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Limit order books are a fundamental and widespread market mechanism.
Large-block transactions, the speed of response, and temporary and permanent stock-price effects
Robert W. Holthausen, Richard W. Leftwich, and David Mayers · 1990
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Data-snooping, technical trading rule performance, and the bootstrap
Ryan Sullivan, Allan Timmermann, and Halbert White · 1999
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Optimal execution of portfolio transactions
Robert Almgren and Neil Chriss · 2001
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Reinforcement learning for optimized trade execution
Yuriy Nevmyvaka, Yi Feng, and Michael Kearns · 2006
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The Evaluation and Optimization of Trading Strategies, 2nd Edition
Robert Pardo · 2008
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Jean-Philippe Bouchaud · 2009
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The (mis) behaviour of markets: a fractal view of risk, ruin and reward
Benoit B Mandelbrot and Richard L Hudson · 2010
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Lobster: Limit order book reconstruction system
Ruihong Huang and Tomas Polak · 2011
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The market impact of a limit order
Nikolaus Hautsch and Ruihong Huang · 2012
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An agent-based model of the nasdaq stock market : Historic validation and future directions
Alexander Outkin · 2012
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High-Frequency Trading: The Faster, the Better?
Rahul Savani · 2012
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Price jump prediction in limit order book
Ban Zheng, Eric Moulines, and Frédéric Abergel · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Limit Order Books
Martin D Gould, Mason A Porter, Stacy Williams, Mark McDonald, Daniel J Fenn, and Sam D Howison · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Market impacts and the life cycle of investors orders
Emmanuel Bacry, Adrian Iuga, Matthieu Lasnier, and Charles-Albert Lehalle · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Beyond the square root: Evidence for logarithmic dependence of market impact on size and participation rate
Elia Zarinelli, Michele Treccani, J Doyne Farmer, and Fabrizio Lillo · 2015
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L. Hyland, and Gunnar Rätsch · 2017
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Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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The problem of calibrating an agent-based model of high-frequency trading
Generating realistic stock market order streams
Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, and Michael P. Wellman · 2020
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Robust Market Making via Adversarial Reinforcement Learning
Thomas Spooner and Rahul Savani · 2020
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Get real: realism metrics for robust limit order book market simulations
Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, and Tucker Balch · 2020
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Towards realistic market simulations: a generative adversarial networks approach
Andrea Coletta, Matteo Prata, Michele Conti, Emanuele Mercanti, Novella Bartolini, Aymeric Moulin, Svitlana Vyetrenko, and Tucker Balch · 2021
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Market making with signals through deep reinforcement learning
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Donovan Platt and Tim Gebbie · 2017
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Trades, quotes and prices: financial markets under the microscope
Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, and Martin Gould · 2018
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Agent-based model calibration using machine learning surrogates
Francesco Lamperti, Andrea Roventini, and Amir Sani · 2018
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Market Making via Reinforcement Learning
Thomas Spooner, John Fearnley, Rahul Savani, and Andreas Koukorinis · 2018
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Recurrent conditional generative adversarial networks for autonomous driving sensor modelling
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Tucker Hybinette Balch, Mahmoud Mahfouz, Joshua Lockhart, Maria Hybinette, and David Byrd · 2019
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Pros and cons of GAN evaluation measures
Ali Borji · 2019
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Improving generalization in reinforcement learning-based trading by using a generative adversarial market model
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Double deep q-learning for optimal execution
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
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Multivariate probabilistic time series forecasting via conditioned normalizing flows
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Trafficsim: Learning to simulate realistic multi-agent behaviors
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Efficient calibration of multi-agent simulation models from output series with bayesian optimization
Yuanlu Bai, Henry Lam, Tucker Balch, and Svitlana Vyetrenko · 2022
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Learning to simulate realistic limit order book markets from data as a world agent
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko, and Tucker Balch · 2022
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Diffusion models in vision: A survey
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Market making with scaled beta policies
Joseph Jerome, Gregory Palmer, and Rahul Savani · 2022
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Towards robust representation of limit orders books for deep learning models
Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni, and Manuela Veloso · 2022
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Conditional generative adversarial networks for modelling fuel sprays
Cihan Ates, Farhad Karwan, Max Okraschevski, Rainer Koch, and Hans-Jörg Bauer · 2023
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