Latency arbitrage in fragmented markets: A strategic agent-based analysis

Wah, Elaine; Wellman, Michael P.

doi:10.3233/af-160060

Cited by 13 publications

(10 citation statements)

References 60 publications

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“…For example, one recent study employed EGTA to explore market making strategies and identify conditions in which market makers were either beneficial or detrimental to market performance (Wah et al, 2017). Other finance topics addressed by EGTA include asset pricing (Cassell and Wellman, 2012), latency arbitrage (Wah and Wellman, 2016), and market manipulation (Wang and Wellman, 2017).…”

Section: Economic Applications Of Egtamentioning

confidence: 99%

Economic Reasoning from Simulation-Based Game Models

Wellman¹

2020

oeconomia

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Simulation modeling in economics has historically been viewed as an alternative to mainstream analytic technique, and as such has generally and intentionally avoided the focus on rational behavior and equilibrium reasoning that is characteristic of game-theoretic approaches. The emerging methodology of empirical game-theoretic analysis (EGTA) attempts to bridge agent-based simulation and game theory, combining the flexibility of simulation with the discipline of rationality criteria. The basic idea is to estimate a restricted game model from data generated by systematic simulation over a space of heuristic strategy profiles. EGTA enables a standard form of strategic analysis for complex economic scenarios previously addressed only in severely stylized form.

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Section: Economic Applications Of Egtamentioning

confidence: 99%

Economic Reasoning from Simulation-Based Game Models

Wellman¹

2020

oeconomia

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“…Having multiple exchanges in simultaneous operation offers opportunities for studying important aspects of contemporary financial markets such as smart order routing to ensure orders are executed at the best price available across multiple trading venues; and cross-market arbitrage where arbitrageur traders exploit discrepancies in prices between two or more trading venues, and/or make profitable use of differences in communications latencies between multiple markets (see e.g. [14,37]).…”

Section: Multiple Exchangesmentioning

confidence: 99%

An Open-Source Limit-Order-Book Exchange for Teaching and Research

Cliff

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Many of the world's major financial markets are electronic, in the sense that all communication among traders and internal record-keeping at exchanges is entirely mediated and executed by digital computer systems and associated communications networks; and many such markets are also highly automated, in the sense that they are heavily populated by automatic algorithmic trading system which have largely replaced human traders at the point of execution in many spot markets. This has created significant demand for people skilled in writing and managing algorithmic trading systems. To provide a complete education and training in this field it is highly desirable to allow students/trainees to study the operation of their own algorithmic trading systems running live on a real financial exchange, interacting dynamically with other automated traders. This paper describes the Bristol Stock Exchange (BSE), a simulator designed and developed to meet that need. BSE provides a full implementation of the Limit Order Book (LOB) at the heart of modern financial exchanges, and includes reference implementations of several well-known leading algorithmic trading systems. BSE allows users to submit a variety of order-types including market, limit, fill-or-kill, timeto-live, immediate-or-cancel, iceberg; orders for specific actions at market-open and market-close; and linked pairs of contingent orders. BSE can be configured to allow empirical studies of issues in order routing between multiple exchanges and the performance of cross-market arbitrage trading algorithms. BSE also has provision for varying the exchange's fee structure, including implementing maker-taker and takermaker pricing models, The Python source-code for BSE, which has been under ongoing development and extension since 2012, along with extensive documentation, is freely available on the GitHub online public repository, and can be used as a publicdomain platform for teaching and research.

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“…Discrete-time call auctions (DCA) have previously been proposed as an alternative to continuous double auctions (CDA) due to their ability, by design, to remove arbitrage opportunities. Wah and Wellman [7], [19] use an agent-based approach and a two-market model to study the effects of the presence of a latency arbitrageur in fragmented markets; demonstrating that a DCA provides efficiency gains. Wah, Hurd and Wellman [20] consider market choice: if DCA markets were available, would anybody use them?…”

Section: Modelling Market Fragmentationmentioning

confidence: 99%

Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets

Matthew

Cartlidge

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Computerisation of the financial markets has precipitated an arms-race for ever-faster trading. In combination, regulatory reform to encourage competition has resulted in market fragmentation, such that a single financial instrument can now be traded across multiple venues. This has led to the proliferation of high-frequency trading (HFT), and the ability to engage in latency arbitrage (taking advantage of accessing and acting upon price information before it is received by others). The impact of HFT and the consequences of latency arbitrage is a contentious issue. In 2013, Wah and Wellman used an agent-based model to study latency arbitrage in a fragmented market. They showed: (a) market efficiency is negatively affected by the actions of a latency arbitrageur; and (b) introducing a discrete-time call auction (DCA) eliminates latency arbitrage opportunities and improves efficiency. Here, we explore and extend Wah and Wellman's model, and demonstrate that results are sensitive to the bid-shading parameter used for zero-intelligence (ZIC) trading agents. To overcome this, we introduce the more realistic, minimally intelligent trading algorithm, ZIP. Using ZIP, we reach contrary conclusions: (a) fragmented markets benefit from latency arbitrage; and (b) DCAs do not improve efficiency. We present these results as evidence that the debate on latency arbitrage in financial markets is far from definitively settled, and suggest that ABM simulation-a form of decentralised collective computational intelligence-is a productive method for understanding and engineering financial systems.

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Latency arbitrage in fragmented markets: A strategic agent-based analysis

Cited by 13 publications

References 60 publications

Economic Reasoning from Simulation-Based Game Models

Economic Reasoning from Simulation-Based Game Models

An Open-Source Limit-Order-Book Exchange for Teaching and Research

Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets

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