Heterogeneous Beliefs and Quote Transparency in an Order-Driven Market

Kovaleva, Polina; Iori, Giulia

doi:10.1007/978-3-319-07470-2_10

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…As was discovered in our earlier order flow analysis (Kovaleva and Iori, 2014), full quote transparency mitigates traders' aggressiveness, limit orders tend to be smaller and more scattered in the book. The estimates in Table 3 reveal that the average transaction price deviates more from the fundamental value, as indicated by the increased price volatility, in the transparent and quasi-transparent markets because agents that demand immediacy in this market trade through several limit orders straightaway, skipping gaps in the book.…”

Section: Price Discovery and Transaction Costsmentioning

confidence: 86%

The impact of reduced pre-trade transparency regimes on market quality

Kovaleva

Iori

2015

Journal of Economic Dynamics and Control

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JEL classification: D4 D8 G1Keywords: Agent based models Market transparency Iceberg orders Liquidity Bid-ask spread a b s t r a c t This paper studies the effects of pre-trade quote transparency on spread, price discovery and liquidity in an artificial limit order market with heterogeneous trading rules. Our agent-based numerical experiments suggest that full quote transparency incurs substantial transaction costs to traders and dampens trading activity in an order-driven market. Our finding reveals that exogenous restriction of displayed depth, up to several best quotes, does not benefit market performance. On the contrary, endogenous restriction of displayed quote depth, by means of iceberg orders, improves market quality in multiple dimensions: it reduces average transaction costs, maintains higher liquidity and moderate volatility, balances the limit order book, and enhances price discovery.

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Section: Price Discovery and Transaction Costsmentioning

confidence: 86%

The impact of reduced pre-trade transparency regimes on market quality

Kovaleva

Iori

2015

Journal of Economic Dynamics and Control

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“…Different from the zero-intelligence models, the heterogenous agent models consider agents' strategic behaviors as potential explanations to the stylized facts. For example, chartist-fundamentalist models find that the chartist behavior contributes to fat tails, volatility clustering (see Anufriev and Panchenko (2009), Chiarella, Iori and Perellò (2009) and Chiarella, He and Pellizzari (2012)) and the diagonal effect in correlations of some order submission types (Kovaleva and Iori (2014)). However, replicating most of these stylized facts simultaneously remains very challenging for agent-based models.…”

Section: Introductionmentioning

confidence: 99%

“…not dependent on individual strategic behavior, but it can emerge from the interplay of the order book and demand/supply functions. More recently, following a similar model as the one in Chiarella et al (2009), Kovaleva and Iori (2014) find that when chartist uses technical rules based on the order book depth, their model can generate significant diagonal effect for some order types, but not for all order types. Our results compliment the existing literature and show that the sentiment trading can generate event clustering for order submission types, namely behavioral sentiment.…”

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confidence: 98%

A behavioural model of investor sentiment in limit order markets

Chiarella

Shi

et al. 2016

Quantitative Finance

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This paper introduces a behavioral sentiment model to explore the stylized facts in limit order markets. Simulation results show that both the noise and sentiment trading can generate the absence of autocorrelation in returns, long memory in the absolute returns and bid-ask spread, and the hump shaped mean depth profile of the order book. However, sentiment trading plays a unique role in explaining the fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, and also the diagonal effect or event clustering in order submission types, all of which cannot be explained by noise trading. Therefore, behavioral sentiment is an important driving force behind some of the well-documented stylized facts in limit order markets.

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Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach

Lucas

Cotsaftis

Bertelle

2018

Int. J. Bifurcation Chaos

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This paper introduces the implementation of a computational agent-based financial market model in which the system is described on both microscopic and macroscopic levels. This artificial financial market model is used to study the system response when a shock occurs. Indeed, when a market experiences perturbations, financial systems behavior can exhibit two different properties: resilience and robustness. Through simulations and different scenarios of market shocks, these system properties are studied. The results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption of the system self-organization. Numerical simulations highlight that the market can absorb strong mono-shocks but can also be led to rupture by low but repeated perturbations.

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Heterogeneous Beliefs and Quote Transparency in an Order-Driven Market

Cited by 4 publications

References 14 publications

The impact of reduced pre-trade transparency regimes on market quality

The impact of reduced pre-trade transparency regimes on market quality

A behavioural model of investor sentiment in limit order markets

Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach

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