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A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 39277

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Special Issue Editors

Dr. Shuangzhe Liu

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Guest Editor

Department of Mathematics and Statistics, University of Canberra, Canberra, Australia
Interests: financial time series; multivariate analysis; statistical diagnostics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tiefeng Ma

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Guest Editor

School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
Interests: casual inference; financial data analytics; statistical algorithms

Dr. Seng Huat Ong

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Guest Editor

Institute of Actuarial Science and Data Analytics, UCSI University, 56000 Kuala Lumpur, Malaysia
Interests: data mining; simulation and computation; statistical modelling and inference

Special Issue Information

Dear Colleagues,

Data analytics and statistical learning have been widely employed to analyze business, financial, economic and other data, with recently developed techniques and applications.

The purpose of this Special Issue is to report and promote the latest progress in advancing specific techniques and methodologies and/or making relevant case studies. Manuscripts are welcome which address any area of financial data analytics, econometric analysis, risk management, statistical modelling, computation and simulation, and their applications.

The Editorial Office is providing several Feature Paper quotas for this Special Issue. When accepted after review, these papers will be published free of charge. A Feature Paper is a high-quality paper; it is up to the Guest Editors to decide whether to grant potential authors a full waiver. Should you have any questions related to Feature Papers, please feel free to contact the Guest Editors or the journal’s Editorial Office (jrfm@mdpi.com).

Dr. Shuangzhe Liu
Prof. Dr. Tiefeng Ma
Dr. Seng Huat Ong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data analytics
data mining and machine learning
econometric techniques and applications
financial engineering
insurance and risk management
multivariate analysis
panel data analysis
time series analysis
simulation and computation in finance
statistical distributions and applications
statistical modelling and inference

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Published Papers (11 papers)

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Research

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19 pages, 3698 KiB

Open AccessArticle

Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation

by Haokun Dong, Rui Liu and Allan W. Tham

J. Risk Financial Manag. 2024, 17(2), 50; https://doi.org/10.3390/jrfm17020050 - 29 Jan 2024

Cited by 13 | Viewed by 4440

Abstract

An accurate prediction of loan default is crucial in credit risk evaluation. A slight deviation from true accuracy can often cause financial losses to lending institutes. This study describes the non-parametric approach that compares five different machine learning classifiers combined with a focus on sufficiently large datasets. It presents the findings on various standard performance measures such as accuracy, precision, recall and F1 scores in addition to Receiver Operating Curve-Area Under Curve (ROC-AUC). In this study, various data pre-processing techniques including normalization and standardization, imputation of missing values and the handling of imbalanced data using SMOTE will be discussed and implemented. Also, the study examines the use of hyper-parameters in various classifiers. During the model construction phase, various pipelines feed data to the five machine learning classifiers, and the performance results obtained from the five machine learning classifiers are based on sampling with SMOTE or hyper-parameters versus without SMOTE and hyper-parameters. Each classifier is compared to another in terms of accuracy during training and prediction phase based on out-of-sample data. The 2 data sets used for this experiment contain 1000 and 30,000 observations, respectively, of which the training/testing ratio is 80:20. The comparative results show that random forest outperforms the other four classifiers both in training and actual prediction. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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27 pages, 590 KiB

Open AccessArticle

Board Gender Diversity and Firm Performance: Recent Evidence from Japan

by Kangyi Wang, Jing Ma, Chunxiao Xue and Jianing Zhang

J. Risk Financial Manag. 2024, 17(1), 20; https://doi.org/10.3390/jrfm17010020 - 5 Jan 2024

Cited by 9 | Viewed by 9806

Abstract

Gender diversity is increasingly recognized as a critical element in corporate management. However, existing research on its impact on firm performance demonstrates inconsistency in a global context. This study employs 1990 publicly listed Japanese companies from 2006 to 2023 and examines the effect of board gender diversity on firm performance in Japan. Findings from the fixed-effects regression model revealed a significant negative impact of board gender diversity on firm performance. This adverse correlation is more pronounced in smaller firms, those with greater leverage and reduced institutional ownership, and regulated and consumer-focused industries, particularly pre-COVID-19. The detrimental impact of board gender diversity on firm performance is transmitted via corporate social responsibility and firm innovation instead of board independence or CEO duality. Notably, the two-stage least squares estimation addresses potential endogeneity, employing an equal opportunity policy as an instrumental variable. Moreover, the robustness of our results is affirmed via the substitution of return on equity for return on assets as an indicator of firm performance. Lastly, our analysis does not reveal a U-shaped nonlinear relationship between board gender diversity and corporate performance. As Japan progressively promotes women’s participation in corporate governance, this research bears significant implications for corporate leaders, investors, and policymakers in Japan. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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18 pages, 1574 KiB

