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Laurent, Marie-Paule. "Essays in financial risk management." Doctoral thesis, Universite Libre de Bruxelles, 2003. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211221.

Zhang, Lequn. "Extreme Risk Forecast for Quantitative Financial Risk Management." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/89362.

Gueye, Djibril. "Some contributions to financial risk management." Thesis, Strasbourg, 2021. http://www.theses.fr/2021STRAD027.

Wang, Mulong. "Financial derivatives in corporate risk management." Access restricted to users with UT Austin EID, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3036610.

Schaumburg, Julia. "Quantile methods for financial risk management." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2013. http://dx.doi.org/10.18452/16675.

Genin, Adrien. "Asymptotic approaches in financial risk management." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC120/document.

Nikoci, Besjana <1989&gt. "Stress Testing for Financial Risk Management." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6935.

Aas, Roar. "Risk management using derivatives." Thesis, Heriot-Watt University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262000.

Eriksson, Kristofer. "Risk Measures and Dependence Modeling in Financial Risk Management." Thesis, Umeå universitet, Institutionen för fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-85185.

Paltalidis, Nikolaos. "Essays on applied financial econometrics and financial networks : reflections on systemic risk, financial stability & tail risk management." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/essays-on-applied-financial-econometrics-and-financial-networks(3534970d-eeba-4748-9812-d18430925664).html.

Černák, Peter. "Risk Management." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76579.

Zou, Lin. "Essays in financial economics and risk management." Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1476.

Graf, Mario. "Financial Risk Management State-of-the-Art /." St. Gallen, 2005. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01665710001/$FILE/01665710001.pdf.

Ewers, Robin B. "Enterprise Risk Management in Responsible Financial Reporting." Thesis, Walden University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10637579.

Despite regulatory guidelines, unreliable financial reporting exists in organizations, creating undue financial risk-harm for their stakeholders. Normal accident theory (NAT) identifies factors in highly complex integrated systems that can have unexpected, undetected, and uncorrected system failures. High-reliability organization (HRO) theory constructs promote reliability in complex, integrated systems prone to NAT factors. Enterprise risk management (ERM) integrates NAT factors and HRO constructs under a holistic framework to achieve organizational goals and mitigate the potential for stakeholder risk-harm. Literature on how HRO constructs promote ERM in responsible integrated financial systems has been limited. The purpose of this qualitative, grounded theory study was to use HRO constructs to identify and define the psychological factors involved in the effective ERM of responsible organizational financial reporting. Standardized, open-ended interviews were used to collect inductive data from a purposeful sample of 13 reporting agents stratifying different positions in organizations that have maintained consistent operational success while attenuating stakeholder risk-harm. The data were interpreted via transcription, and subsequent iterative open, axial, and narrative coding. Results showed that elements of culture and leadership found in the HRO construct of disaster foresightedness and mitigation fostered an internal environment of successful enterprise reporting risk management to ethically achieve organizational goals and abate third-party stakeholder risk-harm. The findings will contribute to positive social change by suggesting an approach for organizations to optimize strategic objectives while minimizing stakeholders’ financial risk-harm.

Siyi, Zhou. "Essays on financial and insurance risk management." Thesis, Imperial College London, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.586894.

Abbas, Sawsan. "Statistical methodologies for financial market risk management." Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547964.

Ben, Hadj Saifeddine. "Essays on risk management and financial stability." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E003/document.

Pillay, Levina. "Risk practitioner experiences of enterprise risk management in financial institutions." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52296.

Shedden, Jason Patrick. "A qualitative approach to financial risk." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-05092007-152751.

Yao, Rui. "Patterns of financial risk tolerance 1983-2001 /." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1060624755.

Yang, Xi. "Applying stochastic programming models in financial risk management." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4068.

MORAES, ALEX SANDRO MONTEIRO DE. "ESSAYS IN FINANCIAL RISK MANAGEMENT OF EMERGING COUNTRIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26131@1.

Haar, Lawrence. "Business cycles and the management of financial risk." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844543/.

Zabarankin, Michael Yurievich. "Optimization approaches in risk management and financial engineering." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001048.

Hays, Douglas C. "Enterprise risk management solutions a case study /." Monterey, Calif. : Naval Postgraduate School, 2008. http://handle.dtic.mil/100.2/ADA483512.

Derrocks, Velda Charmaine. "Risk management." Thesis, Nelson Mandela Metropolitan University, 2010. http://hdl.handle.net/10948/1480.

Bedendo, Mascia. "Density forecasting in financial risk modelling." Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/2661/.

HADJI, MISHEVA BRANKA. "Measuring Financial Risks: The Application of Network Theory in Fintech Risk Management." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1344336.

Chen, Hua. "Contingent Claim Pricing with Applications to Financial Risk Management." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/rmi_diss/22.

Baldwin, Sheena. "Extreme value theory : from a financial risk management perspective." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/53743.

Yamashita, Mamiko. "Three Essays on Financial Risk Management and Fat Tails." Thesis, Toulouse 1, 2020. http://www.theses.fr/2020TOU10056.

Simonson, Peter Douglas. "Limiting Financial Risk from Catastrophic Events in Project Management." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31939.

Madaleno, Mara Teresa da Silva. "Essays on energy derivatives pricing and financial risk management." Doctoral thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7302.

Yazid, Ahmad Shukri. "Perceptions and practices of financial risk management in Malaysia." Thesis, Glasgow Caledonian University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364743.

Masie, Desné Rentia. "Mediating markets : financial news media and reputation risk management." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/14196.

Holifield, Suzanne Marie. "Risk management and hedge accounting decisions at financial institutions." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1267632084.

Awiszus, Kerstin [Verfasser]. "Actuarial and financial risk management in networks / Kerstin Awiszus." Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2020. http://d-nb.info/1215427298/34.

Vuillemey, Guillaume. "Derivatives markets : from bank risk management to financial stability." Thesis, Paris, Institut d'études politiques, 2015. http://www.theses.fr/2015IEPP0007/document.

Anastasio, Edoardo <1996&gt. "The relationship between financial risk management and shareholders value." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/20812.

Kwok, Ying-kit Tony. "A study on treasury risk control in financial institutions in Hong Kong /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14038912.

Siu, Kin-bong Bonny. "Expected shortfall and value-at-risk under a model with market risk and credit risk." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37727473.

Ye, Kang. "Knowledge level modeling for systemic risk management in financial institutions /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-is-b30082274f.pdf.

Neis, Eric. "Three essays in financial economics." Diss., Restricted to subscribing institutions, 2006. http://proquest.umi.com/pqdweb?did=1158520261&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

Weiss, Susan F. "Implications of Executive Succession Upon Financial Risk and Performance." ScholarWorks, 2011. https://scholarworks.waldenu.edu/dissertations/958.

Wang, Letian. "Global supply chain risk management through operational and financial hedges." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95041.

Seidel, Henry [Verfasser], and Alexander [Akademischer Betreuer] Szimayer. "Essays in Financial Risk Management / Henry Seidel ; Betreuer: Alexander Szimayer." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://d-nb.info/1148650563/34.

Reddy, Harry 1963. "Financial supply chain dynamics : operational risk management and RFID technologies." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33729.

Zhu, Yanhui. "Nature and management of financial risk in global stock markets." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/55720/.

Yousefi, Sepehr. "Credit Risk Management in Absence of Financial and Market Data." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188800.

Seidel, Henry Verfasser], and Alexander [Akademischer Betreuer] [Szimayer. "Essays in Financial Risk Management / Henry Seidel ; Betreuer: Alexander Szimayer." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://d-nb.info/1148650563/34.

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80 Financial Risk Management Research Topics

FacebookXEmailWhatsAppRedditPinterestLinkedInAre you a student embarking on the exciting journey of writing a thesis or dissertation in financial risk management? The world of finance offers a plethora of captivating research topics in financial risk management that can enrich your academic pursuit. Whether you’re an undergraduate, master’s, or doctoral student, selecting the right research topic is crucial […]

Are you a student embarking on the exciting journey of writing a thesis or dissertation in financial risk management? The world of finance offers a plethora of captivating research topics in financial risk management that can enrich your academic pursuit. Whether you’re an undergraduate, master’s, or doctoral student, selecting the right research topic is crucial to ensure a meaningful and insightful study.

Managing financial risks involves identifying, assessing, and mitigating potential risks that could negatively impact an organization’s ability to achieve its financial goals. It goes by different names, such as risk management in finance, financial risk assessment, and financial risk analysis. Analyzing uncertainties in financial markets, investments, and economic conditions is crucial to making informed decisions.

This blog post delves into various research topics that align with financial risk management, providing a springboard for your research journey.

A List Of Potential Research Topics In Financial Risk Management :

Systemic risk assessment of interconnected payment and settlement systems.
Regulatory compliance and risk management.
Macroeconomic indicators and market risk exposure in the UK.
Evaluating the effectiveness of financial risk education programs for individual investors.
Stress testing resilience of non-bank financial intermediaries in crisis scenarios.
Risk management practices of non-financial corporations.
Effectiveness of risk models in predicting pandemic-related shocks.
Behavioural biases and their influence on risk assessment in peer-to-peer lending platforms.
Resilience of risk management frameworks during times of crisis: lessons from historical events.
Hedge fund risk management strategies during periods of extreme market turbulence.
Exploring behavioural biases in individual investment decisions and portfolio performance.
Role of risk culture in shaping risk management practices within financial institutions.
Macroprudential policies’ impact on financial stability.
Corporate risk disclosure practices and their impact on investor perception.
Digitalization and operational risk in banking.
Supply chain disruptions and risk management strategies.
Impact of environmental regulations on credit risk in industries with high pollution exposure.
Early warning models for predicting banking crises.
Review of the effectiveness of different risk communication strategies.
Critical evaluation of stress testing methodologies in risk management.
Risk communication strategies during market turbulence.
Analyzing the impact of central bank communication on interest rate risk.
Volatility clustering and its implications for options pricing and risk management.
Cross-border capital flows and emerging market risks.
Cybersecurity breaches and operational risks in financial institutions.
Effectiveness of ESG integration in UK risk assessment.
Investor sentiment’s influence on market volatility.
Government interventions and systemic risk during COVID-19.
Covid-19’s unique risk challenges for the UK financial sector.
Effectiveness of value at risk (VAR) and expected shortfall (ES) in extreme markets.
Risk implications of cross-border mergers and acquisitions in the banking sector.
Impact of inflation volatility on financial market risk.
Investor behaviour changes and market volatility during the pandemic.
Stress testing in the context of pandemic-induced economic uncertainty.
Impact of remote work on cybersecurity and data breach risks.
Risk strategies of insurance companies for catastrophic events.
Integration of fintech innovations in financial risk management for digital finance transformation.
Credit risk assessment for small and medium-sized enterprises (SMEs): current challenges and innovations.
Regulatory adaptations in response to pandemic risks.
Investigating the impact of machine learning algorithms on credit risk assessment.
Risk management strategies in commodity trading firms exposed to supply chain disruptions.
Pandemic-driven changes in operational risk management.
Default prediction models in changing economic conditions.
Basel iii and its impact on global banking risk management: a critique.
Operational risk management in the context of digital transformation in banks.
Role of fintech innovation in UK’s risk management landscape.
Regulatory divergence and cross-border risk management.
Effectiveness of risk management strategies for climate change impacts on markets.
Basel iii framework and global bank risk management.
Assessing stress testing methodologies in predicting systemic risks.
Systemic risk assessment models: a comprehensive review.
The interplay between credit risk and interest rate risk in bond portfolios.
Cyber risk insurance: analyzing the coverage and effectiveness of policies.
Role of derivatives in managing interest rate risk for corporate treasuries.
Liquidity risk impact on asset pricing and portfolio performance.
Brexit’s impact on financial market risk and regulatory frameworks.
Risk management implications of the LIBOR transition to alternative reference rates.
Exploring the relationship between political uncertainty and financial market risk.
Alternative data sources for credit risk assessment.
Risk management implications of technological advancements: a survey.
Credit risk assessment for emerging market sovereign bonds: a cross-country comparison.
Volatility transmission between cryptocurrency markets and traditional assets.
Risk management challenges for UK banks in a post-Brexit environment.
Market risk and performance of algorithmic trading strategies during market stress.
Review of operational risk management practices in fintech companies.
Market liquidity risk during and after the pandemic.
Enhancing risk mitigation strategies in investment Banking through advanced financial risk management.
Behavioural biases in investment decision-making: a literature review.
Financial risk implications of UK-EU trade agreements.
Role of fintech innovations in operational risk management.
Risk implications of central bank digital currencies (CBDCs).
Liquidity risk management strategies in the context of evolving market structures.
Risk-return trade-off in sustainable investment portfolios.
Systemic risk contributions of global systematically important banks (G-SIBs).
ESG factors integration in risk assessment: an overview.
Resilience of risk management frameworks post-COVID-19.
Credit default swaps’ role in credit risk contagion.
Analyzing the dynamic relationship between cryptocurrency volatility and financial market risks.
Macroeconomic indicators and market risk exposure in emerging economies.
Impact of macroeconomic factors on market risk for different asset classes.
Sovereign credit risk assessment in emerging economies: challenges and approaches.

As you contemplate your journey into financial risk management research, remember that the right topic will captivate your interest and contribute significantly to the existing body of knowledge. Whether you’re intrigued by market volatility, credit risk, operational risk, or the application of innovative risk management strategies, the world of finance offers a wealth of possibilities for research at every academic level. Choose a topic that resonates with your educational goals, and immerse yourself in exploring knowledge that can potentially shape the future of financial risk management. Your dissertation will not only be a testament to your scholarly prowess but also a valuable contribution to the ever-evolving landscape of finance.

