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on Market Microstructure |
| By: | Patrick Cheridito; Moritz Weiss |
| Abstract: | We introduce a reinforcement learning framework for market making in a limit order book. Our algorithm aims to maximize trading revenue by dynamically submitting market and limit orders of varying sizes across multiple price levels while controlling inventory size. We use multivariate logistic-normal distributions to model order allocations and employ a deep-set encoder to aggregate features from variable-length order sets into a fixed-dimensional latent representation. Additionally, we incorporate potential-based reward shaping to accelerate learning without altering the optimal policy. We illustrate the performance of the method in three simulated market environments consisting of noise traders who submit random trades, tactical traders who respond to instantaneous volume imbalance, and strategic traders who trade in the direction of an exponentially weighted volume imbalance signal. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18195 |
| By: | Peress, Joël; Schmidt, Daniel Jonas |
| Abstract: | A critical question facing speculators contemplating to trade on private information is whether their signal has already been priced in by the market. In our model, speculators assess the novelty of their information based on recent price movements, and market makers are aware that speculators might be trading on stale news. An asymmetric response to past price movements ensues: after price increases, buy volume—because it may result from stale news trading—has a lower price impact than sell volume (and vice versa after price decreases). Consequently, return skewness is negatively related to lagged returns. We find strong support for these and other predictions using a comprehensive sample of US stocks. |
| Keywords: | Strategic trading |
| JEL: | G11 G14 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19273 |
| By: | Yanzhi Zhang; Yu Ma; Yilin Cheng; Jian Li; Yitong Duan |
| Abstract: | Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets. Since market data reveal only one realized trajectory, many important questions are inherently counterfactual and require realistic trajectory-level simulation. Existing financial generative models, however, often model order events and market states, such as the LOB, in isolation, overlooking the dynamic interaction between order flow and liquidity in market microstructure. We propose the \textbf{M3} (\underline{M}arket \underline{M}icrostructure \underline{M}odel), a state-event generative foundation model for market microstructure dynamics. \textbf{M3} learns to generate future order-flow trajectories, while accounting for the evolving interaction between order events and limit-order-book liquidity. Trained on large-scale order-level real stock market data, \textbf{M3} exhibits predictable scaling behavior, reproduces key market stylized facts, and enables practical simulation-based applications including forecasting, stress testing, and market-impact analysis. These results suggest a scalable foundation-model paradigm for counterfactual market simulation at the microstructure level. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.19227 |