NYU Faculty Digital Archive
metadata only
Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods
U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. market and, potentially, in both markets simultaneously. We develop a general methodology based on a state space model to study 24-hour price discovery in a multiple markets se
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Machine Learning for Time Series Forecasting: Models, Benchmarks, and Challenges – A Case Study#384735
- Temporal Kolmogorov-Arnold Networks (T-KAN) for High-Frequency Limit Order Book Forecasting: Efficiency, Interpretability, and Alpha Decay#974292
- Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models#973692
- A Comparative Analysis of Machine Learning Models for Time Series Forecasting in Finance#354232
- Predicting stock market trends using convolutional neural networks: A deep learning approach#273057
Record · ID 942385
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive