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Lietf Trading Behavior During U.S. – China Trade War Cover

Lietf Trading Behavior During U.S. – China Trade War

Open Access
|Dec 2025

Abstract

This paper examines the trading behavior of investors in Leveraged and Inverse Exchange-Traded Funds (LIETFs) during the early phase of the U.S.–China tariff trade war, from January to November 2018. Using panel data models, we analyze LIETF reactions to 17 trade-war news events identified by China Briefing. The study focuses on the 12 most liquid LIETFs tracking the S&P 500 and Nasdaq-100 indices, with trade and quote data obtained from Reuters DataScope Select. Our first objective is to test whether LIETF investors’ trading activity changes significantly around trade-war announcements, and our results provide strong support for this hypothesis. To further explore trading dynamics, we construct two measures of abnormal activity—the abnormal buy–sell ratio and the abnormal order imbalance—and test whether LIETF investors can predict subsequent market movements. We find little evidence of such predictive ability. Finally, we assess short-term momentum behavior: while investors display trend-chasing in intraday intervals of less than 30 minutes, they tend to bet on reversals of the underlying indices over longer intervals. Overall, this study contributes to understanding how investors in leveraged products respond to geopolitical shocks. Given the growing asset base of U.S.-listed LIETFs and the regulatory importance of investor behavior during periods of heightened uncertainty, our findings are relevant to academics, practitioners, and market supervisors.

DOI: https://doi.org/10.2478/eoik-2025-0100 | Journal eISSN: 2303-5013 | Journal ISSN: 2303-5005
Language: English
Page range: 381 - 398
Submitted on: Jun 18, 2025
Accepted on: Oct 13, 2025
Published on: Dec 1, 2025
Published by: Oikos Institut d.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2025 Petko Stefanov Kalev, Alex Lee, published by Oikos Institut d.o.o.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.