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Carmona, R. and Laurière, M., Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I–The Ergodic Case. arXiv preprint arXiv:1907.05980
Original
1907
Earlier work this paper cites.
Carmona, R. and Laurière, M., Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II–The Finite Horizon Case. arXiv preprint arXiv:1908.01613
Original
1908
Earlier work this paper cites.
Campbell, J.Y., Grossman, S.J. and Wang, J., Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics
1993
Earlier work this paper cites.
Bertsimas, D. and Lo, A.W., Optimal control of execution costs. Journal of Financial Markets
1998
Earlier work this paper cites.
Lehalle, C.A. and Azencott, R., Piecewise affine neural networks and nonlinear control. In Proceedings of the International Conference on Artificial Neural Networks
1998
Earlier work this paper cites.
Almgren, R. and Chriss, N., Optimal execution of portfolio transactions. Journal of Risk
2001
Earlier work this paper cites.
Wilson, D.R. and Martinez, T.R., The general inefficiency of batch training for gradient descent learning. Neural networks
2003
Earlier work this paper cites.
Gobet, E. and Munos, R., Sensitivity analysis using Itô-Malliavin calculus and martingales, and application to stochastic optimal control. SIAM J. Control Optim
2005
Earlier work this paper cites.
Bouchard, B., Dang, N.M. and Lehalle, C.A., Optimal control of trading algorithms: a general impulse control approach. SIAM Journal on financial mathematics
2011
Earlier work this paper cites.
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K. et al
2012
Earlier work this paper cites.
Laruelle, S., Lehalle, C.A. and Pagès, G., Optimal posting price of limit orders: learning by trading. Mathematics and Financial Economics
2013
Earlier work this paper cites.
Kingma, D.P. and Ba, J., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Original
2014
Earlier work this paper cites.
Labadie, M. and Lehalle, C.A., Optimal starting times, stopping times and risk measures for algorithmic trading. The Journal of Investment Strategies
2014
Earlier work this paper cites.
Bacry, E., Iuga, A., Lasnier, M. and Lehalle, C.A., Market impacts and the life cycle of investors orders. Market Microstructure and Liquidity
2015
Earlier work this paper cites.
Cartea, Á., Jaimungal, S. and Penalva, J., Algorithmic and high-frequency trading
2015
Earlier work this paper cites.
Cartea, Á. and Jaimungal, S., Incorporating order-flow into optimal execution. Mathematics and Financial Economics
2016
Earlier work this paper cites.

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Han, J. and E, W., Deep learning approximation for stochastic control problems. Deep Reinforcement Learning Workshop NIPS
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Then

Tan, S., Caruana, R., Hooker, G., Koch, P. and Gordo, A., Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640
Original
2018
Later among the works it cites.
Buehler, H., Gonon, L., Teichmann, J. and Wood, B., Deep hedging. Quantitative Finance
2019
Later among the works it cites.
Carmona, R. and Webster, K., The self-financing equation in limit order book markets. Finance and Stochastics
2019
Later among the works it cites.
Fouque, J.P. and Zhang, Z., Deep Learning Methods for Mean Field Control Problems with Delay. arXiv preprint arXiv:1905.00358
Original
2019
Later among the works it cites.
Guéant, O. and Manziuk, I., Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality. arXiv preprint arXiv:1910.13205
Original

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2020

Learning a functional control for high-frequency finance

Leal, Laura, Laurière, Mathieu, Lehalle, Charles-Albert

Understand

We use a deep neural network to generate controllers for optimal trading on high frequency data.

For the first time, a neural network learns the mapping between the preferences of the trader, i.e.
risk aversion parameters, and the optimal controls.
An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact.

