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

Machine learning for finance

Objectif

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, hedging/pricing of options, calibration of models,  deep portfolio optimization,  numerical resolution of high-dimensional non-linear partial differential equations arising for instance in stochastic control and portfolio selection, market generators, deep reinforcement learning and trading portfolio.   

Plan

 

  •  Part I.   Fundamental concepts from machine learning
  1. Presentation of the main machine learning algorithms and specificities to financial time series
  2. False discovery and back-testing 
  3. Presentation of  scoring techniques 
  4. Deep learning:  Multi-layer feedforward neural networks, LSTM Backpropagation, stochastic gradient for training Implementation with TensorFlow
  • Part II. Applications in finance
  1. Gaussian process regression and financial  applications
  2. Deep optimization in finance: deep hedging, deep calibration, deep simulation (market generators)
  3. Neural networks-based algorithms for high-dimensional non linear problems (stochastic control, non linear PDEs)
  4. Deep reinforcement learning (Q-learning, policy gradient, actor-critic algorithms) and application to trading 

 

 

Références

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