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New research Explores Insights into Cryptocurrencies, Stocks, and US ETFs

This research article, "Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs" by researchers from the DCU School of Computing and UCD investigates herding behavior among investors across three different types of investment vehicles; cryptocurrencies, stocks, and US ETFs. The research was carried out by DCU’s Dr An Pham Ngoc Nguyen, Prof Martin Crane and Dr Marija Bezbradica, as well as UCD’s Dr Thomas Conlon.

The study utilises a recent dataset from April 2019 to May 2023, encompassing significant market events like the US-China trade war, the Covid-19 pandemic, and the Ukraine-Russia conflict. To address limitations in previous research that often focused on single asset types or entire datasets, this study examines herding not only in each asset class separately using the Cross-Sectional Absolute Deviation (CSAD) model but also at a community level by employing graph-based techniques like Minimum Spanning Tree and Louvain community detection to group assets with similar price movements. 

Furthermore, the research explores financial contagion effects between these investment vehicles using Vector Autoregression (VAR). The key findings indicate mostly similar herding patterns for stocks and US ETFs, the existence of herding at a subset level across all asset types stemming from specific events, and that US ETFs exhibit strong contagion effects on stocks and cryptocurrencies, potentially acting as drivers of herding.

Read the full paper here: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316332