Data-Driven Market-Making via Model-Free Learning
By Yueyang Zhong, YeeMan Bergstrom, Amy R. Ward. 2020
132 citations recorded by the source corpus.
This paper studies when a market-making firm should place orders to maximize their expected net profit, while also constraining risk, assuming orders are maintained on an electronic limit order book (LOB). To do this, we use a model-free and off-policy method, Q-learning, coupled with state aggregation, to develop a proposed trading strategy that can be implemented using a simple lookup table. Our main training dataset is derived from event-by-event data recording the state of the LOB. Our proposed trading strategy has passed both in-sample and out-of-sample testing in the backtester of the market-making firm with whom we are collaborating, and it also outperforms other benchmark strategies. As a result, the firm desires to put the strategy into production.
Subfields: Artificial Intelligence, Management Science and Operations Research, Reinforcement Learning, Economics & Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dongwon Lee
- Ting-Hao 'Kenneth' Huang
- William Yang Wang
- Neil Heffernan III
- Bryan R. Routledge
- Xintao Wu
- Lu Zhang
- Mohit Bansal
In Management Science and Operations Research
In Reinforcement Learning
In Economics & Operations Research
Citations in the corpus
- MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education (2021, methodology): “For example, FinBERT [17], ClinicalBERT [11], BioBERT [13], SCIB-ERT [2].”
- FinQA: A Dataset of Numerical Reasoning over Financial Data (2021, methodology): “A few NLP studies in Question Answering (QA) explored the numerical reasoning capabilities needed to answer questions correctly.”