Open AccessArticle

The Impact of Non-Financial and Financial Variables on Credit Decisions for Service Companies in Turkey

by Ali İhsan Çetin, Arzu Ece Çetin and Syed Ejaz Ahmed

J. Risk Financial Manag. 2023, 16(11), 487; https://doi.org/10.3390/jrfm16110487 - 17 Nov 2023

Viewed by 3506

Abstract

This study aims to analyze and generalize the factors influencing credit decision-making in Turkey’s service sector, which has seen substantial growth and increased dynamism post-2000, coinciding with accelerated economic development. The evolving competitive landscape and shifting consumer purchasing perceptions have led companies within this sector to seek differentiation strategies to attain a competitive edge. In this context, access to credit emerges as a crucial enabler for companies to expand and capture market share. The research focuses on the financial and non-financial characteristics of medium-sized service sector firms seeking credit, recognizing that both sets of variables play a pivotal role in the credit allocation process conducted by banks. The core of this study involves applying established assumption tests from extant literature, followed by an extensive regression analysis. The primary objective of this analysis is to identify and underscore the key financial and non-financial factors that significantly impact credit decisions in the service sector. By examining these variables, the study seeks to contribute valuable insights into the credit decision-making process, addressing the nuanced and varied nature of the service sector. This approach not only provides a deeper understanding of the sector’s credit dynamics but also assists in formulating more informed strategies for businesses seeking financial support within this evolving economic landscape. The primary conclusion reached by the study is that non-financial variables exert a greater influence on credit decision-making in the service sector compared to financial variables. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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14 pages, 564 KiB

Open AccessArticle

Circular-Statistics-Based Estimators and Tests for the Index Parameter α of Distributions for High-Volatility Financial Markets

by Ashis SenGupta and Moumita Roy

J. Risk Financial Manag. 2023, 16(9), 405; https://doi.org/10.3390/jrfm16090405 - 11 Sep 2023

Viewed by 1730

Abstract

The distributions for highly volatile financial time-series data are playing an increasingly important role in current financial scenarios and signal analyses. An important characteristic of such a probability distribution is its tail behaviour, determined through its tail thickness. This can be achieved by estimating the index parameter of the corresponding distribution. The normal and Cauchy distributions, and, sometimes, a mixture of the normal and Cauchy distributions, are suitable for modelling such financial data. The family of stable distributions can provide better modelling for such financial data sets. Financial data in high-volatility markets may be better modelled, in many cases, by the Linnik distribution in comparison to the stable distribution. This highly flexible family of distributions is better capable of modelling the inflection points and tail behaviour compared to the other existing models. The estimation of the tail thickness of heavy-tailed financial data is important in the context of modelling. However, the new probability distributions do not admit any closed analytical form of representation. Thus, novel methods need to be developed, as only a few can be found in the literature. Here, we recall a recent novel method, developed by the authors, based on a trigonometric moment estimator using circular distributions. The linear data may be transformed to yield circular data. This transformation is solely for yielding a suitable estimator. Our aim in this paper is to provide a review of the few existing methods, discuss some of their drawbacks, and also provide a universal (

\forall α \in (0, 2]

), efficient, and easily implementable estimator of

α

based on the transformation mentioned above. Novel, circular-statistics-based tests for the index parameter

α

of the stable and Linnik distributions are introduced and also exemplified with real-life financial data. Two real-life data sets are analysed to exemplify the methods recommended and enhanced by the authors. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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15 pages, 487 KiB

