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A Novel Framework for Online Sales Burst Prediction Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Optimal Client Recommendation for Market Makers in Illiquid Financial Products

Authors : Dieter Hendricks, Stephen J. Roberts

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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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.

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previous chapter MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
next chapter Predicting Self-reported Customer Satisfaction of Interactions with a Corporate Call Center
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Metadata
Title
Optimal Client Recommendation for Market Makers in Illiquid Financial Products
Authors
Dieter Hendricks
Stephen J. Roberts
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71273-4_14

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