Algorithmic trading: Intraday profitability and trading behavior

Devika Arumugam

Economic Modelling, 2023, vol. 128, issue C

Abstract: This study examines the intraday profitability and interactions among Buy-side Algorithmic Traders (BATs), High-Frequency Traders (HFTs) and Non-Algorithmic Traders (NATs). When all trades are considered, ATs gain, and NATs lose. Demanding liquidity benefits all traders, with BATs outperforming HFTs. HFTs and NATs lose while providing liquidity, but BATs gain. Intraday timing efficiency increases NATs' trading but not ATs'. Market volatility triggers opposing trading behaviors; As volatility increases, BATs retreat while HFTs intensify trading, possibly driven by opposing hedging and speculative motives, respectively. BATs and HFTs exhibit within-group positive probabilities with their order imbalances. An increase in BATs' order imbalance decreases the likelihood of HFTs’ trading.

Keywords: Algorithmic trading; High-frequency trading; Profitability; Intraday trading; High-frequency finance (search for similar items in EconPapers)
JEL-codes: G12 G14 G20 G23 G40 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:128:y:2023:i:c:s0264999323003334

DOI: 10.1016/j.econmod.2023.106521

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