Performance of a Markovian neural network versus dynamic programming on a fishing control problem

Laurière, Mathieu; Pagès, Gilles; Pironneau, Olivier

doi:10.48550/arxiv.2109.06856

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2022

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“…We also observe that in data-poor environments, the artificial neural networks have an amazing capability to over-learn the data causing poor generalization. This is one of the key results of [31] which was also observed in [28]. Despite this potential, as demonstrated by our experiments, continual data simulation can overcome this difficulty swiftly.…”

Section: Introductionsupporting

confidence: 59%

Deep Stochastic Optimization in Finance

Reppen¹,

Soner²,

Valentin³

2022

Preprint

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This paper outlines, and through stylized examples evaluates a novel and highly effective computational technique in quantitative finance. Empirical Risk Minimization (ERM) and neural networks are key to this approach. Powerful open source optimization libraries allow for efficient implementations of this algorithm making it viable in high-dimensional structures. The free-boundary problems related to American and Bermudan options showcase both the power and the potential difficulties that specific applications may face. The impact of the size of the training data is studied in a simplified Merton type problem. The classical option hedging problem exemplifies the need of market generators or large number of simulations.

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Section: Introductionsupporting

confidence: 59%