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A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data

Roles Data curation, Software, Writing – original draft

Affiliation School of Business, Hunan University of Science and Technology, Xiangtan, China

Roles Data curation, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing

Affiliation School of Geosciences and Info-Physics, Central South University, Changsha, China

Roles Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Business, Hunan University of Science and Technology, Xiangtan, China, Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Xiangtan, China

Roles Funding acquisition, Investigation, Methodology, Project administration

Affiliations Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Xiangtan, China, School of Business, Central South University, Changsha, China

Roles Formal analysis, Investigation, Validation

Dan-Ping Li,
Si-Jie Cheng,
Peng-Fei Cheng,
Jian-Qiang Wang,
Hong-Yu Zhang

Published: December 26, 2018
https://doi.org/10.1371/journal.pone.0208166
Reader Comments

The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data. Subjective and objective indexes are comprehensively taken into consideration in the financial risk assessment index system of the model, which combines fuzzy theory with quantitative data analysis. Moreover, the assessment information obtained from historical financial information of company, credit rating agency and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the ranking order of companies according to their financial risk. Finally, an empirical study of financial risk assessment for companies is conducted, and the results of comparative analysis and sensitivity analysis suggest that the proposed model can effectively and reliably obtain the company with the lowest financial risk.

Citation: Li D-P, Cheng S-J, Cheng P-F, Wang J-Q, Zhang H-Y (2018) A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data. PLoS ONE 13(12): e0208166. https://doi.org/10.1371/journal.pone.0208166

Editor: Baogui Xin, Shandong University of Science and Technology, CHINA

Received: November 30, 2017; Accepted: November 13, 2018; Published: December 26, 2018

Copyright: © 2018 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by a Key Project of Hunan Social Science Achievement Evaluation Committee (XSP2016040508, XSP18ZD1002), the human philosophy social science fund project (15JD21). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Financial risk involves a combination of different methods, models and approaches to reduce the likelihood of a threat and the extent of losses [ 1 ]. Financial analysis can help to companies to detect financial risks in advance, take appropriate actions to minimize the losses, and support better decision-making [ 2 , 3 ]. An accurate understanding and a well assessment of financial risk would have lots of positive consequences such as reduction of insolvency, reduction of bankruptcy rate, reduction of financial hardship. Therefore, the establishment of financial risk assessment model, the early diagnosis of the financial crisis and take appropriate measures to maintain health and safety and sustainable development of enterprises, it is very important [ 4 ]. Consequently, it’s necessary to study and develop an appropriate approach to assess financial risk of companies.

In the field of financial risk assessment, although some remarkable achievements have been made, there are still three shortcomings. First, the quantitative and qualitative analysis which adopts the combination of fuzzy theory and data analysis have not been used in financial risk assessment. Second, the historical data over several years have not been considered. Third, the information is used partially in financial risk assessment. The financial risk can be assessed by historical financial information of company, credit rating agency and decision makers. Hence, fuzziness and accuracy are existed simultaneously in the assessment information. The existing approaches only considered the data information of company, which may lead to information loss [ 5 ]. Therefore, in order to overcome these shortcomings, a novel financial risk assessment model for companies needs to be studied. To sum up, the motivations of this article are as follows:

The assessment of financial risk involves quantitative and qualitative indexes. Some scholars have employed objective financial indexes to assess financial risk quantitatively [ 6 – 9 ]. Subjective indexes such as controlling system of financial risk have not been utilized in extant study. Thus, the subjective indexes combined with objective indexes are employed in the index system of the proposed financial risk assessment model.
With respect to the partial use of information in assessment [ 10 ], it is appropriate to apply historical financial information and fuzzy theory to describe assessment information about financial risk for companies. The assessment information from historical financial information of company mainly involves crisp numbers. Wang et al. [ 11 ] presented that triangular fuzzy numbers can reflect the uncertainty of objective things and the fuzziness of human thought. Thus, it can be used to improve the objectivity and accuracy of the description of credit rating. Zhang et al. [ 12 ] presented that a neutrosophic set is an effective tool for reflecting the fuzziness in text evaluation because the evaluation information from decision makers is text information that represents sentiment values, and every sentiment value has not only a certain degree of truth, but also a falsity degree and an indeterminacy degree [ 13 ]. Thus, it needs to transform sentiment values into neutrosophic numbers with positive, medium, and passive values. For example, when asked to assess whether controlling system of financial risk would be “good”, from the sentiment value of a decision maker, we may deduce that the membership degree of truth is 0.8, the membership degree of indeterminacy is 0.1, and the membership degree of falsity is 0.1. Therefore, assessment information, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers, needs to be taken into account in the financial risk assessment model.
In order to deal with the ranking order of companies according to their financial risk based on historical data and heterogeneous MCDM, a systematic approach need to be employed in the proposed model. Shih et al. [ 14 ] pointed out that TOPSIS is a practical and useful technique for the ranking and selection of a number of externally determined alternatives through distance measures, and it has been applied in multiple-criteria decision-making (MCDM) [ 15 ]. Lourenzutti et al. [ 16 ] and Li et al. [ 17 ] proposed heterogeneous TOPSIS for multi-criteria decision making method. Therefore, the TOPSIS method is used to obtain the ranking order of companies in the financial risk assessment model.

In this paper, a novel financial risk assessment model is developed to help managers assess company’s financial risk by utilizing TOPSIS according to above discussion, which is based on MCDM and heterogeneous information including qualitative data and non-qualitative data. The contributions of this paper are concluded as three aspects. The first one is the establishment of an improved financial risk index system with comprehensive consideration of subjective and objective indexes, which combines fuzzy theory with quantitative data analysis. The second is the consideration of the impact of historical financial position on current financial risk analysis by aggregating historical data over several years which assesses financial risk accurately. The third is the application of heterogeneous information obtained from historical financial information, credit rating and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. The final contribution is that the TOPSIS method for heterogeneous multi-criteria decision-making is employed to get the ranking order of companies based on their financial risk, which can help manager to assess financial risk.

The rest of the paper is organized as follows. In Section 2, previous researches about financial risk assessment are introduced briefly. A brief introduction about research methodology including TOPSIS method, exponential smoothing method, neutrosophic number and triangular fuzzy number is presented in Section 3. Subsequently, a novel financial risk assessment model is developed based on heterogeneous MCDM in Section 4. In Section 5, an empirical study is presented concretely, and the effectiveness of the proposed method is verified by a comparative analysis. Finally, Section 6 summarizes the paper and proposes some directions for future research.

Literature review

Among the studies of financial risk of listing companies, numerous scholars have made great contributions. Some scholars evaluated financial risk by utilizing quantitative analysis. For example, the utility functions that classify the considered alternatives into predefined risk classes were developed in the study by Doumpos and Zopounidis [ 6 ]. It proposed the multi-group hierarchical discrimination method (M.H.DIS) that classified countries into four groups like c 1 , c 2 , c 3 , c 4 from good to bad. Lee et al. [ 7 ] evaluated financial positions of shipping companies using entropy and grey relation analysis. Stochastic frontier analysis (SFA) was illustrated in the study proposed by Wang et al. [ 18 ], which calculated efficiency estimation of risk indicators with determined influence factors of risk assessment indicators computed by panel frontier model. Financial ratios of capital structure risk, liquidity risk and insolvency risk studied by balance sheet, statement of income, expenses and cash flow of dozens of businesses were described to assess financial risk in Kociu et al. [ 8 ]. Furthermore, many fuzzy theory and MCDM method have been applied in business [ 19 ] and risk assessment. Sabokbar et al. [ 20 ], Mardani et al. [ 21 ] and Ribeiro et al. [ 22 ] utilized fuzzy set to assess risk. Kochanek and Tynan [ 23 ] adopted linguistic label to present uncertainty of risk. Chang et al. [ 24 ] adopted the Fuzzy Analytic Network Process (FANP) method to assess ERP implementation risks. Gonçalves et al. [ 25 ] employed the Interactive Multiple Criteria Decision Making (TODIM) approach to analyze credit risk. Kou et al. [ 26 ] presented an MCDM to analyze financial risk. Shaverdi et al. [ 27 ] ranked companies by using fuzzy AHP and fuzzy TOPSIS comparatively to evaluate financial performance. In conclusion, in the extant researches, fuzzy theory and quantitative analysis have not been employed simultaneously to assess financial risk.

With respect to financial risk index system, numerous assessment indexes have been researched. Cui et al. [ 28 ] established a financial evaluation index system of Chinese listing companies with four financial risk criteria and corresponding objective numerical indexes, including financing risk, investment risk, income distribution risk and cash flow at risk. The example for country risk assessment conducted by Doumpos and Zopounidis [ 6 ] used twelve economic indicators like import and export volume growth and GNP growth as risk evaluation indexes. Jurczyk et al. [ 29 ] quantified systemic risks by numerous stock indexes, and without non-numerical indexes. Wang and Liu [ 30 ] evaluated the real estate investment risk by qualitatively analyzing financing risk and investment site risk, etc. Gonçalves et al. [ 25 ] used fuzzy theory to analyze credit risk. In view of the above-mentioned review, a financial risk index system including quantitative and qualitative index needs to be researched.

As studied in numerous risk evaluation researches, existing approaches used partial information. Wang and Liu [ 30 ] utilized crisp numbers to evaluate financing risk qualitatively. Kiliçman and Sivalingam [ 31 ] used triangular fuzzy numbers to represent return rates, etc. Kochanek and Tynan [ 23 ] adopted linguistic label to present uncertainty of risk. And every linguistic value has not only a certain degree of truth, but also a falsity degree and an indeterminacy degree; it needs to transform sentiment values into neutrosophic numbers with positive, medium, and passive values. None of the previous risk evaluation research considered these information types mentioned above simultaneously. Thus, in this paper, the evaluation information obtained from historical financial information, credit rating agency and decision makers includes crisp numbers, triangular fuzzy numbers and neutrosophic numbers, which need to be considered in the assessment progress.

In current methods of subjective weight, numerous scholars have made notable contributions. Wang et al. [ 32 ] used criteria priorities to compute criteria weights. Zhao et al. [ 33 ] calculated weight by using a probabilistic method. Mangla et al. [ 34 ] used fuzzy analytic hierarchy process (AHP) to get subjective weight or provided by decision-makers. Rezaei [ 35 , 36 ] utilized the (best-worst method) BWM method to calculate subjective weight with lesser comparison times and information loss compared with AHP. Tian et al. [ 37 ] presented that the BWM method can require fewer pairwise comparisons than does fuzzy AHP but obtain more highly reliable weights. Therefore, the subjective weight of financial criteria in the proposed model is calculated by BWM.

In conclusion, previous researches about financial risk evaluation should be modified in future study. In order to settle these issues based on the above discussion, we (1) establish a novel financial risk index system combining fuzzy theory with quantitative analysis, (2) consider various types of information including crisp numbers, triangular fuzzy numbers and neutrosophic numbers, (3) utilize BWM method to calculate the subjective weight of financial risk criteria, (4) utilize TOPSIS method to manage heterogeneous information and obtain the ranking order of companies according to their financial risk.

Research methodology

In this section, the specific and processes of TOPSIS method, the concepts, definitions and algorithms of neutrosophic set and triangular fuzzy number are introduced.

TOPSIS method

The TOPSIS method, an MCDM method, was proposed by Hwang and Yoon in 1981 [ 38 ]. It provides the best alternative which is as close as possible to the best solution. Dozens of scholars have applied TOPSIS to solve simple or complex problems in different areas [ 39 ], e.g., weapon selection, alternative evaluation and risk assessment [ 40 – 42 ]. The procedure of the TOPSIS method can be described as shown in the following steps [ 16 ]:

Step 1. Define and normalize the decision matrix R = ( r ij ).
Step 2. Aggregate the weights to the decision matrix by making v ij = w j r ij .

Step 6. Rank the alternatives according to CC i . The bigger CC i is, the better alternative A i will be.

Exponential smoothing method

If the time series is stable comparatively, the selection of ∂ is a lower value as (0.1–0.3); on the contrary, it may bigger like (0.6–0.8). Different ∂ value is selected subjectively, count mean absolute error (MAE) with different ∂ like formula ( 4 ), the ∂ value is best which minimize the error. For s i is predicted value, x i is true value, the solution is depicted as formula ( 5 ).

Let S 0 is the original value; formula ( 6 ) can describe the definite way of S 0 . When t < 20, in general, S 0 is the mean value of three years’ true data initially, in this article we elicit two stages.

Neutrosophic set theory

Definition 1 . [ 43 ] Let X be a space of points (objects), with a generic element in X denoted by x . Then an NS A in X is characterized by three membership functions, including a truth-membership function T A ( x ), indeterminacy-membership function I A ( x ), and falsity-membership function F A ( x ), and is defined as‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ A = {˂ x , T A ( x ), I A ( x ), F A ( x ) ˃ | x ∈ X }, where T A ( x ), I A ( x ), and F A ( x ) are real standard or nonstandard subsets of ]-0, 1+[, i.e. T A ( x ): X →]-0, 1+[, I A ( x ): X →]-0, 1+[, F A ( x ): X →]-0, 1+[. The sum of T A ( x ), I A (x) , and F A ( x ) is unrestricted, and -0 ≤ T A ( x )+ I A ( x )+ F A ( x ) ≤ 3+.

Definition 4 . According to the study by Majumdar and Samanta [ 45 ], single-valued neutrosophic set A = { ˂x , T A ( x ), I A ( x ), F A ( x )˃|| x ∈ X }, an entropy on neutrosophic set A is computed as formula ( 8 ).

