A data-driven framework for consistent financial valuation and risk measurement

Cui, Zhenyu; Kirkby, Justin; Nguyen, Duy

doi:10.1016/j.ejor.2020.07.011

Cited by 26 publications

(12 citation statements)

References 71 publications

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“…They significantly improved the risk prediction ability of the model (Li et al 2021a). Cui et al (2021) proposed a general data-driven framework, which achieved high accuracy in the pricing and risk management of multiple financial contracts (Cui et al 2021). Thus, a single model may perform well in a specific field.…”

Section: Research On Financial Risk Management (Frm)mentioning

confidence: 99%

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Elston

2022

Oper Manag Res

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A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk.

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Section: Research On Financial Risk Management (Frm)mentioning

confidence: 99%

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Elston

2022

Oper Manag Res

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“…References Logsplines [70,71,72,67,124] Smoothing Splines [43,41,42] P-splines [27,74,30] B-splines [37,92,93,80, 87] B-spline Duality [16,17,66] Shape Constrained [83] and B-splines, through a regularization (variance reduction) technique known as spectral filtering which operates on the empirical characteristic function of a sample. This work is organized as follows: the remainder of Section 1 introduces the problem setting and reviews the development of delta sequence and basis density estimators, as well as estimation in a transformed space (logsplines).…”

Section: Methodsmentioning

confidence: 99%

Spline Local Basis Methods for Nonparametric Density Estimation

2023

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“…En tanto, existen diferentes metodologías para la valoración de empresas en las que se puede direccionar la gestión financiera, a partir del conocimiento del negocio y del micro y macroentorno organizacional en el que se encuentra. Según Parra Cui et al (2021), es de vital importancia para el gerente financiero poder identificar las metodologías para valorar una empresa, reconociendo las ventajas y desventajas de cada una de ellas, orientando a los diferentes grupos de interés a la consolidación del valor que les permita alcanzar sus objetivos de manera contundente.…”

Section: Introductionunclassified

Un acercamiento a las metodologías para la valoración financiera: análisis bibliométrico y revisión sistemática de literatura

López-Rodríguez,

Velásquez-Lizcano,

Fajardo-Sierra

et al. 2024

RANAC

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Propósito: Desarrollar un acercamiento hacia las metodologías para la valoración financiera, como herramienta que permite a los gerentes tener una visión objetiva y determinante para generar valor empresarial.Metodología: Se ejecuta un análisis bibliométrico en Scopus utilizando el software R Core Team 2022-Bibliometrix y VOSviewer, y una revisión sistemática de la literatura a partir del protocolo PRISMA.Resultados: Se resaltan como las principales metodologías para la valoración financiera al flujo de caja descontado, opciones reales, el valor presente neto, valoración por múltiplos, el valor económico agregado, y diversas razones financieras que complementan dichos procesos de valoración.Implicaciones: A pesar de que se identifica un incremento regular de la producción bibliográfica, es evidente la escasez de literatura sobre el tema abordado.Originalidad: El uso de estas metodologías permite determinar las decisiones gerenciales asociadas a la valoración de las compañías para su beneficio a largo plazo.

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A data-driven framework for consistent financial valuation and risk measurement

Cited by 26 publications

References 71 publications

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Spline Local Basis Methods for Nonparametric Density Estimation

Un acercamiento a las metodologías para la valoración financiera: análisis bibliométrico y revisión sistemática de literatura

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