A machine learning approach to classification for traders in financial markets

DOI10.1002/STA4.465MaRDI QIDQ6543896

Isaac D. Wright, John C. Liechty, Matthew Reimherr

Publication date: 27 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

clusteringWasserstein distanceslimit order bookstrader classification

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Title not available (Why is that?)
• On properties of the generalized Wasserstein distance
• Schumpeter meeting Keynes: a policy-friendly model of endogenous growth and business cycles
• Equivalent formulations for Monge-Kantorovich equation
• A class of Wasserstein metrics for probability distributions
• The earth mover's distance as a metric for image retrieval
• Fréchet estimation of time-varying covariance matrices from sparse data, with application to the regional co-evolution of myelination in the developing brain
• Generalized Wasserstein distance and its application to transport equations with source
• The price impact of order book events: market orders, limit orders and cancellations
• Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?
• Time-Varying Functional Regression for Predicting Remaining Lifetime Distributions from Longitudinal Trajectories
• Calculation of the Wasserstein Distance Between Probability Distributions on the Line
• Fast Discrete Distribution Clustering Using Wasserstein Barycenter With Sparse Support
• An Introduction to Statistical Learning
• Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

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