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Predicting ETF liquidity

Predicting ETF liquidity Substantial transaction costs are incurred in exchange-traded fund (ETF) trading each year. This article examines a vector autoregressive (VAR) model’s performance and other trading schedules to time trades in a large sample of 1350 ETFs over the 2011–2017 period. We reject the notion of a one-size-fits-all trading schedule that maximizes spread savings for all ETF traders. ETF traders who want to split their orders could save 7.40% of ETF spread costs, whereas trading at the market closing time would be optimal for ETF traders without motives to split trades. The spread savings for ETF traders are diverse across ETF sectors and depend on the spread volatility.JEL Classification:G11, G23 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australian Journal of Management SAGE

Predicting ETF liquidity

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Publisher
SAGE
Copyright
© The Author(s) 2023
ISSN
0312-8962
eISSN
1327-2020
DOI
10.1177/03128962221143494
Publisher site
See Article on Publisher Site

Abstract

Substantial transaction costs are incurred in exchange-traded fund (ETF) trading each year. This article examines a vector autoregressive (VAR) model’s performance and other trading schedules to time trades in a large sample of 1350 ETFs over the 2011–2017 period. We reject the notion of a one-size-fits-all trading schedule that maximizes spread savings for all ETF traders. ETF traders who want to split their orders could save 7.40% of ETF spread costs, whereas trading at the market closing time would be optimal for ETF traders without motives to split trades. The spread savings for ETF traders are diverse across ETF sectors and depend on the spread volatility.JEL Classification:G11, G23

Journal

Australian Journal of Management – SAGE

Published: Jan 1, 2023

Keywords: Bid-ask spread; diversification; ETFs; forecasting; liquidity; portfolio liquidity

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