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https://doi.org/10.1371/journal.pone.0208166.t001

Triangular fuzzy number

Financial risk assessment model

The proposed financial risk assessment model consists five parts, as depicted in S1 Fig . The first part is the establishment of financial risk index system, the important criteria as well as sub-criteria are determined from literature reviews and experts. The second part is the calculation of subjective criteria weight by using BWM method. Moreover, the evaluation information from historical financial information of companies, credit rating agency and decision makers can be obtained. And the evaluation matrix is established including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. In addition, with the computation of objective entropy weight of financial index, the comprehensive index weight can be calculated by multiplying entropy weight and subjective criteria weight. Finally, the ranking order of companies according to financial risk is derived utilizing TOPSIS method based on heterogeneous MCDM. The specific details of this novel model will be described in the rest of this section.

The establishment of the financial risk index system

The assessment of financial risk involves financial condition of company, credit rating and evaluation of decision maker, and it is very complicated. Based on the discussion in the literature review, financial risk can be mainly evaluated from four criteria, which are financing risk, investment risk, income distribution risk and cash flow at risk, denoted as A i ( i = 1, 2, 3, 4) respectively. Moreover, every criterion can be divided into multiple sub-criteria (i.e. financial indexes). Based on the definition of financial risk and previous studies analyzed in the literature review, fuzzy information is taken into account in the financial risk assessment model. Generally, the credit rating indicates the capacity of financing; the contractual capacity of partners represents the fund risk; and the management system of financial risk indicates the risk management ability of company. Therefore, the credit rating index is added to financing risk criterion, and the contractual capacity of partner index and the controlling system of financial risk index are added to investment risk criterion. Hence, an improved risk index system is established as shown in S2 Fig . Because of the complexity of financial condition and the uncertainty of information, the assessment values of financial risk indexes can be divided into multiple types. Therefore, heterogeneous information including crisp numbers, triangular fuzzy numbers and neutrosophic numbers exists in this proposed financial risk index system. The financial risk index system including the definition of the financial risk indexes is established in Table 2 .

https://doi.org/10.1371/journal.pone.0208166.t002

The estimation of criteria weights with BWM

According to the discussion in the literature review, a more efficient method (i.e. BWM method) is used to calculate the subjective weight in this section. The detailed steps of BWM to compute the weights of the four financial risk criteria are summarized as follows [ 35 ].

Step 1. Determine a set of decision criteria. In this step, we consider the criteria { c 1 , c 2 , …, c n } that should be used to arrive at a decision.
Step 2. Determine the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criterion.
Step 3. Determine the preference of the best criterion over all the other criteria, using a number between 1 and 9. The resulting best-to-others vector would be: A B = ( a B 1 , a B 2 , …, a Bn ) where a Bj indicates the preference of the best criterion B over criterion j . It is clear that a BB = 1.
Step 4. Determine the preference of all the criteria over the worst criterion, using a number between 1 and 9. The resulting others-to-worst vector would be: A W = ( a 1 W , a 2 W , …, a nW ) where a jW indicates the preference of the criterion j over the worst criterion W. It is clear that a WW = 1.

Table 3 shows the maximum values of ξ (consistency index) for different values of a BW [ 35 ].

https://doi.org/10.1371/journal.pone.0208166.t003

If consistency ratio ≤ 0.1, it implies a very good consistency which is acceptable. Otherwise we can revise a Bj and a jW to make the solution (more) consistent.

The evaluation matrix of financial risk

The financial risk evaluation of company involves qualitative and quantitative indicators such as financial status, credit rating, decision makers, etc. Different decision makers may make different assessment based on their distinct knowledge and different judgment standards. Therefore, in this section, the evaluation information determined by historical financial information, credit rating and decision makers is heterogeneous, including crisp numbers, interval numbers and linguistic labels. Specifically, the crisp numbers are the evaluation values of asset liability ratio, current ratio, quick ratio, number of times interest earned, main business cost ratio, operating expense ratio, main business revenue growth rate, total asset growth rate, net assets yield, net profit growth rate, shareholder’s equity growth rate, equity ratio, retention ratio, cash debt coverage ratio, cash ratio and security surplus cash multiples; the interval numbers are the evaluations of credit rating; the linguistic labels are the evaluation values of contractual capacity of partner and controlling system of financial risk. Because of the uncertainty information, the interval numbers provided by credit rating agency can be transformed into triangular fuzzy numbers, and the linguistic labels obtained by decision makers can be transformed into neutrosophic numbers. Therefore we can get the evaluation matrix R = ( r ij ) with crisp numbers, triangular fuzzy numbers and neutrosophic numbers.

Step 1. Aggregate financial data over several years.

The evaluation values of financial risk indexes such as asset liability ratio, current ratio, quick ratio, number of times interest earned, main business cost ratio, operating expense ratio, main business revenue growth rate, total asset growth rate, net assets yield, net profit growth rate, shareholder’s equity growth rate, equity ratio, retention ratio, cash debt coverage ratio, cash ratio and security surplus cash multiples are obtained by financial data over the years of company. In consideration of that the financial risk of a company influenced by historical financial condition, the historical data and current data which reflect development trend should be taken into account. In this section, according to the method introduced in Section 3.2, we aggregate financial data over the years by using exponential smoothing method through EViews software to get scientific and reasonable evaluation of financial risk. Then, the evaluation matrix of R = ( r ij ) based on a 11 , a 12 , a 13 , a 14 , a 21 , a 22 , a 23 , a 24 , a 25 , a 26 , a 31 , a 32 , a 33 , a 41 , a 42 , a 43 is computed.

Step 2. Obtain the depiction of credit rating.

The evaluation information of credit rating is determined by credit rating agency. Because of the uncertainty and fuzziness of credit rating, it should be transformed into triangular fuzzy numbers. According to the method mentioned in Section 3.4, the relative descriptions and function of crediting rating can be conducted as seen in Table 4 and S3 Fig . Therefore, the evaluation matrix of R = ( r ij ) based on a 15 is calculated.

https://doi.org/10.1371/journal.pone.0208166.t004

Step 3. Evaluate contractual capacity of partner and financial risk control system.

The fundamental thesis of neutrosophy presented in the study by Rivieccio [ 13 ] is that every idea has not only a certain degree of truth, as is generally assumed in many-valued logic contexts, but also a falsity degree and an indeterminacy degree that have to be considered independently from each other. As mentioned in [ 51 ] and [ 52 ], they can deal with consistent, hesitant, and inconsistent information at the same time, and benefit the management of the evaluation information mentioned in [ 53 ]. The evaluation values of contractual capacity of partner and financial risk control system are linguistic values determined by decision makers, so it must first be transformed into neutrosophic numbers with positive, medium and passive values [ 54 ]. In this section, we transform the linguistic evaluation information of contractual capacity of partner and financial risk control system according to the symmetric linguistic evaluation scale into neutrosophic numbers with truth, indeterminacy and falsity. Then, we can aggregate the neutrosophic numbers using single valued neutrosophic weighted averaging (SVNWA) aggregation operator described by formula ( 10 ). Therefore, the evaluation matrix of R = ( r ij ) based on a 27 and a 28 is calculated eventually, where Ψ = ( Ψ 1 , Ψ 2 , …, Ψ p ) T is the weight vector of decision makers corresponding to these indexes.

The calculation of index weight

In this section, the principle of the combination between subjectivity and objectivity is applied in the calculation of the index weight. First, we compute the entropy weights of financial risk indexes. Then, we can get comprehensive index weight which combines entropy weight of index with subjective weight of financial risk criteria.

Step 1. Determine the entropy weight of data index.

Step 2. Compute the entropy weight of credit rating index.

Step 3. Compute the entropy weight of contractual capacity of partner and controlling system of financial risk.

The formula ( 8 ) and formula ( 9 ) in Section 3.3 is used to calculate entropy weight based on evaluation matrix. Hence, the entropy weight E 27 and E 28 can be computed.

Step 4. Calculate financial risk index weight.

The ranking order of companies according to financial risk by using TOPSIS

Suppose there are n alternatives x j ( j = 1, …, n ), thus the sets of alternatives (i.e. companies) can be denoted by X = { x 1 , x 2 , …, x n }. The TOPSIS method based on heterogeneous MCDM is used to solve the ranking order of companies according to financial risk. Because of the existence of heterogeneous evaluation information of financial risk, the criteria set A = ( A 1 , A 2 , A 3 , A 4 ) can be divided into three subsets O i ( i = 1,2,3), where O i are sets of criteria whose values are crisp numbers, triangular fuzzy numbers and neutrosophic numbers. The procedure of this method is summarized [ 17 ] as follows:

Step 1. Normalize evaluation matrix R .

The normalized evaluation value has already been solved at the time of entropy weight of risk index calculating. So we can get the normalized evaluation matrix R = ( r ij ) directly.

Step 2. Define the positive ideal solution (PIS) and the negative ideal solution (NIS) for each index.

Step 3. Compute the separation measures between each company and the PIS as well as the NIS.

Step 4. Calculate relative closeness degree of companies to the PIS.

Step 5. Rank the companies according to τ i .

The bigger τ i is, the better company x j will be.

Empirical study

Background and data collection.

At present, biological medicine is one of the most important emerging industries in China. And the financial risk assessment is conducive to the risk control and healthy development of pharmaceutical companies. In this section, we selected three companies from Chinese A-share pharmaceutical manufacturing listed companies randomly and conducted an empirical study in order to verify the effectiveness of the proposed model. The stock codes of the three companies are 600196, 600664 and 600085, denoted by A , B , C respectively.

The original financial data and related information can be collected from the website http://www.qianzhan.com or credit rating agency, and the subjective information can be obtained from supervisors of company through questionnaire surveys. In the study, supervisors from the three companies were invited to participate in a questionnaire to obtain linguistic assessment. The raw data on the operation and technology of the three companies from 2010 to 2016, the original evaluation information of credit rating, contractual capacity of partner and financial risk control system were collected, as supporting information; see S1 Raw Data .

According to the financial risk assessment model proposed in Section 4, we compute financial risk criteria weight and get the evaluation matrix through historical financial information of company, credit rating agency and decision makers. Then, the synthetic weight of financial risk index is computed by multiplying criteria weight with the entropy weight of risk index. Finally, the ranking order of the three companies according to their financial risk can be calculated using TOPSIS method based on heterogeneous MCDM. In addition, the effectiveness and reliability of the proposed financial risk assessment model are verified by comparative analysis and sensitivity analysis.

Financial risk criteria weight

https://doi.org/10.1371/journal.pone.0208166.t005

https://doi.org/10.1371/journal.pone.0208166.t006

From formula ( 17 ) and ( 18 ) illustrated in Rezaei’s study [ 36 ], we can get w 1 * = 0.3809, w 2 * = 0.3334, w 3 * = 0.0476, w 4 * = 0.2381, and ξ L * = 0. Based on the proposed method, ξ L * indicates consistency index directly without extra computation. As ξ L * = 0, we can obtain complete consistency. Thus, the subjective criteria weight is w * = (0.3809, 0.3334, 0.0476, 0.2381).

Evaluation matrix

According to the evaluation method proposed in Section 4.3, the evaluation matrix determined by historical financial information of company, credit rating agency and decision makers is obtained, and the information is heterogeneous, including crisp numbers, interval numbers and linguistic labels.

Firstly, the evaluation matrix of numerical index is calculated by aggregating the historical financial data of 2010–2016, according to the exponential smoothing forecasting method introduced in Section 4.3. And the final evaluation values of financial risk on quantitative indices are listed in Table 7 . The data visualization is depicted as shown in S4 – S7 Figs.

https://doi.org/10.1371/journal.pone.0208166.t007

Secondly, we compute the evaluation value of credit rating.

According to the evaluation method of credit rating introduced in Section 4.3, we translate the evaluation information of credit rating into triangular fuzzy numbers transformed by membership function, as shown in Table 8 .

https://doi.org/10.1371/journal.pone.0208166.t008

Thirdly, we get the assessment value of contractual capacity of partner and financial risk control system.

Considering the deficiency of practical information and the efficiency of neutrosophic set mentioned in [ 55 ], we transform decision makers’ linguistic labels into neutrosophic numbers by using the method introduced in Section 4.3, and aggregate the neutrosophic numbers using SVNWA operator described in formula ( 10 ). The description of evaluation values is shown in Table 9 .

https://doi.org/10.1371/journal.pone.0208166.t009

Therefore, the evaluation matrix R = ( r ij ) can be determined directly, as shown in Tables 7 , 8 and 9 .

Weight of financial risk index

The weight of the indexes can be calculated by using the entropy weight method introduced in Section 4.4. According to the normalization method, the normalized evaluation matrix is described in Table 10 .

https://doi.org/10.1371/journal.pone.0208166.t010

The entropy weight is computed by formula ( 20 ), formula ( 22 ) and formula ( 9 ), the result is obtained as E 1 = ( E 11 , E 12 , E 13 , E 14 , E 15 ) = (0.196258983, 0.193583809, 0.265492452, 0.138674702, 0.20599005); E 2 = ( E 21 , E 22 , E 23 , E 24 , E 25 , E 26 , E 27 , E 28 ) = (0.085170054, 0.088203688, 0.085201275, 0.086354441, 0.227198836, 0.08753941, 0.177338538, 0.162993756); E 3 = ( E 31 , E 32 , E 33 ) = (0.296810103, 0.261347451, 0.441842446); E 4 = ( E 41 , E 42 , E 43 ) = (0.525714023, 0.240714367, 0.233571609). The eventual weights of financial risk indexes are calculated by synthesizing subjective and objective weights, which is calculated by formula ( 24 ). The result is described as E 1 = ( E 11 , E 12 , E 13 , E 14 , E 15 ) = (0.074755047, 0.073736073, 0.101126075, 0.052821194, 0.078461612); E 2 = ( E 21 , E 22 , E 23 , E 24 , E 25 , E 26 , E 27 , E 28 ) = (0.028395696, 0.02940711, 0.028406105, 0.028790571, 0.075748092, 0.029185639, 0.059124669, 0.054342118); E 3 = ( E 31 , E 32 , E 33 ) = (0.014128161, 0.012440139, 0.0210317); E 4 = ( E 41 , E 42 , E 43 ) = (0.125172509, 0.057314091, 0.0556134). Thus, according to the description in Section 4.4, the weight of the attributes and indexes are obtained, as in Table 11 .

https://doi.org/10.1371/journal.pone.0208166.t011

Ordering result of TOPSIS method

It is easy to conclude the following ranking order of the three companies: x 3 ≻ x 1 ≻ x 2 . Therefore, the company with the lowest financial risk is x 3 , i.e. 600085.

