for high?dimensional data An overview.pdf. (n.d.).
Yennimar, Kelvin, Suwandi, & Amir. (2022). Comparison Analysis of SVM Algorithm with Linear.
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