arXiv · arXiv · 2023
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose…
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
arXiv · arXiv · 2013
We consider a broker who has to place a large order which consumes a sizable part of average daily trading volume. The broker's aim is thus to minimize execution costs he incurs from the adverse impact of his trades on market prices. By contrast to the previous literature, see, e.g., Obizhaeva and Wang (2005), Predoiu, Shaikhet, and Shreve (2011), we allow the liquidity parameters of market depth and resilience to va…
Peter Bank, Antje Fruth
arXiv · arXiv · 2026
Most of the autocorrelation in trade signs on decentralized exchanges comes from pairs of trades sent by different wallets. For Uniswap v3, this has been read as herding dominating order splitting -- the reverse of what equity markets show. We show that this herding share mixes two quantities: the probability $p$ that consecutive orders come from the same account, which is small when thousands of wallets are trading,…
Yichong Bai, Dingyue Kan
arXiv · arXiv · 2026
Automated markets increasingly route order flow from learning trading agents through automated market makers (AMMs). We model trader archetypes as AI agents defined by their reward functions and analyze the feedback they create on a constant-product AMM. Herd-dominated and arbitrage-dominated markets of equal size have identical return distributions and turnover, so no rule based on volatility, value-at-risk, stress …
Irene Aldridge
arXiv · arXiv · 2026
Generative AI coding tools have lowered the cost of writing trading software, but no systematic dataset records when these services fail. We introduce the AI Incident and Disruption Archive (AIDA), a multi-vendor registry of 5,868 deduplicated outage incidents across 30 providers from 2025 to 2026, with foundation-model incidents verified against primary status-page or postmortem URLs. Pairing AIDA with 5,041,034 ver…
Wenbin Wu
arXiv · arXiv · 2023
Mean-variance analysis is widely used in portfolio management to identify the best portfolio that makes an optimal trade-off between expected return and volatility. Yet, this method has its limitations, notably its vulnerability to estimation errors and its reliance on historical data. While shrinkage estimators and factor models have been introduced to improve estimation accuracy through bias-variance trade-offs, an…
Kwong Yu Chong