Drivers of Economic and Financial Integration: A Machine Learning Approach

We propose a new approach to identify drivers of global market integration using an advanced machine learning technique. We differentiate across economic and financial integration as well as across emerging and developed countries. Our approach allows for nonlinear relationships, corrects for over-fitting, and is less prone to noise. Moreover, it is able to tackle a large number of highly correlated explanatory variables and controls for multicollinearity. Results suggest that general economic growth, increasing international trade, and contained population growth have helped emerging countries to catch up to the level of the economic integration of developed countries. However, slow financial development and a high level of investment riskiness have hindered the speed of emerging countries' financial integration. Furthermore, the results suggest that integration is a gradual process and is not driven by cyclical or transitory events.

Keywords: Determinants of Market Integration, Random Forest Regression, Machine Learning

JEL Classification: F15, F30, G15, E44

Suggested Citation: Suggested Citation

Akbari, Amir and Ng, Lilian and Solnik, Bruno, Drivers of Economic and Financial Integration: A Machine Learning Approach (October 18, 2020). Journal of Empirical Finance, Accepted, Available at SSRN: https://ssrn.com/abstract=3583484 or http://dx.doi.org/10.2139/ssrn.3583484