Reinforcement learning for optimized trade execution

By Yuriy Nevmyvaka, Yi Feng, Michael Kearns. 2006

313 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “…studying recreational games is to develop algorithms that will scale to more complex, real-world games such as airport and network security, ﬁnancial and energy trading, trafﬁc control and routing (Lambert III et al., 2005; Nevmyvaka et al., 2006; Bazzan, 2009; Tambe, 2011; Urieli and Stone, 2014).”
Convolutional neural networks applied to high-frequency market microstructure forecasting (2017, background): “Also, reinforcement learning showed acceptable success in trade execution optimisation [6] and trading [7], based on order-book states.”
Recent advances in reinforcement learning in finance (2021, methodology): “Both these ideas dramatically improve the empirical performance of the algorithm and DQN has been shown to have a strong performance for a variety of ATARI games (Bellemare et al., 2013).”
Autonomous navigation of stratospheric balloons using reinforcement learning (2020, background): “Reinforcement learning has proven effective in optimization problems in which a model of the process is available 13–16 , and has shown promising results in simulations of real applications and back-testing 17–20 with some commercial success in internet applications 21,22 .”
Machine behaviour (2019, background): “For instance, a reinforcement learning agent trained to maximize long-term profit can learn peculiar short-term trading strategies based on its own past actions and concomitant feedback from the market 90 .”
Cloning Strategies from Trading Records using Agent-based Reinforcement Learning Algorithm (2018, background): “[4] demonstrated large-scale applications of reinforcement learning on optimizing trading execution in modern financial market.”
Deep learning for limit order books (2016, background): “Park and Van Roy (2015) and Nevmyvaka et al. (2006) use machine learning for optimal order execution.”
Regularization in reinforcement learning (2011, background): “In addition to robotics and control engineering, researchers have found RL/Planning useful in finance and have applied it to problems such as optimized trade execution [Nevmyvaka et al., 2006] and to learning exercise policy for American options [Li et al.”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “This feature of imbalances has been reported by a variety of researchers [56]–[60].”
Reward (Mis)design for Autonomous Driving (2021, background): “Utilities expressed in currency units are common in RL when proﬁt or cost reduction are the explicit goals of the task, such as stock trading [33] and tax collection [30], but we are unaware of its usage as an optimization objective for AD.”