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on Market Microstructure |
| By: | Glebkin, Sergei; Kuong, John Chi-Fong |
| Abstract: | We consider a market where large investors do not only trade on information about asset fundamentals. When they trade more aggressively, the price becomes less informative. Other investors who learn from prices, in turn, are less concerned about adverse selection and provide more liquidity, causing large investors to trade even more aggressively. This trading complementarity can engender three unconventional results: i) increased competition among large investors makes all investors worse off, ii) more precise private information reduces price informativeness, creating complementarities in information acquisition, and iii) multiple equilibria emerge. Our results have implications for competition and transparency policies in financial markets. |
| JEL: | G0 G01 G12 G14 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19331 |
| By: | Gbenga Ibikunle; Ben Moews; Dmitriy Muravyev; Khaladdin Rzayev |
| Abstract: | We introduce data-driven measures of high-frequency trading (HFT) that distinguish between liquidity-supplying and liquidity-demanding strategies. We train machine learning models on a proprietary dataset with observed HFT activity, then apply these models to public intraday data to generate HFT measures across all U.S. stocks during 2010-2023. Our measures outperform conventional proxies, which struggle to capture the temporal dynamics of HFT. Consistent with theory, our measures respond to a quasi-exogenous speed bump introduction and a data feed upgrade. The measures help uncover the differential impact of HFT on information acquisition. Liquidity-supplying HFT improves price informativeness around earnings announcements, while liquidity-demanding HFT impedes it. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.00858 |
| By: | Peter Cotton |
| Abstract: | We consider a market maker who can only obtain and dispose of inventory by responding to a sequence of sealed-bid enquiries, and whose customers arrive with imbalanced intent: sellers more often than buyers, or the reverse. Under the assumption that the best competing response is exponentially distributed around a commonly discerned fair price, we observe a symmetry in the steady state solution that compresses the imbalanced problem onto the perfectly balanced one. Order imbalance is absorbed, exactly, by a translation of the market maker's skew, a widening of her quotes, and a multiplication of her effective cost of carry. The adjustment is simple even though the solution it adjusts is not, and it involves no free parameter beyond the observable market width. The exponential assumption is needed only locally, at the quotes actually made, and the width that enters is the locally observed one. Among the consequences: a market maker with zero inventory should still skew; skew responds to imbalance at first order whereas width responds only at second order; and the popular "constant width, linear skew" heuristic is recovered as the small-skew solution in the special case of balanced flow and quadratic holding cost. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.07690 |
| By: | Chengqi Zang (Graduate School of Economics, The University of Tokyo, and Gensyn); Gabriel Andrade (Gensyn); Tomoyuki Nakajima (Faculty of Economics, The University of Tokyo) |
| Abstract: | Prediction-market shares differ from traditional financial products in that, with no information or outside utility, classical delta-neutral Central Limit Order Book (CLOB) market making cannot be financed by payoff-uninformative noise flow. Transaction-level evidence from a major prediction-market CLOB platform shows makers profiting not from spread but from carrying an under-priced side to settlement — the empirical signature of behavioral tail demand rather than classical, randomized noise. We build this tail demand directly into the model and study an LMSR and a CLOB on the same event. A pre-shock CLOB quote inside the common-signal band is picked off; competitive quotes therefore screen informed traders out of the book. CLOB makers earn screening rent by carrying the under-priced side to resolution, while informed flow routes to the LMSR. The venues coexist: the CLOB supplies the tail-demand rent margin that lets the LMSR recover part of its loss to informed flow, and AMM depth moves the CLOB premium with a sign set by maker-side contestability—widening it where standing quotes can be undercut, compressing it where a committed maker carries the book. With three or more outcomes, binary-book CLOBs pin switch prices but leave implied beliefs indeterminate, whereas the LMSR prices the outcome simplex coherently and uses collateral more efficiently. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2026cf1277 |
| By: | Massa, Massimo; Zhang, Hong; Zhou, Yijun |
| Abstract: | In the age of big data, investors need to process increasingly complicated, multidimensional data to decipher different aspects of a firm. How do investors deal with such multidimensional data? We find more informed institutional investors tend to specialize in subsets of firm aspects (i.e., data specialists). Such data specialization, however, may hamper market efficiency. Inattention shocks to specialists hinder price efficiency in their specialized aspects of firms; other aspects of firms may also be negatively influenced due to strategic complementarity. Specialist inattention also significantly impacts anomaly returns, impeding the price corrective effect of news arrival. Our results have important implications for how data affects market efficiency. |
| JEL: | G14 G23 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19390 |
