Phil 9.22.19

Getting ready for a fun trip: VA

12th International Conference on Agents and Artificial Intelligence – Dammit, the papers are due October 4th. This would be a perfect venue for the GPT2 agents

Novelist Cormac McCarthy’s tips on how to write a great science paper

Unveiling the relation between herding and liquidity with trader lead-lag networks

  • We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative “opinion” to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times.
    We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minutes time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.

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