Drivers of economic and financial integration: A machine learning approach

Amir Akbari, Lilian Ng and Bruno Solnik

Journal of Empirical Finance, 2021, vol. 61, issue C, 82-102

Abstract: We propose a new approach to identifying drivers of economic and financial integration, separately, and across emerging and developed countries. Our advanced machine learning technique allows for nonlinear relationships, corrects for over-fitting, and is less prone to noise. It also can 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 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 (search for similar items in EconPapers)
JEL-codes: E44 F15 F30 G15 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:61:y:2021:i:c:p:82-102

DOI: 10.1016/j.jempfin.2020.12.005

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