Optimal client recommendation for market makers in illiquid financial products
This addresses the challenge for market makers in illiquid financial products by providing a targeted recommendation system to reduce exposure, though it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of market makers needing to offload illiquid corporate bond positions by proactively identifying interested clients, developing a probabilistic topic-modeling technique based on Latent Dirichlet Allocation to rank client recommendations, and shows promising performance in delivering relevant suggestions for sales traders.
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.