Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures

Abstract

We study the intraday price impact of algorithmic trading (AT) on futures markets. We find that AT exhibits a strong reverse U‐shape intraday pattern, and greater AT activity is related to lower effective spreads, higher realized spreads and lower adverse selection risk, which suggests that algorithmic traders strategically enter the market when transaction costs and information asymmetry are lower. AT is associated with an increase in transaction costs in the subsequent intraday period mainly through an increase in the adverse selection risk, and is positively related to both public and private information. Our results strongly suggest that algorithmic traders are informed and contribute to liquidity and price discovery on the futures markets.

Computerized and High-Frequency Trading

“…The orders also may have an extremely short duration before they are cancelled if not executed, often of a second or less" (p. 3607). Hendershott, Jones andMenkveld (2011) andViljoen, Westerholm, andZheng (2014) use this fact to generate estimates of the amount of algorithmic trading.…”

“…Therefore, proxies for algorithmic trading and the HFT portion thereof have been developed. These include the rate of electronic message traffic normalized by trading volume as used by Hendershott, Jones and Menkveld (2011) and Viljoen, Westerholm, and Zheng (2014), the use of proprietary data to identify specific HFTs in the data as in Brogaard, Hendershott, Hunt, and Ysusi (2014), or the use of account-level trade-by-trade data on certain contracts and schemes for classifying traders into various high-frequency categories, based on their trading volume and inventory management; see Hendershott and Riordan (2012), Brogaard, Hendershott and Riordan (2013) and Baron, Brogaard and Kirilenko (2012).…”

Do High Frequency Traders Care about Earnings Announcements? An Analysis of Trading Activity before, during, and after Regular Trading Hours

“…Research shows that high frequency traders (HFTs) add to price discovery (Brogaard et al (2014); Viljoen et al (2014)) and behave as market makers (Menkveld, 2013). Current research on HFTs focuses primarily on regular trading hours (RTHs), but not on trading before the market opens or after the market closes.…”

Threshold convergence between the federal fund rate and South African equity returns around the colocation period

“…Moreover, others such as Lee (2015) contend that high-frequency trading technologies have an insignificant effect on stock market performance whilst a smaller cluster of studies (e.g . Viljoen et.…”