INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1574
Risk Based Capital and Performance of Insurance Companies in Nigeria
Kolurejo John Olushola, Dr. Idowu Mobolaji Ajike
Accounting Department Joseph Ayo Babalola University
DOI: https://doi.org/10.51244/IJRSI.2025.1210000139
Received: 07 October 2025; Accepted: 14 October 2025; Published: 08 November 2025
ABSTRACT
The stability and financial performance of insurance companies are critical to economic development, one of the
key regulatory tools used to ensure the soundness and solvency of insurance firms is Risk-Based Capital (RBC),
which require insurers to hold capital proportionate to the specific risks they face. This study investigates the
impact of RBC on the financial performance of insurance companies in Nigeria, with a focus on three core
performance indicators: Return on Equity (ROE), Return on Assets (ROA), and Earnings per Share (EPS). The
study adopted an ex-post facto research design and applied judgmental sampling to select five insurance
companies operating in Nigeria. Panel data were extracted from the audited financial statements of these
companies over a 14-year period (2010–2023). Using panel data regression techniques, the Random Effects
Model was employed to estimate the relationship between RBC and firm performance across the selected
indicators. The empirical results revealed that RBC has a positive and statistically significant effect on EPS at
the 5% level of significance (β = 5.5803; p = 0.0210), implying that increased capital adequacy, when aligned
with risk exposures, enhances shareholder value through improved earnings per share. However, the effect of
RBC on ROE (β = 0.2411; p = 0.6250) and ROA (β = 0.6003; p = 0.4411) was found to be negative and
statistically insignificant, suggesting that while RBC may contribute to capital stability, it does not necessarily
lead to higher profitability or better asset utilization in the short term. The study concludes that RBC has
differentiated effects across financial performance metrics and should not be viewed merely as a compliance
requirement. Rather, insurance firms should strategically align capital adequacy practices with broader financial
performance goals. The study recommends that insurance companies adopt risk-sensitive capital management
practices as a tool for strengthening long-term value creation and investor confidence.
Keywords: Risk-Based Capital, Insurance Performance, Return on Equity, Return on Assets, Earnings per Share
INTRODUCTION
The financial performance of Nigerian insurance firms over the years has remained a growing concern, as many
companies continue to exhibit weak profitability, under-reserving, and suboptimal asset utilization, despite
decades of regulatory reforms and capital consolidation (Olaleye & Adeagbo, 2023; Kerim, Alaji, & Innocent,
2019). This persistent underperformance raises fundamental questions about the efficacy of traditional capital
regulation and the sustainability of insurance operations within the Nigerian financial system. Although several
studies have explored corporate performance and capital structure broadly (Sadiq & Jumoke, 2017), there
remains a paucity of insurance-specific evidence in the Nigerian context. A critical element influencing the
financial health of insurers is capital structure, the composition of funding sources that includes both equity and
liabilities. For insurance firms, capital structure extends beyond the conventional debt-equity framework to
encompass technical provisions, including policyholder claims, accruals, and reserves (Florio & Leoni, 2017;
Eling & Marek, 2014). These provisions, particularly non-interest-bearing liabilities, carry significant
opportunity costs, which, if improperly managed, may affect profitability and solvency (Dhaene et al., 2017).
Hence, determining the optimal capital structure that balances operational risk, regulatory compliance, and
profitability is crucial for insurers in developing economies.
To address these structural concerns, insurance regulators globally and in Nigeria have embraced Risk-Based
Capital (RBC) models. Unlike static capital thresholds, RBC aligns capital requirements with the risk exposure
of an insurer across underwriting, investment, operational, and market activities (Cheng & Weiss, 2012a). The
goal is to enhance solvency protection for policyholders, promote better risk management, and ensure long-term
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1575
financial resilience. While RBC has become an international benchmark for solvency regulation, its effectiveness
in improving financial outcomes, particularly in emerging markets with evolving risk systems and low insurance
penetration, remains underexplored (Ghimire & Thorburn, 2020).
Existing literature presents divergent views on the performance implications of capital structure. Some
researchers find a positive relationship between leverage or capital adequacy and firm performance (Fosu, 2013;
Cheng & Weiss, 2012b), while others identify negative or insignificant effects (Avci, 2016; Davydov, 2016;
Chadha & Sharma, 2015). These inconsistencies may be attributed to variations in methodology, scope, and,
more critically, inadequate proxies for capital structure that do not reflect the unique attributes of insurance
firms. To bridge this gap, this study utilizes the Technical Provision Ratio (TPR) as a proxy for capital structure.
