Predicting abnormal capital flow episodes with machine learning methods

Bo Wang, Ruolan Yan and Yang Chen

The Quarterly Review of Economics and Finance, 2025, vol. 103, issue C

Abstract: In recent years, public health emergencies and geopolitical conflicts have constantly triggered volatility in the global economy and financial markets. Such frequent shocks have led to abnormal capital flow episodes, which can destabilize financial systems and foreign exchange markets, and sometimes these episodes are precursors to financial crises. Therefore, we develop an early warning model for abnormal capital flow episodes with a forecast horizon set two quarters in advance, employing two traditional linear regression models and nine machine learning algorithms. We also utilize two ensemble technologies, voting and stacking, to enhance out-of-sample predictive accuracy. This provides monetary authorities across nations with a practical early warning model that allows manual control over the forecast horizon and delivers robust predictive performance on out-of-sample observations, enabling timely interventions and preventative measures against risks associated with volatile capital flows. Furthermore, causal analysis using Shapley value decomposition and Shapley regression reveal drivers and mechanisms of abnormal capital flow episodes that differ from those identified by traditional linear models. For instance, the Shapley-based interpretation uncovers complex nonlinear relationships and highlights previously overlooked variables, such as domestic liability dollarization, as crucial predictors of sudden stops and capital flight. The Shapley-based interpretation reveals that the relative importance of predictors shifts after the 2008 Global Financial Crisis: features such as DLD become far more influential in the post-GFC period, reflecting a transition in investor behavior from profit-seeking to risk-averse. This insight deepens our understanding of the complex dynamics influencing international capital movements and enhances risk management tools in an interconnected world.

Keywords: Abnormal capital flow episodes; Machine learning; Shapley value (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1062976925000675
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:103:y:2025:i:c:s1062976925000675

DOI: 10.1016/j.qref.2025.102026

Access Statistics for this article

The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty

More articles in The Quarterly Review of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().