Titre:	Filling a gap in the taxonomy of phyllachoroid fungi : proposition of Neopolystigma, gen. nov., and the new family Neopolystigmataceae
Auteur(s):	Guterres, Débora Cervieri Santos, Maria do Desterro Mendes dos Furlanetto, Cléber Pinho, Danilo B. Barreto, Robert W. Dianese, José Carmine
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-3902-8487 https://orcid.org/0000-0001-5435-6182 https://orcid.org/0000-0003-0575-0487 https://orcid.org/0000-0003-2624-302X https://orcid.org/0000-0001-8920-4760 https://orcid.org/0000-0002-9100-0545
metadata.dc.contributor.affiliation:	Universidade Federal de Viçosa, Departamento de Fitopatologia Universidade de Brasília, Departamento de Ecologia Universidade de Brasília, Departamento de Fitopatologia Universidade de Brasília, Departamento de Fitopatologia Universidade Federal de Viçosa, Departamento de Fitopatologia Universidade de Brasília, Departamento de Fitopatologia
Assunto::	Fungos - cerrados Taxonomia
Date de publication:	2022
Editeur:	Taylor & Francis
Référence bibliographique:	GUTERRES, Débora C. et al. Filling a gap in the taxonomy of phyllachoroid fungi: proposition of Neopolystigma, gen. nov., and the new family Neopolystigmataceae. Mycologia, [S. l.], v. 114, n. 5, 900-913, 2022. DOI: https://doi.org/10.1080/00275514.2022.2092365.
Abstract:	Researchers and practitioners globally, from a range of perspectives, acknowledge the difficulty in determining the value of a financial asset. This subject is of utmost importance due to the numerous participants involved and its impact on enhancing market structure, function, and efficiency. This paper conducts a comprehensive review of the academic literature to provide insights into the reasoning behind certain conventions adopted in financial value estimation, including the implementation of preprocessing techniques, the selection of relevant inputs, and the assessment of the performance of computational models in predicting asset prices over time. Our analysis, based on 109 studies sourced from 10 databases, reveals that daily forecasts have achieved average error rates of less than 1.5%, while monthly data only attain this level in optimal circumstances. We also discuss the utilization of tools and the integration of hybrid models. Finally, we highlight compelling gaps in the literature that provide avenues for further research.
metadata.dc.description.unidade:	Instituto de Ciências Biológicas (IB) Departamento de Ecologia (IB ECL) Departamento de Fitopatologia (IB FIT)
DOI:	https://doi.org/10.1080/00275514.2022.2092365
metadata.dc.relation.publisherversion:	https://www.tandfonline.com/doi/full/10.1080/00275514.2022.2092365
Collection(s) :	Artigos publicados em periódicos e afins

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