Open AccessArticle

Exponential Stability of Fractional Large-Scale Neutral Stochastic Delay Systems with Fractional Brownian Motion

by T. Sathiyaraj, T. Ambika and Ong Seng Huat

J. Risk Financial Manag. 2023, 16(5), 278; https://doi.org/10.3390/jrfm16050278 - 19 May 2023

Cited by 1 | Viewed by 1532

Abstract

Mathematics plays an important role in many fields of finance. In particular, it presents theories and tools widely used in all areas of finance. Moreover, fractional Brownian motion (fBm) and related stochastic systems have been used to model stock prices and other phenomena in finance due to the long memory property of such systems. This manuscript provides the exponential stability of fractional-order Large-Scale neutral stochastic delay systems with fBm. Based on fractional calculus (FC),

R^{n}

stochastic space and Banach fixed point theory, sufficiently useful conditions are derived for the existence of solution and exponential stability results. In this study, we tackle the nonlinear terms of the considered systems by applying local assumptions. Finally, to verify the theoretical results, a numerical simulation is provided. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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15 pages, 323 KiB

Open AccessArticle

The Naive Estimator of a Poisson Regression Model with a Measurement Error

by Kentarou Wada and Takeshi Kurosawa

J. Risk Financial Manag. 2023, 16(3), 186; https://doi.org/10.3390/jrfm16030186 - 9 Mar 2023

Viewed by 1920

Abstract

We generalize the naive estimator of a Poisson regression model with a measurement error as discussed in Kukush et al. in 2004. The explanatory variable is not always normally distributed as they assume. In this study, we assume that the explanatory variable and measurement error are not limited to a normal distribution. We clarify the requirements for the existence of the naive estimator and derive its asymptotic bias and asymptotic mean squared error (MSE). The requirements for the existence of the naive estimator can be expressed using an implicit function, which the requirements can be deduced by the characteristic of the Poisson regression models. In addition, using the implicit function obtained from the system of equations of the Poisson regression models, we propose a consistent estimator of the true parameter by correcting the bias of the naive estimator. As illustrative examples, we present simulation studies that compare the performance of the naive estimator and new estimator for a Gamma explanatory variable with a normal error or a Gamma error. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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14 pages, 1370 KiB

Open AccessArticle

Modelling of Loan Non-Payments with Count Distributions Arising from Non-Exponential Inter-Arrival Times

by Yeh-Ching Low and Seng-Huat Ong

J. Risk Financial Manag. 2023, 16(3), 150; https://doi.org/10.3390/jrfm16030150 - 23 Feb 2023

Cited by 1 | Viewed by 1553

Abstract

The number of non-payments is an indicator of delinquent behaviour in credit scoring, hence its estimation and prediction are of interest. The modelling of the number of non-payments, as count data, can be examined as a renewal process. In a renewal process, the number of events (such as non-payments) which has occurred up to a fixed time t is intimately connected with the inter-arrival times between the events. In the context of non-payments, the inter-arrival times correspond to the time between two subsequent non-payments. The probability mass function and the renewal function of the count distribution are often complicated, with terms involving factorial and gamma functions, and thus their computation may encounter numerical difficulties. In this paper, with the motivation of modelling the number of non-payments through a renewal process, a general method for computing the probabilities and the renewal function based on numerical Laplace transform inversion is discussed. This method is applied to some count distributions which are derived given the distributions of the inter-arrival times. Parameter estimation with maximum likelihood estimation is considered, with an application to a data set on number of non-payments from the literature. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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18 pages, 2837 KiB

Open AccessArticle

On the Contaminated Weighted Exponential Distribution: Applications to Modeling Insurance Claim Data

by Abbas Mahdavi, Omid Kharazmi and Javier E. Contreras-Reyes

J. Risk Financial Manag. 2022, 15(11), 500; https://doi.org/10.3390/jrfm15110500 - 27 Oct 2022

Cited by 1 | Viewed by 1911

Abstract

Deriving loss distribution from insurance data is a challenging task, as loss distribution is strongly skewed with heavy tails with some levels of outliers. This paper extends the weighted exponential (WE) family to the contaminated WE (CWE) family, which offers many flexible features, including bimodality and a wide range of skewness and kurtosis. We adopt Expectation-Maximization (EM) and Bayesian approaches to estimate the model, providing the likelihood and the priors for all unknown parameters. Finally, two sets of claims data are analyzed to illustrate the efficiency of the proposed method in detecting outliers. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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15 pages, 789 KiB

