Journal
ANNALS OF OPERATIONS RESEARCH
Volume 281, Issue 1-2, Pages 315-347Publisher
SPRINGER
DOI: 10.1007/s10479-019-03150-0
Keywords
Artificial intelligence; Algorithmic trading; Decision analytics; Discrete optimization; FinTech; Liquidity
Categories
Funding
- InfoTech research project [DE320245686]
Ask authors/readers for more resources
Regulatory reform enacted (e.g., the Dodd-Frank Act enforced in the U.S.) requires the financial service industry to consider the reasonably expected near term demand (i.e., RENTD) in trading. To manage the price impact and transaction cost associated with orders submitted to an order driven market, market makers or specialists must determine their trading styles (aggressive, neutral, or passive) based on the market liquidity in response to RENTD, particularly for trading a large quantity of some financial instrument. In this article we introduce a model considering different trading styles to satisfy the predictive near-term customer demand of market liquidity in order to find an optimal order submission strategy based on different market situations. We show some analytical properties and numerical performances of our model in search of optimal solutions. We evaluate the performances of our model with simulations run over a set of experiments in comparison with two alternative strategies. Our results suggest that the proposed model illustrates superiority in performance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available