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Assessment of Credit Risk Management and Financial Performance of Microfinance Banks

  • Gbenga I. Olorunsola  PhD, ACA, FCIB
  • Taslim Ganiyu  Olalekan
  • Oyekunbi Olubukola Dele- Oladejo PhD
  • 833-846
  • Apr 14, 2023
  • Accounting & Finance

Assessment of Credit Risk Management and Financial Performance of Microfinance Banks

Gbenga I. Olorunsola  PhD, ACA, FCIB1, Taslim Ganiyu  Olalekan2 & Oyekunbi Olubukola Dele- Oladejo PhD3
1Department of Management and Accounting, Lead City University, Ibadan
2Department of Banking and Finance, Covenant University, Ota, Nigeria.
3Department of Management Studies, Dominican University , Samonda Ibadan, Nigeria 

Received: 08 March 2023;  Accepted: 17 March 2023; Published: 14 April 2023

ABSTRACT

Many times, microfinance companies price credit incorrectly and fail to take all embedded costs into account, which increases the risks involved and makes mitigation a herculean task. This study used panel data from the income statement and balance sheet of the five selected Nigerian microfinance banks (five cross-sections) for ten (10) years spanning between 2012 and 2021 to examine the effect of credit risk management (via Non-performing loan to total deposit ratio, Non-performing loan to total loan and advance ratio, Capital adequacy ratio) on financial performance (proxied by returns on asset) while making provision for key control variables like Leverage ratio and Firm size. This study made use of panel data methodology and the random effect was found most appropriate for modeling the data. The result shows that credit risk management is not significant in explaining the performance of microfinance banks in Nigeria. This is evident from the fact that the selected banks were over-prudent in their credit risk management policy which includes not giving out enough loans and hence resulting in low income. The over-prudent credit risk management by these banks is evident from their descriptive statistics. The selected microfinance banks are keeping too much money as reserves. When banks are not using their assets (giving loans and advances) to generate income, they will eventually lose money. As long as it’s tied to returns for microfinance banks, credit risk is not a bad thing. According to empirical theory, bank returns increase as risk increases, but the bank must moderate its risk and anticipate returns.

Keywords: Credit risk management, Non-performing loan to total deposit ratio, Non-performing loan to total loan and advance ratio, Capital adequacy ratio, Financial performance, Returns on asset

INTRODUCTION

The financial sector particularly microfinance is one of the key drivers of economic development in a country. In the quest for economic progress, particularly in a developing nation like Nigeria, their lending activities to people, Small and Medium-scale Enterprises (SMEs), and industries are essential (Afolabi, Obamuyi & Egbetunde, 2020; Kajola, Adedeji, Olabisi & Babatolu, 2018).

Microfinance banks are specific organizations that offer low-income people with severally financial and wealth management services which include micro-credit, savings, etc. intending to enhance the economic stability of small-scale businesses in urban as well as rural areas (Abubakar, Tobi & Abdullahi, 2020). However, due to their evolving nature and the challenging business climate in which these companies operate, microfinance banks just like any other conventional banks are faced with several risks (Ramazan & Gulden, 2019). The equity, liabilities, market value and profitability of these financial institutions could all be adversely affected by each of these risks (Koch & MacDonald, 2014). Credit risk, however, is one of the most momentous risks that confront banks generally since loans that these banks grant are the main source of revenue for the banking industry (Ramazan & Gulden, 2019). Credit risk is probably the major risk confronting these institutions, and the sustainability of banks’ operations rests more on how well this risk is managed (Gieseche, 2004).  Due to the unexpected devaluation of the Naira against other foreign currencies, increase in global oil prices and drastic drop in equities market indices, banks in Nigeria have been put under strain because of the declining quality of their loan assets (BGL Banking Report, 2010). Microfinance institutions commonly find themselves wrongly pricing loans and neglecting to include all intrinsic costs; as a result, the risks incurred increase and are harder to offset (Sudi & Maniagi, 2020). Inaccurate credit grading and identification among these businesses are the cause of these issues. Vulnerability and defaulting become the norm as a result of the inadequate examination of the risks connected with loans and disregard for the impact on the organization. Managing these risks is what is referred to as credit risk management.

