arXiv:2608. 19227v1 Announce Type: cross Abstract: Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 09230v1 Announce Type: cross Abstract: Building event-conditioned market models requires separating macro-event labels from persistent microstructure state.
The paper studies Limit Order Books (LOBs) that are populated only by autonomous reinforcement‑learning agents. It shows that such agentic LOBs exhibit clear phase boundaries between orderly price discovery and hyper‑volatile cascade states, determined by critical thresholds in agent number and market depth. Additionally, it finds that market impact in these systems departs from the classic square‑root law, revealing distinct dissipative, balanced, and non‑dissipative regimes driven by nonlinear feedback loops.
arXiv:2606. 31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment.
SAiFE-gym is a Python module that offers simulation environments for studying trading in Constant Product Markets with Concentrated Liquidity. It decomposes the microstructure of these markets into interactive components, allowing researchers to model various economic settings. The environments are vectorized for scalability in high‑dimensional reinforcement learning workflows, and the paper demonstrates their usefulness by evaluating RL agents under uncertain market parameters.
arXiv:2510. 17088v3 Announce Type: replace-cross Abstract: Financial anomalies arise from heterogeneous mechanisms - price shocks, liquidity freezes, contagion cascades, and momentum reversals - yet existing detectors produce uniform anomaly scores without revealing which mechanism is failing or where risks concentrate.
Large language models (LLMs) are being tested as simulators of individual financial decision-making. In a controlled paper‑trading experiment with 120 volunteers, the study evaluated whether an LLM could predict a participant’s next‑day trading action, chosen security, and transaction size using only pre‑cutoff information. Results showed that including market context improved predictions of actions and tickers, but sizing remained challenging, and the models tended to over‑predict hold actions, under‑predict sells, and simplify multi‑security trades.