Impact of Palestine-Israel conflict on multinational stock prices use neural network and support vector machine comparison
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Published:
May 30, 2024
Keywords:
Geopolitical, MAE, Neural Network, RMSE, Support Vector Machine
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Computer Science
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Abstract
One form of prolonged geopolitical event is the conflict between Palestine and Israel, which has complex historical, political, and religious roots in the Middle East. This research aims to determine whether this conflict influences the share prices of the companies Unilever, McDonald's, and KFC. These three large companies are known as allies of one of the disputing countries. The method used by the Neural Network is compared with Support Vector Machine to find the best accuracy using RMSE and MAE. The greater the error value, the more affected the company is by this geopolitical factor. As a result, the accuracy of the SVM method is better than NN; the company most affected is KFC, with the RMSE value of 0.111, MAE of 0.020, followed by Unilever with RMSE 0.034, MAE 0.025 then McDonald's with RMSE 0.026 and MAE 0.116, is expected to help investors choose to invest in the company McDonald’s then Unilever.
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Andriani, W. ., Gunawan, G. and Wahyuning Naja, N. N. P. (2024) “Impact of Palestine-Israel conflict on multinational stock prices use neural network and support vector machine comparison ”, Jurnal Mantik, 8(1), pp. 768-777. doi: 10.35335/mantik.v8i1.5196.
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Ampomah, E. K., Nyame, G., Qin, Z., Addo, P. C., Gyamfi, E. O., & Gyan, M. (2021). Stock market prediction with gaussian naïve bayes machine learning algorithm. Informatica (Slovenia), 45(2), 243–256. https://doi.org/10.31449/inf.v45i2.3407
Ashenfelter, O., & Jurajda, Š. (2022). Minimum Wages, Wages, and Price Pass-Through: The Case of McDonald’s Restaurants. Journal of Labor Economics, 40(S1), S179–S201. https://doi.org/10.1086/718190
Benhar, H., Idri, A., & Fernández-Alemán, J. L. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635. https://doi.org/https://doi.org/10.1016/j.cmpb.2020.105635
?alasan, M., Abdel Aleem, S. H. E., & Zobaa, A. F. (2020). On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Conversion and Management, 210(March), 112716. https://doi.org/10.1016/j.enconman.2020.112716
Chen, L., Li, M., Su, W., Wu, M., Hirota, K., & Pedrycz, W. (2021). Adaptive Feature Selection-Based AdaBoost-KNN With Direct Optimization for Dynamic Emotion Recognition in Human–Robot Interaction. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(2), 205–213. https://doi.org/10.1109/TETCI.2019.2909930
Chen, Y., & Zhu, G. (2023). Using machine learning to alleviate the allometric effect in otolith shape-based species discrimination: The role of a triplet loss function. ICES Journal of Marine Science, 80(5), 1277–1290. https://doi.org/10.1093/icesjms/fsad052
Ciulla, G., & D’Amico, A. (2019). Building energy performance forecasting: A multiple linear regression approach. Applied Energy, 253, 113500. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.113500
Divine, D. R. (2019). Word Crimes: Reclaiming The Language of The Israeli-Palestinian Conflict. Israel Studies, 24(2), 1–16. https://doi.org/10.2979/israelstudies.24.2.01
Felix, E. A., & Lee, S. P. (2019). Systematic literature review of preprocessing techniques for imbalanced data. IET Software, 13(6), 479–496. https://doi.org/10.1049/iet-sen.2018.5193
Ferreira, L. B., da Cunha, F. F., de Oliveira, R. A., & Fernandes Filho, E. I. (2019). Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach. Journal of Hydrology, 572, 556–570. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.03.028
Hajimirzaei, B., & Navimipour, N. J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1), 56–59. https://doi.org/10.1016/j.icte.2018.01.014
Hong, S., & Lynn, H. S. (2020). Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Medical Research Methodology, 20(1), 1–12. https://doi.org/10.1186/s12874-020-01080-1
Liao, W., Bak-Jensen, B., Pillai, J. R., Wang, Y., & Wang, Y. (2022). A Review of Graph Neural Networks and Their Applications in Power Systems. Journal of Modern Power Systems and Clean Energy, 10(2), 345–360. https://doi.org/10.35833/MPCE.2021.000058
Nõu, A., Lapitskaya, D., Eratalay, M. H., & Sharma, R. (2023). Predicting stock return and volatility with machine learning and econometric models – a comparative case study of the Baltic stock market. International Journal of Computational Economics and Econometrics, 13(4), 446–489. https://doi.org/10.1504/IJCEE.2023.133923
Obomwan, I. B. (2022). Effect of Marketing Mix on Quick Service Restaurant (Qsr) Consumption in Bangkok, Thailand. a Study of Kfc, Mcdonald’S and …. http://dspace.bu.ac.th/handle/123456789/5078%0Ahttp://dspace.bu.ac.th/jspui/bitstream/123456789/5078/1/Thesis For D space FINAL.pdf
Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(5), 1467–1474. https://doi.org/10.1016/j.dsx.2020.07.045
Smales, L. A. (2021a). Geopolitical risk and volatility spillovers in oil and stock markets. The Quarterly Review of Economics and Finance, 80, 358–366. https://doi.org/https://doi.org/10.1016/j.qref.2021.03.008
Smales, L. A. (2021b). Macroeconomic news and treasury futures return volatility: Do treasury auctions matter? Global Finance Journal, 48, 100537. https://doi.org/https://doi.org/10.1016/j.gfj.2020.100537
Sun, F., Wang, R., Wan, B., Su, Y., Guo, Q., Huang, Y., & Wu, X. (2019). Efficiency of extreme gradient boosting for imbalanced land cover classification using an extended margin and disagreement performance. ISPRS International Journal of Geo-Information, 8(7). https://doi.org/10.3390/ijgi8070315
Tang, C., Ostrikov, K. (Ken), Sanvito, S., & Du, A. (2021). Prediction of room-temperature ferromagnetism and large perpendicular magnetic anisotropy in a planar hypercoordinate FeB3 monolayer. Nanoscale Horizons, 6(1), 43–48. https://doi.org/10.1039/D0NH00598C
Wong, T.-T., & Yeh, P.-Y. (2020). Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586–1594. https://doi.org/10.1109/TKDE.2019.2912815
Wood, B., Robinson, E., Baker, P., Paraje, G., Mialon, M., van Tulleken, C., & Sacks, G. (2023). What is the purpose of ultra-processed food? An exploratory analysis of the financialisation of ultra-processed food corporations and implications for public health. Globalization and Health, 19(1), 1–20. https://doi.org/10.1186/s12992-023-00990-1
Wood, B., Williams, O., Baker, P., & Sacks, G. (2023). Behind the ‘creative destruction’ of human diets: An analysis of the structure and market dynamics of the ultra-processed food manufacturing industry and implications for public health. Journal of Agrarian Change, 23(4), 811–843. https://doi.org/10.1111/joac.12545
Wu, D., Wang, X., Su, J., Tang, B., & Wu, S. (2020). A Labeling Method for Financial Time Series Prediction Based on Trends. In Entropy (Vol. 22, Issue 10). https://doi.org/10.3390/e22101162
Yan, M., Wang, X., Wang, B., Chang, M., & Muhammad, I. (2020). Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. ISA Transactions, 98, 471–482. https://doi.org/https://doi.org/10.1016/j.isatra.2019.08.058
Yilmazkuday, H. (2024). Geopolitical risk and stock prices. European Journal of Political Economy, 102553. https://doi.org/https://doi.org/10.1016/j.ejpoleco.2024.102553
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