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Abstract
Supply chain finance is newly emerging concept and grab attend of financial sercive providers, buyers and suppliers. This study empirically examines the impact of supply chain finance solutions (SCFS), banks financial risk on financial service providers’ financial performance using panel dataset of Asian Development Bank registered countries (Pakistan, China and Bangladesh) from 2012–2021. By breaking new ground, supply chain finance solution index is developed by combining several solutions to measure its impact on financial service provider financial performance. The results show a significant impact of supply chain finance solutions on financial performance of financial service providers. Furthermore, by offering SCF solutions a bank is able to reduce its financial risk for the external parties (e.g., investors, shareholders) This research encourages financial service providers (banks) to embrace the supply chain finance solution to enhance financial performance and allows them to evaluate their supply chain finance solutions investments as a technique to mitigate financial risk.
Citation: Munir M, Bhutta NT (2023) Light in the tunnel or just a train; impact of supply chain finance solutions on financial service providers’ financial performance by mitigating financial risk. PLoS ONE 18(12): e0292497. https://doi.org/10.1371/journal.pone.0292497
Editor: João Zambujal-Oliveira, University of Madeira / NOVA Lincs, PORTUGAL
Received: May 23, 2023; Accepted: September 21, 2023; Published: December 13, 2023
Copyright: © 2023 Munir, Bhutta. 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: By using the following URLs others can access the dataset •https://www.adb.org/what-we-do/trade-supply-chain-finance-program/participating-banks •https://www.sbp.org.pk/reports/annual/FSAFS/2023/2023.htm •https://www.bankasia-bd.com/about/annualreport •https://www.thecitybank.com/report/annualreports •https://www.ebl.com.bd/annual-reports •https://www.dutchbanglabank.com/investor-relations/financial-statements.html •https://www.eximbankbd.com/report/Annual_Reports •https://www.mutualtrustbank.com/regulatory-disclosure/financial-statements/ •https://www.primebank.com.bd/index.php/home/financial_reports •https://www.pubalibangla.com/Annual-Reports.asp •https://www.southeastbank.com.bd/?page=annual_reports •https://ir.ctbcholding.com/html/financial_reports.php •https://www.hsbc.com/investors/results-and-announcements/annual-report.
Funding: The authors received no specific funding for this work
Competing interests: The authors have declared that no competing interests exist
Introduction
The Supply chain finance is a newly inovative and emerging practice that has grabbed the attention of suppliers, buyers, and financial service providers [1–4]. Supply chain finance ensures efficient financial flow through the information and goods flow phases. Different research shows that it is a “win-win” situation for all actors (i.e., buyers, suppliers, and financial service providers) [5, 6]. In any economy, the major contributors to the growth and survival of the banking sector are financial service providers and managing buyer-supplier relationships to reduce risk. Financial service providers, specifically banks, focus on to enhance their involvement in financial side of the global supply chain by offering different supply chain finance solutions according to the need of the supply chain participants [7].
Nevertheless, a financial service provider faces many risks that can destabilize their financial position, jeopardizing the stability of the whole economy and the financial performance of the financial service provider [8].
Due to financial crisis, banking sector distress and mainly financial risk and performance of the banks. Financial risk may not be avoided to enhance good financial performance; these two interdependent components must be evaluated simultaneously [9].
Supply chain finance program main pillar are finance providers [3, 4, 10, 11]. All participants risk level in supply chain finance lower by finance providers, if any participant not payback loan to provider. That’s why finance providers are risk-takers and generate profits by performing this activity [7]. Different studies show that initiatives of supply chain finance allow finance providers to have low-risk exposure by offering different but attractive discount rates [7, 12]. Financial service providers’ financial risk may be influenced by supply chain initiatives [13].