TPR represents the ratio of total liabilities (including reserves, claims, and accruals) to total assets and is more
reflective of insurers’ capital realities (Shim, 2010; De Haan & Kakes, 2010). Furthermore, traditional
performance metrics such as Return on Assets (ROA) and Return on Equity (ROE), though widely used, may
not fully capture the strategic positioning and shareholder value of a firm. Earnings per Share (EPS), often
underutilized in insurance research, offers a unique perspective on profitability and investor attractiveness. EPS
is particularly useful in valuation, executive compensation, and mergers and acquisitions, critical factors in the
strategic management of insurers (Berger & Patti, 2006; Margaritis & Psillaki, 2007).
This study therefore investigates the effect of Risk-Based Capital (RBC), as measured by TPR, on the financial
performance of insurance companies in Nigeria, using ROE, ROA, and EPS as performance indicators. The data
spans the period 2010 to 2023, chosen to reflect the post-consolidation phase in the Nigerian insurance industry,
when firms had regularized their capital positions and financial reporting formats. This timeline captures key
regulatory shifts, including IFRS adoption and NAICOM’s intensified solvency monitoring. The study
contributes to the literature by offering an industry-specific evaluation of capital structure under a risk-sensitive
regulatory framework, and by integrating multiple performance dimensions often ignored in previous works. In
doing so, it provides fresh insights for policymakers, investors, and scholars into how capital adequacy
requirements influence the financial outcomes of insurers in developing markets. The remaining parts of the
paper are discussed under literature review, data and methodology, results and discussion, and conclusion.
LITERATURE REVIEW
2.1 Risk-Based Capital (RBC)
Risk-Based Capital (RBC) refers to a dynamic capital adequacy framework designed to ensure that insurance
companies maintain sufficient capital in proportion to the specific risks they assume. Unlike traditional fixed
capital requirements, which prescribe a uniform minimum irrespective of risk exposure, RBC frameworks adjust
capital thresholds based on the insurer’s underwriting, market, credit, and operational risk profiles (Cheng &
Weiss, 2012a; Florio & Leoni, 2017). The core objective is to safeguard policyholders by reducing the probability
of insurer insolvency, especially during periods of financial stress (Falade & Oyedokun, 2022). RBC has gained
prominence globally as a more robust solvency regulation tool, capable of promoting financial system stability
through tailored risk assessment. Oyugi and Mutuli (2014) affirm that RBC requires higher capital buffers from
riskier insurers, thereby enforcing discipline in risk management and encouraging prudent capital allocation. It
replaces the “one-size-fits-all” approach of non-risk-based capital (NRBC) models with a responsive mechanism
that internalizes firm-specific vulnerabilities. In the Nigerian context, RBC is being gradually implemented
under the supervision of the National Insurance Commission (NAICOM). The regulatory shift emphasizes
ongoing solvency assessment and introduces capital requirements that are responsive to the unique risk
exposures of individual insurers rather than static benchmarks. This evolution is expected to align Nigerian
insurers more closely with international best practices in solvency regulation and risk governance (Ghimire &
Thorburn, 2020).
2.2 Technical Provisions and Capital Structure in Insurance Firms
In insurance finance, capital structure extends beyond the traditional debt-to-equity paradigm often applied in
corporate finance literature. Unlike conventional firms, insurers rely on a dual-capital framework comprising
owner’s equity and technical provisions, the latter consisting of both interest-bearing and non-interest-bearing
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1576
liabilities (Dhaene et al., 2017). These provisions include outstanding claims, insurance funds, policyholder
reserves, and accruals, forming the core financial obligations through which insurers meet their commitments to
policyholders and manage underwriting risk (Eling & Marek, 2014). For the purpose of this study, capital
structure is operationalized through the Technical Provision Ratio (TPR), defined as the ratio of total technical
provisions to total assets. TPR serves as a practical proxy for Risk-Based Capital (RBC), capturing the degree
to which insurers’ operations are supported by internally retained reserves and liabilities aligned with assumed
risks (Shim, 2010; De Haan & Kakes, 2010). As RBC frameworks emphasize the alignment of capital with risk
exposure, TPR offers a nuanced reflection of capital adequacy under varying financial and operational stress
conditions.
From a performance perspective, technical provisions, especially non-interest-bearing liabilities such as
outstanding claims and accrued expenses, carry implicit opportunity costs, including limited liquidity, forgone
investment returns, and diminished financial flexibility (Dhaene et al., 2015). While essential for solvency
protection and regulatory compliance, overly conservative or misaligned technical provisions can erode capital
efficiency and negatively impact key performance indicators such as Return on Assets (ROA), Return on Equity
(ROE), and Earnings per Share (EPS). Florio and Leoni (2017) conclude that treating technical provisions as a
central element of capital structure rather than a separate accounting obligation enables a more accurate and risk-
sensitive assessment of an insurer’s financial health. In this context, the present study employs TPR not merely
as a regulatory compliance metric, but as a strategic indicator of how well capital structure supports operational
performance in Nigeria’s evolving insurance landscape.
