Machine-learning a family of solutions to an optimal pension investment problem

John Armstrong, Cristin Buescu, James Dalby and Rohan Hobbs

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Abstract: We use a neural network to identify the optimal solutions to a family of pension investment problems, where the parameters determining an investor's risk and consumption preferences are given as inputs to the neural network in addition to economic variables. Training a single network across such a family fails without modification. Our main contribution is a scaling of the loss function that resolves this, together with a proof that the resulting algorithm converges. We use this to develop a practical tool for exploring how pension outcomes vary with preference parameters. We use a Black-Scholes economic model so that we may validate the accuracy of the network using a classical and provably convergent numerical method developed using the duality approach.

Date: 2025-11, Revised 2026-08
New Economics Papers: this item is included in nep-age, nep-cmp and nep-mac
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