Event-Driven Market Co-Movement Dynamics in Critical Mineral Equities: An Empirical Framework Using Change Point Detection and Cross-Sectional Analysis

TL;DR

This paper introduces an empirical framework combining change-point detection and cross-sectional analysis to study co-movement dynamics in critical mineral equities, revealing how market shocks influence investor behavior and herd behavior patterns.

Contribution

It develops a novel analytical approach to identify change points and analyze co-movement patterns in critical mineral ETFs, highlighting the impact of global events on investor behavior.

Findings

Major change points aligned with global shocks like oil crisis, COVID-19, and Ukraine invasion.

Herding increased after crises, shifting to anti-herding following positive vaccine news and geopolitical events.

Investor coordination varies with market conditions and is most pronounced during downturns.

Abstract

This study examines market behavior in critical mineral investments using a novel analytical framework that combines change-point detection (PELT algorithm) with cross-sectional analysis. This research analyzes ESG-ranked critical mineral ETFs from March 31, 2014, to April 19, 2024, using the S&P 500 as a benchmark to evaluate market co-movements. The findings demonstrate that different critical mineral investments experienced change points at distinct times, but three major dates, July 23, 2015; March 17, 2020; and December 1, 2020, were common and aligned with global events such as the oil market shock, the COVID-19 pandemic, and later market adjustments. Herding behavior among investors increased after these shocks, following the 2015 and 2020 crises, but shifted to anti-herding after positive vaccine news in late 2020 and after the Russian invasion of Ukraine in 2022. The sensitivity analysis shows that investor coordination is strongest during market downturns but exhibits greater variation during stable periods or after major developments, with these dynamics sensitive to the length of the observation period. Additionally, anti-herding became more apparent during crises, suggesting investors reacted to specific risks rather than moving in lockstep, especially in response to geopolitical shocks.