However, it is still unclear how the supply chain finance solutions index influences financial service providers’ financial risk. Some researchers suggested that financial services providers are evaluated by stakeholders based on their financial risk mitigation capability using supply chain finance solutions [14]. However, there is very limited empirical evidence regarding the supply chain finance impact on their banks financial risk. Since Financial risk is one of the widely used market-based measurement ways and grabs the attention of many company investors and shareholders [15, 16]. There is a need to figure out this research gap in the literature and whether by offering supply chain solutions, finance providers reduce/mitigate financial risk.
Underpinned by a risk management perspective. Morck [17], suggested that an organization’s internal control system with risk propensity leads toward risk level. Financial service providers take the risk contingent upon financial performance with the condition of financial risk propensity. Risk sharing reduces the quality risk and enhances performance levels [17]. Finance providers offer a SCFS contingent upon financial risk as it enhances performance. This study mainly focuses on the supply chain finance solution impact on the firm performance of financial service providers (banks) with the moderating role of financial risk. Different research has highlighted this research as having a high impact on the successful use of supply chain finance solutions [13]. For example, according to Selvaraj, [13], initiative of supply chain financial play a critical role in the financial risk of finance providers. Financial service providers’ financial risk reduces by initiating supply chain finance, but the impact of financial service providers’ financial performance remains empirically unexamined. Furthermore, other than examination of direct impact of combined supply chain finance solutions on finance provider’s performance, this study also analyzes the moderating role of financial risk of financial service providers. Thus, this study attempts to answer the following question:
- What is the impact of supply chain finance solutions on financial service provider’s financial performance?
- What is the moderating role of financial risk on SCFS and finance providers’ financial performance?
To answer these questions, collect and use secondary panel data from multiple sources to open a new window with significant insights into the abovementioned relationships. The scarcity of empirical studies and the importance of supply chain finance solutions in the survival and economic situation make this paper an appealing substance of research. First, Actors of supply chain finance has grabbed the attention [1, 17]; there is little research contribution that connects supply chain finance solutions impact with financial service providers finance performance. This research is the first to study the combined supply chain solutions relationship with financial service providers’ financial performance. By identifying the combined supply chain finance solutions relationship with financial service provider financial performance, this study gives future direction to scholars to further investigate the implications of financial performance on supply chain finance service users from buyers and suppliers as supply chain finance participants. Moreover, this study increases the supply chain finance solutions knowledge by investigating the impact of financial risk on financial service providers’ financial performance. Therefore, the findings allow finance providers to evaluate their offered supply chain finance solutions based on different firm resources.
Theoretical background
Bargaining power theory in which all parties are in an argumentative situation like contract writing, making agreements, or barraging contracts where one party has more influence over the other party. Bargaining power is the relative ability of parties to exert influence over each other in a situation [18]. In supply chain finance, bargaining is called bargaining if one party has strong power and exercises power to gain more over the weaker party [19]. Financial service providers may not determine the risk level by offering supply chain finance solutions, but how financial risk affects their financial performance leads to a strong bargaining power exercise.
With more financial visibility for the financial service provider, they will gain more from the transaction over financial risk [20]. When banks offer supply chain finance solutions and act as risk-taker for the other two parties (buyers and suppliers) they can influence the other parties when they make the contract or negotiate the agreement. Banks with strong bargaining power towards other parties suggested that as risk-taker, they are more in the situation to extract influence over others to get offset against risk [21]. Bargaining power impact on financial service provider performance will be clearer via financial risk level, which forces the other parties to do what they will otherwise be less willing to do, for example, more growth [22].
In this case, if all parties have equal bargaining rights, they perform perfect rights equally. In actual business or bank loan situation is different, and one party may have more power over the other. Banks hold longer cash and create a deficit for the other two parties that have to find finance solutions for different banks. In this case, banks have more bargaining power, and the other two parties have less access to credit, ultimately leading them to high costs [23]. The weak bargaining power of parties has forced them to bear more risk and cost through the use of finance [2]. In the case of financial service providers, banks put pressure on buyers and suppliers to accept late payments if they believe that other parties have resource slack. With this behavior, banks might be possible to increase their bargaining power as the other two parties prepare to formally file for bankruptcy [21]. As a financial service provider with strong bargaining power has an opportunity to have more favorable finance provider terms against other parties, a financial service provider may choose not to use power against a weak party due to concern about the party disruptions and overall will affect financial performance and growth [24].
