ENSAE Paris - École d’ingénieurs pour l’économie, la data science, la finance et l’actuariat

Machine learning for finance

Objective

The aim of this lecture is to introduce some fundamental concepts and techniques from machine learning and deep learning with a view towards important and recent applications in finance : This includes advanced techniques in scoring, text mining for stock market prediction, hedging/pricing of options, calibration of models, optimal transport and robust finance, numerical resolution of high-dimensional non-linear partial differential equations arising for instance in stochastic control and portfolio selection, market generators.

Planning

Part I: Fundamental concepts from machine learning (9h)

  • Presentation of the main machine learning algorithms
  • Overlearning: penalization, regularization, cross-validation
  • Presentation of the main scoring techniques
  • Deep learning
    • Multi-layer feedforward neural networks, convolutional, recurrent networks.
    • Backpropagation, stochastic gradient for training.
    • Implementation with TensorFlow

Part II: Applications in finance (15h)

  • Text data processing and stock market prediction (3h)
  • Deep hedging and deep calibration (3h)
  • Deep reinforcement learning and applications (6h)
    • Q-learning algorithms, policy gradient, actor-critic algorithm
    • Stochastic control and portfolio optimization
    • Nonlinear PDE, American option pricing, counterparty risk (CVA).

4. Market generators and deep simulation (3h)

 

References

A. Bachouch, C. Huré, N. Langrené, H. Pham : Deep neural networks algorithms for stochastic control problems on finite horizon, part II, numerical applications : Methodology and Computing in Applied Probability, https://doi.org/10.1007/s11009-019-09767-9
C. Bayer, B. Horvath, A. Muguruza, B. Stemper, M. Tomas : On deep calibration of (rough) stochastic volatility models, arXiv : 1908.08806
D. Bloch : Machine learning : models and algorithms, Quantitative Analytics, 2020.
H. Buehler, L. Gonon, J. Teichmann, B. Wood : Deep hedging, Quantitative Finance, 19(8), 1271-1291, 2019.
I. Goodfellow, Y. Bengio, A. Courville : Deep learning, 2016.
C. Huré, H. Pham, X. Warin : Deep backward schemes for high-dimensional nonlinear PDEs, Mathematics of Computation, Mathematics of Computation, 2020, vol 89(324), pp. 1547-1580
P. Henry-Labord_ere : Generative models for financial data, SSRN 3408007, 2019
M. Lopez de Prado : Advances in machine learning, Wiley, 2016.
R. Sutton, A. Barto : Reinforcement Learning, An Introduction, 2011