Combining random forest and copula functions: A heuristic approach for selecting assets from a financial crisis perspective

Luca, Giovanni De; Rivieccio, Giorgia; Zuccolotto, Paola

doi:10.1002/isaf.315

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…The random forests (RFs) method was implemented to identify the main determinants of the Central Bank Independence (CBI) index from a large database of institutional, political, and economic variables. To the best of our knowledge, RF has not been previously used for the identification of CBI determinants, although it has been utilized in finance (e.g., Creamer & Freund, 2010;De Luca, Rivieccio, & Zuccolotto, 2010;Booth, Gerding, & McGroarty, 2015;Ward, 2017). RF has been utilized to overcome limitations such as omitted variables, collinearity, overfitting, and linear functional form of the regression.…”

Section: Resultsmentioning

confidence: 99%

Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

Cavicchioli

Papana

Dagiasis

et al. 2019

J. Mod. Appl. Stat. Methods

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A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.

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Section: Resultsmentioning

confidence: 99%

Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

Cavicchioli

Papana

Dagiasis

et al. 2019

J. Mod. Appl. Stat. Methods

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show abstract

“…Finally, in this study, we introduced a machine learning technique to define variable importance measurement. Random Forest (RF) is powerful tool that has recently been used to solve the problems of prediction and variable importance measurement [16,62,63]). For this study, we used decision trees [64][65][66]), more specifically, classification and regression trees [67].…”

Section: Methodsmentioning

confidence: 99%

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

et al. 2021

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Financial knowledge is the main element of financial literacy, which is important for the sustainable development of individuals and society. Sustainability is a complex concept that spans many fields, including financial knowledge for all ages. Financial knowledge requires significant scientific research showing its impact on individuals and the economy, including non-cash payments. Consumer payment knowledge and its association with consumer financial behavior have long been a matter of widespread interest by researchers, but no in-depth, empirically based scientific research has been completed for Poland. The objective of this study was to examine factors associated with cashless payment behavior with an emphasis on the role of consumer financial knowledge. A total of 1100 interviews were carried out with Polish nationals aged 15 and above. The collected data were analyzed with the use of statistical methods, including analysis of variances (ANOVA), in order to examine consumers’ financial knowledge by basic economic and non-economic factors. Additionally, a data-mining method known as Random Forests was implemented for finding the variable importance in correlations between consumer financial knowledge and preferred methods of payment. The results revealed the diversity of factors influencing consumer behavior. Among the consumers’ personal traits, financial knowledge was one of the most important determinants of their payment choices. The results have implications for the design of payment processes. The results can be used by central banks to determine the directions of financial inclusion, as well as for stakeholders in the payments market.

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“…In 1932, Luca et al [3] used a univariate analysis model for financial early warning research, which is the earliest study of financial early warning models and has some research significance.…”

Section: Related Workmentioning

confidence: 99%

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

Zhang

Zhong

2022

Mobile Information Systems

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The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.

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Combining random forest and copula functions: A heuristic approach for selecting assets from a financial crisis perspective

Cited by 13 publications

References 15 publications

Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

Consumer Financial Knowledge and Cashless Payment Behavior for Sustainable Development in Poland

Research on Early Warning of Financial Crisis of Listed Companies Based on Random Forest and Time Series

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