Comparison analysis and discussion

As described in Section 4, the proposed model can be used to assess the financial risk of company considering historical financial information, credit rating, the conditions of partners and risk control, and heterogeneous information. To validate that the proposed model can effectively and reliably identify which company has the lowest financial risk, a comparative analysis is made with heterogeneous TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) [ 56 ] and heterogeneous VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) [ 57 ] in the empirical study. Table 12 shows the ranking order of the three companies as obtained using these methods. Based on Table 12 , the ranking order calculated by the proposed hybrid assessment model are the same as those computed by heterogeneous TODIM method and heterogeneous VIKOR method, so the effectiveness of the model is proved. Compared with other assessment methods of financial risk for companies, the advantages of the proposed model in the paper can be generalized as the following:

https://doi.org/10.1371/journal.pone.0208166.t012

The proposed model considers both subjective and objective indexes in the financial risk index system, and combines fuzzy theory with quantitative data analysis. Thus effectively ensuring that the financial risk assessment for companies can be more in line with reality.
The evaluation information is evaluated from historical financial data of the company, credit rating agency and decision-makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. So that the financial risk assessment model is more accurate and reliable.
In the proposed model, TOPSIS method is used to determine the ranking order of financial risk of the companies, which is more flexible and simple in solving MGCDM problem [ 16 ]. Therefore, the proposed financial risk assessment model for companies can obtain the best company with the least financial risk reliably.

Sensitivity analysis

In order to monitor the robustness of the financial risk assessment model for companies, the sensitivity analysis is conducted according to the change of the weight coefficient ∂ and the evaluator’s attitude λ . The corresponding ranking order of the three companies can be obtained when the value of ∂ is changed, which are listed in Table 13 . And the influence on the proposed financial risk assessment model with different values of ∂ listed in Table 13 can be figured in S8 Fig .

https://doi.org/10.1371/journal.pone.0208166.t013

According to Table 13 and S8 Fig , it is clear that the ranking order calculated are the same as in the above experimental example, when the value of ∂ is changed from 0 to 1. This means that the ranking order is insensitive to the changes of parameter ∂ . That is to say, despite the assessment process involving different values of the weighting coefficient ∂ , the final ranking order is consistent.

When the evaluator’s attitude λ is changed, the evaluation values of credit rating are transformed into triangular fuzzy number, the influence of values λ on the proposed model is shown in Table 14 and S9 Fig . Obviously, the changes of the evaluator’s attitude λ do not influence the ranking order of the three companies, and the results are the same as that of the above experimental example.

https://doi.org/10.1371/journal.pone.0208166.t014

According to the visualized results shown in S8 and S9 Figs, the final ranking order is consistent in the experimental example of the sensitivity analysis. In other words, although the different selection of weighting coefficient ∂ and evaluator’s attitude λ , x 3 is the best company with the least financial risk. The two sensitivity analysis results indicate that the ranking order of the proposed model is insensitive to the values of ∂ and λ in the example. Therefore, to a certain degree, the robustness of the proposed model is verified.

Conclusion and future research

In this paper, a multi-level fuzzy comprehensive financial risk assessment model for companies has been developed. In order to assess the company’s financial risk accurately, subjective and objective indexes have been utilized simultaneously in the financial risk index system, which combines fuzzy theory with quantitative data analysis. Moreover, heterogeneous information obtained from historical financial information of company, credit rating agency and the decision makers’ estimation, such as crisp numbers, triangular fuzzy numbers and neutrosophic numbers, has been employed to decrease the information loss. In addition, TOPSIS based on heterogeneous MCDM has been employed to obtain the ranking order of companies according to their financial risk.

The proposed model has been used in empirically study to assess the financial risks of the listed pharmaceutical manufacturing companies of Chinese A-share. Moreover, the comparison results with the two other methods show that the proposed model is effective and reliable. In addition, the sensitivity analysis has been carried out and the results verify the robustness of the proposed model.

In summary, this paper not only contributes to the development of theory, but also contributes to practical application. First, the proposed model uses both quantitative historical data analysis and fuzzy theory; it is helpful to enrich the contents of risk research. Second, the proposed model will optimize the financial risk assessment method for companies. Third, the proposed model can be applied to provide rational support for decision makers in the process of financial risk management.

There are several possible directions of further research. First, more information types of financial risk assessment could be considered in the proposed model in order to adapt to the dynamic financial environment in future research. Second, the proposed model can obtain the ranking order of companies according to financial risk by investigating MULTIMOORA (multi–objective optimization by ratio analysis plus the full multiplicative form) method because of its simple computation. Finally, the proposed model can be adopted for risk assessment for some other fields in future study.

Supporting information

S1 fig. summary of the process of the proposed model..

https://doi.org/10.1371/journal.pone.0208166.s001

S2 Fig. Financial risk index system.

https://doi.org/10.1371/journal.pone.0208166.s002

S3 Fig. The membership function of credit rating.

https://doi.org/10.1371/journal.pone.0208166.s003

S4 Fig. Financing risk.

https://doi.org/10.1371/journal.pone.0208166.s004

S5 Fig. Investment risk.

https://doi.org/10.1371/journal.pone.0208166.s005

S6 Fig. Income distribution risk.

https://doi.org/10.1371/journal.pone.0208166.s006

S7 Fig. Cash flow at risk.

https://doi.org/10.1371/journal.pone.0208166.s007

S8 Fig. The result of the sensitivity analysis with different ∂ .

https://doi.org/10.1371/journal.pone.0208166.s008

S9 Fig. The radar plot displaying the result of the sensitivity analysis.

https://doi.org/10.1371/journal.pone.0208166.s009

S1 Raw Data.

https://doi.org/10.1371/journal.pone.0208166.s010

Acknowledgments

This work was supported by a Key Project of Hunan Social Science Achievement Evaluation Committee (XSP2016040508, XSP18ZD1002), the human philosophy social science fund project (15JD21).

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Published: 03 September 2022

A literature review of risk, regulation, and profitability of banks using a scientometric study

Shailesh Rastogi 1 ,
Arpita Sharma 1 ,
Geetanjali Pinto 2 &
Venkata Mrudula Bhimavarapu ORCID: orcid.org/0000-0002-9757-1904 1 , 3

Future Business Journal volume 8 , Article number: 28 ( 2022 ) Cite this article

11k Accesses

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Metrics details

This study presents a systematic literature review of regulation, profitability, and risk in the banking industry and explores the relationship between them. It proposes a policy initiative using a model that offers guidelines to establish the right mix among these variables. This is a systematic literature review study. Firstly, the necessary data are extracted using the relevant keywords from the Scopus database. The initial search results are then narrowed down, and the refined results are stored in a file. This file is finally used for data analysis. Data analysis is done using scientometrics tools, such as Table2net and Sciences cape software, and Gephi to conduct network, citation analysis, and page rank analysis. Additionally, content analysis of the relevant literature is done to construct a theoretical framework. The study identifies the prominent authors, keywords, and journals that researchers can use to understand the publication pattern in banking and the link between bank regulation, performance, and risk. It also finds that concentration banking, market power, large banks, and less competition significantly affect banks’ financial stability, profitability, and risk. Ownership structure and its impact on the performance of banks need to be investigated but have been inadequately explored in this study. This is an organized literature review exploring the relationship between regulation and bank performance. The limitations of the regulations and the importance of concentration banking are part of the findings.

Introduction

Globally, banks are under extreme pressure to enhance their performance and risk management. The financial industry still recalls the ignoble 2008 World Financial Crisis (WFC) as the worst economic disaster after the Great Depression of 1929. The regulatory mechanism before 2008 (mainly Basel II) was strongly criticized for its failure to address banks’ risks [ 47 , 87 ]. Thus, it is essential to investigate the regulation of banks [ 75 ]. This study systematically reviews the relevant literature on banks’ performance and risk management and proposes a probable solution.

Issues of performance and risk management of banks

Banks have always been hailed as engines of economic growth and have been the axis of the development of financial systems [ 70 , 85 ]. A vital parameter of a bank’s financial health is the volume of its non-performing assets (NPAs) on its balance sheet. NPAs are advances that delay in payment of interest or principal beyond a few quarters [ 108 , 118 ]. According to Ghosh [ 51 ], NPAs negatively affect the liquidity and profitability of banks, thus affecting credit growth and leading to financial instability in the economy. Hence, healthy banks translate into a healthy economy.

Despite regulations, such as high capital buffers and liquidity ratio requirements, during the second decade of the twenty-first century, the Indian banking sector still witnessed a substantial increase in NPAs. A recent report by the Indian central bank indicates that the gross NPA ratio reached an all-time peak of 11% in March 2018 and 12.2% in March 2019 [ 49 ]. Basel II has been criticized for several reasons [ 98 ]. Schwerter [ 116 ] and Pakravan [ 98 ] highlighted the systemic risk and gaps in Basel II, which could not address the systemic risk of WFC 2008. Basel III was designed to close the gaps in Basel II. However, Schwerter [ 116 ] criticized Basel III and suggested that more focus should have been on active risk management practices to avoid any impending financial crisis. Basel III was proposed to solve these issues, but it could not [ 3 , 116 ]. Samitas and Polyzos [ 113 ] found that Basel III had made banking challenging since it had reduced liquidity and failed to shield the contagion effect. Therefore, exploring some solutions to establish the right balance between regulation, performance, and risk management of banks is vital.

Keeley [ 67 ] introduced the idea of a balance among banks’ profitability, regulation, and NPA (risk-taking). This study presents the balancing act of profitability, regulation, and NPA (risk-taking) of banks as a probable solution to the issues of bank performance and risk management and calls it a triad . Figure 1 illustrates the concept of a triad. Several authors have discussed the triad in parts [ 32 , 96 , 110 , 112 ]. Triad was empirically tested in different countries by Agoraki et al. [ 1 ]. Though the idea of a triad is quite old, it is relevant in the current scenario. The spirit of the triad strongly and collectively admonishes the Basel Accord and exhibits new and exhaustive measures to take up and solve the issue of performance and risk management in banks [ 16 , 98 ]. The 2008 WFC may have caused an imbalance among profitability, regulation, and risk-taking of banks [ 57 ]. Less regulation , more competition (less profitability ), and incentive to take the risk were the cornerstones of the 2008 WFC [ 56 ]. Achieving a balance among the three elements of a triad is a real challenge for banks’ performance and risk management, which this study addresses.

Triad of Profitability, regulation, and NPA (risk-taking). Note The triad [ 131 ] of profitability, regulation, and NPA (risk-taking) is shown in Fig. 1

Triki et al. [ 130 ] revealed that a bank’s performance is a trade-off between the elements of the triad. Reduction in competition increases the profitability of banks. However, in the long run, reduction in competition leads to either the success or failure of banks. Flexible but well-expressed regulation and less competition add value to a bank’s performance. The current review paper is an attempt to explore the literature on this triad of bank performance, regulation, and risk management. This paper has the following objectives:

To systematically explore the existing literature on the triad: performance, regulation, and risk management of banks; and

To propose a model for effective bank performance and risk management of banks.

Literature is replete with discussion across the world on the triad. However, there is a lack of acceptance of the triad as a solution to the woes of bank performance and risk management. Therefore, the findings of the current papers significantly contribute to this regard. This paper collates all the previous studies on the triad systematically and presents a curated view to facilitate the policy makers and stakeholders to make more informed decisions on the issue of bank performance and risk management. This paper also contributes significantly by proposing a DBS (differential banking system) model to solve the problem of banks (Fig. 7 ). This paper examines studies worldwide and therefore ensures the wider applicability of its findings. Applicability of the DBS model is not only limited to one nation but can also be implemented worldwide. To the best of the authors’ knowledge, this is the first study to systematically evaluate the publication pattern in banking using a blend of scientometrics analysis tools, network analysis tools, and content analysis to understand the link between bank regulation, performance, and risk.

This paper is divided into five sections. “ Data and research methods ” section discusses the research methodology used for the study. The data analysis for this study is presented in two parts. “ Bibliometric and network analysis ” section presents the results obtained using bibliometric and network analysis tools, followed by “ Content Analysis ” section, which presents the content analysis of the selected literature. “ Discussion of the findings ” section discusses the results and explains the study’s conclusion, followed by limitations and scope for further research.

Data and research methods

A literature review is a systematic, reproducible, and explicit way of identifying, evaluating, and synthesizing relevant research produced and published by researchers [ 50 , 100 ]. Analyzing existing literature helps researchers generate new themes and ideas to justify the contribution made to literature. The knowledge obtained through evidence-based research also improves decision-making leading to better practical implementation in the real corporate world [ 100 , 129 ].

