Financial Crisis Detection in Thailand Based on Currency Crisis Index Approach Using Artificial Neural Network

Sugiyanto; Winita  Sulandari; Etik  Zukhronah; Riskhia  Hapsari; Ihsan  Fathoni Amri

Authors

Sugiyanto Department of Statistics, Faculty of Mathematics and Science, Sebelas Maret University, Central Java, Indonesia
Winita Sulandari Department of Statistics, Faculty of Mathematics and Science, Sebelas Maret University, Central Java, Indonesia
Etik Zukhronah Department of Statistics, Faculty of Mathematics and Science, Sebelas Maret University, Central Java, Indonesia
Riskhia Hapsari Department of Statistics, Faculty of Mathematics and Science, Sebelas Maret University, Central Java, Indonesia
Ihsan Fathoni Amri Department of Data Science, Faculty Science and Agricultural Technology, Muhammadiyah Semarang University, Jawa Tengah, Indonesia

Keywords:

Financial crisis detection, multilayer perceptron, currency crisis index, Nadam optimization, macroeconomic indicators

Abstract

The 1997 financial crisis in Thailand had a variety of adverse effects, ranging from the depreciated baht, negative economic growth, increased unemployment, to declining stock prices. Thailand took a long time to recover from these impacts. Therefore, financial crisis detection is needed to prevent or minimize the impact of the crisis. This study aims to build a model that can predict the financial crisis in Thailand using multilayer perceptron (MLP). Four different optimizations were applied to obtain the optimal model, namely SGD, Adam, Nadam, and AdaBound. The model employs eleven macroeconomic indicators as input variables and uses the perfect signal derived from the Currency Crisis Index (CCI) as the target variable. This study used the CCI approach to determine Thailand’s crisis conditions from January 1991 to December 2022. Based on the results, the best model for predicting Thailand’s financial crisis is the MLP model with Nadam optimization trained using 11-6-1 network architecture, learning rate of 0.2, batch size of 128, and epoch of 100. The model produced an accuracy of 99.44% on validation data and 98.61% on testing data. The model prediction results suggest a low likelihood of a financial crisis in Thailand from January 2023 to December 2024.

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