The impact of algorithmic trading on market quality: evidence from the Johannesburg Stock Exchange

Abstract

High Frequency Trading (HFT) is a trading method that relies on sophisticated algorithms to analyse markets and execute large amounts of orders within milliseconds. In the last two decades, this new technology has gained traction globally and now accounts for the majority of the trading volume on the Johannesburg Stock Exchange. Despite the dominance of HFT, studies on the topic have been scarce outside of the U.S. This study seeks to examine the effects of HFT on market quality in a South African context. Market quality is proxied by two measures, market liquidity and short-term volatility. Firstly, the study makes use of a set of proxies for algorithmic trading (AT), namely average trade size, odd-lot volume ratio and trade-to-order volume ratio. This captures the level of AT activity from intra-day trade and quote data for all JSE-listed stocks from 1 January 2019 to 30 June 2019. Secondly, panel regressions are used to determine the relationship between these proxies and the market quality measures, while controlling for market capitalisation. The panel data estimators indicated a strong positive relationship between average trade size and the liquidity measures. However, they revealed an inverse relationship between the odd-lot volume ratio and the trade-to-order volume ratio. These findings thus suggest that the proxies capture the activity of different types of algorithmic traders (ATs), market-makers in the first case, opportunistic in the second. Finally, the study found a strong positive relationship with short-term volatility. The study concludes that overall, AT has a positive impact on market quality, despite carrying the risk of causing instability in certain markets.