2.3 Return on Assets (ROA)
Return on Assets (ROA) is a key indicator used in financial literature to assess a firm’s efficiency in converting
its total assets into net income. It is widely recognized in insurance performance analysis as it captures both
underwriting outcomes and investment returns, two primary functions of insurers (Berger & Patti, 2006). ROA
is calculated as the ratio of net income to total assets and is considered a holistic measure of managerial
effectiveness in asset utilization (Florio & Leoni, 2017). Within the insurance context, scholars like Dhaene et
al. (2017) and Eling and Marek (2014) argue that ROA is particularly important due to the capital-intensive
nature of the business and the critical role of asset investment in overall profitability. In emerging markets, De
Haan and Kakes (2010) observed that a firm’s capital structure significantly affects ROA, particularly when
insurers rely heavily on technical provisions as a source of internal funding. Empirical studies such as those by
Cheng and Weiss (2012a) and Fosu (2013) found mixed results, with some suggesting a positive influence of
capital structure on ROA, while others observed negative or insignificant relationships, depending on market
conditions, regulatory frameworks, and asset allocation strategies.
2.4 Earnings Per Share (EPS)
Earnings per Share (EPS) represents the amount of earnings available to each unit of common stock and is a
widely used metric in corporate finance to assess profitability and shareholder value. It is computed as net income
minus preferred dividends, divided by the weighted average number of shares outstanding. While not as
frequently employed as ROE or ROA in insurance-specific studies, EPS has gained scholarly attention for its
strategic relevance in investment decisions and firm valuation (Berger & Patti, 2006; Margaritis & Psillaki,
2007). EPS is often used to evaluate the attractiveness of a company to investors and its ability to generate
sustainable returns on a per-share basis. Sarfaraz et al. (2021) argue that in the context of capital adequacy
frameworks, EPS may serve as a more precise indicator of how well capital is being converted into shareholder
returns, especially under stringent regulatory environments like Risk-Based Capital regimes. Despite its limited
use in traditional insurance performance literature, recent studies including Yaudil et al. (2023) and Akpan et al.
(2020) have begun to incorporate EPS into performance models, recognizing its growing relevance for
stakeholders, particularly in emerging insurance markets where market capitalization and share performance are
gaining prominence.
2.5 Return on Equity (ROE)
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1577
Return on Equity (ROE) is a foundational profitability measure that reflects how effectively a company is using
shareholders’ equity to generate profit. Defined as net income divided by equity, ROE is frequently used in
insurance literature to evaluate firm performance, solvency resilience, and capital efficiency (Fosu, 2013). In
capital structure theory, higher equity levels should support greater financial stability, yet the relationship
between equity and profitability is not always linear. For instance, Modigliani and Miller’s (1958) irrelevance
theory suggests that under perfect market conditions, capital structure has no impact on firm value, but
subsequent theories—such as the trade-off theory and pecking order theory—acknowledge that capital
composition can influence firm performance depending on the cost of capital and information asymmetry (Jensen
& Meckling, 1976; Myers & Majluf, 1984). Empirical findings on the impact of capital adequacy on ROE have
been inconclusive. While Cheng and Weiss (2012b) and Lai (2011) reported a positive association between
capital strength and ROE in developed markets, studies by Avci (2016), Chadha and Sharma (2015), and Akpan
et al. (2017a) observed either a negative or non-significant impact in developing economies. These variations
may be explained by firm-specific factors such as governance structure, investment strategy, and risk appetite,
as well as market-level issues like regulatory rigidity and capital market depth.
2. 6 Theoretical Review
This study is anchored on four key theories that explain the relationship between capital structure, risk, and
performance in insurance firms. The Dynamic Trade-Off Theory posits that insurers adjust their capital structure
over time to balance solvency and profitability, a process influenced by risk exposure and adjustment costs under
Risk-Based Capital (RBC) regimes (Dhaene et al., 2017; Eling & Marek, 2014). The Pecking Order Theory
suggests a preference for internal funds, particularly retained earnings and technical provisions over external
financing due to cost and information asymmetries (Myers & Majluf, 1984). The Risk Capital Theory frames
capital as a buffer against adverse outcomes, aligning with RBC’s requirement for capital adequacy proportional
to risk, with empirical support for the performance benefits of strong Tier 1 capital (Perold, 2005; Iroh &
Orobator, 2025). Finally, the Modigliani-Miller Theory (with taxes) underscores the trade-offs between tax
advantages of debt and the cost of holding large technical reserves in insurance, especially under regulatory
constraints like RBC (Dhaene et al., 2015; Fier et al., 2013). Together, these theories provide a comprehensive
lens for evaluating capital-performance dynamics in Nigeria’s insurance sector.
