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Algorithmic trading: Intraday profitability and trading behavior

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  • Arumugam, Devika

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.

Suggested Citation

  • Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:ecmode:v:128:y:2023:i:c:s0264999323003334
    DOI: 10.1016/j.econmod.2023.106521
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    References listed on IDEAS

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    More about this item

    Keywords

    Algorithmic trading; High-frequency trading; Profitability; Intraday trading; High-frequency finance;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G40 - Financial Economics - - Behavioral Finance - - - General

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