Literature review and hypothesis development
Supply chain management is collaborating and coordinating different supply chain parties’ goods flow, information, and finance flow optimization [4]. Different studies contribute to it as supply chain finance (SCF) can create a win-win situation for all the participants like suppliers, buyers, and financial service providers [25, 26]. Supply chain finance has a significant intersection of trade finance and the supply chain management field. Overall, all supply chain movement is to convert the material and information flow into the desired form along with the effective use of financial flows for all the supply chain parties [10]. The basic idea of supply chain finance all partices of supply chain get mutal value [13]. Morck [17], added classic firm-oriented practices in supply chain finance now extended to deal with cash conversion cycle, Weighted average cost of capital (WACC), cash flow management, receivables and payables.
When measuring a firm financial performance and different financial decision phenomena, the Tobin’s Q ratio is one of the indicators used in literature. Wernerfelt [27], used Tobin’s Q ratio to explain cross-sectional returns implying a proxy for risk. Mansyur [28], used Tobin’s q to measure firm performance with the relative importance to measure share effect. In literature, the Tobins q ratio is not limited and is used to measure firm performance. All financial performance combines all the management factors that may be used for optimal profit achievement in any firm resources [29].
Bank’s financial performance measurement, also known as profitability [30, 31], measured financial performance by using return on assets (ROA) and net interest margin (NIM). Kassi [32], examined USA banks’ failure factors and concluded return on assets has a significant performance factor for banks’ failure.
H1: Supply chain finance solutions have a positive impact on financial service providers’ financial performance.
Common risks are liquidity, credit, market, and non-financial risks [33]. Out of all financial risks directly affect the company’s financial position internally and in the market. Any fluctuation in return will lead to financial risk [34]. Due to financial movement in financial markets arises financial risk [35]. Usually, it is linked with leverage and risk about all the due obligations and liabilities or not meeting with current assets. Due to the asymmetry of information among service providers, financial risk increases, and supply chain finance reduce this fact and reduce the uncertainty effect [36, 37]. Many service providers may fail to evaluate small businesses and their financial perspectives with conventional financing solutions [38]. Conversely, supply chain finance is offered by service providers and many substantial relevant business solutions. Supply chain participants maintained transaction history and credit informaiton [39].
Financial risk includes credit, liquidity, and operating risks, contributing to financial performance volatility [40].
Bolton [41], examined that financial risk significantly impacts financial performance. Financial success is measured using ROA and ROE, net equity to total assets, gross debt to total assets, and loan-deposit ratio for financial risk management. For this analysis, the population was 10 Botswana commercial banks with secondary data set time 2011–2018 using different research tests (descriptive statistics, correlation, and regression analysis. Finding of the study was interest rate had a significant negative effect on ROA and ROE.
Malkiel [42], examined the financial risk relationship with ownership structure using Tobin’s q ratio. They concluded that financial leverage and business risk significantly negative impact inside ownership structure and firm financial performance.
H2: Supply chain finance solutions enable the finance providers to reduce/mitigate their financial risk by improving their performance.
Methodology
In this research, Asian Development bank registered countries, Pakistan, China and Bangladesh 25 banks data used as sample from 2012–2021. This research sample covers conventional banks and excludes Islamic and saving investment banks due to their different business models. It focuses on the country level rather than the individual level of firm and soltuions. All variables’ data are collected on an annual base from the bank’s official websites and annual reports of banks.
Variables description
Financial performance.
