Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar

Unstable fluctuations in financial markets caused by the 2008 financial crisis and currently by the Covid-19 crisis have generated greater concern among investors regarding their capital protection. In view of this situation, the consideration of alternative investments has taken a relevant position...

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Autores principales:	Antonio Carlos Alcázar-Blanco, Jessica Paule-Vianez, Miguel Prado-Román, José Luis Coca-Pérez
Formato:	article
Lenguaje:	ES
Publicado:	Elsevier 2021
Materias:	G11 G17 G41 C45 Business HF5001-6182
Acceso en línea:	https://doaj.org/article/49547f7fee5e4fc484e9e7bbe8a0bb6f
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spelling	oai:doaj.org-article:49547f7fee5e4fc484e9e7bbe8a0bb6f2021-11-14T04:34:39ZGeneralized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar2444-883410.1016/j.iedeen.2021.100167https://doaj.org/article/49547f7fee5e4fc484e9e7bbe8a0bb6f2021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2444883421000267https://doaj.org/toc/2444-8834Unstable fluctuations in financial markets caused by the 2008 financial crisis and currently by the Covid-19 crisis have generated greater concern among investors regarding their capital protection. In view of this situation, the consideration of alternative investments has taken a relevant position to protect their wealth and obtain profits. Due to the relevance of these investments in these times, this study proposes using artificial intelligence to predict the value of alternative investments, specifically the numismatic asset the Walking Liberty Half Dollar. To achieve this objective, the use of Generalized Regression Neural Networks has been proposed over a sample 25 coins of the Walking Liberty Half Dollar with several qualities valued in the period 2000-2019. Two models were proposed, one for the entire selected sample and the other one for each type of coin depending on its year of minting. Thus, it has been found that the model proposed for the entire sample has a success rate of between 86.12% and 97% while the approach for each type of coin obtained success rates that even reach 100%. The variables that have the greatest influence within the model are the state of conservation of the coin, its age, and its exclusivity. In this way, these results provide fundamental information to investors to understand the behaviour of these assets, and to be able to formulate more profitable investment portfolios, especially in times of great economic instability.Antonio Carlos Alcázar-BlancoJessica Paule-VianezMiguel Prado-RománJosé Luis Coca-PérezElsevierarticleG11G17G41C45BusinessHF5001-6182ESEuropean Research on Management and Business Economics, Vol 27, Iss 3, Pp 100167- (2021)
institution	DOAJ
collection	DOAJ
language	ES
topic	G11 G17 G41 C45 Business HF5001-6182
spellingShingle	G11 G17 G41 C45 Business HF5001-6182 Antonio Carlos Alcázar-Blanco Jessica Paule-Vianez Miguel Prado-Román José Luis Coca-Pérez Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
description	Unstable fluctuations in financial markets caused by the 2008 financial crisis and currently by the Covid-19 crisis have generated greater concern among investors regarding their capital protection. In view of this situation, the consideration of alternative investments has taken a relevant position to protect their wealth and obtain profits. Due to the relevance of these investments in these times, this study proposes using artificial intelligence to predict the value of alternative investments, specifically the numismatic asset the Walking Liberty Half Dollar. To achieve this objective, the use of Generalized Regression Neural Networks has been proposed over a sample 25 coins of the Walking Liberty Half Dollar with several qualities valued in the period 2000-2019. Two models were proposed, one for the entire selected sample and the other one for each type of coin depending on its year of minting. Thus, it has been found that the model proposed for the entire sample has a success rate of between 86.12% and 97% while the approach for each type of coin obtained success rates that even reach 100%. The variables that have the greatest influence within the model are the state of conservation of the coin, its age, and its exclusivity. In this way, these results provide fundamental information to investors to understand the behaviour of these assets, and to be able to formulate more profitable investment portfolios, especially in times of great economic instability.
format	article
author	Antonio Carlos Alcázar-Blanco Jessica Paule-Vianez Miguel Prado-Román José Luis Coca-Pérez
author_facet	Antonio Carlos Alcázar-Blanco Jessica Paule-Vianez Miguel Prado-Román José Luis Coca-Pérez
author_sort	Antonio Carlos Alcázar-Blanco
title	Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
title_short	Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
title_full	Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
title_fullStr	Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
title_full_unstemmed	Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar
title_sort	generalized regression neuronal networks to predict the value of numismatic assets. evidence for the walking liberty half dollar
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/49547f7fee5e4fc484e9e7bbe8a0bb6f
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Generalized regression neuronal networks to predict the value of numismatic assets. Evidence for the walking liberty half dollar

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