2.7 Empirical Review
Empirical studies on the relationship between Risk-Based Capital (RBC) and the performance of insurance
companies have produced mixed results across jurisdictions. While some studies support a positive link between
RBC and firm performance, others find no significant relationship or even negative effects, depending on the
measurement techniques, market dynamics, and institutional maturity of the regulatory framework. In the United
States, Devieux, Kovalerchik, and Ragusa (2021) observed that insurers adjusted their portfolios towards more
capital-efficient instruments following bond factor changes introduced by the National Association of Insurance
Commissioners (NAIC). Van Bragt (2021) similarly found that RBC implementation promoted conservative
investment strategies by encouraging insurers to focus on high-quality assets. Grace, Klein, and Phillips (2004)
demonstrated that RBC ratios were useful predictors of solvency risk, thereby enhancing regulatory supervision.
Findings from macro-level assessments also highlight the conditional effectiveness of RBC frameworks. For
instance, the International Monetary Fund (2020) conducted stress tests during the COVID-19 pandemic, which
revealed that while RBC ratios initially held firm, they deteriorated under extreme economic stress, indicating
that capital adequacy may require dynamic adjustments during crises. In Canada and Europe, Soumaré and
Tafolong (2016) applied a dynamic RBC model that accounted for business cycle fluctuations and found that
adjusting capital in line with macroeconomic conditions improved insurer stability. Heinrich, Sabuco, and
Farmer (2019) cautioned that uniform internal modeling under Solvency II could exacerbate systemic risk.
Ágoston and Varga (2024) theorized that overly stringent RBC thresholds in oligopolistic markets could reduce
competition and distort pricing.
In the Nigerian context, Falade and Oyedokun (2022) found that net claims, premium income, and RBC were
significantly associated with profitability. Abiola and Akanbi (2022), using a Data Envelopment Analysis (DEA)
model, reported that RBC implementation enhanced operational efficiency by lowering underwriting losses and
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1578
improving cost management. Adegbite and Ogunyomi (2020) confirmed a significant positive association
between RBC compliance and return on assets (ROA), while Okafor and Onwumere (2018) linked strong capital
structures to improved underwriting outcomes. However, other studies present contrasting views. Ezekwesili
and Ojo (2021) found no statistically significant relationship between RBC and profitability metrics such as ROE
and net profit margin, which they attributed to weak regulatory enforcement and inconsistent industry adaptation.
Akpan et al. (2017a) noted that while the equity ratio had an insignificant negative impact on performance,
technical provision ratios had a significant positive effect, highlighting the relevance of alternative capital
structure metrics in insurance research.
Further contributions by Akpan and Etukafia (2019) demonstrated that capital structure, when moderated by
risk-taking behavior, significantly influenced ROA but not EPS, implying that behavioral and governance factors
may mediate capital-performance outcomes. Chukwuma and Uche (2019) also underscored the importance of
institutional quality, noting that the benefits of RBC are more likely to manifest where strong corporate
governance frameworks exist. Internationally, Putra (2017) found that RBC and claims ratio significantly
influenced profitability among Indonesian life insurers, though the effects of asset size and revenue growth were
marginal. Bishnu (2016), analyzing Nepalese manufacturing firms, reported that leverage had a negative effect
on profitability, while Amraoui, Jianmu, and Bouarara (2018) identified similar adverse effects of liquidity and
debt on Moroccan firms' returns. Dincer et al. (2011) compared RBC with the CAMELS rating system and
concluded that while RBC captured solvency, it failed to reflect liquidity and management quality. Studies from
Malaysia by Jaaman, Ismail, and Majid (2007), as well as Lazam et al. (2012), reported instances of capital
surpluses under RBC, warning that idle capital could lower efficiency. In Ghana, Anafo, Amponteng, and Yin
(2015) found a positive relationship between capital structure and profitability, affirming that industry-specific
conditions affect capital dynamics.