Financial performance is measured in two ways; internal and external. Internal financial performance is estimated using the ROA of the year. First, ROA is computed as the net income ratio to total assets [43]. The value has been transformed into a log of ROA to improve the data normality. The ROA is selected because the return on assets shows the management’s ability to make a profit from the bank’s assets
ROA is the return on assets, PAT is profit after tax, and TA is total assets.
Second, the external market is measured by Tobin’s Q. this ratio is consistent with the [44], established efficient market hypothesis. This ratio is used to measure the company’s potential future growth and existing assets. Tobin’s Q ratio measures the investor’s future expectations with the business’s current strategies for evaluation [43, 45–47].
Where the Q ratio is Tobin’s Q, the company’s total market value is outstanding stock and debt, and the value of the total assets is the replacement cost of the company’s assets (book value) (Christensen et al., 2010).
Supply chain finance index.
In literature, there is list of 21 supply chain finance solutions offered by banks (S1 Annex). Each bank website check and mark how many supply chain finance solutions offered by particular bank out of list of the supply chain finance solutions, if a bank offers any one solution like account receivables finance or an early payment discount program it is coded as 1. On the other side if bank is not offering any particular supply chain finance solution it is coded as 0. The Principal Component Analysis technique of [48], is applied to quantify the supply chain finance solutions index using supply chain finance solutions as proxies.
Where SCFSIt represents the supply chain finance solutions index, SSt represents supply chain finance solutions.
Financial risk.
Financial risk is the proability of losing profit based on the bank’s financial characteristics [11]. Financial risk is the average of two risks, credit risk and liquidity risk, contributing to financial performance [40]. Credit risk is measured as capital adequacy ratio (CAR) and non-performing loans (NPL). At the same time, the liquidity ratio is measured as total loans divided by total deposits.
Where FR is financial risk, CR is credit risk, and LR is liquidity risk.
Control variables. The research model includes bank-specific control variables. The bank-specific variables are tier 1 capital ratio (T1), loan-to-asset ratio (LA), other earnings assets (OEA), and bank size (BS). T1 is used to control for differences in banking sector development, while the other variables (LA, OEA, and BS) are used to control for systemic, idiosyncratic, and market risks of the bank.
Empirical model
In this research, a panel data analysis is used. To measure the model variables:
(2)
i and t represent, banks and year indices; ai & δt represent firm & year level effect respectively; SCFSI is supply chain finance solutions index, FR is financial risk, BS is bank size, LA is leverage, TCA is tier 1 capital ratio, EA is earning assets, ai = country-level effect δt = year-level effect, β are the coefficients variables and ε = error term regression investigation in this study will focus on two main approaches: fixed and random effects models.
Regression model-fixed-effect
The objective of this research, analyze the supply chain finance solutions impact on finance providers (banks) financial performance (H1) and moderating role of financial risk (H2). In the analysis, there were many challenges. First, although by adding many control variables like bank size, earning assets, tier-1 capital ratio, and loan-to-assets ratio, there are many other characteristics are unobservable which may relate the firm’s decision to offer supply chain finance solutions and simultaneously its financial performance, raising possible concerns of endogeneity [49]. Second, this research sample time covered 10 years period of 2012 to 2021, during this time any event or trend like (COVID-10) is unobservable might also relation the financial performance. To address these challenges, First, all sample firms provided supply chain finance solutions between 2012–2021, with this firm-level fixed-effect estimation possible.
Furthermore, this research was interested in how firms’ supply chain finance solutions affect their financial performance over time. Firm-level fixed effect approach used, there are many times variant firm characteristics that may link with the firm’s supply chain finance solutions and financial performance of financial service providers such as firm culture and corporate decision-making; this will lead to reduced endogeneity concern and make within-firm consistent. Furthermore, there may be any event or trend effect which can effect model, to resolve this year-level fixed effect included. The fixed effect regression equation is:
(3)
(4)
i and t represent, banks and year indices; ai & δt represent firm & year level effect respectively, and ε is an error term. SCFSI supply chain solutions index on financial service providers’ financial performance (H1) and FR the financial risk moderating role (H2)
The first is the F-test to determine whether a mixed regression or fixed effect model should be used. The F-test p-value is 0.0000, which is significant and uses a fixed-effect model. Hausmann test p-value is 0.0000 rejecting the null hypothesis and using the fixed-effect model over the random-effect model.