As Kumar et al. [ 77 , 78 ] and Rowley and Slack [ 111 ] recommended conducting an SLR, this study also employs a three-step approach to understand the publication pattern in the banking area and establish a link between bank performance, regulation, and risk.

Determining the appropriate keywords for exploring the data

Many databases such as Google Scholar, Web of Science, and Scopus are available to extract the relevant data. The quality of a publication is associated with listing a journal in a database. Scopus is a quality database as it has a wider coverage of data [ 100 , 137 ]. Hence, this study uses the Scopus database to extract the relevant data.

For conducting an SLR, there is a need to determine the most appropriate keywords to be used in the database search engine [ 26 ]. Since this study seeks to explore a link between regulation, performance, and risk management of banks, the keywords used were “risk,” “regulation,” “profitability,” “bank,” and “banking.”

Initial search results and limiting criteria

Using the keywords identified in step 1, the search for relevant literature was conducted in December 2020 in the Scopus database. This resulted in the search of 4525 documents from inception till December 2020. Further, we limited our search to include “article” publications only and included subject areas: “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Social sciences” only. This resulted in a final search result of 3457 articles. These results were stored in a.csv file which is then used as an input to conduct the SLR.

Data analysis tools and techniques

This study uses bibliometric and network analysis tools to understand the publication pattern in the area of research [ 13 , 48 , 100 , 122 , 129 , 134 ]. Some sub-analyses of network analysis are keyword word, author, citation, and page rank analysis. Author analysis explains the author’s contribution to literature or research collaboration, national and international [ 59 , 99 ]. Citation analysis focuses on many researchers’ most cited research articles [ 100 , 102 , 131 ].

The.csv file consists of all bibliometric data for 3457 articles. Gephi and other scientometrics tools, such as Table2net and ScienceScape software, were used for the network analysis. This.csv file is directly used as an input for this software to obtain network diagrams for better data visualization [ 77 ]. To ensure the study’s quality, the articles with 50 or more citations (216 in number) are selected for content analysis [ 53 , 102 ]. The contents of these 216 articles are analyzed to develop a conceptual model of banks’ triad of risk, regulation, and profitability. Figure 2 explains the data retrieval process for SLR.

Data retrieval process for SLR. Note Stepwise SLR process and corresponding results obtained

Bibliometric and network analysis

Figure 3 [ 58 ] depicts the total number of studies that have been published on “risk,” “regulation,” “profitability,” “bank,” and “banking.” Figure 3 also depicts the pattern of the quality of the publications from the beginning till 2020. It undoubtedly shows an increasing trend in the number of articles published in the area of the triad: “risk” regulation” and “profitability.” Moreover, out of the 3457 articles published in the said area, 2098 were published recently in the last five years and contribute to 61% of total publications in this area.

Articles published from 1976 till 2020 . Note The graph shows the number of documents published from 1976 till 2020 obtained from the Scopus database

Source of publications

A total of 160 journals have contributed to the publication of 3457 articles extracted from Scopus on the triad of risk, regulation, and profitability. Table 1 shows the top 10 sources of the publications based on the citation measure. Table 1 considers two sets of data. One data set is the universe of 3457 articles, and another is the set of 216 articles used for content analysis along with their corresponding citations. The global citations are considered for the study from the Scopus dataset, and the local citations are considered for the articles in the nodes [ 53 , 135 ]. The top 10 journals with 50 or more citations resulted in 96 articles. This is almost 45% of the literature used for content analysis ( n = 216). Table 1 also shows that the Journal of Banking and Finance is the most prominent in terms of the number of publications and citations. It has 46 articles published, which is about 21% of the literature used for content analysis. Table 1 also shows these core journals’ SCImago Journal Rank indicator and H index. SCImago Journal Rank indicator reflects the impact and prestige of the Journal. This indicator is calculated as the previous three years’ weighted average of the number of citations in the Journal since the year that the article was published. The h index is the number of articles (h) published in a journal and received at least h. The number explains the scientific impact and the scientific productivity of the Journal. Table 1 also explains the time span of the journals covering articles in the area of the triad of risk, regulation, and profitability [ 7 ].

Figure 4 depicts the network analysis, where the connections between the authors and source title (journals) are made. The network has 674 nodes and 911 edges. The network between the author and Journal is classified into 36 modularities. Sections of the graph with dense connections indicate high modularity. A modularity algorithm is a design that measures how strong the divided networks are grouped into modules; this means how well the nodes are connected through a denser route relative to other networks.

Network analysis between authors and journals. Note A node size explains the more linked authors to a journal

The size of the nodes is based on the rank of the degree. The degree explains the number of connections or edges linked to a node. In the current graph, a node represents the name of the Journal and authors; they are connected through the edges. Therefore, the more the authors are associated with the Journal, the higher the degree. The algorithm used for the layout is Yifan Hu’s.

Many authors are associated with the Journal of Banking and Finance, Journal of Accounting and Economics, Journal of Financial Economics, Journal of Financial Services Research, and Journal of Business Ethics. Therefore, they are the most relevant journals on banks’ risk, regulation, and profitability.

Location and affiliation analysis

Affiliation analysis helps to identify the top contributing countries and universities. Figure 5 shows the countries across the globe where articles have been published in the triad. The size of the circle in the map indicates the number of articles published in that country. Table 2 provides the details of the top contributing organizations.

Location of articles published on Triad of profitability, regulation, and risk

Figure 5 shows that the most significant number of articles is published in the USA, followed by the UK. Malaysia and China have also contributed many articles in this area. Table 2 shows that the top contributing universities are also from Malaysia, the UK, and the USA.

Key author analysis

Table 3 shows the number of articles written by the authors out of the 3457 articles. The table also shows the top 10 authors of bank risk, regulation, and profitability.

Fadzlan Sufian, affiliated with the Universiti Islam Malaysia, has the maximum number, with 33 articles. Philip Molyneux and M. Kabir Hassan are from the University of Sharjah and the University of New Orleans, respectively; they contributed significantly, with 20 and 18 articles, respectively.

However, when the quality of the article is selected based on 50 or more citations, Fadzlan Sufian has only 3 articles with more than 50 citations. At the same time, Philip Molyneux and Allen Berger contributed more quality articles, with 8 and 11 articles, respectively.

Keyword analysis

Table 4 shows the keyword analysis (times they appeared in the articles). The top 10 keywords are listed in Table 4 . Banking and banks appeared 324 and 194 times, respectively, which forms the scope of this study, covering articles from the beginning till 2020. The keyword analysis helps to determine the factors affecting banks, such as profitability (244), efficiency (129), performance (107, corporate governance (153), risk (90), and regulation (89).

The keywords also show that efficiency through data envelopment analysis is a determinant of the performance of banks. The other significant determinants that appeared as keywords are credit risk (73), competition (70), financial stability (69), ownership structure (57), capital (56), corporate social responsibility (56), liquidity (46), diversification (45), sustainability (44), credit provision (41), economic growth (41), capital structure (39), microfinance (39), Basel III (37), non-performing assets (37), cost efficiency (30), lending behavior (30), interest rate (29), mergers and acquisition (28), capital adequacy (26), developing countries (23), net interest margin (23), board of directors (21), disclosure (21), leverage (21), productivity (20), innovation (18), firm size (16), and firm value (16).

Keyword analysis also shows the theories of banking and their determinants. Some of the theories are agency theory (23), information asymmetry (21), moral hazard (17), and market efficiency (16), which can be used by researchers when building a theory. The analysis also helps to determine the methodology that was used in the published articles; some of them are data envelopment analysis (89), which measures technical efficiency, panel data analysis (61), DEA (32), Z scores (27), regression analysis (23), stochastic frontier analysis (20), event study (15), and literature review (15). The count for literature review is only 15, which confirms that very few studies have conducted an SLR on bank risk, regulation, and profitability.

Citation analysis

One of the parameters used in judging the quality of the article is its “citation.” Table 5 shows the top 10 published articles with the highest number of citations. Ding and Cronin [ 44 ] indicated that the popularity of an article depends on the number of times it has been cited.

Tahamtan et al. [ 126 ] explained that the journal’s quality also affects its published articles’ citations. A quality journal will have a high impact factor and, therefore, more citations. The citation analysis helps researchers to identify seminal articles. The title of an article with 5900 citations is “A survey of corporate governance.”

Page Rank analysis

Goyal and Kumar [ 53 ] explain that the citation analysis indicates the ‘popularity’ and ‘prestige’ of the published research article. Apart from the citation analysis, one more analysis is essential: Page rank analysis. PageRank is given by Page et al. [ 97 ]. The impact of an article can be measured with one indicator called PageRank [ 135 ]. Page rank analysis indicates how many times an article is cited by other highly cited articles. The method helps analyze the web pages, which get the priority during any search done on google. The analysis helps in understanding the citation networks. Equation 1 explains the page rank (PR) of a published paper, N refers to the number of articles.

T 1,… T n indicates the paper, which refers paper P . C ( Ti ) indicates the number of citations. The damping factor is denoted by a “ d ” which varies in the range of 0 and 1. The page rank of all the papers is equal to 1. Table 6 shows the top papers based on page rank. Tables 5 and 6 together show a contrast in the top ranked articles based on citations and page rank, respectively. Only one article “A survey of corporate governance” falls under the prestigious articles based on the page rank.

Content analysis

Content Analysis is a research technique for conducting qualitative and quantitative analyses [ 124 ]. The content analysis is a helpful technique that provides the required information in classifying the articles depending on their nature (empirical or conceptual) [ 76 ]. By adopting the content analysis method [ 53 , 102 ], the selected articles are examined to determine their content. The classification of available content from the selected set of sample articles that are categorized under different subheads. The themes identified in the relationship between banking regulation, risk, and profitability are as follows.

Regulation and profitability of banks

The performance indicators of the banking industry have always been a topic of interest to researchers and practitioners. This area of research has assumed a special interest after the 2008 WFC [ 25 , 51 , 86 , 114 , 127 , 132 ]. According to research, the causes of poor performance and risk management are lousy banking practices, ineffective monitoring, inadequate supervision, and weak regulatory mechanisms [ 94 ]. Increased competition, deregulation, and complex financial instruments have made banks, including Indian banks, more vulnerable to risks [ 18 , 93 , 119 , 123 ]. Hence, it is essential to investigate the present regulatory machinery for the performance of banks.

There are two schools of thought on regulation and its possible impact on profitability. The first asserts that regulation does not affect profitability. The second asserts that regulation adds significant value to banks’ profitability and other performance indicators. This supports the concept that Delis et al. [ 41 ] advocated that the capital adequacy requirement and supervisory power do not affect productivity or profitability unless there is a financial crisis. Laeven and Majnoni [ 81 ] insisted that provision for loan loss should be part of capital requirements. This will significantly improve active risk management practices and ensure banks’ profitability.

Lee and Hsieh [ 83 ] proposed ambiguous findings that do not support either school of thought. According to Nguyen and Nghiem [ 95 ], while regulation is beneficial, it has a negative impact on bank profitability. As a result, when proposing regulations, it is critical to consider bank performance and risk management. According to Erfani and Vasigh [ 46 ], Islamic banks maintained their efficiency between 2006 and 2013, while most commercial banks lost, furthermore claimed that the financial crisis had no significant impact on Islamic bank profitability.

Regulation and NPA (risk-taking of banks)

The regulatory mechanism of banks in any country must address the following issues: capital adequacy ratio, prudent provisioning, concentration banking, the ownership structure of banks, market discipline, regulatory devices, presence of foreign capital, bank competition, official supervisory power, independence of supervisory bodies, private monitoring, and NPAs [ 25 ].

Kanoujiya et al. [ 64 ] revealed through empirical evidence that Indian bank regulations lack a proper understanding of what banks require and propose reforming and transforming regulation in Indian banks so that responsive governance and regulation can occur to make banks safer, supported by Rastogi et al. [ 105 ]. The positive impact of regulation on NPAs is widely discussed in the literature. [ 94 ] argue that regulation has multiple effects on banks, including reducing NPAs. The influence is more powerful if the country’s banking system is fragile. Regulation, particularly capital regulation, is extremely effective in reducing risk-taking in banks [ 103 ].

Rastogi and Kanoujiya [ 106 ] discovered evidence that disclosure regulations do not affect the profitability of Indian banks, supported by Karyani et al. [ 65 ] for the banks located in Asia. Furthermore, Rastogi and Kanoujiya [ 106 ] explain that disclosure is a difficult task as a regulatory requirement. It is less sustainable due to the nature of the imposed regulations in banks and may thus be perceived as a burden and may be overcome by realizing the benefits associated with disclosure regulation [ 31 , 54 , 101 ]. Zheng et al. [ 138 ] empirically discovered that regulation has no impact on the banks’ profitability in Bangladesh.

Governments enforce banking regulations to achieve a stable and efficient financial system [ 20 , 94 ]. The existing literature is inconclusive on the effects of regulatory compliance on banks’ risks or the reduction of NPAs [ 10 , 11 ]. Boudriga et al. [ 25 ] concluded that the regulatory mechanism plays an insignificant role in reducing NPAs. This is especially true in weak institutions, which are susceptible to corruption. Gonzalez [ 52 ] reported that firm regulations have a positive relationship with banks’ risk-taking, increasing the probability of NPAs. However, Boudriga et al. [ 25 ], Samitas and Polyzos [ 113 ], and Allen et al. [ 3 ] strongly oppose the use of regulation as a tool to reduce banks’ risk-taking.

Kwan and Laderman [ 79 ] proposed three levels in regulating banks, which are lax, liberal, and strict. The liberal regulatory framework leads to more diversification in banks. By contrast, the strict regulatory framework forces the banks to take inappropriate risks to compensate for the loss of business; this is a global problem [ 73 ].

