The power of patience: a behavioural regularity in limit-order placement

Zovko, Ilija I.; Farmer, J. Doyne

doi:10.1088/1469-7688/2/5/308

Cited by 102 publications

(59 citation statements)

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“…2) but for a different reason: An increase in the rate of market order submission reduces liquidity and thus increases price volatility. We certainly believe that agents respond in important ways to changing market conditions such as volatility, and indeed we have demonstrated this in previous work (16). Nonetheless, we argue that it is also necessary to understand the impact of agents' actions on market conditions.…”

Section: Resultssupporting

confidence: 57%

“…We thus start from almost zero rationality and work our way up, in contrast to the canonical approach of starting from perfect rationality and working down (see ref. 16).…”

Section: Resultsmentioning

confidence: 99%

“…However, a problem occurs in measuring the parameters ␣ and ␦ due to the simplifying assumption of a uniform distribution of prices for limit order placement and a uniform cancellation rate. In the real data, limit order placement and cancellation are concentrated near the best prices (12,16). To cope with this, we make an auxiliary assumption that order placement is uniform inside a price window around the best prices and zero outside this window.…”

Section: Testing the Scaling Lawsmentioning

confidence: 99%

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The Predictive Power of Zero Intelligence in Financial Markets

2004

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Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where constraints imposed by market institutions dominate strategic agent behavior. We use data from the London Stock Exchange to test a simple model in which minimally intelligent agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction and yields simple laws relating order-arrival rates to statistical properties of the market. We test the validity of these laws in explaining cross-sectional variation for 11 stocks. The model explains 96% of the variance of the gap between the best buying and selling prices (the spread) and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The nondimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view, because it demonstrates the existence of simple laws relating prices to order flows and, in a broader context, suggests there are circumstances where the strategic behavior of agents may be dominated by other considerations. double auction market ͉ market microstructure ͉ agent-based models T he traditional paradigm in economics is one of rational utility maximizing agents. Recognizing limitations in human cognition, economists have increasingly explored models in which agents have bounded rationality. We take this direction even further here by testing a model of trading in financial markets that drops agent rationality almost altogether. These results are particularly striking because the model predicts simple quantitative laws relating different properties of markets that are borne out well when tested against data.Although no one would dispute that agents in financial markets behave strategically, and that for some purposes taking this into account is essential, we show in this paper that there are some problems where other factors may be more important. Previous work along these lines includes that of Becker (1), who showed that random agent behavior and budget constraint are sufficient to guarantee the proper slope of supply and demand curves, and Gode and Sunder (2), who demonstrated that if one replaces the students in a standard classroom economics experiment by zero-intelligence agents with a budget constraint, they perform surprisingly well. More specifically, the model we test here builds on earlier work on the double auction in financial economics (3-6) and physics (7-11). [See also interesting subsequent work (12, 13).] The model makes the simple assumption that agents place orders to buy or sell at random (14, 15), subject to constraints imposed by current prices. Although one might argue that tracking prices requires at least som...

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Section: Resultssupporting

confidence: 57%

“…We thus start from almost zero rationality and work our way up, in contrast to the canonical approach of starting from perfect rationality and working down (see ref. 16).…”

Section: Resultsmentioning

confidence: 99%

Section: Testing the Scaling Lawsmentioning

confidence: 99%

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The Predictive Power of Zero Intelligence in Financial Markets

2004

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“…Agents trade an asset for T periods and transactions are executed through a limit-order book (LOB) where the information about the type, the size and the price of all agents' orders is stored (see, for instance, Maslov, 2000;Zovko and Farmer, 2002;Avellaneda and Stoikov, 2008;Bartolozzi, 2010). The market is populated by two groups of agents depending on their trading frequency (i.e., the average amount of time elapsed between two order 4 placements), namely N L low-frequency (LF) and N H high-frequency (HF) traders (N = N L + N H ).…”

Section: The Modelmentioning

confidence: 99%

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

Leal¹,

Napoletano

2019

Journal of Economic Behavior & Organization

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low-and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of flash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes flash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-off between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off.

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“…The zero-intelligence model of the limit order book was first proposed by [6], and has since been studied by [2,8,17], and [19]. Because traders are assumed to have zerointelligence, the orders are randomly placed as a Poisson process with an i.i.d.…”

Section: Limit Order Book With Zero-intelligence Tradersmentioning

confidence: 99%

Equivalence of Particle Survival Model and Record Value Process, and Its New Application to Limit Order Books

Toyoizumi

2017

JORSJ

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The particle survival model, which was originally proposed to analyze the dynamics of species' coexistence, has surprisingly found to be related to a non-homogeneous Poisson process. It is also well known that successive record values of independent and identically distributed sequences have the spatial distribution of such processes. In this paper, we show that the particle survival model and the record value process are indeed equivalent. Further, we study their application to determine the optimal strategy for placing selling orders on stock exchange limit order books. Our approach considers the limit orders as particles, and assumes that the other traders have zero intelligence.

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The power of patience: a behavioural regularity in limit-order placement

Cited by 102 publications

References 10 publications

The Predictive Power of Zero Intelligence in Financial Markets

The Predictive Power of Zero Intelligence in Financial Markets

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

Equivalence of Particle Survival Model and Record Value Process, and Its New Application to Limit Order Books

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