KnE Life Sciences

ISSN: 2413-0877

The latest conference proceedings on life sciences, medicine and pharmacology.

Implementation of Neural Network in Early Detection of Financial Crisis in Singapore

Published date: Mar 27 2024

Journal Title: KnE Life Sciences

Issue title: International Conference On Mathematics And Science Education (ICMScE 2022): Life Sciences

Pages: 253–264

DOI: 10.18502/kls.v8i1.15587

Authors:

Fadia MulyartiEmail: N/A
Affiliation: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret , Indonesia.
Biography:

Sugiyanto .Email: sugiyanto61@staff.uns.ac.id
Affiliation: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret , Indonesia.
Biography:

Sri SubantiEmail: N/A
Affiliation: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret , Indonesia.
Biography:

Etik ZukhronahEmail: N/A
Affiliation: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret , Indonesia.
Biography:

Winita SulandariEmail: N/A
Affiliation: Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret , Indonesia.
Biography:

Fadia Mulyarti

Sugiyanto . - sugiyanto61@staff.uns.ac.id

Sri Subanti

Etik Zukhronah

Winita Sulandari

Abstract:

The financial crisis that occurred in 1997 and 2008 had a negative impact on several countries, including Singapore. A financial crisis can occur suddenly so it can endanger a country’s economy if it is not prepared for it. Therefore, early detection of financial crises is needed as a form of crisis warning so that the government can anticipate and prepare appropriate policies. The independent variables used are monthly data of 11 key macroeconomic and financial indicators of Singapore’s economy from January 1990 to June 2021. The Perfect signal is used as the dependent variable in the crisis early detection system. This study aims to build a model of a financial crisis detection system in Singapore using Multilayer Perceptron Backpropagation (MLPBP) as a neural network algorithm by comparing the optimization of Stochastic Gradient Descent (SGD) and Nesterov-accelerated Adaptive Moment Estimation (Nadam). The optimal hyperparameter value in the model was searched using the grid search method based on the accuracy and obtained the best model with 11-11-1 network architecture, best optimization is Nadam, learning rate = 0.1; μ = 0.975; v =0.999; ϵ =[10]^(-8) ; batch size = 128, epoch = 100, and sigmoid activation function. Testing the model with data testing obtained an accuracy of 95.89%, a sensitivity of 98.36%, and a specificity of 83.33%. The results of the Perfect Signal prediction show that from January to June 2021 it is predicted that there will be no financial crisis in Singapore.

Keywords: neural network, early detection, financial, crisis, Singapore

References:

[1] Jie WJ. “Singapore’s approach to managing economic crises,” p. 2018.

[2] Mishkin FS. Understanding financial crises: a developing country perspective. Mass., USA: National Bureau of Economic Research Cambridge; 1996. https://doi.org/10.3386/w5600.

[3] Goldstein M, Kaminsky GL, Reinhart CM. Assessing financial vulnerability: an early warning system for emerging markets. Peterson Institute; 2000.

[4] Salvatore D. Could the financial crisis in East Asia have been predicted? J Policy Model. 1999;21(3):341–7.

[5] Siriwardana M, Schulze D. Singapore and the Asian economic crisis: an assessment of policy responses. ASEAN Econ Bull. 2000;17(3):233–56.

[6] C.S.R. Sulaeman and V. Lisna, “Analisis EMP Indonesia dan empat negara ASEAN pada masa krisis.,” Jurnal Ekonomi dan Pembangunan Indonesia. vol. 16, no. 2, p. 2, 2016.

[7] Abdushukurov N. The impact of currency crises on economic growth and foreign direct investment: the analysis of emerging and developing economies. Russian Journal of Economics. 2019;5(3):220–50.

[8] Imansyah MH. Krisis keuangan di Indonesia, dapatkah di ramalkan? Elex Media Komputindo; 2008. [9] Sevim C, Oztekin A, Bali O, Gumus S, Guresen E. Developing an early warning system to predict currency crises. Eur J Oper Res. 2014;237(3):1095–104.

[10] Kaminsky G, Lizondo S, Reinhart CM. Leading indicators of currency crises. Staff Pap Int Monet Fund. 1998;45(1):1–48.

[11] Bluwstein K, Buckmann M, Joseph A, Kapadia S, Şimşek Ö. Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach. J Int Econ. 2023;145:103773.

[12] Wanto A. Prediksi angka partisipasi sekolah dengan fungsi pelatihan gradient descent with momentum & adaptive LR. Algoritma: Jurnal Ilmu Komputer Dan Informatika. 2019;3(1):9.

[13] Dozat T. “Incorporating nesterov momentum into adam.,” p. 2016.

[14] Edison HJ. Do indicators of financial crises work? an evaluation of an early warning system. Int J Finance Econ. 2003;8(1):11–53.

[15] Venkatesan P, Anitha S. “Application of a radial basis function neural network for diagnosis of diabetes mellitus.,” current science. vol. 91, no. 9, pp. 1195–1199, 2006.

[16] Fausett LV. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India; 2006.

[17] Van Rijn JN, Hutter F. “Hyperparameter importance across datasets.,” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2367–2376 (2018).

[18] Géron A. Hands-on machine learning with scikit-learn, keras, and tensorflow. O’Reilly Media, Inc.; 2022.

[19] Han J, Kamber M, Pei J. Data mining: concepts and techniques. Volume 10. Waltham (MA): Morgan Kaufman Publishers; 2012. pp. 971–8.

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