Results
Table 1 shows the correlation, mean and standard deviations of all variables. Fixed-effect model regression results are shown in Tables 2 and 3. There are 4 regression models in Tables 2 and 3: Model-1 results show that control variables with bank and year level fixed effect; Model-2 results show that financial risk direct effect and Model-3 results show that direct effect of the supply chain finance solutions index. Model-4 results show that financial risk moderating role is statistically significant at (p<0.01), whereas F-statistics and R-squared 0.54 to 0.98 respectively.
Table 2 shows the results of the return of assets effect on supply chain finance solutions is significant (p<0.10); in model 4, its coefficient is 0.9888, and with Tobin’s Q, its coefficient is 0.0106 means it has a positive relation with finance providers financial performance. This means that a 1% increase in supply chain finance solutions will also increase financial performance by 0.9888 and 0.0106, respectively. Thus, H1 is accepted based on model number 4.
Financial risk as a moderating effect, the interaction between supply chain finance solutions and financial risk is significant (p<0.01), as shown in model 4 Table 3. The coefficient is negative 0.0067, which means the moderating effect is interference rather than reinforcement. Financial risk reduces when finance providers offer SCF solutions with reinforcement of financial performance. In Table 2, the coefficient is positive 1.0209. Financial risk as moderating effect reinforcement not an interference.
Conclusion
This research reveals the supply chain fiancé solutions effect on finance provide financial performance. First, it demonstrates that finance provider finance performance may enhance by offering supply chain finance supply chain finance solutions enhance the financial performance of financial service providers. Moreover, financial service providers’ financial performance financial risk mitigates with supply chain finance solutions. As per [10], finance provider financial performance increase and relaxation in financing term is capital level strategy of supply chain finance. Following the literature pattern, with the help of supply chain finance solutions, financial service providers’ financial risk can mitigate and enhance financial performance because financial risk impacts financial service providers and overall stakeholders [50]. It has to consider for supply chain finance solutions development and implementation. Therefore, the research findings indicate that financial service providers can use supply chain finance solutions as a strategic move to enhance financial performance and mitigate their financial risk, as shown in Tables 2 and 3 (model 4).
Implications of research
Future research and implication of this research, first, some researchers have emphasized that supply chain finance solutions can provide a “win-win” situation, the financial risk implication of SCF solutions and its impact of finance providers financial performance are not clear. This is the unique study, how empirically supply chain finance solution index affects financial service providers’ financial performance with financial risk. Scholars in finance allow them to extend the finance literature boundaries and investigate supply chain finance solutions associations with non-financial strategies linked with financial risk.
References
- 1. Zhao X., Yeung K., Huang Q., and Song X. (2015). Improving the predictability of business failure of supply chain finance clients by using external big datasets. Industrial Management & Data Systems.
- 2. Wuttke D. A., Blome C., Foerstl K., and Henke M. (2013). Managing the innovation adoption of supply chain finance empirical evidence from six European case studies. Journal of Business Logistics, 34:148–166.
- 3. Jia F., Blome C., Sun H., Yang Y., and Zhi B. (2020). Towards an integrated conceptual framework of supply chain finance: An information processing perspective." International Journal of Production Economics 219 (2020): 18–30.
- 4. Gelsomino L. M., Mangiaracina R., Perego A., and Tumino A. (2016). Supply chain finance: a literature review. International Journal of Physical Distribution & Logistics Management, 46:2014–0173.
- 5. Ma H.-L., Wang Z., and Chan F. T. (2020). How important is supply chain collaborative factors in supply chain finance? a view of financial service providers in china. International Journal of Production Economics, 219:341–346
- 6. Martin J. and Hofmann E. (2017). Involving financial service providers in supply chain finance practices: Company needs and service requirements. Journal of Applied Accounting Research.
