Volume 24 , Issue 4 , PP: 165-175, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Jamil J. Jaber 1 * , Anwar Al-Gasaymeh 2 , Maha Shehadeh 3 , Asma S. Alzwi 4
Doi: https://doi.org/10.54216/IJNS.240412
This study focuses on improving the predicting accuracy of the daily ASE's weighted price index of the insurance sector (ICI) using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6). Using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions—Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6)—this study aims to increase the daily ASE's weighted price index of the insurance sector's (ICI) prediction accuracy. The model utilizes a genetic fuzzy system based on Thrift's methodology (GFS.Thrift). The Amman Stock Exchange (ASE) supplied a dataset with 4,478 observations for the purpose of the study. The dataset represented daily data from January 2, 2006, to March 24, 2024. The adaptive GFS.THRIFT model was trained with 90% of the dataset, while the remaining 10% was used to test its prediction performance. Multiple egressions and multicollinearity tests were used to select input variables such as standardized foreign direct investment (FDI), standardized value traded (VT) and consumer price index (CPI). Insights from this study indicate that all input variables are positively related to the output variable. Secondly, the proposed model (MODWT-Haar-GFS. Thrift) significantly outperforms other existing models including the GFS. Thrift model.
wavelet transform , FDI , GFS.Thrift model , fuzzy logic , fuzzy genetic algorithm.
[1] T. Saadi-Sedik and M. Petri, "The Jordanian Stock Market: should you invest in it for risk diversification or performance?," 2006.
[2] H. Yaseen, G. Omet, M. J. E. J. o. B. Abdel-Halim, and Management, "The 2008 Global financial crisis: The case of a market with consistent losses ever since," vol. 3, no. 1, 2015.
[3] R. G. J. J. J. o. B. A. Aldeki, "The Effect of the Spread of Coronavirus (COVID-19) on Stock Markets: The Case of Amman Stock Exchange (ASE)," vol. 20, no. 1, 2024.
[4] N. Mousazadeh Abbassi, M. Ali Aghaei, M. J. I. J. o. Q. Moradzadeh Fard, and R. Management, "An integrated system based on fuzzy genetic algorithm and neural networks for stock price forecasting: Case study of price index of Tehran Stock Exchange," vol. 31, no. 3, pp. 281-292, 2014.
[5] O. J. Akindote et al., "Comparative review of big data analytics and GIS in healthcare decision-making," vol. 20, no. 3, pp. 1293-1302, 2023.
[6] R. Al Omari, R. S. Alkhawaldeh, and J. J. J. E. Jaber, "Artificial neural network for classifying financial performance in Jordanian insurance sector," vol. 11, no. 4, p. 106, 2023.
[7] A. Al-Gasaymeh and M. R. J. G. B. R. Samarah, "Does country risk affect banking efficiency: empirical evidence from Turkey and selected countries from the European union," vol. 24, no. 6, pp. 1401-1417, 2023.
[8] O. A. Sayed, H. J. I. J. o. F. Eledum, and Economics, "The short‐run response of Saudi Arabia stock market to the outbreak of COVID‐19 pandemic: An event‐study methodology," vol. 28, no. 3, pp. 2367-2381, 2023.
[9] A. O. J. P. C. S. Aseeri, "Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series," vol. 9, p. e1205, 2023.
[10] H. Maqsood et al., "A local and global event sentiment based efficient stock exchange forecasting using deep learning," vol. 50, pp. 432-451, 2020.
[11] M. R. Hossain, M. T. Ismail, and S. A. B. A. J. I. A. Karim, "Improving stock price prediction using combining forecasts methods," vol. 9, pp. 132319-132328, 2021.
[12] S. Al Wadi, O. Al Singlawi, J. J. Jaber, M. H. Saleh, A. A. J. J. o. R. Shehadeh, and F. Management, "Enhancing Predictive Accuracy through the Analysis of Banking Time Series: A Case Study from the Amman Stock Exchange," vol. 17, no. 3, p. 98, 2024.
[13] K. A. Rashedi et al., "Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns," vol. 17, no. 2, p. 69, 2024.
[14] A. RAWASHDEH and M. BAKHIT, "THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND ENVIRONMENTAL DETERMINANTS IN AUDITING FIRMS."
[15] A. Rawashdeh, M. Bakhit, and S. Al-Okdeh, "The mediating role of control risk in the relationship between technological factors and AI-based predictive analytics adoption: evidence from audit firms in the US," in 2023 International Conference on Business Analytics for Technology and Security (ICBATS), 2023, pp. 1-7: IEEE.
[16] A. Rawashdeh, L. Abaalkhail, and M. J. D. S. L. Bakhit, "A two-stage SEM-artificial neural network analysis of the organizational effects of Internet of things adoption in auditing firms," vol. 12, no. 2, pp. 255-266, 2023.
[17] H. S. Sim, H. I. Kim, and J. J. J. C. Ahn, "Is deep learning for image recognition applicable to stock market prediction?," vol. 2019, 2019.
[18] Q. Chen, "Stock market prediction using machine learning," in 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022), 2022, pp. 458-465: Atlantis Press.
[19] L. AlQasrawi, M. Awad, and R. Hodrob, "A Hybrid Model of Neural Networks and Genetic Algorithms for Prediction of the Stock Market Price: Case Study of Palestine," 2024.
[20] Y. Bayar, M. D. Gavriletea, D. C. Danuletiu, A. E. Danuletiu, and E. J. M. Sakar, "Pension Funds, Insurance Companies and Stock Market Development: Evidence from Emerging Markets," vol. 10, no. 13, p. 2335, 2022.
[21] P. Naik, Y. J. M. Reddy, and F. i. E. M. Economies, "Determinants of stock market liquidity–a macroeconomic perspective," vol. 17, no. 1, pp. 153-173, 2024.
[22] H. Krisnandi, K. Digdowiseiso, L. A. J. I. J. o. E. R. Ghani, Law, and S. Sciences, "THE DETERMINANTS OF THE COMPOSITE STOCK PRICE INDEX IN INDONESIA," vol. 4, no. 1, pp. 276-291, 2024.
[23] A. B. McBratney, B. Minasny, and U. Stockmann, Pedometrics. Springer, 2018.
[24] A. H. Alenezy, M. T. Ismail, S. A. Wadi, and J. J. J. R. Jaber, "Predicting Stock Market Volatility Using MODWT with HyFIS and FS. HGD Models," vol. 11, no. 7, p. 121, 2023.
[25] J. J. JABER, N. A. YAACOB, and S. ALWADI, "Hybrid Lee-Carter Model with Adaptive Network of Fuzzy Inference System and Wavelet Functions," Sains Malaysiana, vol. 52, no. 3, pp. 1011-1021, 2023.
[26] P. R. Thrift, "Fuzzy Logic Synthesis with Genetic Algorithms," in ICGA, 1991, pp. 509-513.
[27] H. Henderi, T. Wahyuningsih, E. J. I. J. o. I. Rahwanto, and I. Systems, "Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer," vol. 4, no. 1, pp. 13-20, 2021.
[28] S. Ribaric and I. Fratric, "Experimental evaluation of matching-score normalization techniques on different multimodal biometric systems," in MELECON 2006-2006 IEEE Mediterranean Electrotechnical Conference, 2006, pp. 498-501: IEEE.
