ScholarLens Back to search
2021•Financial Analysts JournalRequires access

Active Trading in ETFs: The Role of High-Frequency Algorithmic Trading

Archana Jain, Chinmay Jain, Christine X. Jiang

Open publisher page 17 citations

Abstract

In the study reported here, we explored high-frequency algorithmic trading and its effect on exchange-traded funds (ETFs). Using the cancel rate, the trade-to-order ratio, percentage odd-lot volume, and trade size as proxies for algorithmic trading, we found that more algorithmic trading in ETFs results in smaller and less persistent deviations of fund prices from their net asset values (NAVs). Arbitrage strategies adopted by algorithmic traders directly help reduce the magnitude and persistence of ETF price deviations from NAVs. Also, algorithmic trading improves ETF liquidity by lowering spreads and facilitates arbitrage.

About this research paper

What this paper is about

In the study reported here, we explored high-frequency algorithmic trading and its effect on exchange-traded funds (ETFs). Using the cancel rate, the trade-to-order ratio, percentage odd-lot volume, and trade size as proxies for algorithmic trading, we found that more algorithmic trading in ETFs results in smaller and less persistent deviations of fund prices from their net asset values (NAVs). Arbitrage strategies adopted by algorithmic traders directly help reduce the magnitude and persistence of ETF price deviations from NAVs. Also, algorithmic trading improves ETF liquidity by lowering spreads and facilitates arbitrage.

Why it matters

OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In the study reported here, we explored high-frequency algorithmic trading and its effect on exchange-traded funds (ETFs). Using the cancel rate, the trade-to-order ratio, percentage odd-lot volume, and trade size as proxies for algorithmic trading, we found that more algorithmic trading in ETFs results in smaller and less persistent deviations of fund prices from their net asset values (NAVs). Arbitrage strategies adopted by algorithmic traders directly help reduce the magnitude and persistence of ETF price deviations from NAVs. Also, algorithmic trading improves ETF liquidity by lowering spreads and facilitates arbitrage.

Key concepts: Pairs trade, Statistical arbitrage, High-frequency trading, Arbitrage, Market liquidity, Algorithmic trading, Asset (computer security), Volume-weighted average price

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Active Trading in ETFs: The Role of High-Frequency Algorithmic Trading — Research Paper | ScholarLens