On fairness in continuous electronic markets

Melton, Hayden

doi:10.1145/3194770.3194771

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…The constant l represents the various transmission delays of the exchange's infrastructure (e.g., network link latency for market updates, order transmission latency, order processing latency). A more relaxed version of this definition that seeks to bring slower and faster participants onto "equal footing" has been discussed in [27,29,32,33]. We can now define when an exchange is temporally fair.…”

Section: Unbounded Temporal Fairnessmentioning

confidence: 99%

“…Operators can try to decrease (and increase δ) by upgrading their infrastructure but similarly participants are also getting consistently faster and more sensitive to subtle differences. We argue that noncontinuous market designs at timescales could solve this problem by uniformly distributing the victories between participants for races beneath [27,29,32,33]. In these market designs, the majority of the participants will experience a FIFO market, while the small minority of ultra-fast traders will be still guaranteed at least equal treatment at all scales.…”

Section: Bounded Temporal Fairnessmentioning

confidence: 99%

“…This is primarily due to FIFO's implementation and infrastructural requirements that are hard to consistently meet in practice. For example, exchanges (whose participants may have nanosecond-scale reaction times) must ensure that their infrastructure does not introduce any delays that could alter the relative arrival times of competing orders [25,28,31]. However, networking equipment (e.g., network switches) is rarely certified to guarantee equal latency on all ports at a nanosecond scale [25].…”

Section: Introductionmentioning

confidence: 99%

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Bounded Temporal Fairness for FIFO Financial Markets

Mavroudis

2020

Security Protocols XXVII

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Financial exchange operators cater to the needs of their users while simultaneously ensuring compliance with the financial regulations. In this work, we focus on the operators' commitment for fair treatment of all competing participants. We first discuss unbounded temporal fairness and then investigate its implementation and infrastructure requirements for exchanges. We find that these requirements can be fully met only under ideal conditions and argue that unbounded fairness in FIFO markets is unrealistic. To further support this claim, we analyse several real-world incidents and show that subtle implementation inefficiencies and technical optimizations suffice to give unfair advantages to a minority of the participants. We finally introduce, -fairness, a bounded definition of temporal fairness and discuss how it can be combined with non-continuous market designs to provide equal participant treatment with minimum divergence from the existing market operation.1 Orders who occupy the top of the order book have better access to liquidity [10].

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Section: Unbounded Temporal Fairnessmentioning

confidence: 99%

Section: Bounded Temporal Fairnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bounded Temporal Fairness for FIFO Financial Markets

Mavroudis

2020

Security Protocols XXVII

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“…Much effort has been put into uncovering fairness violations [7,8,11,16,23,26,29,43,53]. The most common method is fairness testing [2,3,9,22,24,52,56,57], which solves this problem by generating as many instances as possible.…”

Section: Introductionmentioning

confidence: 99%

NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

Zheng¹,

Chen²,

Du³

et al. 2021

Preprint

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Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable -it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective -it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic -it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (∼ ×5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems. The code of NeuronFair is open-sourced at https:// github.com/ haibinzheng/ NeuronFair. CCS CONCEPTS• Computing methodologies → Artificial intelligence; • Software and its engineering → Software reliability.

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“…This is primarily due to FIFO's implementation and infrastructural requirements that are hard to consistently meet in practice. For example, exchanges (whose participants may have nanosecondscale reaction times) must ensure that their infrastructure does not introduce any delays that could alter the relative arrival times of competing orders [25,28,31]. However, networking equipment (e.g., network switches) is rarely certified to guarantee equal latency on all ports at a nanosecond scale [25].…”

Section: Introductionmentioning

confidence: 99%