Credit risk management refers to the approaches, practices, and controls put in place at a company to achieve optimal receivables of customers’ payments, hence lowering the risk of nonpayment (Mokogi, 2003). The long-term survival of a financial organization depends on the effectiveness of credit risk management, which is a vital part of a comprehensive risk management plan. It helps cut down on bank losses. As a crucial step in the lending process, credit risk management is fundamental for the banking sector, according to Misker (2015). Retaining credit risk exposure lowers bank risk while safeguarding the bank from the detrimental effects of credit risk. Any institution that offers financial services, particularly the microfinance business, must have a strong credit risk management program. Operational credit risk management occurs, when a microfinance institution has set policies or measures to govern their actions when extending credits in ways that minimize the detrimental effect on their capital and incomes (Abubakar, Tobi & Abdullahi, 2020).

Financial performance, on the other hand, is commonly defined as a firm’s stability and profitability. The risk elements are referred to as its stability, and the financial return is referred to as its profitability (Mohamed & Onyiego, 2018). Financial performance, which is assessed by net income and cash from operations, refers to a firm’s capacity to make fresh resources from ongoing operations over a given time (Danson, 2012; Ally, 2013). The amount of money a firm makes or loses over a specific period is measured by its financial performance. To evaluate the financial performance of banks, various metrics have been employed. These measures include Net Interest Margin (NIM), Return on Asset (ROA), and Return on Equity (ROE) (Murthy & Sree, 2003; Alexandru, Genu & Romanescu, 2008).

The nexus between credit risk management and financial performance has been established and empirical works of the literature show that credit risk management has a positive effect on the financial performance of banks (Abubakar, Tobi & Abdullahi, 2020; Kajola, Adedeji, Olabisi & Babatolu, 2018; Kurawa & Garba, 2014; Asfaw & Veni, 2015; Harcourt, 2017). Bank failures will be eliminated if risks are well-managed in banks, and the economy would be steady (Jaseviciene & Valiuliene, 2013). Controlling the non-performance of advances is essential for the functioning of a specific microfinance foundation and the financial environment of the economy (Kangethe, Oluoch, & Nyangau, 2019).

Even though many studies have looked at the effect of credit risk management on the financial performance of banks, the majority of the studies are centered on commercial banks. Very few studies have considered microfinance banks. Also, to the best of my knowledge, there seems to be no study that has investigated how the financial performance of microfinance banks (dependent variable measured by ROA) is influenced by credit risk management (independent variable) in terms of Non-performing loan to total deposit ratio, Non-performing loan to total loan and advances ratio, Capital adequacy ratio while allowing for control variables such as Leverage ratio and Firm size. Against this background, this article aims at investigating the effect of credit risk management on the financial performance of Microfinance banks in Nigeria. Consequent to that, this article specifically:

  1. Investigate the effect of Non-performing loan to total deposit ratio on the financial performance of Microfinance banks in Nigeria.
  2. Ascertain the effect of Non-performing loans to total loan and advance ratio on the financial performance of Microfinance banks in Nigeria.
  3. Analyze the effect of the Capital adequacy ratio on the financial performance of Microfinance banks in Nigeria.

This article is divided into five parts. A general summary of the study is given in Section 1 (problem statement and research objectives), and the empirical assessment of relevant literature is presented in Section 2. Materials and methods are covered in Section 3. Data analysis and empirical findings are described in Section 4. The last section which is Section 5 contains the study’s conclusions and recommendations for policy.

LITERATURE REVIEW

Abubakar, Tobi, and Abdullahi (2020) analyzed the impact of credit risk management on the financial performance of Nigerian-listed microfinance banks. Data were acquired from two microfinance banks listed on the Nigerian Stock Exchange from 2012 to 2017 through their annual reports and accounts. Using multiple regression, panel regression, and Pearson correlation, the collected data were statistically analyzed. The results showed that the loan loss provision ratio, and capital adequacy ratio indirectly and significantly predicted the financial performance of the selected. However, the non-performing loan to total loan ratio showed to be a positive and significant predictor of financial performance. Bank size and inflation are the two controls that are unrelated to financial performance. According to the anticipated income theory that underpins this study, credit risk management was found to have a substantial impact on the financial performance of microfinance banks in Nigeria.

Sudi and Maniagi (2020) analyzed the effect of credit risk management techniques on the financial performance of Nairobi microfinance enterprises. The study’s specific objective was to determine the effect of credit reminder practices, credit risk control practices, viability identification practices, and credit risk grading practices on the financial performance of Kenyan microfinance enterprises. With a sample size of 96 replies, this study’s population consisted of 1147 employees of Nairobi’s microfinance organizations. Utilizing questionnaires distributed to each branch’s branch managers and credit managers, this study gathered primary data. The results of the study supported that all the enlisted credit risk measures were the significant determinant of the performance of Kenyan microfinance firms. The study suggests that organizations should concentrate more on strengthening credit risk management techniques to be more competitive and handle more volatile environments. To improve performance, organizations should streamline their risk management culture.

