Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)

Prado, José Willer do; Alcântara, Valderí Castro; Carvalho, Francisval Melo; Vieira, Kelly Carvalho; Machado, Luiz Kennedy Cruz; Tonelli, Dany Flávio

doi:10.1007/s11192-015-1829-6

Cited by 113 publications

(112 citation statements)

References 68 publications

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“…(), Prado et al . (), Qasim (), and Costa et al . () who have consubstantiated the relevance of this scientific database in their pontifications.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, relying on quantitative techniques, this sort of analysis allows the possibility to unveil significant aspects, such as the citations, co‐citations, authors, co‐authors, periodicals, keywords, among other characteristics, such as the growth and distribution of the bibliographic production (Hsu and Chiang, ; Sanchez‐Riofrio et al. , 2015; Costa and Carvalho, ; Prado et al. , 2016; Costa et al.…”

Section: Methodsmentioning

confidence: 99%

“…The bibliometric analysis should be systematic, in other words, it must be based on primary studies, it must encompass goals and methods that are clearly stated, it also should be conducted using a clear and reproducible methodology (Greenhalgh, 1997). Therefore, relying on quantitative techniques, this sort of analysis allows the possibility to unveil significant aspects, such as the citations, co-citations, authors, co-authors, periodicals, keywords, among other characteristics, such as the growth and distribution of the bibliographic production (Hsu and Chiang, 2015;Sanchez-Riofrio et al, 2015;Costa and Carvalho, 2016;Prado et al, 2016;Costa et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Behavioral Economics and Behavioral Finance: A Bibliometric Analysis of the Scientific Fields

Costa

Carvalho

Moreira

2018

Journal of Economic Surveys

101

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The aim of the study is to conduct a bibliometric analysis of the scientific field of Behavioral Economics and Behavioral Finance. The research was conducted using the Web of Science database, which returned 2617 articles, revealing that the amount of research within these fields has grown over time. Furthermore, the results also prove the relevance of the works of Daniel Kahneman and Amos Tversky for the field of Behavioral Economics and Finance, and Steven Hursh to Behavioral Economics. It is still possible to note that the field of Behavioral Economics encompasses subjects that connect human behavior with demand, consumption and price, with investments and with managerial decisions, as well as with the role played by heuristics and cognitive biases in decision‐making processes. In turn, the field of Behavioral Finance is more focused on the study of errors of judgment and of decision‐making characteristics in financial investments. Additionally, it is inferred that the field of Behavioral Economics is more wide‐ranging than the field of Behavioral Finance, as the latter is a byproduct of Behavioral Economics. Finally, a conclusion is then reached, demonstrating that the fields of Behavioral Economics and Finance have turned into an important field of study.

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“…(), Prado et al . (), Qasim (), and Costa et al . () who have consubstantiated the relevance of this scientific database in their pontifications.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Behavioral Economics and Behavioral Finance: A Bibliometric Analysis of the Scientific Fields

Costa

Carvalho

Moreira

2018

Journal of Economic Surveys

101

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“…According to Do Prado et al . (), this analysis can be understood as the first condition for conducting new research, since it allows the identification of gaps and opportunities within the field of knowledge, in new theoretical constructions and empirical research.…”

Section: Methodological Proceduresmentioning

confidence: 99%

Bibliometric Analysis of the Quantitative Methods Applied to the Measurement of Industrial Clusters

Chain

Santos

Castro

et al. 2018

Journal of Economic Surveys

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Literature on methods for analysing interindustry interdependence and geographical concentration of firms began to multiply since the 1990s. The aim of this paper was to systematize the literature on methods and measures applied to the analysis of industrial clusters, as well as to identify trends in this knowledge field. The method used was bibliometrics, which consisted of a frequency evaluation of the publications and the relationship network between them. It was verified an exponential increase in the number of papers that composed this area, based mainly on the theories of New Economic Geography. Recently, the literature has focused on the geographic location in relation to interindustry linkages, and the frontier of knowledge has shifted from traditional methods of regional science to areas such as spatial statistics, econophysics and artificial intelligence. There are still relevant questions being explored, as Modifiable Area Unit Problem (aggregation bias), nevertheless, spatial anisotropy (directional bias) is still neglected and indicates a new research path.

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“…The interested readers may refer to Prado et al [30] for the detailed description and some previous results of these baseline classifiers.…”

Section: Base Classifiermentioning

confidence: 99%

Chinese Small Business Credit Scoring: Application of Multiple Hybrids Neural Network

Chi¹,

Abedin²,

Moula³

2017

IJDTA

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Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)

Cited by 113 publications

References 68 publications

Behavioral Economics and Behavioral Finance: A Bibliometric Analysis of the Scientific Fields

Behavioral Economics and Behavioral Finance: A Bibliometric Analysis of the Scientific Fields

Bibliometric Analysis of the Quantitative Methods Applied to the Measurement of Industrial Clusters

Chinese Small Business Credit Scoring: Application of Multiple Hybrids Neural Network

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