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on Market Microstructure |
| By: | Umut \c{C}etin; Mingwei Lin |
| Abstract: | We study a one period limit order market with informed traders, noise traders, and competitive liquidity suppliers, in which the number of informed traders is random. Liquidity suppliers know the distribution of the informed trader count, but not its realization, and therefore face uncertainty about both the presence and the intensity of informed trading. We characterize equilibrium by a fixed point integral equation for the marginal cost function and establish existence of equilibrium for bounded asset values. We then analyse large order asymptotics. For bounded asset values with power law endpoint behaviour, equilibrium price impact follows a power law whose exponent is determined jointly by the asset value tail and the full distribution of the informed trader count. In particular, this exponent is not determined by the expected number of informed traders alone. In the light endpoint regime, price impact is instead logarithmic. Finally, we solve the fixed point numerically across several asset value and informed trader count distributions. The numerical results are consistent with the theoretical asymptotics in the cases covered by the theory and provide comparative statics beyond them. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.04221 |
| By: | Chan Kim; Peter Reinhard Hansen |
| Abstract: | Cryptocurrency markets exhibit periodic bursts in volatility and volume at one-, five-, and quarter-hour marks. Using trade data for six Binance perpetual contracts, we associate these bursts with algorithmic trading: trade-size roundness declines sharply within them, a behavioral signature of algorithmic participation. The Autocorrelation Map, a clock-phase-resolved display, reveals serial dependence in order flow and returns at the quarter-hour openings that conventional measures conceal. This opening activity is not only predictable out of sample but also informative: its order imbalance forecasts four-to-twelve-hour returns, weaker at finer marks. Our results characterize periodic algorithmic trading and its cross-frequency variation. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.09426 |
| By: | Hendershott, Terrence; Li, Dan; Livdan, Dmitry; Schürhoff, Norman; Venkataraman, Kumar |
| Abstract: | Dealer quotes in corporate bonds, though indicative, lower trading costs and increase trading volume. Dealers offering higher quality quotes attract more order flow and execute trades at favorable prices. Dealers advertise quotes to manage their inventories and attract orders from non-relationship clients. However, quote competition is imperfect; the best quotes often fail to attract orders, and trade-throughs are common. Nevertheless, quote competition is important as clients exploit quotes from other dealers in negotiations, forcing dealers with lower quality quotes to offer price improvements. Quoting is not a zero-sum game, as higher bond-level quoting leads to higher bond-level trading. |
| JEL: | G12 G14 G24 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20205 |
| By: | Irene Aldridge |
| Abstract: | We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under an AR(1) cost process, the same statistic equals the product of strategy size and the squared Roll (1984) implied spread, making the correction a direct proxy for prevailing illiquidity. Extending to endogenous price impact and aggregating across N correlated strategies yields a liquidity-balance condition whose violation produces welfare loss scaling as N squared, a closed-form fire-sale externality. We calibrate to CRSP equity data (2016-2025), tracking implied spreads through the COVID-19 and 2022 rate-shock episodes, with an estimator computable in O(Tnd) time. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.29018 |
| By: | Omar Inverso; Emilio Tuosto; Dragisa Zunic |
| Abstract: | This paper argues that AI-agent alignment in markets should not be understood only as a property of agents, but also as a property of the interaction infrastructure in which agents act. In financial markets, this infrastructure is the market core: the rule system that determines how orders enter, interact, match, persist, and stabilize. If this fundamental interaction layer allows or rewards undesired behaviour, then higher-level alignment of agents may be insufficient. We propose to view fundamental market design as a layer of AI-agent alignment. Alongside the important work of computational economics in modelling agents, strategies, and learning, we focus on a complementary but more fundamental layer: the formal modelling of the market core itself. Market design, especially at the level of the core mechanism, can benefit from a rigour characteristic of theoretical computer science. This gives a transparent-box model of the market, whose core properties can be formally specified and reasoned about. It also lets us treat the trading venue not as a static order book, but as a computational process combining resident orders with incoming order flow, and ask which computational model, perhaps yet unknown, naturally lies at its core. This perspective is especially relevant for markets populated by adaptive or AI agents. Such agents may learn what the mechanism rewards, including speed, delay, liquidity provision, or manipulation. These behaviours are not only properties of individual agents, but may emerge from the agent-mechanism system. We therefore argue that transparent formal models of market cores can support incentive-oriented analysis and the design of mechanisms in which desirable behaviours are structurally favoured and undesirable behaviours are harder to sustain. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.09702 |