In Nairobi County, Kenya, Kangethe, Oluoch, and Nyangau (2019) assessed the effect of credit risk management on the loan performance of deposit-taking Microfinance organizations. All 13 of Nairobi’s microfinance institutions that accept deposits served as the study’s target population. The Krejcie and Morgan (1970) method was used to select a sample of 118 microfinance employees. The study found that changes in credit evaluation techniques, credit risk management, credit terms, and credit approvals significantly affect how well microfinance organizations perform in terms of their loan performance. According to study findings, microfinance institutions should enhance their processes for recognizing, analyzing and assessing risks emerging from credits.

The impact of credit risk management on the financial performance of ten Nigerian-listed deposit money banks from 2005 to 2016 was quantitatively examined by Kajola, Adedeji, Olabisi, and Babatolu (2018). The independent variable, credit risk management, was substituted by three factors: Capital Adequacy Ratio, Non-performing Loan to total Deposit Ratio and Non-performing Loan to Total Loan Ratio. The study made use of two financial performance measures which include Return on Equity and Return on Asset. The findings revealed that proposed credit risk measures show a noteworthy link with financial performance. Based on the study’s findings, deposit money bank management should implement strict credit rules that would assist banks in efficiently evaluating the creditworthiness of their clients. Current methods for monitoring, detecting, and controlling credit risk should be developed by regulatory bodies.

Juma, Otuya and Kibati (2018)focused on how credit management (debt recovery and credit standard) impacted the financial performance of deposit-taking SACCOS in Nakuru town. About 74 workers were sampled out of the total population consisting of 220 staff. Employees completed questionnaires to collect the data. The simple linear regression analysis showed that credit management (debt recovery and credit standard) has a significant impact on the SACCOS’ financial performance. According to the article’s findings, all the investigated constructs are significant in explaining SACCO’s financial performance. According to the report, SACCOs should improve debt recovery procedures and establish efficient credit management standards.

MATERIALS AND METHODS

The secondary data for this article were gotten from the income statement and balance sheets of the selected microfinance banks in Nigeria. The data contained a panel data of five microfinance banks (five cross-sections) for ten (10) years spanning between 2012 and 2021 for some credit risk management indicators such as Non-performing loan to total deposit ratio, Non-performing loan to total loan and advance ratio, Capital adequacy ratio. The data also captured the financial performance indicator which is returns on assets and finally the control variables such as Leverage ratio and Firm size.

Multiple regression analysis was done in this study. Using the panel data regression approach, the models are estimated. The three commonlyadopted regression models (i.e. fixed effect and random effect method, pooled OLS) for panel data were employed to examine the causal link between the response and predictor variables. Table 1 below provides an overview of the model option.

Table 1: Model Selection Criteria

Fixed Effect Random Effect Selection
If no fixed effect If no random effect Choose the Pooled OLS
If there is a fixed effect If no random effect Select fixed effect model
If no fixed effect If there is a random effect Choose the Random effect model
If there is a fixed effect If there is a random effect Use Hausman test to select the best model from either fixed or random effect

Source: Park, 2011.

The regression equation for this work is specified as:

Where  are the regression coefficients, Return on asset (ROA), Non-performing loan to total deposit ratio (ND), Non-performing loan to total loan and advance ratio (NT), Capital adequacy ratio (CA). The data will also capture the financial performance indicator which is returns on assets and finally, the control variables such as Leverage ratio (LR), Firm size (FZ), and ε is the error term. Variables Description and Formulas are accessible in Table 2 below

 

 