Open AccessArticle

On Financial Distributions Modelling Methods: Application on Regression Models for Time Series

by Paul R. Dewick

J. Risk Financial Manag. 2022, 15(10), 461; https://doi.org/10.3390/jrfm15100461 - 13 Oct 2022

Cited by 2 | Viewed by 2707

Abstract

The financial market is a complex system with chaotic behavior that can lead to wild swings within the financial system. This can drive the system into a variety of interesting phenomenon such as phase transitions, bubbles, and crashes, and so on. Of interest in financial modelling is identifying the distribution and the stylized facts of a particular time series, as the distribution and stylized facts can determine if volatility is present, resulting in financial risk and contagion. Regression modelling has been used within this study as a methodology to identify the goodness-of-fit between the original and generated time series model, which serves as a criterion for model selection. Different time series modelling methods that include the common Box–Jenkins ARIMA, ARMA-GARCH type methods, the Geometric Brownian Motion type models and Tsallis entropy based models when data size permits, can use this methodology in model selection. Determining the time series distribution and stylized facts has utility, as the distribution allows for further modelling opportunities such as bivariate regression and copula modelling, apart from the usual forecasting. Determining the distribution and stylized facts also allows for the identification of the parameters that are used within a Geometric Brownian Motion forecasting model. This study has used the Carbon Emissions Futures price between the dates of 1 May 2012 and 1 May 2022, to highlight this application of regression modelling. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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20 pages, 514 KiB

Open AccessArticle

Modeling Bivariate Dependency in Insurance Data via Copula: A Brief Study

by Indranil Ghosh, Dalton Watts and Subrata Chakraborty

J. Risk Financial Manag. 2022, 15(8), 329; https://doi.org/10.3390/jrfm15080329 - 25 Jul 2022

Cited by 5 | Viewed by 4060

Abstract

Copulas are a quite flexible and useful tool for modeling the dependence structure between two or more variables or components of bivariate and multivariate vectors, in particular, to predict losses in insurance and finance. In this article, we use the VineCopula package in R to study the dependence structure of some well-known real-life insurance data and identify the best bivariate copula in each case. Associated structural properties of these bivariate copulas are also discussed with a major focus on their tail dependence structure. This study shows that certain types of Archimedean copula with the heavy tail dependence property are a reasonable framework to start in terms modeling insurance claim data both in the bivariate as well as in the case of multivariate domains as appropriate. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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Review

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18 pages, 768 KiB

Open AccessReview

On Asymmetric Correlations and Their Applications in Financial Markets

by Linyu Cao, Ruili Sun, Tiefeng Ma and Conan Liu

J. Risk Financial Manag. 2023, 16(3), 187; https://doi.org/10.3390/jrfm16030187 - 9 Mar 2023

Cited by 4 | Viewed by 3208

Abstract

Progress on asymmetric correlations of asset returns has recently advanced considerably. Asymmetric correlations can cause problems in hedging effectiveness and overstate the value of diversification. Furthermore, considering the asymmetric correlations in portfolio construction significantly enhances performance. The purpose of this paper is to trace developments and identify areas that require further research. We examine three aspects of asymmetric correlations: first, the existence of asymmetric correlations between asset returns and their significance tests; second, the test on the existence of asymmetric correlations between different markets and financial assets; and third, the root cause analysis of asymmetric correlations. In the first part, the contents of extreme value theory, the H statistic and a model-free test are covered. In the second part, commonly used models such as copula and GARCH are included. In addition to the GARCH and copula formulations, many other methods are included, such as regime switching, the Markov switching model, and the multifractal asymmetric detrend cross-correlation analysis method. In addition, we compare the advantages and differences between the models. In the third part, the causes of asymmetry are discussed, for example, higher common fundamental risk, correlation of individual fundamental risk, and so on. Full article

(This article belongs to the Special Issue Financial Data Analytics and Statistical Learning)

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