High-frequency trading, liquidity withdrawal and the breakdown of conventions in foreign exchange markets

Alexis Stenfors, Masayuki Susai

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    Abstract

    Conventions, or “that the existing state of affairs will continue indefinitely, except in so far as we have specific reasons to expect a change” (Keynes, 1936), play a central role in over-the-counter markets. For instance, by allowing expectations about the future to become more harmonised and orderly, they act as stabilisers for the provision of liquidity. Conventions might, of course, change at any time. Nonetheless, by being attached to the daily trading routine and/or integrated within the institutional structure, the confidence in their relevance and validity can be long-lasting. In the foreign exchange market, in particular, where prices are quoted to end-users on demand, market-making banks rely upon a convention to quote each other prices to maintain liquidity. However, the rise of algorithmic and high-frequency trading poses a practical as well as a theoretical challenge to such conventions. By reacting ultra-fast to new information, including to new limit orders submitted by others, markets largely populated with algorithmic traders have become susceptible to a withdrawal of liquidity at an unprecedented speed and scale. Using a high-frequency dataset provided by EBS, this paper investigates the process of liquidity withdrawal from the foreign exchange spot market. By doing so, it considers the crowding out of conventions associated with liquidity provision, traditionally upheld through mutual understanding among financial institutions – in other words, reciprocity and trust among humans.
    Original languageEnglish
    Pages (from-to)387-395
    Number of pages9
    JournalJournal of Economic Issues
    Volume52
    Issue number2
    Early online date11 Jun 2018
    DOIs
    Publication statusPublished - 11 Jul 2018

    Keywords

    • conventions
    • liquidity
    • foreign exchange
    • limit order book
    • high-frequency trading
    • algorithmic trading

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