- 7. Berger Allen N., Deyoung Robert, 1997. Problem loans and cost efficiency in commercial banks board of governors of the federal reserve system. J. Bank. Finance 21 (6), 849–870.
- 8. Boermans M.A. (2011). Firm Performance under Financial Constraints and Risks: Recent Evidence from Micro Finance Clients In Tanzania, HU University of Applied Sciences Utrecht.
- 9.
Hofmann E., Belin O., et al. (2011). Supply chain finance solutions. Springer.
- 10. Peng Y. et al., (2011). An Empirical Study of Classification Algorithm Evaluation for Financial Risk Prediction, Applied Soft Computing, 11, pp. 2906–2915.
- 11. Fellenze M.R., Augustenborg C., Brady M., and Greene J., 2009. Requirements for an evolving model of supply chain finance: A technology and service providers perspective. Communications of the IBIMA, 10(29), 227–235.
- 12. Lam H. K., and Zhan Y. (2021). The impacts of supply chain finance initiatives on firm risk: evidence from service providers listed in us. International Journal of Operations & Production Management, 41:383–409.
- 13. Selvaraj J. J. A. and Wesley J. R. (2020). Integrated supply chain risk management capabilities and its impact on supply chain demand management-an empirical study. International Journal of Business Information Systems, 34:447–462.
- 14. Jo H., and Na H., 2012. Does CSR reduce firm risk? Evidence from controversial industry sectors. Journal of Business Ethics, 110(4), 441–456.
- 15. Sitkin S.B., and Pablo A.L., 1992. Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17(1), 9–38.
- 16. Tse Y.K., Zhang M., and Jia F., 2018. The effects of risk and reward sharing on quality performance. International Journal of Operations & Production Management, 38(12), 2367–2388.
- 17. Morck R., Shleifer A., and Vishny R. W. (1988). Management ownership and market valuation: An empirical analysis. Journal of financial economics, 20:293–315.
- 18. Crook T. R., and Combs J. G. (2007). Sources and consequences of bargaining power in supply chains. Journal of operations management, 25:546–555.
- 19. Cho W., Ke J.-y. F., and Han C. (2019). An empirical examination of the use of bargaining power and its impacts on supply chain financial performance. Journal of Purchasing and Supply Management, 25:100550.
- 20. Fabbri D. and Klapper L. F. (2016). Bargaining power and trade credit. Journal of corporate finance, 41:66–80.
- 21. Lavie D., Stettner U., and Tushman M. L. (2010). Exploration and exploitation within and across organizations. Academy of Management Annals, 4:109–155
- 22. Oliveira M., Kadapakkam P.-R., and Beyhaghi M. (2017). Effects of customer financial distress on supplier capital structure. Journal of Corporate Finance, 42:131–149.
- 23. Munson C. L., Rosenblatt M. J., and Rosenblatt Z. (1999). The use and abuse of power in supply chains. Business Horizons, 42:55–56.
- 24. Mentzer J. T., DeWitt W., Keebler J. S., Min S., Nix N. W., Smith C. D., et al. (2001). Defining supply chain management. Journal of Business Logistics, 22:1–25.
- 25. Wang Z., Wang Q., Lai Y., and Liang C. (2020). Drivers and outcomes of supply chain finance adoption: An empirical investigation in china. International Journal of Production Economics, 220:107453.
- 26. Cavenaghi E. (2014). Supply-chain finance: The new frontier in the world of payments. Journal of Payments Strategy & Systems, 7:290–293.
- 27. Wernerfelt B. and Montgomery C. A. (1988). Tobin’s q and the importance of focus in firm performance. The American Economic Review pages 246–250.