Table 2: Variables Description and Formulas

Variables Classification Description
Return on asset (ROA) Dependent Variable It offers details on how well management has made revenue from the company’s assets. ROA=Profit after tax/Total Asset
Non-performing loan to total deposit ratio (ND) Independent Variable This is an effective method of managing credit risk. An extremely low ratio indicates minimal risk for the bank, but it also indicates that it is not employing its assets to create revenue and might even experience a financial loss. ND=Non-performing loan/Total deposit.
Non-performing loan to total loan and advance ratio (NT) Independent Variable This represents the proportion of non-performing loans in a bank’s loan portfolio compared to all of its outstanding debt.NT= Non-performing loan/Total loan and advance.
Capital adequacy ratio (CA) Independent Variable It is a measure of the bank’s long-term ability to meet its commitments. CA=Shareholders’ fund/Total assets.
Leverage ratio (LR) Control Variable This is used to evaluate a company’s capacity to fulfill its financial obligations. LR= Long-term debts/Total assets
Firm size (FZ) Control Variable Firm size is frequently cited as a crucial, essential firm attribute. It is the extent of available resources. FZ=Log of Total Asset

DATA ANALYSIS AND RESULT

The panel data of five microfinance banks (five cross sections) for a ten (10) year interval spanning from 2012 to 2021 is presented below. These include data on Return on asset (ROA), Non-performing loan to total deposit ratio (ND), Non-performing loan to total loan and advance ratio (NT), Capital adequacy ratio (CA), Leverage ratio (LR), and Firm size (FZ).

Table 3: The Data

Microfinance banks Years ROA ND NT CA LR FZ
NPF MFB 2012 0.0687 0.0284 0.0195 0.4943 0.0000 6.8916
2013 0.0451 0.0328 0.0227 0.4512 0.0000 6.9386
2014 0.0440 0.0292 0.0215 0.3755 0.0459 7.0360
2015 0.0417 0.0435 0.0365 0.3447 0.0511 7.0911
2016 0.0449 0.0240 0.0179 0.3763 0.0283 7.0921
2017 0.0396 0.0196 0.0198 0.2916 0.0972 7.2028
2018 0.0111 0.0822 0.0812 0.2640 0.1181 7.2455
2019 0.0407 0.0836 0.0687 0.2721 0.1004 7.2919
2020 0.0245 0.0334 0.0297 0.2184 0.1194 7.3996
2021 0.0221 0.0325 0.0304 0.1793 0.0847 7.5047
ACCION MFB 2012 0.1053 0.7957 0.2292 0.5246 0.0674 6.4321
2013 0.0994 0.4774 0.1655 0.4148 0.1213 6.5969
2014 0.1224 0.1955 0.0699 0.5048 0.1090 6.7064
2015 0.0804 0.3214 0.1287 0.4419 0.1601 6.8318
2016 0.0714 0.4729 0.1634 0.4457 0.1562 6.8773
2017 0.0926 0.3681 0.1265 0.4465 0.1686 6.9418
2018 0.0954 0.3859 0.1319 0.4186 0.2134 7.0419
2019 0.0749 0.2693 0.1150 0.4294 0.1617 7.0869
2020 0.0102 0.3414 0.1765 0.4165 0.1970 7.1100
2021 0.0388 0.4717 0.1539 0.4033 0.2505 7.1679
INFINITY MFB 2012 0.1579 0.0584 0.0425 0.3366 0.1663 5.6857
2013 0.1305 0.0505 0.0331 0.3807 0.1493 5.7710
2014 0.1605 0.0580 0.0297 0.4546 0.1405 5.8501
2015 0.0910 0.0651 0.0324 0.4213 0.1975 5.9804
2016 0.1019 0.0733 0.0368 0.4651 0.1303 6.0338
2017 0.0956 0.0967 0.0559 0.4819 0.0884 6.1035
2018 0.0724 0.0854 0.0488 0.4585 0.1046 6.1961
2019 0.0789 0.0733 0.0408 0.4633 0.0797 6.2673
2020 0.0653 0.1479 0.0720 0.5827 0.4025 6.2146
2021 0.0594 0.1578 0.0725 0.3623 0.2680 6.4945
CAPSTONE MFB 2012 0.0105 0.0008 0.0006 0.3699 0.0000 5.1782
2013 0.0133 0.0041 0.0023 0.3430 0.0000 5.2281
2014 0.0051 0.0136 0.0180 0.2106 0.1792 5.4457
2015 0.0125 0.0133 0.0125 0.2606 0.1681 5.6494
2016 -0.0029 0.0230 0.0141 0.3302 0.1433 5.5426
2017 0.0001 0.0170 0.0111 0.3073 0.1333 5.5740
2018 0.0005 0.0256 0.0273 0.2896 0.0000 5.6005
2019 0.0006 0.0199 0.0186 0.2425 0.0000 5.6787
2020 0.0059 0.0497 0.0505 0.2190 0.0000 5.7347
2021 0.0126 0.0809 0.0671 0.2320 0.0000 5.7342
ASHA MFB 2012 0.1650 0.0019 0.0012 0.4148 0.0000 5.9157
2013 0.1197 0.0013 0.0009 0.4265 0.0000 6.0463
2014 0.1560 0.0018 0.0011 0.4654 0.0000 6.1859
2015 0.1058 0.0007 0.0004 0.2758 0.1373 6.3545
2016 0.1119 0.0016 0.0008 0.4131 0.1509 6.3162
2017 0.1144 0.0045 0.0021 0.4131 0.1671 6.3162
2018 0.1510 0.0172 0.0078 0.2468 0.0010 6.5718
2019 0.0992 0.0669 0.0295 0.6205 0.0000 6.7084
2020 0.0037 0.2268 0.0857 0.4792 0.0753 7.1899
2021 0.1357 0.2180 0.0800 0.5215 0.0000 7.2903

