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