Capital regulation reduces banks’ risk-taking [ 103 , 110 ]. Capital regulation leads to cost escalation, but the benefits outweigh the cost [ 103 ]. The trade-off is worth striking. Altman Z score is used to predict banks’ bankruptcy, and it found that the regulation increased the Altman’s Z-score [ 4 , 46 , 63 , 68 , 72 , 120 ]. Jin et al. [ 62 ] report a negative relationship between regulation and banks’ risk-taking. Capital requirements empowered regulators, and competition significantly reduced banks’ risk-taking [ 1 , 122 ]. Capital regulation has a limited impact on banks’ risk-taking [ 90 , 103 ].

Maji and De [ 90 ] suggested that human capital is more effective in managing banks’ credit risks. Besanko and Kanatas [ 21 ] highlighted that regulation on capital requirements might not mitigate risks in all scenarios, especially when recapitalization has been enforced. Klomp and De Haan [ 72 ] proposed that capital requirements and supervision substantially reduce banks’ risks.

A third-party audit may impart more legitimacy to the banking system [ 23 ]. The absence of third-party intervention is conspicuous, and this may raise a doubt about the reliability and effectiveness of the impact of regulation on bank’s risk-taking.

NPA (risk-taking) in banks and profitability

Profitability affects NPAs, and NPAs, in turn, affect profitability. According to the bad management hypothesis [ 17 ], higher profits would negatively affect NPAs. By contrast, higher profits may lead management to resort to a liberal credit policy (high earnings), which may eventually lead to higher NPAs [ 104 ].

Balasubramaniam [ 8 ] demonstrated that NPA has double negative effects on banks. NPAs increase stressed assets, reducing banks’ productive assets [ 92 , 117 , 136 ]. This phenomenon is relatively underexplored and therefore renders itself for future research.

Triad and the performance of banks

Regulation and triad.

Regulations and their impact on banks have been a matter of debate for a long time. Barth et al. [ 12 ] demonstrated that countries with a central bank as the sole regulatory body are prone to high NPAs. Although countries with multiple regulatory bodies have high liquidity risks, they have low capital requirements [ 40 ]. Barth et al. [ 12 ] supported the following steps to rationalize the existing regulatory mechanism on banks: (1) mandatory information [ 22 ], (2) empowered management of banks, and (3) increased incentive for private agents to exert corporate control. They show that profitability has an inverse relationship with banks’ risk-taking [ 114 ]. Therefore, standard regulatory practices, such as capital requirements, are not beneficial. However, small domestic banks benefit from capital restrictions.

DeYoung and Jang [ 43 ] showed that Basel III-based policies of liquidity convergence ratio (LCR) and net stable funding ratio (NSFR) are not fully executed across the globe, including the US. Dahir et al. [ 39 ] found that a decrease in liquidity and funding increases banks’ risk-taking, making banks vulnerable and reducing stability. Therefore, any regulation on liquidity risk is more likely to create problems for banks.

Concentration banking and triad

Kiran and Jones [ 71 ] asserted that large banks are marginally affected by NPAs, whereas small banks are significantly affected by high NPAs. They added a new dimension to NPAs and their impact on profitability: concentration banking or banks’ market power. Market power leads to less cost and more profitability, which can easily counter the adverse impact of NPAs on profitability [ 6 , 15 ].

The connection between the huge volume of research on the performance of banks and competition is the underlying concept of market power. Competition reduces market power, whereas concentration banking increases market power [ 25 ]. Concentration banking reduces competition, increases market power, rationalizes the banks’ risk-taking, and ensures profitability.

Tabak et al. [ 125 ] advocated that market power incentivizes banks to become risk-averse, leading to lower costs and high profits. They explained that an increase in market power reduces the risk-taking requirement of banks. Reducing banks’ risks due to market power significantly increases when capital regulation is executed objectively. Ariss [ 6 ] suggested that increased market power decreases competition, and thus, NPAs reduce, leading to increased banks’ stability.

Competition, the performance of banks, and triad

Boyd and De Nicolo [ 27 ] supported that competition and concentration banking are inversely related, whereas competition increases risk, and concentration banking decreases risk. A mere shift toward concentration banking can lead to risk rationalization. This finding has significant policy implications. Risk reduction can also be achieved through stringent regulations. Bolt and Tieman [ 24 ] explained that stringent regulation coupled with intense competition does more harm than good, especially concerning banks’ risk-taking.

Market deregulation, as well as intensifying competition, would reduce the market power of large banks. Thus, the entire banking system might take inappropriate and irrational risks [ 112 ]. Maji and Hazarika [ 91 ] added more confusion to the existing policy by proposing that, often, there is no relationship between capital regulation and banks’ risk-taking. However, some cases have reported a positive relationship. This implies that banks’ risk-taking is neutral to regulation or leads to increased risk. Furthermore, Maji and Hazarika [ 91 ] revealed that competition reduces banks’ risk-taking, contrary to popular belief.

Claessens and Laeven [ 36 ] posited that concentration banking influences competition. However, this competition exists only within the restricted circle of banks, which are part of concentration banking. Kasman and Kasman [ 66 ] found that low concentration banking increases banks’ stability. However, they were silent on the impact of low concentration banking on banks’ risk-taking. Baselga-Pascual et al. [ 14 ] endorsed the earlier findings that concentration banking reduces banks’ risk-taking.

Concentration banking and competition are inversely related because of the inherent design of concentration banking. Market power increases when only a few large banks are operating; thus, reduced competition is an obvious outcome. Barra and Zotti [ 9 ] supported the idea that market power, coupled with competition between the given players, injects financial stability into banks. Market power and concentration banking affect each other. Therefore, concentration banking with a moderate level of regulation, instead of indiscriminate regulation, would serve the purpose better. Baselga-Pascual et al. [ 14 ] also showed that concentration banking addresses banks’ risk-taking.

Schaeck et al. [ 115 ], in a landmark study, presented that concentration banking and competition reduce banks’ risk-taking. However, they did not address the relationship between concentration banking and competition, which are usually inversely related. This could be a subject for future research. Research on the relationship between concentration banking and competition is scant, identified as a research gap (“ Research Implications of the study ” section).

Transparency, corporate governance, and triad

One of the big problems with NPAs is the lack of transparency in both the regulatory bodies and banks [ 25 ]. Boudriga et al. [ 25 ] preferred to view NPAs as a governance issue and thus, recommended viewing it from a governance perspective. Ahmad and Ariff [ 2 ] concluded that regulatory capital and top-management quality determine banks’ credit risk. Furthermore, they asserted that credit risk in emerging economies is higher than that of developed economies.

Bad management practices and moral vulnerabilities are the key determinants of insolvency risks of Indian banks [ 95 ]. Banks are an integral part of the economy and engines of social growth. Therefore, banks enjoy liberal insolvency protection in India, especially public sector banks, which is a critical issue. Such a benevolent insolvency cover encourages a bank to be indifferent to its capital requirements. This indifference takes its toll on insolvency risk and profit efficiency. Insolvency protection makes the bank operationally inefficient and complacent.

Foreign equity and corporate governance practices help manage the adverse impact of banks’ risk-taking to ensure the profitability and stability of banks [ 33 , 34 ]. Eastburn and Sharland [ 45 ] advocated that sound management and a risk management system that can anticipate any impending risk are essential. A pragmatic risk mechanism should replace the existing conceptual risk management system.

Lo [ 87 ] found and advocated that the existing legislation and regulations are outdated. He insisted on a new perspective and asserted that giving equal importance to behavioral aspects and the rational expectations of customers of banks is vital. Buston [ 29 ] critiqued the balance sheet risk management practices prevailing globally. He proposed active risk management practices that provided risk protection measures to contain banks’ liquidity and solvency risks.

Klomp and De Haan [ 72 ] championed the cause of giving more autonomy to central banks of countries to provide stability in the banking system. Louzis et al. [ 88 ] showed that macroeconomic variables and the quality of bank management determine banks’ level of NPAs. Regulatory authorities are striving hard to make regulatory frameworks more structured and stringent. However, the recent increase in loan defaults (NPAs), scams, frauds, and cyber-attacks raise concerns about the effectiveness [ 19 ] of the existing banking regulations in India as well as globally.

Discussion of the findings

The findings of this study are based on the bibliometric and content analysis of the sample published articles.

The bibliometric study concludes that there is a growing demand for researchers and good quality research

The keyword analysis suggests that risk regulation, competition, profitability, and performance are key elements in understanding the banking system. The main authors, keywords, and journals are grouped in a Sankey diagram in Fig. 6 . Researchers can use the following information to understand the publication pattern on banking and its determinants.

Sankey Diagram of main authors, keywords, and journals. Note Authors contribution using scientometrics tools

Research Implications of the study

The study also concludes that a balance among the three components of triad is the solution to the challenges of banks worldwide, including India. We propose the following recommendations and implications for banks:

This study found that “the lesser the better,” that is, less regulation enhances the performance and risk management of banks. However, less regulation does not imply the absence of regulation. Less regulation means the following:

Flexible but full enforcement of the regulations

Customization, instead of a one-size-fits-all regulatory system rooted in a nation’s indigenous requirements, is needed. Basel or generic regulation can never achieve what a customized compliance system can.

A third-party audit, which is above the country's central bank, should be mandatory, and this would ensure that all three aspects of audit (policy formulation, execution, and audit) are handled by different entities.

Competition

This study asserts that the existing literature is replete with poor performance and risk management due to excessive competition. Banking is an industry of a different genre, and it would be unfair to compare it with the fast-moving consumer goods (FMCG) or telecommunication industry, where competition injects efficiency into the system, leading to customer empowerment and satisfaction. By contrast, competition is a deterrent to the basic tenets of safe banking. Concentration banking is more effective in handling the multi-pronged balance between the elements of the triad. Concentration banking reduces competition to lower and manageable levels, reduces banks’ risk-taking, and enhances profitability.

No incentive to take risks

It is found that unless banks’ risk-taking is discouraged, the problem of high NPA (risk-taking) cannot be addressed. Concentration banking is a disincentive to risk-taking and can be a game-changer in handling banks’ performance and risk management.

Research on the risk and performance of banks reveals that the existing regulatory and policy arrangement is not a sustainable proposition, especially for a country where half of the people are unbanked [ 37 ]. Further, the triad presented by Keeley [ 67 ] is a formidable real challenge to bankers. The balance among profitability, risk-taking, and regulation is very subtle and becomes harder to strike, just as the banks globally have tried hard to achieve it. A pragmatic intervention is needed; hence, this study proposes a change in the banking structure by having two types of banks functioning simultaneously to solve the problems of risk and performance of banks. The proposed two-tier banking system explained in Fig. 7 can be a great solution. This arrangement will help achieve the much-needed balance among the elements of triad as presented by Keeley [ 67 ].

Conceptual Framework. Note Fig. 7 describes the conceptual framework of the study

The first set of banks could be conventional in terms of their structure and should primarily be large-sized. The number of such banks should be moderate. There is a logic in having only a few such banks to restrict competition; thus, reasonable market power could be assigned to them [ 55 ]. However, a reduction in competition cannot be over-assumed, and banks cannot become complacent. As customary, lending would be the main source of revenue and income for these banks (fund based activities) [ 82 ]. The proposed two-tier system can be successful only when regulation especially for risk is objectively executed [ 29 ]. The second set of banks could be smaller in size and more in number. Since they are more in number, they would encounter intense competition for survival and for generating more business. Small is beautiful, and thus, this set of banks would be more agile and adaptable and consequently more efficient and profitable. The main source of revenue for this set of banks would not be loans and advances. However, non-funding and non-interest-bearing activities would be the major revenue source. Unlike their traditional and large-sized counterparts, since these banks are smaller in size, they are less likely to face risk-taking and NPAs [ 74 ].

Sarmiento and Galán [ 114 ] presented the concerns of large and small banks and their relative ability and appetite for risk-taking. High risk could threaten the existence of small-sized banks; thus, they need robust risk shielding. Small size makes them prone to failure, and they cannot convert their risk into profitability. However, large banks benefit from their size and are thus less vulnerable and can convert risk into profitable opportunities.

India has experimented with this Differential Banking System (DBS) (two-tier system) only at the policy planning level. The execution is impending, and it highly depends on the political will, which does not appear to be strong now. The current agenda behind the DBS model is not to ensure the long-term sustainability of banks. However, it is currently being directed to support the agenda of financial inclusion by extending the formal credit system to the unbanked masses [ 107 ]. A shift in goal is needed to employ the DBS as a strategic decision, but not merely a tool for financial inclusion. Thus, the proposed two-tier banking system (DBS) can solve the issue of profitability through proper regulation and less risk-taking.

The findings of Triki et al. [ 130 ] support the proposed DBS model, in this study. Triki et al. [ 130 ] advocated that different component of regulations affect banks based on their size, risk-taking, and concentration banking (or market power). Large size, more concentration banking with high market power, and high risk-taking coupled with stringent regulation make the most efficient banks in African countries. Sharifi et al. [ 119 ] confirmed that size advantage offers better risk management to large banks than small banks. The banks should modify and work according to the economic environment in the country [ 69 ], and therefore, the proposed model could help in solving the current economic problems.

This is a fact that DBS is running across the world, including in India [ 60 ] and other countries [ 133 ]. India experimented with DBS in the form of not only regional rural banks (RRBs) but payments banks [ 109 ] and small finance banks as well [ 61 ]. However, the purpose of all the existing DBS models, whether RRBs [ 60 ], payment banks, or small finance banks, is financial inclusion, not bank performance and risk management. Hence, they are unable to sustain and are failing because their model is only social instead of a much-needed dual business-cum-social model. The two-tier model of DBS proposed in the current paper can help serve the dual purpose. It may not only be able to ensure bank performance and risk management but also serve the purpose of inclusive growth of the economy.