Source: Balance sheet and income statement for the selected Microfinance banks

Table 4: Descriptive statistics of Data

ROA ND NT CA LR FZ
 Mean  0.068144  0.123270  0.054090  0.384038  0.102658  6.426904
 Median  0.070050  0.054250  0.032750  0.413100  0.106800  6.393300
 Maximum  0.165000  0.795700  0.229200  0.620500  0.402500  7.504700
 Minimum -0.002900  0.000700  0.000400  0.179300  0.000000  5.178200
 Std. Dev.  0.050775  0.168225  0.054810  0.102172  0.087462  0.647206
 Skewness  0.241322  1.987099  1.323594 -0.106085  0.750599 -0.135165
 Kurtosis  1.912587  6.917262  4.063095  2.410009  4.047879  1.791922
 Jarque-Bera  2.948778  64.87331  16.95369  0.818971  6.982596  3.192771
 Probability  0.228919  0.000000  0.000208  0.663992  0.030461  0.202628
 Sum  3.407200  6.163500  2.704500  19.20190  5.132900  321.3452
 Sum Sq. Dev.  0.126328  1.386683  0.147205  0.511513  0.374830  20.52489
 Observations  50  50  50  50  50  50

Source: Eviews 9.0. Output (Computed by the Author)

The table demonstrates that the average for ROA, ND, NT, CA, LR and FZclusters around 0.068144, 0.123270, 0.054090, 0.384038, 0.102658 and 6.426904respectively. As a result, since the means of each series are consistently between its maximum and lowest values, all of the series display high levels of consistency.In addition, the computed average ROA is an indication the selected microfinance banks are performing well financially. All the computed credit risk management measures also show that the selected banks have been prudent in credit risk management practices. This is an indication of a significant reduction in credit risk.  ROA, ND, NT, and LR are all positively skewed, suggesting that the series’ extent of departure demonstrates an upward trend from 2012 to 2021. But CA and FZ are negatively skewed. ROA, CA, and FZ have a platykurtic distribution (Kurtosis < 3) but ND, NT and LR have a leptokurtic distribution (Kurtosis < 3). This shows that all the series are not normally distributed.

The three mostadopted regression models (fixed effect and random effect method, pooled OLS) for panel data were employed to examine the causal link between the response and predictor variables. In case the fixed and random effects don’t work, pooled OLS estimation was also done. The researcher first contrasted the random effects with the alternative, the fixed effect, to determine between fixed and random effects. Table 5 below provides an overview of the model selection.

Table 5: Regression Results

Fixed Effect Model Random Effect Model Pooled OLS
Coefficient p-value Coefficient p-value Coefficient p-value
const 0.423951 <0.0001 0.403336 <0.0001 −0.0376513 0.5963
ND 0.106874 0.2323 0.114879 0.1862 0.265508 0.0839
NT −0.515017 0.0780 −0.533137 0.0571 −0.958416 0.0468
CA −0.0602014 0.1933 −0.0546043 0.2237 0.230682 0.0018
LR −0.124711 0.0156 −0.122497 0.0118 0.0275592 0.7268
FZ −0.0474877 0.0023 −0.0446508 0.0015 0.00521129 0.6375
Goodness of Fit
Mean dependent var 0.068144 0.068144 0.068144
Sum squared resid 0.020602 0.216441 0.085423
R-squared 0.836882 0.323657
LSDV F(9, 40) 22.80238
F(5, 44) 4.211157
Log-likelihood 123.9128 65.11457 88.35713
Schwarz criterion −208.7054 −106.7570 −153.2421
rho 0.063552 0.063552 0.570298
S.D. dependent var 0.050770 0.050770 0.050770
S.E. of regression 0.022695 0.069353 0.044062
Within R-squared 0.522788
Adjusted R-squared 0.050770
P-value(F) 0.000000 0.044062
Akaike criterion −227.8256 −118.2291 0.050770
Hannan-Quinn −220.5445 −113.8605 0.044062
Durbin-Watson 1.154069 1.154069 0.050770
Breusch-Pagan test(p-value) 0.000118
Hausman test     0.424785(p>0.05)