2.8 Conceptual Framework
Independent Variable Dependent Variable
Author’s compilation (2025)
Data and Methodology
This study adopted an ex-post facto research design, which is appropriate for analyzing historical data where the
variables of interest cannot be manipulated. This design allows for the empirical evaluation of relationships
between existing financial indicators, making it suitable for assessing the effect of Risk-Based Capital (RBC),
proxied by the Technical Provision Ratio (TPR), on the financial performance of Nigerian insurance firms. The
study builds on the econometric model employed by Akpan et al. (2017) to explore the link between RBC and
key performance metrics in the insurance sector. The adapted model facilitates the examination of how variations
in capital adequacy influence firm-level outcomes over time. The model was specified as:
Risk Based Capital
(RBC)
Performance
Returns on Assets
(ROA)
Earnings per Share
(EPS)
Returns on Equity
(ROE)
Technical Provision
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1579
Perfi,t = α0+ β1TPR i,t + Ɛ i,t
Where
Perf= performance
TPR= technical provision ratio
α0= Constant Term
β1= Parameters of the Model
ε= Error term
t time period under study (2010 -2023)
The model was modified in order to determine how risk-based capital impacts on performance of insurance
companies in Nigeria. Technical provision ratio (TPR) was used as proxy for risk-based capital while Return on
Asset, Earnings per Share and Returns on Equity were used as proxies for performance. Flowing from the above
model, the first model for this study using ROA as a proxy for performance would be specified mathematically
as:
ROA = f(TPR)
However, the model is further stated in its econometric form as given below:
ROA t = α0 + β1TPRt +εt - - - - - - - - - - - - - 1
Where
ROA= Return on Asset
TPR= Technical provision ratio
β1= Parameters of the Model
ε= Error term
t= time period under study (2010 -2023)
The second model for this study using EPS as a proxy for performance was specified mathematically as:
EPS = f(TPR)
However, the model was further stated in its econometric form as given below:
EPS t = α0 + β1TPRt +εt - - - - - - - - - - - - - 2
Where
EPS= Earnings per Share
TPR= Technical provision ratio
β1= Parameters of the Model
ε= Error term
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1580
t= time period under study (2010 -2023)
The last model for this study using ROE as a proxy for performance would be specified mathematically as:
ROE = f(TPR)
However, the model is further stated in its econometric form as given below:
ROE t = α0 + β1TPRt +εt - - - - - - - - - - - - - 3
Where
ROE = Returns on Equity
TPR= Technical provision ratio
β1= Parameters of the Model
ε= Error term
t= time period under study (2014 -2023)
RESULT AND PRESENTATION
4.1 Descriptive Statistics
The descriptive statistics reveal the data characteristics
Table 4.1: Descriptive statistics
ROE ROA EPS RBC
Mean 0.107150 0.061574 0.928929 0.683916
Median 0.104371 0.037000 0.114070 0.549302
Maximum 0.426000 0.503000 4.371781 5.922258
Minimum -0.547589 -0.196445 -0.034771 0.123889
Std. Dev. 0.136902 0.100159 1.224119 0.937009
Skewness -1.617309 2.399774 1.050481 5.068544
Kurtosis 10.13391 12.20746 2.753320 28.74723
Jarque-Bera 178.9531 314.4547 13.05176 2233.234
Probability 0.000000 0.000000 0.001465 0.000000
Sum 7.500517 4.310165 65.02505 47.87414
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1581
Sum Sq. Dev. 1.293202 0.692190 103.3943 60.58099
Observations 70 70 70 70
Source: Author’s Compilation (2025)
Table 4.1 presents the descriptive statistics for the variables, highlighting their central tendencies, dispersion,
and normality. The mean ROE is 0.107 with a standard deviation of 0.137, ranging from -0.548 to 0.426; it is
negatively skewed with a peaked distribution, and the Jarque-Bera (JB) test confirms normality (p > 0.05). ROA
has a mean of 0.062 and standard deviation of 0.100, with values ranging from -0.196 to 0.503. It is positively
skewed with leptokurtic distribution, and the JB test also confirms normality. EPS averages 0.929 with higher
variability (SD = 1.224), ranging from -0.035 to 4.372, and shows positive skewness with platykurtic tendencies;
the JB statistic again supports normality. RBC (proxied by TPR) has a mean of 0.684 and standard deviation of
0.937, with values between 0.124 and 5.922. It is positively skewed and leptokurtic, and the JB test indicates
normal distribution across the sample (p > 0.05).
