Journal of Economics, Finance and Administrative Science

URI permanente para esta comunidadhttps://hdl.handle.net/20.500.12640/4090

La Journal of Economics, Finance and Administrative Science (JEFAS), de la Universidad ESAN, es una publicación académica de acceso abierto que presenta investigaciones revisadas por pares en administración, economía y finanzas, con un enfoque en el contexto latinoamericano e iberoamericano. Fundada en 1992 como Cuadernos de Difusión, en 2009 cambió de nombre a su actual denominación como JEFAS. Ha evolucionado en colaboración con importantes editoriales, como Elsevier y actualmente Emerald Publishing. La revista publica investigaciones de alta calidad sin costo para los autores, con el respaldo de ESAN y su compromiso con la difusión del conocimiento científico y académico, y la práctica gerencial.

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2021 - 20232

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Idioma: search.filters.itemlanguage.InglésTema: search.filters.subject.Calificación crediticia

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Mostrando 1 - 2 de 2
  • Miniatura
    ÍtemSolo Metadatos
    A recent review on optimisation methods applied to credit scoring models
    (Universidad ESAN. ESAN Ediciones, 2023-12-11) Kamimura, Elias Shohei; Pinto, Anderson Rogerio Faia; Nagano, Marcelo Seido
    Purpose: This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs). Design/methodology/approach: The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs). Findings: The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs. Practical implications: The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs. Originality/value: The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.
  • Miniatura
    ÍtemSolo Metadatos
    The impact of rating classifications on stock prices of Brazilian companies
    (Universidad ESAN. ESAN Ediciones, 2021-06-30) Pagin, Fernanda; Gomes, Matheus da Costa; Antônio, Rafael Moreira; Júnior, Tabajara Pimenta; Gaio, Luiz Eduardo
    Purpose. This paper aims to identify if there is an impact of the rating announcements issued by the agencies on the returns of the stocks of Brazilian companies listed on Brasil Bolsa Balcão, from August 2002 to August 2018, identifying which types of announcement (upgrade, downgrade or the same initial classification) cause variations in prices around the date of disclosure of the rating. Design/methodology/approach. The event study methodology was applied to verify the market reaction around the announcement dates in a 21-day event window (−10, +10). The market model was used to calculate the abnormal returns (ARs), and subsequently, the accumulated ARs. Findings. The hypotheses tests allowed to verify that the accumulated ARs are different, before and after the three types of rating announcements (upgrades, downgrades and the same classification); in upgrades, the mean of accumulated ARs increases in the days before the event, while in downgrades, this increase occurs after the event. This paper concluded that the rating announcements have an impact on the return of stock of the Brazilian market and that the market reaction occurs most of the time before the event happens, which indicates that the market can anticipate the information contained in the changes in credit ratings. Practical implications. The results have considerable implications for portfolio managers, institutional investors and traders. It facilitates investment decision-making in the face of rating classification announcements. Market participants can pay more attention to their investment strategies and asset allocation during periods of risk rating announcements. Additionally, traders can understand the form of investment strategy for superior earnings. Originality/value. The importance of the study is related to the fact that the results may explain the causes of specific movements in the Brazilian financial market related to a source of information that may or may not be able to influence the decisions of the financial agents that operate in this market. The justification is centred on the idea that, for investors who somehow react to the announcements, it is relevant to understand the impact of rating classifications on companies, as access to such information allows for more conscious decision-making.