Source: Researcher computation using Gretl Econometric Software

This section compares the results of the three most used panel regression models. First, the Fixed Effect model was compared with the Random Effects model. The outcomes revealed that both the fixed and random effect models have a good fit. The fixed effect model’s goodness of fit was determined using the LSDV F(9, 40) = 22.80238 (p<0.05). Likewise, The Breusch-Pagan test (p<0.05) was also used for determining the fit for a random effect. Since both Fixed and Random Effect models have a good fit; a Hausman’s test was carried out. Hausman’s test shows that the Random Effect model has a better fit than the Fixed Effect model, consequently, the interpretation of the result is based on the random effect model.

Regarding to the selected random effect model, the findings shows that ND (β1 =0.114879, p> 0.05), NT (β2=−0.533137, p> 0.05)and CA (β3=−0.0546043, p> 0.05)have an insignificant effect on ROA. This is an indication that the credit risk management measure is not a significant predictor of the financial performance of microfinance banks in Nigeria. However, LR (β4=0.114879, p< 0.05), NT (β5=−0.533137, p< 0.05) has a significant effect on ROA.

As suggested by the formulated specific objectives of the study, the result shows that the financial performance of Microfinance institutions in Nigeria is not significantly influenced Non-performing loan-to-total deposit ratio, Non-performing loan-to-total loan and advance ratio, and Capital adequacy ratio.

CONCLUSION AND RECOMMENDATION

This article used panel data from five microfinance banks to explore the connection between credit risk management (an independent variable) and financial performance (a dependent variable) of microfinance banks in Nigeria. The researcher found it easy to compare the study’s findings to earlier research and more recent studies on the topic.

According to the findings, it is concluded that credit risk management has no significant effect on the performance of microfinance institutions in Nigeria.  This study outcome is divergent from the work of Abubakar, et al (2020); Kajola, et al (2018). The implication of these findings is that the selected banks were over-prudent in their credit risk management policy which includes not giving out enough loans and hence resulting in low income. These banks’ over-prudent credit risk management is evident from their descriptive statistics. The selected microfinance is keeping too much money as a reserve. When banks are not using their assets (giving loans and advances) to generate income, they will eventually lose money. As long as it’s tied to returns for microfinance banks, credit risk is not a bad thing. According to empirical theory, bank returns increase as risk increases, but the bank must moderate its risk and anticipate returns. That is, a microfinance bank needs to keep a healthy balance and plan for its profits. In addition to this, the microfinance bank is required to maintain a sizable capital reserve to cover credit risk in the event of failure. To reduce the risk of default, the microfinance bank must also enhance its credit mitigation practices, portfolio grading, and lending standards. Between reserves and loan disbursements, good credit risk management should strike a balance.

