Stock Price Forecasting and Hypothesis Testing Using Neural Networks

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal statistical test

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Year of publication:	2020
Authors:	Varaku, Kerda
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Theorie \| Theory \| Neuronale Netze \| Neural networks \| Börsenkurs \| Share price \| Prognoseverfahren \| Forecasting model

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Extent:	1 Online-Ressource (20 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 10, 2020 erstellt
Other identifiers:	10.2139/ssrn.3597684 [DOI]
Classification:	C45 - Neural Networks and Related Topics ; C12 - Hypothesis Testing ; G14 - Information and Market Efficiency; Event Studies ; C53 - Forecasting and Other Model Applications ; c58
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012834485