Optimal client recommendation for market makers in illiquid financial products

TL;DR

This paper introduces a probabilistic method using Latent Dirichlet Allocation to recommend clients for trading illiquid corporate bonds, aiming to improve market makers' targeting efficiency.

Contribution

It develops a novel client recommendation technique based on topic modeling, specifically LDA, for corporate bond trading in illiquid markets.

Findings

LDA-based model effectively ranks clients by interest probability.

The approach improves recommendation relevance for sales traders.

Model shows promising performance in empirical tests.

Abstract

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.

Full text

See pages 1-last of ECML_submission_002.pdf