A Machine Learning Framework for Asset Pricing

This paper introduces a unified machine learning framework for solving general asset pricing problems. Building on representations of asset prices in discrete-time and continuous-time models, we develop a solution strategy using neural networks and further machine learning techniques to determine asset prices. We exemplify and numerically study our methodology for consumption-based asset pricing models

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Year of publication:	[2022]
Authors:	Dillschneider, Yannick
Publisher:	[S.l.] : SSRN
Subject:	Theorie \| Theory \| Künstliche Intelligenz \| Artificial intelligence \| CAPM

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Extent:	1 Online-Ressource (30 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 29, 2022 erstellt
Other identifiers:	10.2139/ssrn.4097100 [DOI]
Classification:	C45 - Neural Networks and Related Topics ; C63 - Computational Techniques ; G12 - Asset Pricing ; G13 - Contingent Pricing; Futures Pricing
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013290180