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Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

Arnulf Jentzen / Christian Beck / Weinan E

deep learning / machine learningResource overview

Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

Paper2017

by Arnulf Jentzen, Christian Beck, Weinan E

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Suggested foundations for financial machine learning and optimisation and numerical methods. Specialist prerequisites vary by paper.

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Time-Ordered Train, Validation and Test Design
Preserve time order and avoid future information in training.
Linear Regression
Establish an interpretable baseline before a flexible model.
Vectors, Matrices and Linear Systems
Read the linear algebra used to define a numerical problem.
Constrained and Convex Optimisation
Identify the objective, constraints and conditions for an optimum.

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Finance-aware validation
Account for dependence and overlapping observations.
Feature importance
Examine which information contributes to predictions.
Calibration as an Inverse Problem
Understand identifiability and fitted model parameters.

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From trading ideas to honest backtests
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.

Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

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