analyze market microstructure

This study addresses the decomposition of permanent price movements from transient microstructure noise using tick-by-tick transaction data and provides a microstructural foundation for rough noise observed at macroscopic scales. To this end, the authors develop a structural microstructure model that explicitly distinguishes between these two components and demonstrate its weak convergence to a semimartingale with a rough noise term in the macroscopic limit. This work establishes, for the first time, a microfounded theoretical basis for rough noise models grounded in high-frequency data, revealing their non-universality and pronounced intraday variability. Employing generalized method of moments (GMM) estimation coupled with formal statistical tests—validated through simulations to perform well in finite samples—the empirical analysis of 2024 Dow Jones constituents shows that rough noise is statistically significant only on days dominated by short-term price reversals, with estimated roughness exponents typically near zero.

This study systematically investigates the impact of relative tick size on limit order book (LOB) microstructure, aiming to identify and quantify distinct characteristics across assets with large, medium, and small tick sizes. We propose the first multidimensional Hawkes process model capable of smoothly capturing cross-tick-size LOB dynamics—unifying the modeling of order sparsity, multi-level price jumps, and bid-ask spread shape. We introduce the first formal microstructural criterion for classifying small-tick stocks. Furthermore, we uncover a universal mapping between tick size and order flow dynamics. The model is validated against empirical stylized facts, successfully reproducing key small-tick LOB features—including sparsity, spread shape, and heavy-tailed, leptokurtic return distributions—and enables continuous parameter-driven simulation of LOB evolution from large- to small-tick regimes, demonstrating its cross-asset generalizability.

This study investigates how institutional liquidity provision affects bid-ask spreads, price discovery efficiency, and the welfare of slower traders in prediction markets. Addressing the challenge in existing literature of disentangling liquidity injection channels from causal identification, the authors develop a market quality analysis framework and innovatively employ a synthetic market microstructure experimental approach to conduct stress tests. The findings reveal that distinct liquidity mechanisms—such as market maker coverage, incentive schemes, and automation—operate through significantly different pathways. Moreover, aggregate improvements in liquidity do not uniformly benefit all participants; particularly under informational shocks or extreme market conditions, the welfare gains for disadvantaged traders are markedly limited. These results highlight the heterogeneous effects of liquidity policies and offer critical insights for market design.

This paper investigates price transmission mechanisms in coupled Constant Function Market Maker (CFMM) markets within decentralized finance, focusing on nonlinear price linkages and basket inflation/deflation arising when intermediate assets serve as cross-market oracles. We model a two-CFMM intermediate market structure—comprising constant-product AMMs—and integrate market microstructure theory to quantify the relative impacts of price drift, trade size, and market depth on inflation dynamics. Our key contribution is uncovering a path-dependent, micro-level transmission mechanism and proposing an interpretable analytical framework that systematically characterizes oracle-driven price spillovers and their structural impact on the value of asset baskets priced in risk-free assets. The results provide theoretical foundations and quantitative tools for CFMM coupling design, oracle risk modeling, and stability optimization in decentralized markets.

This work addresses key limitations of Hawkes processes in market microstructure modeling—namely, instability, calibration difficulty, and irreproducible simulation. We propose a reproducible research framework integrating a deterministic C++ limit-order-book (LOB) simulator with a multivariate marked Hawkes process. Methodologically, we provide the first rigorous theoretical proofs of stability and ergodicity for both linear and nonlinear Hawkes models, revealing the critical role of the subcritical regime in capturing order-flow clustering. Calibration employs an exponential–power-law hybrid kernel, combined with time-rescaling and goodness-of-fit diagnostics for precision. Our framework successfully reproduces temporal clustering characteristics of real order flows on Binance BTC/USDT and LOBSTER AAPL datasets. All code, data, and configuration files are publicly released, enabling fully reproducible high-frequency market dynamics research.

This study addresses the empirical gap in understanding the microstructure of decentralized prediction markets by systematically characterizing eight stylized facts across 600 markets on Polymarket, leveraging 30 billion order book events and on-chain trade records. Through precise alignment of high-frequency order book data with Ethereum’s OrderFilled events, a pre-registered cross-sectional panel design, and a reproducible analytical framework, the authors find that inferring trade direction from public order book data alone achieves only about 59% accuracy—significantly lower than in traditional markets—highlighting the critical necessity of on-chain execution data. Key findings include a long-tailed spread premium, liquidity depth approximately following a uniform geometric distribution, a median wash trading rate of just 1%, and a decay rate of 0.55 in market depth as settlement approaches. The complete replication toolkit is openly released.

This study addresses the trade-off among execution probability, adverse selection, and opportunity cost in limit order trading by proposing a mesoscale optimal passive execution strategy. Embedding two empirically observed microstructural features—namely, the exponential decay of limit order fill probability with quote distance and the short-term linear price response to order flow imbalance—into a stochastic control framework, the work derives for the first time a passively induced market impact rate exhibiting exponential decay and solves for the corresponding optimal liquidation policy. The model is validated on both NASDAQ equity and foreign exchange data and extends naturally to settings involving heterogeneous decay rates, instantaneous impact, and target execution schedules, thereby establishing a theoretical foundation and practical mechanism for tactical passive execution.

This study addresses the challenge of identifying whether algorithmic trading strategies act as net consumers or providers of liquidity using only observable transaction and price history, and quantifies their impact on market liquidity and welfare. By integrating multi-period regret decomposition, an AR(1) transaction cost model, and Roll’s implied spread estimator, the authors develop an O(Tnd)-efficient algorithm that, without access to internal signals or objective functions, uniquely recovers the informed trader–market maker dichotomy central to Kyle’s model. The work introduces a liquidity balance condition, uncovers quadratic (N²-scale) fire-sale externalities, and successfully calibrates the framework on CRSP U.S. equity data from 2016 to 2025, effectively capturing liquidity dynamics during the COVID-19 pandemic and the 2022 interest rate shocks.

Current LLM-driven trading research lacks standardized execution assumptions and reproducibility criteria, hindering cross-study comparisons and economic interpretability. This work systematically reviews 30 related studies and introduces the first evidence matrix encompassing execution semantics, turnover handling, and temporal control to evaluate transparency across dimensions such as data recency, backtest partitioning, and transaction cost modeling. Through bibliometric coding, methodological analysis of backtesting practices, and friction sensitivity experiments on ten stocks, the study quantifies how execution details compress strategy returns. It reveals that most papers inadequately disclose execution assumptions and proposes a standardized reporting framework emphasizing execution transparency as critical for result credibility, advocating for stricter community-wide standards of realism and reproducibility.