Conclusion of the study

The study’s conclusions have some significant ramifications. This study can assist researchers in determining their study plan on the current topic by using a scientific approach. Citation analysis has aided in the objective identification of essential papers and scholars. More collaboration between authors from various countries/universities may help countries/universities better understand risk regulation, competition, profitability, and performance, which are critical elements in understanding the banking system. The regulatory mechanism in place prior to 2008 failed to address the risk associated with banks [ 47 , 87 ]. There arises a necessity and motivates authors to investigate the current topic. The present study systematically explores the existing literature on banks’ triad: performance, regulation, and risk management and proposes a probable solution.

To conclude the bibliometric results obtained from the current study, from the number of articles published from 1976 to 2020, it is evident that most of the articles were published from the year 2010, and the highest number of articles were published in the last five years, i.e., is from 2015. The authors discovered that researchers evaluate articles based on the scope of critical journals within the subject area based on the detailed review. Most risk, regulation, and profitability articles are published in peer-reviewed journals like; “Journal of Banking and Finance,” “Journal of Accounting and Economics,” and “Journal of Financial Economics.” The rest of the journals are presented in Table 1 . From the affiliation statistics, it is clear that most of the research conducted was affiliated with developed countries such as Malaysia, the USA, and the UK. The researchers perform content analysis and Citation analysis to access the type of content where the research on the current field of knowledge is focused, and citation analysis helps the academicians understand the highest cited articles that have more impact in the current research area.

Practical implications of the study

The current study is unique in that it is the first to systematically evaluate the publication pattern in banking using a combination of scientometrics analysis tools, network analysis tools, and content analysis to understand the relationship between bank regulation, performance, and risk. The study’s practical implications are that analyzing existing literature helps researchers generate new themes and ideas to justify their contribution to literature. Evidence-based research knowledge also improves decision-making, resulting in better practical implementation in the real corporate world [ 100 , 129 ].

Limitations and scope for future research

The current study only considers a single database Scopus to conduct the study, and this is one of the limitations of the study spanning around the multiple databases can provide diverse results. The proposed DBS model is a conceptual framework that requires empirical testing, which is a limitation of this study. As a result, empirical testing of the proposed DBS model could be a future research topic.

Availability of data and materials

SCOPUS database.

Abbreviations

Systematic literature review

World Financial Crisis

Non-performing assets

Differential banking system

SCImago Journal Rank Indicator

Liquidity convergence ratio

Net stable funding ratio

Fast moving consumer goods

Regional rural banks

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Rastogi, S., Sharma, A., Pinto, G. et al. A literature review of risk, regulation, and profitability of banks using a scientometric study. Futur Bus J 8 , 28 (2022). https://doi.org/10.1186/s43093-022-00146-4

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Sustainable finance: An analysis of the ASEAN-4 universal banking sector's sustainable growth rate (SGR) and its risk factors , Bianca Elise S. Alejandrino, Armandeep K. Bhuller, Arnel Jorge N. Francisco II, and Jean Christian C. Peralta

The long memory effect in ASEAN-6 stock markets from 2006 to 2022: A rescaled range analysis , Tricia Q. Almandres, Sharmaine Rose G. Estor, Michaela Wencee S. Ng, and Audrey T. Reyes

An analysis of the effect and influence of macroeconomic factors on 10-year government bond Yields in the ASEAN-4 , Dan Joseph L. Andres, Ronnie-Lans T. Ayuyao, Nathan John N. Deypalan, and Jan Marcus C. Naguit

The effects of behavioral biases on investment decisions of Filipino millennials and generation Z: Moderating role of financial literacy , Liezl Katherine C. Ang, Jean Ashley A. Masanque, Johannah Mae C. Nacario, and Renna Mae M. Paguntalan

Panel regression analysis of the link between ESG indicators and financial performance in the energy and transportation Industry of ASEAN 5 countries: A sectoral perspective , Rafael Antonio C. Arellano, Skye Orin L. Libarnes, Meg Allen G. Maayo, Joaquin Francisco T. Sun, and Pedro Enrique S.A. Villamejor

Smart beta investing: A comparative study of fundamental accounting metrics and traditional market capitalization indices to measure the performance of the Philippine Stock Exchange , Miguel Benedicto Ramirez Argamosa, Faith Robles Del Rosario, Veronica Marielle Ferrer Delmo, and Miguel Antonio Rodriguez Merino II

The moderating effect of institutional quality on the relationship between financial inclusion and the profitability of commercial banks in selected ASEAN-5 countries: An analysis , Miguel Rene Q. Balboa, Timothy Karl R. Dela Torre, Alexandra Yzabella H. Lazaro, and Nishie S. Yao

The impacts of oil price shocks on the stock market index of the ASEAN-4 countries from January 2012 to October 2022: An analysis , Miguel Enrico V. Banzon, Beatriz Colleen S. Calimlim, Hesed Heindrick S. Cariño, and Siegfried Paolo B. Malabanan

Granger causality between stock market and selected macroeconomic indicators: Evidence from the Philippines , Kenneth Richard O. Bardullas, Vinzze Joseph T. Co, Aaron Henric P. Leung, and Alyzza Ariane J. Tadeo

Sustainable finance: The impact of selected green bonds on issuing firms' greenhouse gas emission (GHG) levels in selected ASEAN countries , Jomabelle C. Bautista, Mary Aubrey C. Calma, Ezekiel C. Camilo, and Krystell Abigail L. Tan

Can it walk the talk? Determining the validity of random walk hypothesis and technical analysis in the Philippine stock market , Hazel P. Bongolan, Bea Eliza A. Delos Reyes, Jenielle Joye T. Ho, and Cale Robert S. Rasco

Total factor productivity using Malmquist DEA on selected ASEAN-5 life insurers from 2007 to 2021: An analysis , John Allen C. Caballa, Riana Jade Y. Ng, Cherilyn G. Tan, and Jon Calvin C. Uy

The relationship between economic indicators and stock exchange index of ASEAN-4 countries: Indonesia, Malaysia, Philippines, and Thailand , Craig Jimver Mikael C. Camino, Micko Briel D. Del Pilar, Paul A. Fuentebella Jr., and Bryan Stephen A. Hong

Utilization of financial ratios in selected financial models to predict financial distress among food manufacturing companies in the Philippines , Simon Hongying G. Chen, Jelline C. Cheng, Lyka Mari C. Javier, and Janine F. Ong

Analyzing the causal effects of the major ASEAN-4 countries exchange rates against the Philippine peso on the volatility of the Philippine stock market returns , Richmond Ryan S. Chua, Yung Ching S. Shi, Willy W. Tang, and Rico J. Wu

Impact of mega-sporting events on the host countries’ stock market performance and economic growth: Evidenced from the Southeast Asian Games , Wendy Cai Chua, Se Jin Kaibigan Jeong, Catherine Ke Ke, and Michelle Chen Lin

An evaluation of logistic regression and random forest model as early warning system models for assessing an equity market crisis in ASEAN-5 + 3 countries , Allister James R. del Rosario, Anne Ysobel P. Guzman, Michelle O. Kohzai, and Jay Ruel B. Zape

A comparative analysis of the performance of machine learning models for predicting stock prices from the years 2012 to 2022: Evidence from the ASEAN 5 stock market indices , Sameer D. Dhanani, Hugh Leon B. Escaño, Jasmine L. Lim, and Isaiah Franz Dominique L. Pascual

Evaluating the volatility spillovers in the foreign exchange market during extreme events from 2007 to 2022 using the EGARCH model: Evidence from the ASEAN-5 countries , Helen L. Diaz, Jan Peter T. Ignacio, Melanie Grace V. Namol, and Abby Gail C. So

An analysis on the impact of crude oil prices and macroeconomic indicators on the ASEAN-5 stock market index: The 2022 Russian invasion of Ukraine , Sophia Anne B. Gallardo, Jasmine F. Kau, Christelle Joy V. Remegio, and Jaylyn M. Vibar

An analysis of the relationship between stock prices and financial ratios of banks based on the ASEAN-4 , Rica Anne A. Ko, Clarea Felice C. Lim, and Stacey Elaine T. Yap

Determinants of property sector profitability: Empirical evidence from the selected publicly listed real estate companies in the ASEAN-5 , France Gabriel D. Palileo, Miguel Faustino O. Mallari, Paul John S. Sison, and Mary Angeleen V. Teodosio

Financial literacy and fintech adoption among millennials in Metro Manila, Philippines: An analysis , Alyanna Marie L. Toh, Stacey Eunice U. Lee, Jason W. Su, and Athena Micah B. De Guzman

Theses/Dissertations from 2022 2022

Effects of volatilities on property sector indices of ASEAN-6 pre, during, and after the Global Financial Crisis and during the COVID-19 pandemic from 2006 to 2022 , Maria Charizza Acuña, Ernest Joseph Coronel, Margarita Lauren Cortez, and Nathalie Raika Julio

The impact of exchange rate volatility on the stock market index returns of select developed and developing Asian countries: An analysis , Alianne J. Alfonso, Mariela C. Cai, Erika Anne D. Jaurigue, and Sofia Eloisa U. Placino

Philippine financial institutions' counterparty default risk and stock price relationships: An analysis , Gerard Constantine Amano, Juwan Kenzie Gomez, Gilbert Angelo Juan, and Jan Michael Pioquinto

The effect of ESG activities on the financial performance of PSE listed companies during the COVID-19 pandemic—Evidence from the Philippines , Andrea Danielle S. Amil, Raizen Philippe M. King, Rondel Y. Ortiz, and Gweneth Allona Mikaela B. Te Tan

The impact of COVID-19 and specific control indicators on the performance of selected universal banks in the Philippines , Keana Aedrielle Modesto Ang, Bea Alexis Gotay Lim, Issey Miuccia Domminiq Uy Tan, and Jenny Huang Zhang

A study on the determinants of dividend payout policy: Evidence from the ASEAN-5 countries , Princess Askha Intal Artates, Mary Coshey Israel Dabatos, Claire Aimy Padilla Sendin, and Reynalyn Del Mundo Tenorio

Forecasting value-at-risk during crises in select ASEAN stock market indices through GARCH-EVT models , Janelle Fatima A. Balmaceda, Maxim Anthonnae M. Miranda, Mary Haniel Joy M. Parba, and Patricia Anne M. Zapanta

The relationship of the daily number of COVID-19 cases, lockdown classifications in the National Capital Region, and Philippine stock returns: An analysis , Beatrice Q. Bañagale, Dazle M. Edralin, Joaquin Pierre T. Guinto, and Isabelle Rhein D. Rivera

Financial development in the ASEAN 8: Impact of foreign direct investment and institutional quality , Katherine Marie F. Batto, Julie T. Caguioa, Sophia L. Cruz, and Sofia Julia S. Uy

The rise of fintech in the Philippines: A study on the impact of digital finance and demographics on financial inclusion and its effect on economic growth , Sofia Angeli M. Bobier, Gillian Clare O. Carbonilla, Alessandra Rayne L. Mallari, and Erika Marie D. Moleno

Investigating the long-term co-movement and spillover effects of the stock markets between the United States and the ASEAN-5 countries for the periods up to and after the 2008 Global Financial Crisis and the COVID-19 pandemic , James Paul Misa Calub, Stephanie Joyce Chua Chan, and Elisa Kyle Agulto Lim

The effect of credit and liquidity risk management practices on the profitability ratios of selected Philippine thrift banks , Renee Ysobelle S. Canlas, Jerlene E. Coronado, Joana Raquel S. Gianan, and Stephanie Mae C. Hu

A relationship between world oil prices and Philippine mining and oil sector index: A comparative study of multivariate GARCH approaches , Errol Stephen Santos Chan, Justin Matthew Carrasco Hou, Mychael John Llamado Ong, and Marcus Adrian Garyth Ejercito Tan

Evaluating the impact of competition on the profitability and the stability of the commercial banking sector: A case of selected Asian countries (2008-2020) , Gianina Jewel Paredes Chan, John Matthew Menco Chua, Jeanne Marie Lim Si, and Christian Rosales Sy

Examining financial performance of ASEAN REITs from 2020-2022 , Lorenz Dominick Santos Chon, Martin Clifford King Ornido, Bryce Harvey Angsanto Tan, and John Henderson Co Tan

An analysis on the total factor productivity of selected commercial banks in the ASEAN-5 countries using Malmquist-DEA analysis from 2006-2020 , Kervin T. Chua, Maria Jeanette C. Mallari, Joacquin Carlo A. Navales, and Chelsea Ann A. Yu

The impact of human development in the ASEAN 5 countries on financial inclusion (2015-2019): An analysis , Danica Deryll C. Condes, Justin Nicholas D. Nocum, and Kenneth C. Sevilla

Behavioral biases and demographic factors influencing Filipino’s investment decision during the COVID-19 pandemic: An empirical study , Chevy Louise G. De Guzman, Pierre Angelo A. Lopez, Alley Jill Q. Ocampo Tan, and Jannah Andre A. Seville

Relationship of information and communication technology (ICT) and stock market development (SMD): Empirical evidence using a panel of ASEAN-6 and East Asian-3 countries , Erica Jillian Allison S. Dela Cruz, Huihuang Shi, Edward Spencer D. Tan, and Micah Lovell V. Tan

