Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning

Duarte, Victor Fonseca

doi:10.2139/ssrn.3012602

Cited by 9 publications

(8 citation statements)

References 72 publications

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“…Outputs with high resolution may be aggregated via clustering methods to provide insights [183], while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield [249], wind speed [479] or surface temperature [481]. ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].…”

Section: Assessing Policy Optionsmentioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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Section: Assessing Policy Optionsmentioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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“…Machine learning has been applied to solve nonlinear models in continuous time in macroeconomics and finance by Duarte [40] where the problem of solving the corresponding nonlinear partial differential equations can be reformulated as a sequence of supervised learning problems. A single variable time-series model that combines two financial volatility metrics to predict and forecast one of them is proposed by Stefani et al [41]; the method is based on artificial neural networks with a multilayer perceptron in a single-hidden-layer configuration, the k-nearest neighbors as a local nonlinear model used for classification and regression and finally support vector machine in a regression methodology.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Genetic and deep learning clusters based on neural networks for management decision structures

Serrano

2019

Neural Comput & Applic

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Judgments are taken in a structured way; both human and business management decisions involve a hierarchical process that requires a level of compromise between risk, cost, reward, experience and knowledge. This article proposes a management decision structure that emulates the human brain approach based on genetic and deep learning cluster algorithms and the random neural network. Reinforcement learning takes quick and specific local decisions, deep learning clusters enables identity and memory, and deep learning management clusters make final strategic decisions. The presented genetic algorithm transmits the learned information to future generations in the network weights rather than the neurons. Because the subject's information, a combination of memory, identity and decision data, is never lost but transmitted, the genetic algorithm provides immortality. The management decision structure has been applied and validated in a smart investment Fintech application: an intelligent banker that makes buy and sell asset decisions with an associated market and risk that entirely transmits itself to a future generation. Results are rewarding; the management decision structure with genetics and machine learning based on the random neural network algorithm that emulates the human brain and biology transmits information to future generations and learns autonomously, gradually and continuously while adapting to the environment. Keywords Genetic learning Á Deep learning clusters Á Reinforcement learning Á Random neural network Á Fintech Á Smart investment & Will Serrano

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“…Other papers that use machine learning to solve economic models include Scheidegger and Bilionis (2019), Azinovic, Gaegauf, and Scheidegger (2022), Fernández-Villaverde, Hurtado, and Nuño (2023), and Duarte, Duarte, and Silva (2023. Fernández-Villaverde, Hurtado, and Nuño (2023) use deep neural networks to approximate the aggregate laws of motion in a heterogeneous agents model featuring strong nonlinearities and aggregate shocks.…”

Section: Introductionmentioning

confidence: 99%

“…Fernández-Villaverde, Hurtado, and Nuño (2023) use deep neural networks to approximate the aggregate laws of motion in a heterogeneous agents model featuring strong nonlinearities and aggregate shocks. Duarte, Duarte, and Silva (2023) casts the economic model in continuous time and uses neural networks to approximate the Bellman equation. Maliar, Maliar, and Winant (2021) and Azinovic, Gaegauf, and Scheidegger (2022) approximate all the model equilibrium conditions using neural networks and use the simulated data to train them.…”

Section: Introductionmentioning

confidence: 99%

A machine learning projection method for macro‐finance models

Valaitis,

Villa

2024

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We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity. We show that a neural network‐based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities. We find that the optimal policy prescribes an active role for the newly added medium‐term maturities, enabling the planner to raise financial income without increasing its total borrowing in response to expenditure shocks. Through this mechanism, the government effectively subsidizes the private sector during recessions.

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Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning

Cited by 9 publications

References 72 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Genetic and deep learning clusters based on neural networks for management decision structures

A machine learning projection method for macro‐finance models

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