3 research outputs found
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
One of the key decisions in execution strategies is the choice between a
passive (liquidity providing) or an aggressive (liquidity taking) order to
execute a trade in a limit order book (LOB). Essential to this choice is the
fill probability of a passive limit order placed in the LOB. This paper
proposes a deep learning method to estimate the filltimes of limit orders
posted in different levels of the LOB. We develop a novel model for survival
analysis that maps time-varying features of the LOB to the distribution of
filltimes of limit orders. Our method is based on a convolutional-Transformer
encoder and a monotonic neural network decoder. We use proper scoring rules to
compare our method with other approaches in survival analysis, and perform an
interpretability analysis to understand the informativeness of features used to
compute fill probabilities. Our method significantly outperforms those
typically used in survival analysis literature. Finally, we carry out a
statistical analysis of the fill probability of orders placed in the order book
(e.g., within the bid-ask spread) for assets with different queue dynamics and
trading activity