Table 4.2: Correlation statistics
ROE ROA RBC EPS
ROE 1 0.6409 -0.0094 -0.0949
ROA 0.64090 1 -0.0656 -0.1257
RBC -0.0094 -0.0656 1 0.1655
EPS -0.0949 -0.1257 0.1655 1
Source: Author’s Compilation (2025)
Table 4.2 presents the correlation matrix for the study variables, with particular attention to the relationship
between Risk-Based Capital (RBC) and performance indicators. ROE is positively correlated with ROA (r =
0.6409) but shows weak negative correlations with RBC (r = –0.0094) and EPS (r = –0.0940). ROA also has
negative correlations with RBC (r = –0.0656) and EPS (r = –0.1257). Conversely, EPS is positively correlated
with RBC (r = 0.1655). While these correlations offer initial insights into the direction and strength of
associations, they are not indicative of causality. Therefore, regression analysis is employed to explore the causal
relationships between the variables more rigorously.
4.3 Unit root test
To ensure the reliability of data used for regression analysis, the Augmented Dickey-Fuller (ADF) test was
employed to examine the stationarity of the variables. A variable is considered stationary if its ADF test statistic
exceeds the 5% critical value in absolute terms and has a p-value less than 0.05.
Table 4.3 Augmented Dickey Fuller (ADF) Test at Levels
Variables ADF test statistics p-value Remark
ROE 26.2570 0.0034 STATIONARY
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1582
ROA 18.5842 0.0459 STATIONARY
EPS 21.5223 0.0177 STATIONARY
Source: Researcher’s Compilation (2025) using E-views.
From the unit root presentation in Table 4.3, it was observed that ROE ROA EPS were stationary at level while
RBC was non-stationary at level.
Table 4.4 Augmented Dickey Fuller (ADF) Test at First Difference
Variables ADF test statistics p-value Remark
RBC 26.7867 0.0028 STATIONARY
ROE 26.2570 0.0034 STATIONARY
ROA 18.5842 0.0459 STATIONARY
EPS 21.5223 0.0177 STATIONARY
Source: Researcher’s Compilation (2025) using E-views
From the unit root presentation in Table 4.4, it was observed that RBC is stationary at first difference.
4.4 Hausman Test
The Hausman test was conducted to determine the appropriate model between fixed and random effects. For all
three models, the p-values exceeded 0.05, indicating a preference for the random effects model. The pooled,
cross sectional fixed and random effects panel data results were presented and discussed as follows.
Table 4.5 Hausman Test for Model 1
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 0.818085 1 0.3657
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
RBC -0.010064 -0.008144 0.000005 0.3657
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1583
Source: Researcher’s Compilation (2025) using E-views
From Table 4.5, the Hausman test evidenced that the random effects model should be selected to test for
hypothesis 1. This was based on the chi-square statistic probability of 0.3657 which suggested that the
corresponding effect was not statistically significant, hence the null hypothesis was accepted by the data and the
random effects model was preferred to analyse the model.
Table 4.6 Hausman Test for Model 2
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 0.334681 1 0.5629
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
RBC -0.010727 -0.009673 0.000003 0.5629
Source: Researcher’s
Compilation (2025)
Source: Researcher’s Compilation (2025) using E-views
From Table 4.6, the Hausman test evidenced that the random effects model should be selected to test for
hypothesis 2. This was based on the chi-square statistic probability of 0.5629 which suggested that the
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1584
corresponding effect was not statistically significant, hence the null hypothesis was accepted by the data and the
random effects model was preferred to analyse the model.
Table 4.7 Hausman Test for Model 3
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 0.003340 1 0.9539
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
RBC 0.206314 0.206506 0.000011 0.9539
Source: Researcher’s Compilation (2025) using E-views
From Table 4.7, the Hausman test evidenced that the random effects model should be selected to test for
hypothesis 3. This was based on the chi-square statistic probability of 0.9539 which suggested that the
corresponding effect was not statistically significant, hence the null hypothesis was accepted by the data and the
random effects model was preferred to analyse the model.