Analysis of the impact of selected financial ratios and macroeconomic factors on share price: Empirical evidence from selected emerging ASEAN countries mining and oil sector , Marielle C. Dela Cruz, Kieza Francesca C. Garra, Samantha Rose V. Pontines, and Serin Hanbyeol A. You

Will the renminbi emerge as a safe-haven currency? Evidence from the tiger cub economies’ stock market volatility from 2016 to 2021 , Paolo Manuel D. Delfin, Kay Lin Ding, Hana Juniela N. Sebe, and Sofia Nicole C. Villanueva

Financial integration in ASEAN emerging markets: The relationship between macroeconomic variables and stock index performance from 2011 to 2020 , Loren Margaret Malabanan Dizon, Mico Angelo Pasion Magturo, Maria Nicole Sebastian Molina, and Rainiele Clarice Galaura San Juan

A comparative analysis of the risk-adjusted performance of Philippine active and passive equity funds before and during the COVID-19 pandemic , Luisa L. Dizon, Irvin Avery F. Ng, Audrey Nicole F. See, and Lance Spencer T. Yu

Information and communication technology (ICT), economic indicators, and banking sector indicators: Its impact on the ASEAN-5 countries' banking sector performance: An analysis , Piero Antonio D. Dominguez, Samantha Colleen M. Francisco, and Maria Regina T. Ignacio

Influence of digital finance accessibility on financial inclusion and bank stability: Evidence from the ASEAN , Jameson Esparas and Faustine Angela B. Zipagan

A study on Filipino investors and their intention to invest in mutual funds in the Philippines , Jaan Alexander Lacap Gana, Mauro Ramon Campos Lacson, Dheeraj Motiani, and Yu-jin Dacula Nam

A comparative study on the impact of COVID-19 pandemic and exchange rate on the stock market returns of the Philippines and Thailand , Jan Gavin Santos Go, Bea Jilian Banaag Llana, Reignard Alric Chong Uy, and Aaron Elian Lardizabal Yaneza

The significance of microfinance to Pototan rice farmers in the Philippines: An analysis , Mikaela Luis A. Gutierrez and John Dominic D. Hechanova

Comparative analysis of top cryptocurrencies to other financial markets , Chadwick Wayne G. Ilagan, Stephen C. Ong, and Juanito P. Valdecantos IV

The impact of sustainability reporting on corporate financial performance: A multilevel modelling approach using evidence from publicly listed companies in the Philippines , Julienne Elisha Q. Juan, David Joshua T. Marin, John Raymond D. Reyes, and Shaila Kimberly U. Sy

A comparative assessment of Benjamin Graham's stock selection criteria as quantitative investment strategy in ASEAN-5 markets post global financial crisis: An examination of the defensive and enterprising investor approach , Geoffrey James O. Lim, Jose Rafael M. Malamug, and Ethen Aldrich P. Panugayan

The effects of the adoption of blockchain technology on selected ASEAN banks’ stock performance: An event study , Angelo Gabriel G. Lopez, Joachim Santino G. Palacios, Romualdo Anton T. Rosas, and Samantha T. Santos

The effect of fintech on the efficiency of selected Philippine universal banks using DEA from 2010 to 2019 , Sarah Frances O. Ludo, Joaquin Vicente C. Sese, Leah Beatrice S.D. Tagabucba, and Regine Elixhea S. Torres

An assessment of risk management's mediating effect on financial innovation and bank's performance: A study of selected ASEAN-5 listed commercial banks from 2018-2020 , Raphael Gerardo Nisce, Louise Kate R. Ramirez, and Kataaki M. Watanabe

The effect of selected financial ratios and macroeconomic factors on stock price: A study of Bursa Malaysia Berhad's listed energy companies from 2015 to 2019 , Rexwin Anthony Osida, Joanne Chelsea B. Pecson, and Miguel Benedicto C. Tupas

Efficiency, financial performance, and stock returns of the food and beverage industry: A study on the ASEAN 5 , Jan Dominique O. Tan, Hanns Dominic W. Chen, Christian Lance L. Haw, and Queenie Yvette S. Shi

Theses/Dissertations from 2021 2021

A comparative study: Underpricing and long-run performance of initial public offerings in Singapore Exchange and Bursa Malaysia from 2007-2016 , Ann Nicole Louise L. Ang, Alexa May B. Domingo, Paula M. Maniulit, and Ysabel T. Maniulit

Women empowerment through microfinance: Twenty year historical data analysis of selected microfinance institutions in NCR and CALABARZON , Bianca Erica D. Bala, Cristina Mae Y. Chu, Paolo Tristan L. Chu, and Dustinmico S. Wee

The relationship of stock returns with systematic risk in the ASEAN-5 Region: A panel data approach analysis of the relationship prior to and during the COVID-19 pandemic , Wren Angelo Encarnacion Banaag, Maxinne Vaughn Julia Catoto De Guzman, and Kassandra Mari Banawa Luces

The impact of financial literacy, attitude, and behavior on financial well-being among Metro Manila residents , Erika Shaine Chong Bana Lim, Domingo Maria Carmona Garcia IV, Joiecel Labung Tan, and Alyssa Janine Hong Yao

The effect of the coronavirus (COVID-19) on the optimal portfolio composition of select industry sectors in the Philippines , Laxmir Roselle Magpantay Biacora, Ida Augusta Ramos Lim, Marc Ivan Pagtama Lanuza, and Denise Nicole Pimentel Lim

Cardinality-constrained approach: Small portfolios breakthrough in the Philippine market from January 2015 to December 2019 , Judely Ann Calipusan Cabador, Clarissa Lingat Calo-oy, Krisma Allu Gapasin Duldulao, and Juliene Faye Palmares Zamora

Empirical analysis: Application of specific GARCH models in examining stock market volatility , Adrianne Nicole J. Canonizado, Charles Lawrence L. Chua, Jon Pryce Y. Go, and Jackie C. Yu

The relationship between financial literacy and fraud detection between generation X and Y Filipinos in Metro Manila, Philippines , Elijah Climaco Castañeda, Eric Paul Mariano, and Mark Ildefonso M. Zurbano III

Evaluating early warning systems for currency crisis in select emerging ASEAN economies , Mikhaela Kristine R. Chan, Berndhart S. Co, Mary Khristine P. Juan, and Erryl Ron M. Lacanlale

A PLS-structural equation modelling of the role of financial inclusion, financial technology, financial stability, and bank competition on economic growth in ASEAN , Lou Marie Princess Dimalibot Chua, Richelyn May Pantig Chua, Kim Borja Fernandez, and Ericka Christian Ando Javate

A panel analysis of Philippine banks’ loan portfolio quality in relation to their bank lending rates, bank performance, and key accounts , Antonio Miguel Tayag Coronel, Lorenzo Jose Morales Prieto, Zach Gabriel Server Rapanot, and Xavier Maria Castro Roxas

An analysis on the effect of demographic characteristics and e-money usage towards bank account ownership in the Philippines , Miguel Carlos S. De Guzman, Zores Miguel A. Declaro, Vincent Thomas F. Garcia, and Dana Erika E. Julaton

A comparative analysis of the inflation hedging properties of gold, stocks, corporate bonds, and foreign currency in the Philippines from years 2011-2019 , Ma. Danielle Kyle L. de Jesus, Eduardo Wolfgang U. Gargarita, Dessa Fay A. Isubol, and Eira Jasmine H. Javaluyas

The impact of the COVID-19 pandemic on revenue diversification of selected banks in the ASEAN 5 countries , Alma Grace De Vera, Adrian Keith Deparene, Regina Sofia Ong, and Jonas Marvin Villar

Examining the impacts of environmental, social, and governance (ESG) considerations on millennials and generation Z’s investment attitudes and behaviors , Micaella Danielle Go, Mary Agnes Alita Grino, and Tyrone Kyle Jambalos

Behavioral factors influencing retail investors’ decision making during the COVID-19 pandemic: A study on the Philippine stock exchange , Justin Martin Meneses Jacaria, Ma. Isabel Anacleta San Jose Paredes, Matthew Jeremy Sacdalan Quismorio, and Gonzalo Philip Centennial Caligagan Exconde

Value investing and technical analysis in the Philippine Stock Exchange, investing in the five different sectors after the financial crisis of 2008 from the years 2010-2019 , Mathew Luis L. Marzan, James Ryan A. Sese, and Bryan Michael C. Yap

An analysis of the relationship between financial performance of Microfinance Institutions and the Sustainable Development Goals , Samuel Villacruz, Dallin Torio, Jing-Jing Go, and Carolyn Tan

Theses/Dissertations from 2020 2020

The effect of ASEAN-4 stock market volatility on the Japanese yen as a safe haven asset from 2003 to 2019 , Angela Angie Wu Chen, Kymberlin Rae Chan Cua, and Shiela Camille Chua Lao

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Risk Management Dissertation Ideas

Published by Owen Ingram at January 2nd, 2023 , Revised On August 18, 2023

Identifying and assessing risks in various life situations is the focus of risk management dissertation topics. The key focus of risk management research topics is on risk prevention and risk mitigation. This field is growing in popularity among students every day because of the need for businesses and organisations to prevent and manage risks as part of their damage control strategies.

The decision of what to write about for your dissertation can be difficult. But there is no need to panic yet because you’ve come to the right place if you’re looking for risk management dissertation topics .

For Your Consideration, Here Are Some Excellent Risk Management Dissertation Ideas.

Investigating the relationship between risk management and organizational performance.
A review of the literature on the effects of decision support on risk management strategies in business contexts.
How do insurance companies approach risk management in their organizations? Is it fair, or do some changes need to be made to improve it?
Earthquake risk management should concentrate on potential barriers and opportunities.
A descriptive analysis of the relationship between earthquake risk management and earthquake insurance.
How social and environmental factors relate to risk management, either directly or indirectly.
A review of empirical evidence on long-term risk management.
Geotechnical risk management: a comparison of developed and developing countries.
Investigating the guidelines and principles related to the risk management domain.
The impact of the relationship between key individuals and business concepts, as well as the degree to which risk management tools are related.
Investigating the connection between consumer safety and risk management.
A quantitative study focuses on the factors for optimizing risk management in services.
A detailed review of empirical evidence for a futuristic analysis of the risk management domain.
Which of the following factors is a business’s most important risk management?
Smart grid security risk management is a new area to research.
Investigating the risk management strategies used in organizations in the UK.
A correlational study of risk management and population health.
Investigating the relationship between supply chain risk management and performance measurement.
International comparison of traditional versus modern risk management strategies.
A review of the literature on an international disaster risk management system.
A descriptive analysis of risk management strategies in the pharmaceutical development industry.
A correlational analysis of the relationship between risk perception and risk management.
Focus on potential challenges and interventions in enterprise risk management.
Risk management and big data in engineering and science projects.
A review of empirical evidence on community-based disaster risk management.
Portfolio risk management should emphasize the significance of six sigma quality principles.
Using financial tools and operational methods to integrate supply chain risk management.
Discovering risk management’s practical applications in Third World countries. Risk Management in a Supply Chain: How Have Current Trends in Global Supply Chain Management Influenced the Evolution of Risk-Management Strategies?
Critical Success Factors for Financial Services Organizations Implementing an Operational Management System.

Nothing is more critical to a business than managing risks, whether large or small and bringing positive results to their customers. There is no doubt that the course will be interesting, and you will be able to find topics to write about using research methods such as diversity. Get expert assistance with your dissertation topics by placing an order for our dissertation topic and outline service today. You can take inspiration from the above-mentioned risk management dissertation ideas as well.

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Dissertations / Theses on the topic 'Financial risk management'

80 Financial Risk Management Research Topics

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A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data

Introduction

Literature review

Research methodology

TOPSIS method

Exponential smoothing method

Neutrosophic set theory

Triangular fuzzy number

Financial risk assessment model

The establishment of the financial risk index system

The estimation of criteria weights with BWM

The evaluation matrix of financial risk

The calculation of index weight

The ranking order of companies according to financial risk by using TOPSIS

Empirical study

Financial risk criteria weight

Evaluation matrix

Weight of financial risk index

Ordering result of TOPSIS method

Comparison analysis and discussion

Sensitivity analysis

Conclusion and future research

Supporting information

S2 Fig. Financial risk index system.

S3 Fig. The membership function of credit rating.

S4 Fig. Financing risk.

S5 Fig. Investment risk.

S6 Fig. Income distribution risk.

S7 Fig. Cash flow at risk.

S8 Fig. The result of the sensitivity analysis with different ∂ .

S9 Fig. The radar plot displaying the result of the sensitivity analysis.

S1 Raw Data.

Acknowledgments

Finance, Risk Management & Banking: Theses and Dissertations

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Research Proposals

South African

Unisa Finance/Banking Theses/Dissertations

International

Open Access Theses

A literature review of risk, regulation, and profitability of banks using a scientometric study

Introduction

Issues of performance and risk management of banks

Data and research methods

Determining the appropriate keywords for exploring the data

Initial search results and limiting criteria

Data analysis tools and techniques

Bibliometric and network analysis

Source of publications

Location and affiliation analysis

Key author analysis

Keyword analysis

Citation analysis

Page Rank analysis

Content analysis

Regulation and profitability of banks

Regulation and NPA (risk-taking of banks)

NPA (risk-taking) in banks and profitability

Triad and the performance of banks

Concentration banking and triad

Competition, the performance of banks, and triad

Transparency, corporate governance, and triad

Discussion of the findings

The bibliometric study concludes that there is a growing demand for researchers and good quality research

Research Implications of the study

Competition

No incentive to take risks

Conclusion of the study

Practical implications of the study

Limitations and scope for future research

Availability of data and materials

Abbreviations

Acknowledgements