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APPENDIX

Raw Data

Microfinance banks Years Deposit Amount (N’000) Non-performing loan amount (N’000) Loan and advances Amount (Net)(N’000) Shareholders fund (N’000) Total Asset (N’000) Long term debt (N’000) Profit After tax (N’000)
NPF MFB 2012 3,271,585 93,049 4,780,335 3,850,844 7,790,984 535,541
2013 3,858,052 126,460 5,559,453 3,916,894 8,680,638 391,320
2014 4,803,374 140,184 6,527,210 4,079,893 10,865,189 499,113 477,816
2015 6,610,113 287,288 7,881,519 4,251,493 12,334,021 630,795 514,598
2016 6,792,391 163,058 9,095,801 4,652,289 12,361,872 349,249 554,903
2017 9,095,801 178,052 9,008,675 4,652,289 15,952,341 1,550,468 631,890
2018 10,465,119 860,250 10,593,635 4,646,591 17,597,552 2,078,843 195,749
2019 11,327,058 946,724 13,776,931 5,327,939 19,583,717 1,965,665 796,425
2020 14,838,805 495,507 16,667,615 5,481,584 25,096,975 2,995,809 614,417
2021 16,278,901 529,548 17,447,816 5,730,965 31,967,345 2,708,090 707,493
ACCION MFB 2012 543,310 432,299 1,886,176 1,418,739 2,704,335 182,364 284,683
2013 1,048,751 500,642 3,025,012 1,639,619 3,953,163 479,550 392,948
2014 1,421,819 277,945 3,975,266 2,567,489 5,086,236 554,614 622,555
2015 2,120,599 681,556 5,294,462 3,000,360 6,789,014 1,087,259 545,941
2016 2,013,517 952,251 5,826,119 3,359,645 7,538,090 1,177,734 538,220
2017 2,392,578 880,702 6,959,938 3,905,640 8,746,431 1,474,453 809,761
2018 2,809,253 1,084,099 8,219,748 4,609,714 11,012,082 2,349,494 1,050,137
2019 4,013,511 1,080,765 9,394,157 5,245,587 12,216,158 1,975,445 915,342
2020 4,309,677 1,471,411 8,337,995 5,365,527 12,881,605 2,538,050 132,007
2021 3,810,250 1,797,184 11,674,684 5,936,384 14,719,897 3,687,058 570,857
INFINITY MFB 2012 222,481 12,993 305,496 163,236 484,935 80,625 76,548
2013 256,456 12,959 391,664 224,639 590,143 88,125 76,998
2014 267,716 15,540 523,729 321,952 708,168 99,533 113,681
2015 340,407 22,146 682,939 402,688 955,898 188,791 86,950
2016 381,699 27,984 759,912 502,728 1,080,915 140,799 110,104
2017 505,600 48,908 874,298 611,568 1,269,190 112,225 121,337
2018 642,860 54,931 1,126,357 720,240 1,570,713 164,250 113,779
2019 781,370 57,303 1,403,000 857,308 1,850,615 147,472 145,940
2020 896,822 132,620 1,842,208 955,108 1,639,215 659,792 107,114
2021 970,822 153,219 2,114,743 1,131,436 3,122,710 836,892 185,642
CAPSTONE MFB 2012 92,272 77 126,960 55,747 150,720 0 1,576
2013 101,425 420 183,403 58,003 169,102 0 2,256
2014 148,497 2,020 111,936 58,765 279,072 50,000 1,411
2015 246,258 3,280 261,519 116,241 446,037 75,000 5,568
2016 176,996 4,070 287,864 115,189 348,845 50,000 -1,002
2017 203,299 3,450 311,227 115,236 374,995 50,000 47
2018 277,059 7,100 259,639 115,431 398,572 194
2019 356,768 7,115 382,583 115,738 477,251 307
2020 320,079 15,903 314,986 118,925 542,917 3,188
2021 312,188 25,271 376,626 125,767 542,195 6,841
ASHA MFB 2012 366,992 692 563,394 341,554 823,475 0 135,865
2013 550,412 701 794,198 474,554 1,112,600 0 133,192
2014 649,035 1,200 1,141,623 714,055 1,534,328 0 239,309
2015 773,540 580 1,488,824 623,850 2,262,241 310,667 239,286
2016 735,909 1,156 1,363,310 855,677 2,071,126 312,435 231,827
2017 876,295 3,910 1,828,185 855,677 2,071,126 346,037 237,004
2018 1,010,987 17,360 2,232,894 920,681 3,730,463 3,714 563,193
2019 1,263,569 84,530 2,866,285 3,170,340 5,109,735 506,954
2020 4,596,335 1,042,535 12,158,811 7,421,158 15,486,249 1,165,498 57,211
2021 5,939,986 1,295,096 16,182,363 10,176,146 19,513,335 2,648,385

Descriptive stat

ROA ND NT CA LR FZ
 Mean  0.068144  0.123270  0.054090  0.384038  0.102658  6.426904
 Median  0.070050  0.054250  0.032750  0.413100  0.106800  6.393300
 Maximum  0.165000  0.795700  0.229200  0.620500  0.402500  7.504700
 Minimum -0.002900  0.000700  0.000400  0.179300  0.000000  5.178200
 Std. Dev.  0.050775  0.168225  0.054810  0.102172  0.087462  0.647206
 Skewness  0.241322  1.987099  1.323594 -0.106085  0.750599 -0.135165
 Kurtosis  1.912587  6.917262  4.063095  2.410009  4.047879  1.791922
 Jarque-Bera  2.948778  64.87331  16.95369  0.818971  6.982596  3.192771
 Probability  0.228919  0.000000  0.000208  0.663992  0.030461  0.202628
 Sum  3.407200  6.163500  2.704500  19.20190  5.132900  321.3452
 Sum Sq. Dev.  0.126328  1.386683  0.147205  0.511513  0.374830  20.52489
 Observations  50  50  50  50  50  50

