Machine Learning and High-Frequency Algorithms during Batch Auctions

Yergeau, Gabriel

doi:10.2139/ssrn.3170378

SSRN Journal

2018

DOI: 10.2139/ssrn.3170378

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Machine Learning and High-Frequency Algorithms during Batch Auctions

Gabriel Yergeau

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2019

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“…change. Accordingly, Yergeau [2018] finds typical patterns of high-frequency algorithmic trading in the auctions of XTRA. Challet and Gourianov [2018] analyze anonymous data from US equities and compute the response functions of the final auction price to the addition or cancellation of auction orders as a function of the time remaining until the auction, which have strikingly different behaviors in the opening and closing auction phases.…”

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Strategic Behaviour and Indicative Price Diffusion in Paris Stock Exchange Auctions

Challet

2019

New Economic Windows

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We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. A third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa.Research in market micro-structure has focused on the dynamical properties of open markets [O'Hara, 1997, Bouchaud et al., 2009]. However, main stock exchanges have been using auction phases when they open and close for a long time 1 .Auctions are known to have many advantages, provided that there are enough participants: for example, auction prices are well-defined, correspond to larger liquidity, and decrease price volatility (and bid-ask spreads) shortly after the opening time and before closing time (see e.g. Pagano and Schwartz [2003], Chelley-Steeley [2008], Pagano et al. [2013]).Only a handful of papers are devoted to the dynamics of auction phases, i.e., periods during which market participants may send limit or market orders specifically for the auction. Boussetta et al. [2016] investigate when fast and slow traders send their orders during the opening auction phase of the Paris Stock Exchange and find markedly different behaviors: the slow brokers are active first, while highfrequency traders are mostly active near the end of auctions. In the same vein, Bellia et al. [2016] show how and when low-latency traders (identified as high frequency traders) add or remove liquidity in the pre-opening auction of the Tokyo Stock Ex-

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Strategic Behaviour and Indicative Price Diffusion in Paris Stock Exchange Auctions

Challet

2019

New Economic Windows

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Machine Learning and High-Frequency Algorithms during Batch Auctions

Cited by 1 publication

References 42 publications

Strategic Behaviour and Indicative Price Diffusion in Paris Stock Exchange Auctions

Strategic Behaviour and Indicative Price Diffusion in Paris Stock Exchange Auctions

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