Slow Decay of Impact in Equity Markets

Brokmann, X.; Sérié, Emmanuel; Kockelkoren, Julien; Bouchaud, Jean‐Philippe

doi:10.2139/ssrn.2471528

Cited by 26 publications

(49 citation statements)

References 27 publications

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“…Further studies have shown that impact is to a large part transient: after execution the price falls from its peak to some intermediate level, that some studies (Bershova and Rakhlin 2013, Gomes and Waelbroeck 2015, Moro et al 2009) argue to be close to 2/3 of the peak impact, in agreement with equilibrium models (Farmer et al 2013). In Brokmann et al (2014) the authors find that this high permanent level may be due to the correlation between the trader's execution decision and the residual order flow 2 and to the price signal that triggered the decision to trade: By taking into account these effects they argue that the "bare" permanent impact (or mechanical permanent impact) is much lower and possibly even zero. Finally, Gomes and Waelbroeck (2015) conduct a separate impact study of informed trades and cash-flow (uninformed) trades, to find that the latter have no permanent effect on the price (although the transient impact are similar for both types of trades).…”

Section: Comparison With Related Literaturementioning

confidence: 99%

A Million Metaorder Analysis of Market Impact on the Bitcoin

Donier

Bonart

2015

Mark. Microstructure Liq.

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We present a thorough empirical analysis of market impact on the Bitcoin/USD exchange market using a complete dataset that allows us to reconstruct more than one million metaorders. We empirically confirm the "square-root law" for market impact, which holds on four decades in spite of the quasi-absence of statistical arbitrage and market marking strategies. We show that the square-root impact holds during the whole trajectory of a metaorder and not only for the final execution price. We also attempt to decompose the order flow into an "informed" and "uninformed" component, the latter leading to an almost complete long-term decay of impact. This study sheds light on the hypotheses and predictions of several market impact models recently proposed in the literature and promotes heterogeneous agent models as promising candidates to explain price impact on the Bitcoin market -and, we believe, on other markets as well.

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Section: Comparison With Related Literaturementioning

confidence: 99%

A Million Metaorder Analysis of Market Impact on the Bitcoin

Donier

Bonart

2015

Mark. Microstructure Liq.

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“…The obtained model can then be used in an optimal trading scheme (see [Almgren et al, 2005a], [Gatheral, 2010] and [Lehalle and Dang, 2010] and for link with optimal trading see [Almgren and Chriss, 2000], [Gatheral and Schied, 2012] and [Bouchard et al, 2011]), or used by an investment firm to understand its trading costs (like in [Engle et al, 2012], [Bershova and Rakhlin, 2013], [Brokmann et al, 2014] or [Mastromatteo et al, 2013] written by author involved in investment firms).…”

Section: Selection Of the Explanatory Variablesmentioning

confidence: 99%

“…The purpose of the econophysicist is to understand the behaviour of these two forces and to find the law at the root of the impact of buying/selling pressure on prices' dynamic. See [Gabaix et al, 2006], and [Brokmann et al, 2014].…”

Section: Positioningmentioning

confidence: 99%

“…Recently [Brokmann et al, 2014] exposed a new methodology to remove the informational content of the studied proprietary metaorder database. Each metaorder is characterized by the intensity of the trading signal which triggered it.…”

Section: Positioningmentioning

confidence: 99%

“…Since we use the database of an executing broker, we do not know explicitly what triggered the creation of the metaorders, contrary to authors of [Brokmann et al, 2014] who had access to the prior belief of the metaorder issuer on future price changes. Still we can assume that the clients of the broker globally constitute a good representative sample of the diversity of market investors.…”

Section: Positioningmentioning

confidence: 99%

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Market Impacts and the Life Cycle of Investors Orders

Bacry

Iuga

Lasnier³

et al. 2014

SSRN Journal

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In this paper, we use a database of around 400,000 metaorders issued by investors and electronically traded on European markets in 2010 in order to study market impact at different scales.At the intraday scale we confirm a square root temporary impact in the daily participation, and we shed light on a duration factor in 1/T γ with γ 0.25. Including this factor in the fits reinforces the square root shape of impact. We observe a power-law for the transient impact with an exponent between 0.5 (for long metaorders) and 0.8 (for shorter ones). Moreover we show that the market does not anticipate the size of the meta-orders. The intraday decay seems to exhibit two regimes (though hard to identify precisely): a "slow" regime right after the execution of the meta-order followed by a faster one. At the daily time scale, we show price moves after a metaorder can be split between realizations of expected returns that have triggered the investing decision and an idiosynchratic impact that slowly decays to zero.Moreover we propose a class of toy models based on Hawkes processes (the Hawkes Impact Models, HIM) to illustrate our reasoning. We show how the Impulsive-HIM model, despite its simplicity, embeds appealing features like transience and decay of impact. The latter is parametrized by a parameter C having a macroscopic interpretation: the ratio of contrarian reaction (i.e. impact decay) and of the "herding" reaction (i.e. impact amplification).

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Mean field game of controls and an application to trade crowding

2017

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In this paper we formulate the now classical problem of optimal liquidation (or optimal trading) inside a Mean Field Game (MFG). This is a noticeable change since usually mathematical frameworks focus on one large trader facing a "background noise" (or "mean field"). In standard frameworks, the interactions between the large trader and the price are a temporary and a permanent market impact terms, the latter influencing the public price.In this paper the trader faces the uncertainty of fair price changes too but not only. He also has to deal with price changes generated by other similar market participants, impacting the prices permanently too, and acting strategically.Our MFG formulation of this problem belongs to the class of "extended MFG", we hence provide generic results to address these "MFG of controls", before solving the one generated by the cost function of optimal trading. We provide a closed form formula of its solution, and address the case of "heterogenous preferences" (when each participant has a different risk aversion). Last but not least we give conditions under which participants do not need to instantaneously know the state of the whole system, but can "learn" it day after day, observing others' behaviors.

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Slow Decay of Impact in Equity Markets

Cited by 26 publications

References 27 publications

A Million Metaorder Analysis of Market Impact on the Bitcoin

A Million Metaorder Analysis of Market Impact on the Bitcoin

Market Impacts and the Life Cycle of Investors Orders

Mean field game of controls and an application to trade crowding

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