- 28. Mansyur N. (2017). Impact financial risk on financial performance bank in Indonesia. The International Journal Of Business & Management, 5:305–31
- 29. Qin X. and Pastory D. (2012). Commercial banks profitability position: The case of Tanzania. International Journal of Business and Management.
- 30. Ruziqa A. (2013). The impact of credit and liquidity risk on bank financial performance: the case of Indonesian conventional bank with total assets above 10 trillion rupiahs. International Journal of Economic Policy in Emerging Economies, 6:93–106.
- 31. Said R.M. & Tumin M.H. (2011). Performance and Financial Ratios of Commercial Banks in Malaysia and China, International Review of Business Research Papers, 7 (2), pp. 157–169.
- 32. Kassi D. F., Rathnayake D. N., Louembe P. A., and Ding N. (2019). Market risk and financial performance of non-financial companies listed on the Moroccan stock exchange. Risks, 7:20.
- 33. Kioko C., Olweny T., and Ochieng L. (2019). Effect of financial risk on the financial performance of commercial banks in Kenya listed on the Nairobi stock exchange. The Strategic Journal of Business & Change Management, 6:1936–1952.
- 34. Jorion P. and Khoury S. (1996). Financial risk management. Cambridge/Massachusetts
- 35. Pfohl H.-C. and Gomm M. (2009). Supply chain finance: optimizing financial flows in supply chains. Logistics Research, 1:149–161.
- 36. Shen B., Choi T.-M., and Minner S. (2019). A review on supply chain contracting with information considerations: information updating and information asymmetry. International Journal of Production Research, 57:4898–4936.
- 37. Moretto A., Grassi L., Caniato F., Giorgino M., and Ronchi S. (2019). Supply chain finance: From traditional to supply chain credit rating. Journal of Purchasing and Supply Management, 25:197–217.
- 38. Xu X., Chen X., Jia F., Brown S., Gong Y., and Xu Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204:160–173.
- 39. Dimitropoulos P. E., Asteriou D., and Koumanakos E. (2010). The relevance of earnings and cash flows in a heavily regulated industry: Evidence from the greek banking sector. Advances in Accounting, 26:290–303.
- 40. Sathyamoorthi C., Mapharing M., Mphoeng M., Dzimiri M., et al. (2020). Impact of financial risk management practices on financial performance: Evidence from commercial banks in Botswana. Applied Finance and Accounting, 6:25–39.
- 41. Bolton P., Chen H., and Wang N. (2011). A unified theory of Tobin’s q, corporate investment, financing, and risk management. The Journal of Finance, 66:1545–1578.
- 42. Malkiel B. G., & Fama E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
- 43. Christensen J., Kent P., & Stewart J. (2010). Corporate governance and company performance in Australia. Australian Accounting Review, 20(4), 372–386.
- 44. Demsetz H., & Villalonga B. (2001). Ownership structure and corporate performance. Journal of Corporate Finance, 7(3), 209–233.
- 45. Ehikioya B. I. (2009). Corporate governance structure and firm performance in developing economies: Evidence from Nigeria. The International Journal of Business in Society, 9(3), 231–243.
- 46. Rodriguez-Fernandez M. (2016). Social responsibility and financial performance: The role of good corporate governance. BRQ Business Research Quarterly, 19(2), 137–151.
- 47. Baker M. and Wurgler J., “Investor Sentiment in the Stock Market,” vol. 21, no. 2, pp. 129–151, 2007.
- 48. Luo X., and Bhattacharya C.B., 2009. The debate over doing good: Corporate social performance, strategic marketing levers, and firm-idiosyncratic risk. Journal of Marketing, 73(6), 198–213.
- 49. Wooldridge J. M. (2019). Correlated random effects models with unbalanced panels. Journal of Econometrics, 211(1), 137–150.
- 50. Agrawal N., Dai Q., and Walden E.A., 2011. The more, the merrier.How the number of partners in a standard-setting initiative affects shareholder’s risk and return. MIS Quarterly, 35(2), 445–462.