| dc.description.abstract | Determinants of systemic risk have been extensively investigated in developed markets
and using the Ordinary Least Squares (OLS) regression models. However, several studies in the top-tier journals in finance and banking about the prediction of stock returns
and default risks show that the prediction of the OLS regression models is not as good
as that of machine learning models, namely the Ridge, the LASSO, and the Elastic
Net. In this study, I employ machine learning techniques to predict systemic risk in
Emerging Asian Markets to fill out the gaps of the shortage of research of systemic
risk in emerging markets and the lack of the application of machine learning methods
in predicting systemic risk. I find that the Elastic Net is the best among the four
regression models: Stepwise Regression, Ridge Regression, LASSO Regression, and
Elastic Net Regression and that non-performance loans are the best predicted factor
and is negatively related to systemic risk as well as its two components. Thus, too low
high-quality non-performance loans give the signals or early warning about systemic
risk. Thereby, increasing high-quality non-performance loans, in that case, may help
prevent the damage that systemic risk can bring. | en_US |
