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on Market Microstructure |
| By: | Mateusz Wilinski; Juho Kanniainen |
| Abstract: | There are multiple explanations for stylized facts in high-frequency trading, including adaptive and informed agents, many of which have been studied through agent-based models. This paper investigates an alternative explanation by examining whether, and under what circumstances, interactions between traders placing limit order book messages can reproduce stylized facts, and what forms of interaction are required. While the agent-based modeling literature has introduced interconnected agents on networks, little attention has been paid to whether specific trading network topologies can generate stylized facts in limit order book markets. In our model, agents are strictly zero-intelligence, with no fundamental knowledge or chartist-like strategies, so that the role of network topology can be isolated. We find that scale-free connectivity between agents reproduces stylized facts observed in markets, whereas no-interaction does not. Our experiments show that regular lattices and Erdos-Renyi networks are not significantly different from the no-interaction baseline. Thus, we provide a completely new, potentially complementary, explanation for the emergence of stylized facts. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20672 |
| By: | Patrick Cheridito; Moritz Weiss |
| Abstract: | In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06345 |
| By: | Przemys{\l}aw Rola |
| Abstract: | In this paper, we introduce a parametrized family of prices derived from the Maximum Entropy Principle. The price is obtained from the distribution that minimizes bias, given the bid and ask volume imbalance at the top of the order book. Under specific parameter choices, it closely approximates the mid-price or the weighted mid-price. Using probabilities of bid and ask states, we propose a model of price dynamics in which both drift and volatility are driven by volume imbalance. Compared to standard models like Bachelier or Geometric Brownian Motion with constant volatility, our model can generate higher kurtosis and heavy-tailed distributions. Additionally, the drift term naturally emerges as a consequence of the order book imbalance. We validate the model through simulation and demonstrate its fit to historical equity data. The model provides a theoretical framework, integrating price, volume imbalance, and spread. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09734 |
| By: | Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant; Malo Lemmel |
| Abstract: | Over the past decade, many dealers have implemented algorithmic models to automatically respond to RFQs and manage flows originating from their electronic platforms. In parallel, building on the foundational work of Ho and Stoll, and later Avellaneda and Stoikov, the academic literature on market making has expanded to address trade size distributions, client tiering, complex price dynamics, alpha signals, and the internalization versus externalization dilemma in markets with dealer-to-client and interdealer-broker segments. In this paper, we tackle two critical dimensions: adverse selection, arising from the presence of informed traders, and price reading, whereby the market maker's own quotes inadvertently reveal the direction of their inventory. These risks are well known to practitioners, who routinely face informed flows and algorithms capable of extracting signals from quoting behavior. Yet they have received limited attention in the quantitative finance literature, beyond stylized toy models with limited actionability. Extending the existing literature, we propose a tractable and implementable framework that enables market makers to adjust their quotes with greater awareness of informational risk. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20225 |
| By: | Nobuyuki Hanaki (Institute of Social and Economic Research, Osaka University); Giulia Iori (City, University of London); Pietro Vassallo (Bank of Italy) |
| Abstract: | In this paper we present the results of experiments and computational analyses of trading in decentralized markets with asymmetric information. We consider three trading configurations, namely the ring, the small-world, and the Erdös-Rényi random network, which allow us to introduce heterogeneity in nodes degree, centrality and clustering, while keeping the number of possible trading relationships fixed. We analyze how the prices of a traded risky asset and the profits of differently informed traders are affected by the distribution of the trading links, and by the location of the traders in the network. This allows us to infer key features in the dynamics of learning and information diffusion through the market. Experimental results show that learning is enhanced locally by clustering rather than degree, pointing to a learning dynamic driven by interdependent, successive trading events, rather than independent exposures to informed traders. By calibrating a behavioural agent-based model to the experimental data we are able to estimate the speed at which agents learn and to locate where information accumulates in the market. Interestingly, simulations indicate that proximity to the insiders leads to more information in regular networks but not so in random networks. |
| Keywords: | OTC markets; Asymmetric information; Learning; Information diffusion; Networks; Insider trading |
| JEL: | G1 C6 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:12 |