SearcharxivSearch

arXiv · 2607.08907

Herding and Liquidity in Order-Book Markets. I. A Robust Liquidity-Stress Crossover and its Reflexive Mechanism

Abstract

Agent-based models of markets readily produce emergent instabilities, but telling a genuine collective effect apart from a parameter artefact takes discipline. We apply Bouchaud's phase-diagram method to a continuous-double-auction order-book model. The method is to map the full phase diagram, test its robustness to rule changes, and rule out degenerate and numerical origins before we call any feature a tipping point. The model has fundamental-anchored zero-intelligence liquidity and a mid-anchored chartist herding layer, controlled by the fraction $\varphi$ and the strength $\kappa$ of herders. A 7x6 grid (336 runs, each with a scrambled-sign null) locates an emergent liquidity-stress crossover. The order parameter, the fraction of events with a one-sided book, rises to about 0.34 at $(\varphi,\kappa)=(0.9,1.0)$, is zero across all 42 scrambled cells, and forms a smooth crossover rather than a discontinuous Dark Corner. The dry-up is rule-robust (it recurs under an order-flow-imbalance rule), horizon-robust (about 0.32-0.35 across a 16x range of momentum window), and has a monotone onset boundary $\varphi^*(\kappa) = \{0.55, 0.45, 0.36\}$. We then decompose the mechanism at a matched directional-bias amplitude (mean |p_buy - 0.5| about 0.269). Price-momentum herding carries a large, comparator-robust reflexive component (+0.29; buying begets buying), whereas the order-flow rule's component is about 0 and comparator-dependent. The RMS-mispricing gradient is a placement artefact, largest at $\kappa=0$. A companion two-market analysis finds no directional cross-market contagion across a signal-only herding link.

Explore related subjects

Keep this discovery

BibTeXRIS

Jan Novotny. 2026-07-09. Herding and Liquidity in Order-Book Markets. I. A Robust Liquidity-Stress Crossover and its Reflexive Mechanism. https://arxiv.org/abs/2607.08907

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deep Learning of Robust Market Making under Regime-Switching Order Flow

Classical market-making strategies based on stochastic control, such as the Avellaneda-Stoikov and the Gu\'{e}ant-Lehalle-Fernandez-Tapia (GLFT) extension, provide closed-form quoting rules, but rest on assumptions that break down at realistic microstructure timescales. One of them is that order flow is stationary, while empirical evidence points to the existence of regimes, possibly associated with algorithmic execution of metaorders. In this case, existing methods provide negative PnL. In this paper, we develop a deep reinforcement-learning market maker (RLMM) - a Rainbow-style distributional DQN (C51) which is calibrated and tested in a zero-intelligence limit order book. We find that, in the stationary setting, RLMM outperforms GLFT across the entire observed risk-return frontier. The RLMM is more robust to flow asymmetry than GLFT, but, like any stationarily trained strategy, it still suffers large drawdowns from inventory saturation under persistent directional imbalance. Augmenting the state of RLMM with two auxiliary signals - a Bayesian online change-point filter over the directional flow bias and a queue-adjusted quote-exposure imbalance -restores profitability. A final scenario-bandit step that reweights low-return regime scenarios further improves performance under random-persistence and correlated-direction stress.

q-fin.TR

dexamine: A Python package for Uniswap event data on Ethereum

Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata. dexamine is a Python package that parses Uniswap v2 and v3 events on Ethereum. It converts transaction receipt logs into observations of trades and liquidity changes, with token quantities, pool state, transaction order, and gas information. The package separates data retrieval, contract metadata, protocol interpretation, and output construction. The repository provides recorded Ethereum responses and an offline reproducible example, and version 1 has been used to construct data for an empirical study of price discovery in decentralized markets.

q-fin.TR

The Double-Edged Sword of Short-Selling Bans

We develop a theoretical model that endogenizes the regulator's decision to impose short-selling bans to prevent large stock price declines. Empirically, we test the model's predictions using the cross-sectional variation in short-selling restrictions implemented across European countries in 2020. Consistent with our model, we find that bans had a detrimental effect on liquidity and failed to support the average price levels, but were effective in limiting large price drawdowns. Finally, we show that the effectiveness of the bans depends on the share of informed stockholders, a central variable in our framework, thus informing the design of more effective regulatory responses.

q-fin.TR