Drivers of Economic and Financial Integration: A Machine Learning Approach

52 Pages Posted: 20 May 2020 Last revised: 2 Jan 2021

See all articles by Amir Akbari

Amir Akbari

Ontario Tech. University

Lilian Ng

Schulich School of Business, York University; European Corporate Governance Institute (ECGI)

Bruno Solnik

Hong Kong University of Science & Technology (HKUST) - Department of Finance ; HEC Paris - Departement Finance et Economie

Date Written: October 18, 2020

Abstract

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

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

Amir Akbari (Contact Author)

Ontario Tech. University ( email )

2000 Simcoe Street North
Oshawa, Ontario L1H 7K4
Canada

HOME PAGE: http://www.amir-akbari.com

Lilian Ng

Schulich School of Business, York University ( email )

N223, Seymour Schulich Building
4700 Keele Street
Toronto, Ontario ON M3J 1P3
Canada
+1.416.736.2100 x77994 (Phone)

European Corporate Governance Institute (ECGI) ( email )

c/o the Royal Academies of Belgium
Rue Ducale 1 Hertogsstraat
1000 Brussels
Belgium

Bruno Solnik

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

HEC Paris - Departement Finance et Economie ( email )

1, rue de la Liberation
Jouy-en-Josas Cedex, 78351
France
+33 1 39 67 72 84 (Phone)
+33 1 39 67 70 85 (Fax)

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