Model: Fixed-effects, using 50 observations

Included 5 cross-sectional units

Time-series length = 10

Dependent variable: ROA

Coefficient Std. Error t-ratio p-value
const 0.423951 0.0906734 4.676 <0.0001 ***
ND 0.106874 0.0881225 1.213 0.2323
NT −0.515017 0.284715 −1.809 0.0780 *
CA −0.0602014 0.0455001 −1.323 0.1933
LR −0.124711 0.0493918 −2.525 0.0156 **
FZ −0.0474877 0.0145887 −3.255 0.0023 ***

 

Mean dependent var  0.068144 S.D. dependent var  0.050770
Sum squared resid  0.020602 S.E. of regression  0.022695
LSDV R-squared  0.836882 Within R-squared  0.522788
LSDV F(9, 40)  22.80238 P-value(F)  4.39e-13
Log-likelihood  123.9128 Akaike criterion −227.8256
Schwarz criterion −208.7054 Hannan-Quinn −220.5445
rho  0.063552 Durbin-Watson  1.154069

Joint test on named regressors –

 Test statistic: F(5, 40) = 8.76402

with p-value = P(F(5, 40) > 8.76402) = 1.11435e-05

Test for differing group intercepts –

 Null hypothesis: The groups have a common intercept

 Test statistic: F(4, 40) = 31.4634

with p-value = P(F(4, 40) > 31.4634) = 7.17103e-12

Model: Random-effects (GLS), using 50 observations

Included 5 cross-sectional units

Time-series length = 10

Dependent variable: ROA

Coefficient Std. Error z p-value
const 0.403336 0.0946590 4.261 <0.0001 ***
ND 0.114879 0.0869070 1.322 0.1862
NT −0.533137 0.280183 −1.903 0.0571 *
CA −0.0546043 0.0448754 −1.217 0.2237
LR −0.122497 0.0486294 −2.519 0.0118 **
FZ −0.0446508 0.0141062 −3.165 0.0015 ***

 

Mean dependent var  0.068144 S.D. dependent var  0.050770
Sum squared resid  0.216441 S.E. of regression  0.069353
Log-likelihood  65.11457 Akaike criterion −118.2291
Schwarz criterion −106.7570 Hannan-Quinn −113.8605
rho  0.063552 Durbin-Watson  1.154069

            ‘Between’ variance = 0.00516255

            ‘Within’ variance = 0.00041204

            theta used for quasi-demeaning = 0.911016

Joint test on named regressors –

 Asymptotic test statistic: Chi-square(5) = 42.8935

with p-value = 3.88328e-08

Breusch-Pagan test –

 Null hypothesis: Variance of the unit-specific error = 0

 Asymptotic test statistic: Chi-square(1) = 14.8249

with p-value = 0.00011797

Hausman test –

 Null hypothesis: GLS estimates are consistent

 Asymptotic test statistic: Chi-square(4) = 3.86357

 with p-value = 0.424785

Model: Pooled OLS, using 50 observations

Included 5 cross-sectional units

Time-series length = 10

Dependent variable: ROA

Coefficient Std. Error t-ratio p-value
const −0.0376513 0.0705560 −0.5336 0.5963
ND 0.265508 0.150105 1.769 0.0839 *
NT −0.958416 0.468476 −2.046 0.0468 **
CA 0.230682 0.0693629 3.326 0.0018 ***
LR 0.0275592 0.0783920 0.3516 0.7268
FZ 0.00521129 0.0109818 0.4745 0.6375

 

Mean dependent var  0.068144 S.D. dependent var  0.050770
Sum squared resid  0.085423 S.E. of regression  0.044062
R-squared  0.323657 Adjusted R-squared  0.050770
F(5, 44)  4.211157 P-value(F)  0.044062
Log-likelihood  88.35713 Akaike criterion  0.050770
Schwarz criterion −153.2421 Hannan-Quinn  0.044062
rho  0.570298 Durbin-Watson  0.050770

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