| By: | Zhuohan Wang; Carmine Ventre |
| Abstract: | Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data. Their appeal is both structural and practical: they offer stable likelihood-based training, strong mode coverage, flexible conditioning, and a stochastic-differential-equation formulation that aligns naturally with the It\^o calculus and stochastic control frameworks widely used in finance. This survey reviews the growing literature on diffusion-family generative models for financial applications. We organize prior work primarily by financial data type, covering time series, limit order books, tabular data, and other structured financial objects, while discussing the modeling goals and application contexts that arise within each category. To the best of our knowledge, this is the first survey dedicated specifically to diffusion-family models for financial data. For more detailed information, we have open-sourced a repository https://github.com/ZhuoHan1998/Diffusion -Models-In-Finance. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12583 |
| By: | Michael J. Fleming; Or Shachar |
| Abstract: | In March 2020, the Financial Industry Regulatory Authority (FINRA) began reporting aggregate trading volume for securities issued by the U.S. Treasury Department. The public data do not, however, include information about the trading activity of Separate Trading of Registered Interest and Principal of Securities (STRIPS). STRIPS are created from existing Treasury securities and offer risk management benefits, yield curve insights, and investment opportunities for a diverse range of market participants. In this post, we provide the first detailed analysis of STRIPS trading activity using FINRA’s Trade Reporting and Compliance Engine (TRACE) transactions data. |
| Keywords: | Separate Trading of Registered Interest and Principal of Securities (STRIPS); Treasury securities; trading; TRACE |
| JEL: | G12 |
| Date: | 2026–08–10 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103628 |
| By: | Lee, Woongki (Yonsei University) |
| Abstract: | This study develops an equilibrium framework for asset price dynamics under heterogeneous investor beliefs. It shows that price changes admit three equivalent representations, each linked to a distinct channel: group dominance, order imbalance, and participation tilt. As an application of the third representation, the study introduces investor mobility as a measure of how actively investors shift their participation across belief groups over time. Higher mobility indicates greater changes in investor composition and, for a given level of forecast dispersion, larger price movements. The study implements this concept empirically by constructing and analyzing a market-wide series of investor mobility. |
| Date: | 2026–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:vpxqk_v1 |
| By: | Peter Korsbakke Christensen; Anders Norlyk |
| Abstract: | Recently, it has been proposed to model the microstructure noise in prices by a continuous-time process with continuous sample paths that are rougher than those of a standard Brownian motion. In this paper, we propose a microstructural model for the tick-by-tick price changes that explicitly separates the permanent price changes from the fleeting price changes due to noise. We show how this model converges to a standard semimartingale model for the permanent price process, plus a rough noise term originating from the fleeting price changes on the macro scale. This provides a microstructural foundation for the rough-noise model. We then develop a GMM estimation method applicable to tick-by-tick data, together with a formal test for rough noise. We show that the estimator and test work in finite samples through a simulation study, and apply them to tick-by-tick data on Dow Jones Industrial Average constituents in 2024. Because our estimator is designed for tick-by-tick data, we estimate roughness at the daily level, revealing substantial day-to-day variation. We find that rough noise, while present, is not universal: even when detected, the roughness index is typically close to zero, and it is most pronounced on days dominated by short-run price reversals. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.29442 |
| By: | Zhuohan Wang; Andreea Bacalum; Ollie Olby; Carmine Ventre; Namid Stillman |
| Abstract: | Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially. We present \textbf{FlowLOB}, a conditional \textbf{flow}-matching generator of \textbf{LOB} trajectories, trained on multiple Hong Kong Exchange (HKEX) symbols at three sampling frequencies ($0.1$s, $1$s, $10$s) in tick-relative representation that transfers to unseen instruments. Because flow and diffusion models admit a common formulation, we train both with identical data, architecture, and budget, and sample both through the same fixed-step ODE solvers, yielding a controlled comparison of sampling efficiency and fidelity. Flow matching attains its best quality with only $10$ ODE-solver steps, whereas diffusion needs many more function evaluations to approach the same fidelity. At this efficient operating point, FlowLOB improves realism over baselines, two learned and two agent-based models, in most distributional metrics at the two finer sampling frequencies. We evaluate counterfactual controllability with a distributional test that asks whether changing a scenario condition moves the generated statistic toward the corresponding real tail regime; FlowLOB satisfies this criterion in most tested settings. Both realism and control effects transfer zero-shot on a held-out symbol. We additionally conduct ablation studies on the network architecture and the learning rate. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.13096 |