4.5 Inferential Test
Based on the results from the Hausman test performed the random effect will be used to test the three models
formulated for the study
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1585
Model 1: ROE t = α0 + β1TPRt +εt - - - - - - - - - - - - - 1
Model 2: ROA t = α0 + β1TPRt +εt - - - - - - - - - - - - - 2
Model 3: EPS t = α0 + β1TPRt +εt - - - - - - - - - - - - -- 3
Table 4.8 Random Effect for Model 1
Dependent Variable: ROE
Method: Panel EGLS (Cross-section random effects)
Date: 06/05/25 Time: 13:17
Sample: 2010 2023
Periods included: 14
Cross-sections included: 5
Total panel (balanced) observations: 70
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 0.112720 0.034776 3.241282 0.0018
RBC -0.008144 0.016609 -0.490324 0.6255
Effects Specification
S.D. Rho
Cross-section random 0.065398 0.2136
Idiosyncratic random 0.125493 0.7864
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1586
Weighted Statistics
R-squared 0.003533 Mean dependent var 0.048897
Adjusted R-squared -0.011121 S.D. dependent var 0.124634
S.E. of regression 0.125325 Sum squared resid 1.068036
F-statistic 0.241062 Durbin-Watson stat 2.296383
Prob(F-statistic) 0.625021
Unweighted Statistics
R-squared -0.002059 Mean dependent var 0.107150
Sum squared resid 1.295864 Durbin-Watson stat 1.892652
Source: Researcher’s Compilation (2025) using E-views
The regression results show that RBC explains only 0.4% of the variation in ROE (R² = 0.0035), indicating
minimal explanatory power. The Durbin-Watson statistic (1.89) suggests no first-order serial correlation. RBC
has a negative but statistically insignificant effect on ROE (coefficient = -0.0081; p = 0.6255). The F-statistic
(0.2411; p = 0.6250) confirms the model is not statistically significant at the 5% level.
Table 4.9 Random Effect for Model 2
Dependent Variable: ROA
Method: Panel EGLS (Cross-section random effects)
Date: 06/05/25 Time: 13:38
Sample: 2010 2023
Periods included: 14
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1587
Cross-sections included: 5
Total panel (balanced) observations: 70
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 0.068189 0.023525 2.898614 0.0050
RBC -0.009673 0.012546 -0.771019 0.4434
Effects Specification
S.D. Rho
Cross-section random 0.041882 0.1627
Idiosyncratic random 0.095017 0.8373
Weighted Statistics
R-squared 0.008751 Mean dependent var 0.031924
Adjusted R-squared -0.005826 S.D. dependent var 0.094277
S.E. of regression 0.094551 Sum squared resid 0.607916
F-statistic 0.600345 Durbin-Watson stat 1.449735
Prob(F-statistic) 0.441133
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1588
Unweighted Statistics
R-squared 0.003688 Mean dependent var 0.061574
Sum squared resid 0.689637 Durbin-Watson stat 1.277943
Source: Researcher’s Compilation (2025) using E-views
The regression analysis indicates that RBC explains only 0.8% of the variation in ROA (R² = 0.0088), with the
Durbin-Watson statistic (1.45) suggesting no first-order serial correlation. RBC has a negative but statistically
insignificant effect on ROA (coefficient = -0.0097; p = 0.4434). The model’s F-statistic (0.6003; p = 0.4411)
confirms it lacks statistical significance at the 5% level.
Table 4.10 Random Effect for Model 3
Dependent Variable: EPS
Method: Panel EGLS (Cross-section random effects)
Date: 06/05/25 Time: 13:42
Sample: 2010 2023
Periods included: 14
Cross-sections included: 5
Total panel (balanced) observations: 70
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 0.787697 0.590917 1.333008 0.1870
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1589
RBC 0.206506 0.088066 2.344890 0.0220
Effects Specification
S.D. Rho
Cross-section random 1.302541 0.7955
Idiosyncratic random 0.660513 0.2045
Weighted Statistics
R-squared 0.075840 Mean dependent var 0.124755
Adjusted R-squared 0.062249 S.D. dependent var 0.677067
S.E. of regression 0.655655 Sum squared resid 29.23204
F-statistic 5.580299 Durbin-Watson stat 0.979631
Prob(F-statistic) 0.021032
Unweighted Statistics
R-squared 0.027326 Mean dependent var 0.928929
Sum squared resid 100.5690 Durbin-Watson stat 0.284746
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1590
Source: Researcher’s Compilation (2025) using E-views
The analysis shows that RBC accounts for 8% of the variation in EPS (R² = 0.0758), with no evidence of first-
order serial correlation (Durbin-Watson = 0.98). RBC has a positive and statistically significant impact on EPS
(coefficient = 0.2065; p = 0.0220), indicating that a unit increase in RBC leads to a 21% rise in EPS. The model
is statistically significant overall, with an F-statistic of 5.58 (p = 0.0210).
DISCUSSION ON FINDINGS
This study examined the effect of Risk-Based Capital (RBC), proxied by the Technical Provision Ratio (TPR),
on the financial performance of insurance companies in Nigeria using three performance indicators: Return on
Equity (ROE), Return on Assets (ROA), and Earnings per Share (EPS). The first hypothesis tested whether RBC
has a significant effect on ROE. The regression analysis showed a negative coefficient (-0.0081), indicating an
inverse relationship between RBC and ROE. However, this relationship was not statistically significant at the
5% level (p = 0.6255). Thus, the study concludes that there is no significant effect of RBC on ROE. This result
contrasts with the findings of Akpan et al. (2020) and Falade & Oyedokun (2022), who reported a significant
positive relationship between RBC and ROE. The inconsistency may be attributed to differences in sample
composition, capital structure dynamics, or macroeconomic conditions over time.