Built on

Carmona, R. and Laurière, M., Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I–The Ergodic Case. arXiv preprint arXiv:1907.05980
Original
1907
Earlier work this paper cites.
Carmona, R. and Laurière, M., Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II–The Finite Horizon Case. arXiv preprint arXiv:1908.01613
Original
1908
Earlier work this paper cites.
Campbell, J.Y., Grossman, S.J. and Wang, J., Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics
1993
Earlier work this paper cites.
Bertsimas, D. and Lo, A.W., Optimal control of execution costs. Journal of Financial Markets
1998
Earlier work this paper cites.
Lehalle, C.A. and Azencott, R., Piecewise affine neural networks and nonlinear control. In Proceedings of the International Conference on Artificial Neural Networks
1998
Earlier work this paper cites.
Almgren, R. and Chriss, N., Optimal execution of portfolio transactions. Journal of Risk
2001
Earlier work this paper cites.
Wilson, D.R. and Martinez, T.R., The general inefficiency of batch training for gradient descent learning. Neural networks
2003
Earlier work this paper cites.
Gobet, E. and Munos, R., Sensitivity analysis using Itô-Malliavin calculus and martingales, and application to stochastic optimal control. SIAM J. Control Optim
2005
Earlier work this paper cites.
Bouchard, B., Dang, N.M. and Lehalle, C.A., Optimal control of trading algorithms: a general impulse control approach. SIAM Journal on financial mathematics
2011
Earlier work this paper cites.
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K. et al
2012
Earlier work this paper cites.
Laruelle, S., Lehalle, C.A. and Pagès, G., Optimal posting price of limit orders: learning by trading. Mathematics and Financial Economics
2013
Earlier work this paper cites.
Kingma, D.P. and Ba, J., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Original
2014
Earlier work this paper cites.
Labadie, M. and Lehalle, C.A., Optimal starting times, stopping times and risk measures for algorithmic trading. The Journal of Investment Strategies
2014
Earlier work this paper cites.
Bacry, E., Iuga, A., Lasnier, M. and Lehalle, C.A., Market impacts and the life cycle of investors orders. Market Microstructure and Liquidity
2015
Earlier work this paper cites.
Cartea, Á., Jaimungal, S. and Penalva, J., Algorithmic and high-frequency trading
2015
Earlier work this paper cites.
Cartea, Á. and Jaimungal, S., Incorporating order-flow into optimal execution. Mathematics and Financial Economics
2016
Earlier work this paper cites.

Similar

Han, J. and E, W., Deep learning approximation for stochastic control problems. Deep Reinforcement Learning Workshop NIPS
2016
Cited alongside, same era.
Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M. and Tang, P.T.P., On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836
Original
2016
Cited alongside, same era.
Zhang, Q., Cao, R., Wu, Y.N. and Zhu, S.C., Growing interpretable part graphs on convnets via multi-shot learning. arXiv preprint arXiv:1611.04246
Original
2016
Cited alongside, same era.
E, W., Han, J. and Jentzen, A., Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Communications in Mathematics and Statistics
2017
Cited alongside, same era.

Then

Tan, S., Caruana, R., Hooker, G., Koch, P. and Gordo, A., Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640
Original
2018
Later among the works it cites.
Buehler, H., Gonon, L., Teichmann, J. and Wood, B., Deep hedging. Quantitative Finance
2019
Later among the works it cites.
Carmona, R. and Webster, K., The self-financing equation in limit order book markets. Finance and Stochastics
2019
Later among the works it cites.
Fouque, J.P. and Zhang, Z., Deep Learning Methods for Mean Field Control Problems with Delay. arXiv preprint arXiv:1905.00358
Original
2019
Later among the works it cites.
Guéant, O. and Manziuk, I., Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality. arXiv preprint arXiv:1910.13205
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Frosst, N. and Hinton, G., Distilling a neural network into a soft decision tree. arXiv preprint arXiv:1711.09784

Original

2017

Cited alongside, same era.

Murdoch, W.J. and Szlam, A., Automatic rule extraction from long short term memory networks. arXiv preprint arXiv:1702.02540

Original

2017

Cited alongside, same era.

Zhang, Q., Cao, R., Shi, F., Wu, Y.N. and Zhu, S.C., Interpreting cnn knowledge via an explanatory graph. arXiv preprint arXiv:1708.01785

Original

2017

Cited alongside, same era.

Bouchaud, J.P., Bonart, J., Donier, J. and Gould, M., Trades, quotes and prices: financial markets under the microscope

2018

Cited alongside, same era.

Cardaliaguet, P. and Lehalle, C.A., Mean field game of controls and an application to trade crowding. Mathematics and Financial Economics

2018

Cited alongside, same era.

Han, J., Jentzen, A. and E, W., Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences

2018

Cited alongside, same era.

Harradon, M., Druce, J. and Ruttenberg, B., Causal learning and explanation of deep neural networks via autoencoded activations. arXiv preprint arXiv:1802.00541

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2018

Cited alongside, same era.

Laruelle, S. and Lehalle, C.A., Market microstructure in practice

2018

Cited alongside, same era.

2019