Main Article Content


Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset.  In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis.  


Crisis Financial Machine Learning Optimization Prediction

Article Details

Author Biographies

Dimas, Universitas Muhammadiyah Surakarta

Informatics Engineering Department

Muhibah Fata Tika, Universitas Muhammadiyah Surakarta

Informatics Engineering Department

Fitri Cahya Kusumawati, Universitas Muhammadiyah Surakarta

Informatics Engineering Department

How to Cite
Maryam, M., Anggoro, D. A., Tika, M. F., & Kusumawati, F. C. (2022). An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction. Pakistan Journal of Statistics and Operation Research, 18(4), 1015-1025.

Funding data


  1. Albashrawi, M., & Lowell, M. (2016). Detecting Financial Fraud Using Data Mining Techniques : a. Journal of Data Science, 14(3), 553–570.改10-Detecting+Financial+Fraud+Using+Data+Mining+Techniques-JDS_V3.pdf DOI:
  2. Aly, R. H. M., Rahouma, K. H., & Hamed, H. F. A. (2019). Brain Tumors Diagnosis and Prediction Based on Applying the Learning Metaheuristic Optimization Techniques of Particle Swarm, Ant Colony and Bee Colony. Procedia Computer Science, 163, 165–179. DOI:
  3. Anggoro, D. A., & Novitaningrum, D. (2021). Comparison of accuracy level of support vector machine (SVM) and artificial neural network (ANN) algorithms in predicting diabetes mellitus disease. ICIC Express Letters, 15(1), 9–18.
  4. Camero, A., Toutouh, J., & Alba, E. (2020). Random error sampling-based recurrent neural network architecture optimization. Engineering Applications of Artificial Intelligence, 96, 103946. DOI:
  5. Comelli, F. (2016). Comparing the Performance of Logit and Probit Early Warning Systems for Currency Crises in Emerging Market Economies. Journal of Banking and Financial Economics, 2016(2), 5–22. DOI:
  6. Corsetti, G., Pesenti, P., & Roubini, N. (1999). What caused the Asian currency and financial crisis? Japan and the World Economy, 11(3), 305–373. DOI:
  7. Dai, H. P., Chen, D. D., & Zheng, Z. S. (2018). Effects of random values for particle swarm optimization algorithm. Algorithms, 11(2), 1–20. DOI:
  8. Dewi, S., Abd. Majid, M. S., Aliasuddin, & Kassim, S. (2018). Dynamics of Financial Development, Economic Growth, and Poverty Alleviation: The Indonesian Experience. South East European Journal of Economics and Business, 13(1), 17–30. DOI:
  9. Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374–393. DOI:
  10. Erzurum Cicek, Z. I., & Kamisli Ozturk, Z. (2021). Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting. Applied Soft Computing, 102, 107091. DOI:
  11. Fausset L. (1994). Fundamentals of Neural Network : rchitecture, Algorithm, And Applications. Prentice Hall.
  12. Fricke, D. (2017). Financial Crisis Prediction: A Model Comparison. SSRN Electronic Journal, 2(4), 6–10. DOI:
  13. Grosan, C., Abraham, A., & Chis, M. (2006). Swarm intelligence in data mining. Studies in Computational Intelligence, 34(2006), 1–20. DOI:
  14. Gudise, V. G., & Venayagamoorthy, G. K. (2003). Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No.03EX706), 110–117. DOI:
  15. Kaminsky, G., Lizondo, S., & Reinhart, C. M. (1998). Leading Indicators of Currency Crises. In IMF Staff Papers (Vol. 45, Issue 1, pp. 1–48). DOI:
  16. Kılıç, F., Yılmaz, İ. H., & Kaya, Ö. (2021). Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting. Renewable Energy, 171, 176–190. DOI:
  17. Qi, C., Fourie, A., & Chen, Q. (2018). Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill. Construction and Building Materials, 159, 473–478. DOI:
  18. Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an early warning system to predict currency crises. European Journal of Operational Research, 237(3), 1095–1104. DOI:
  19. Sugiyanto, Zukhronah, E., & Sari, S. P. (2018). Detection method of financial crisis in Indonesia using {MSGARCH} models based on banking condition indicators. Journal of Physics: Conference Series, 1025, 12118. DOI:
  20. Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020). A Survey of Optimization Methods from a Machine Learning Perspective. IEEE Transactions on Cybernetics, 50(8), 3668–3681. DOI:
  21. Tarmidi, L. T. (2003). KRISIS MONETER INDONESIA : SEBAB, DAMPAK, PERAN IMF DAN SARAN. Buletin Ekonomi Moneter Dan Perbankan, 1(4 SE-Articles). DOI:
  22. Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K. (2018). Intelligent hybrid model for financial crisis prediction using machine learning techniques. Information Systems and E-Business Management, 0123456789. DOI:
  23. Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K. (2020). Financial crisis prediction model using ant colony optimization. International Journal of Information Management, 50, 538–556. DOI:
  24. Zhang, X., Nguyen, H., Bui, X. N., Anh Le, H., Nguyen-Thoi, T., Moayedi, H., & Mahesh, V. (2020). Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization. Tunnelling and Underground Space Technology, 103(March), 103517. DOI:
  25. Zhou, G., Moayedi, H., Bahiraei, M., & Lyu, Z. (2020). Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. Journal of Cleaner Production, 254, 120082. DOI: