Evaluation of Effectiveness of Arima Model Predictions in Investment Portfolio Formation and Management
References
- Allen, P. G., & Fildes, R. (2001). Econometric forecasting. In J. S. Armstrong (Ed.), Principles of forecasting. International series in operations research & management science, 30 (pp. 303-362). Springer. https://doi.org/10.1007/978-0-306-47630-3_15
- Armstrong, J. S., & Green, K. C. (2018). Forecasting methods and principles: Evidence-based checklists. Journal of Global Scholars of Marketing Science, 28(2), 103-159. https://doi.org/10.1080/21639159.2018.1441735
- Bodie, Z., Kane, A., & Marcus, A. (2020). Investments (12th ed.). McGraw-Hill Education.
- Box, G. E. P., Jenkins, G. M., Reinsel, G., C., & Ljung, G. M. (2016). Time series analysis: Forecasting and control (5th ed.). John Wiley & Sons, Inc.
- Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed.). Springer.
- Cardoso, F., & Duarte, C. (2006). The use of qualitative information for forecasting exports. Economic Bulletin / Banco de Portugal, 67-74.
- Fatemi, Z., Huynh, M., Syed, Z., Di, X., & Zheleva, E. (2023). Mitigating cold-start forecasting using cold causal demand forecasting model. Cornell University. https://doi.org/10.48550/arXiv.2306.09261
- Hassen, O. A., Darwish, S. M., Abu, N. A., & Abidin, Z. Z. (2020). Application of cloud model in qualitative forecasting for stock market trends. Entropy, 22(9), 991. https://doi.org/10.3390/e22090991
- Ingsrisawang, L., & Wongoutong, C. (2023). A novel approach for combined forecasting model systems based on the correlation coefficient ranking of the individual forecasting models. Thailand Statistician, 21(4), 887-909.
- Jiang, Z. (2023). Analysis and prediction of the trend of chinese and american stock market indexes based on ARIMA model. Advances in Economics, Management and Political Sciences, 49(1), 176-188. https://doi.org/10.54254/2754-1169/49/20230482
- Kalebos, A. H., & Rudianto, D. (2022). Optimization of stock portfolio of value30 index and growth30 index using the Markowitz model and Sharpe model. Journal of International Conference Proceedings, 5(3), 192-205. https://doi.org/10.32535/jicp.v5i3.1864
- Kenmei, B., Antoniol, G., & di Penta, M. (2008). Trend analysis and issue prediction in large-scale open source systems. 12th European Conference on Software Maintenance and Reengineering, 73-82. https://doi.org/10.1109/CSMR.2008.4493302
- Khadka, M. S., George, K.M., Park, N., & Kim, J. B. (2012). Performance analysis of hybrid forecasting model in stock market forecasting. International Journal of Managing Information Technology, 4(3), 81-88. https://doi.org/10.5121/ijmit.2012.4307
- Lee, C.-K., Song, H.-J., & Mjelde, J. W. (2008). The forecasting of International Expo tourism using quantitative and qualitative techniques. Tourism Management, 29(6), 1084-1098. https://doi.org/10.1016/j.tourman.2008.02.007
- Liu, K., Liu, Y., & Zhu, X. (2022). Portfolio risk investment strategy model based on ARIMA and LSTM. BCP Business & Management, 22, 196-203. https://doi.org/10.54691/bcpbm.v22i.1229
- Lyulyov, O., Chygryn, O., Pimonenko, T., Zimbroff, A., Makiela, Z., & Kwilinski, A. (2024). Green competitiveness forecasting as an instrument for sustainable business transformation. Forum Scientiae Oeconomia, 12(2), 8-20. https://doi.org/10.23762/FSO_VOL12_NO2_1
- Malik, P., Thakur, A. S., Parihar, A. P.S., Dangi, A. S., Sharma, U., & Mishra, L. (2023). An analysis of time series analysis and forecasting techniques. International Journal of Advance Research and Innovative Ideas in Education, 9(5), 1364-1374.
- Mo, H. (2023). Comparative analysis of linear regression, polynomial regression, and ARIMA model for short-term stock price forecasting. Advances in Economics, Management and Political Sciences, 49(1), 166-175. https://doi.org/10.54254/2754-1169/49/20230509
- Ndikum, P. (2020). Machine learning algorithms for financial asset price forecasting. Cornell University. https://doi.org/10.48550/arXiv.2004.01504
- Pan, L. (2023). Cryptocurrency price prediction based on ARIMA, random forest and LSTM algorithm. BCP Business & Management, 38, 3396-3404. https://doi.org/10.54691/bcpbm.v38i.4313
- Saha, A., & Sinha, K. (2020). Usage of Holt’s linear trend exponential smoothing for time series forecasting in agricultural research. Food and Scientific Reports, 1, 9-11.
- Shao, Y. (2023). Prediction of Moderna adjusted closing stock price trend using ARIMA model. BCP Business & Management, 44, 108-114. https://doi.org/10.54691/bcpbm.v44i.4800
- Thoplan, R. (2014). Qualitative v/s quantitative forecasting of yearly tourist arrival in Mauritius. International Journal of Statistics and Applications, 4(4), 198-203.
- Tian, H. (2023). Bitcoin price forecasting using ARIMA model. Theoretical and Natural Science, 26(1), 105-112.
- Upe, A. (2023). Innovation and technological adaptation of business actors in the digital age: A digital sociology perspective. Indonesian Journal of Innovation and Applied Sciences (IJIAS), 3(3), 218-227. https://doi.org/10.47540/ijias.v3i3.737
- Yu, H., Ming, L. J., Sumei, R., & Shuping, Z. (2020). A hybrid model for financial time series forecasting - integration of EWT, ARIMA with the improved ABC optimized ELM. IEEE Access, 8, 84501–84518.
- Zhang, C., Sjarif, N. N. A., & Ibrahim, R. (2024). Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022. WIREs Data Mining and Knowledge Discovery, 14(1), e1519. https://doi.org/10.1002/widm.1519
DOI: https://doi.org/10.2478/jec-2025-0009 | Journal eISSN: 2256-0173
Language: English
Page range: 108 - 122
Submitted on: Oct 21, 2024
Accepted on: Jun 6, 2025
Published on: Jun 25, 2025
Published by: University College of Economics and Culture
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year
Keywords:
Related subjects:
© 2025 Iryna Brolinska, Grigorij Žilinskij, published by University College of Economics and Culture
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.