Comparison of different market making strategies for high frequency traders

This paper utilizes agent-based simulation to compare different market making strategies for high frequency traders (HFTs). After proposing a model representing HFTs' activities in financial market when they act as market makers, we carry out simulations to explore how different quoting strategies affect their profit. The results show that combination of (i) offering prices based on the latest trading price, and (ii) using the information about market volatility and order imbalance, increase market makers' daily returns. In addition, other scenarios including the competition environment of increased competitors and decreased latencies are incorporated in the model, in order to find out how these factors change the performance of market making strategy.

[1]  Terrence Hendershott,et al.  Price Pressures , 2014 .

[2]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[3]  G. Fagiolo,et al.  Rock around the clock: An agent-based model of low- and high-frequency trading , 2014, Journal of Evolutionary Economics.

[4]  A. Menkveld.  High frequency trading and the new market makers , 2013 .

[5]  Shinobu Yoshimura,et al.  Investigating the Impact of Trading Frequencies of Market Makers: A Multi-Agent Simulation Approach , 2013 .

[6]  G. Kendall,et al.  Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[7]  Giorgio Fagiolo,et al.  Rock Around the Clock: An Agent-Based Model of Low- and High-Frequency Trading , 2014 .

[8]  T. Hendershott,et al.  High Frequency Trading and Price Discovery , 2013, SSRN Electronic Journal.

[9]  Duane J. Seppi,et al.  Electronic Market Making: Initial Investigation , 2003 .

[10]  Michael P. Wellman,et al.  Welfare Effects of Market Making in Continuous Double Auctions , 2015, AAMAS.

[11]  Flaminio Squazzoni,et al.  Individual behavior and macro social properties. An agent-based model , 2008, Comput. Math. Organ. Theory.

[12]  Huyen Pham,et al.  Optimal high-frequency trading with limit and market orders , 2011, ArXiv.

[13]  Elizabeth M. Murphy,et al.  Re: File Number S7-02010, " Concept Release on Equity Market Structure " , 2022 .

[14]  Björn Hagströmer,et al.  The Diversity of High-Frequency Traders , 2013 .

[15]  Allen Carrion.  Very Fast Money: High-Frequency Trading on the NASDAQ , 2013 .

[16]  Michel A. Robe,et al.  Electronic Market Makers, Trader Anonymity and Market Fragility , 2014 .

[17]  B. LeBaron,et al.  Long-memory in an order-driven market , 2007 .

[18]  Olivier Guéant,et al.  Dealing with the inventory risk: a solution to the market making problem , 2011, 1105.3115.

[19]  Peter A. Beling,et al.  An agent based model of the E-Mini S&P 500 applied to flash crash analysis , 2011, 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).