Machine learning for finance


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.   



  •  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 




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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
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