Detecting Toxic Flow

In foreign exchange (FX), as in other asset classes, broker-client relationships are ubiquitous. The broker streams bid and ask quotes to her clients and the clients decide when to trade on these quotes, so the broker bears the risk of adverse selection when trading with better informed clients. These risks are borne by both liquidity providers who stream quotes to individual parties and by market participants who provide liquidity in the books of electronic exchanges. However, in contrast to electronic order books in which trading is anonymous for all participants (e.g., in Nasdaq, LSE, Euronext), in broker-client relationships the broker knows which client executed the order. This privileged information can be used by the broker to classify flow, i.e., toxic or benign, and to devise strategies that mitigate adverse selection costs. In the literature, models generally classify traders as informed or uninformed; see e.g., Bagehot (1971), Copeland and Galai (1983), Grossman and Stiglitz (1980), Amihud and Mendelson (1980), Kyle (1989), Kyle (1985), and Glosten and Milgrom (1985). In equity markets, many studies focus on informed flow (i.e., asymmetry of information) across various traded stocks, see e.g., Easley et al. (1996) who study the probability of informed trading at the stock level, while our study focuses on We thank Andrew Stewart, Alistair Sturgiss, Fayçal Drissi, Patrick Chang, Álvaro Arroyo, Sergio Calvo Ordoñez, and participants at the Oxford Victoria Seminar for comments. ChatGPT suggested the name PULSE for our algorithm.