The second hypothesis tested the effect of RBC on ROA. Similarly, the regression coefficient was negative (-
0.0097), suggesting that increases in RBC are associated with marginal declines in asset profitability. Yet, this
effect was also statistically insignificant (p = 0.4434), leading to the conclusion that RBC does not significantly
influence ROA of Nigerian insurers. This finding aligns with Putra (2017), who found a negative association
between RBC and profitability in the Indonesian insurance market. It suggests that while capital buffers are
essential for solvency, they may also reduce operational efficiency if not matched with proportionate revenue-
generating activities. Conversely, Akpan (2021) argued that RBC contributes positively to ROA when prudently
managed, indicating that firm-specific governance and risk appetite may moderate the relationship.
The third hypothesis examined the effect of RBC on EPS. The regression revealed a positive coefficient (0.2065)
and a statistically significant p-value (0.0220), indicating that RBC has a significant and positive effect on
EPS. This implies that higher technical provisions relative to total assets enhance shareholder returns per unit of
stock, possibly due to increased investor confidence or better claims management. The finding supports Akpan
et al. (2020), Falade & Oyedokun (2022), and Yaudil et al. (2023), who observed similar positive impacts of
RBC on performance metrics. In conclusion, while RBC does not significantly affect ROE and ROA in this
study, it significantly improves EPS, suggesting that its value may be better reflected in shareholder-oriented
performance indicators than in operational metrics.
CONCLUSION AND RECOMMENDATIONS
The Nigerian insurance industry continues to face persistent structural challenges including inadequate
capitalization, suboptimal performance, and limited operational efficiency, despite regulatory efforts aimed at
strengthening financial soundness. This study investigated the effect of Risk-Based Capital (RBC), measured
through the Technical Provision Ratio (TPR), on the performance of insurance companies in Nigeria between
2010 and 2023, using Return on Equity (ROE), Return on Assets (ROA), and Earnings per Share (EPS) as
performance indicators. The findings revealed a significant and positive impact of RBC on EPS, indicating that
well-capitalized insurers tend to generate stronger shareholder returns, likely due to improved solvency
confidence and earnings sustainability. However, RBC had no significant effect on ROE and ROA, with both
coefficients being negative and statistically insignificant. This suggests that capital adequacy alone does not
guarantee operational efficiency or optimal asset utilization, especially when firms are burdened with under-
leveraged capital or inefficient deployment strategies. These outcomes highlight the need for capital regulation
to be complemented by reforms in governance, investment planning, and underwriting discipline. Accordingly,
NAICOM is advised to adopt a flexible, risk-tiered RBC framework that aligns capital thresholds with insurers’
size, risk exposure, and business models, rather than a one-size-fits-all approach. Insurance firms should also
reframe capital requirements as strategic levers for growth and profitability, embedding them within enterprise-
wide risk management, claims optimization, and dynamic asset allocation. In addition, coordinated efforts by
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1591
regulators, industry stakeholders, and government are essential to stimulate insurance penetration, enhance
capital productivity, and strengthen the macroeconomic conditions that underpin performance. Future research
could explore the mediating role of firm-specific variables like reinsurance strategies, corporate governance, and
market maturity, as well as undertake comparative studies across African or emerging markets to deepen
understanding of how RBC frameworks influence sectoral resilience and long-term financial performance.
REFERENCES
1. Abdul, K., John, A., & Idachaba O.I. (2019). Effect of Capital Structure on the Profitability of Listed
Insurance Firms in Nigeria. American International Journal of Economics and Finance Research, 1(2),
36-45.
2. Abiola, R. O., & Akanbi, M. A. (2022). Risk-based capital and operational efficiency of insurance
companies in Nigeria. African Journal of Business Management, 16(4), 101–112.
https://doi.org/10.5897/AJBM2022.9334
3. Adegbite, A. A., & Ogunyomi, O. (2020). Risk-based capital and performance of Nigerian insurance
companies. International Journal of Finance and Accounting, 9(2), 45–53.
https://doi.org/10.5923/j.ijfa.20200902.01
4. Ágoston, K., & Varga, K. (2024). Solvency II and value-at-risk capital constraints in oligopolistic
insurance markets. arXiv. https://arxiv.org/abs/2404.17915
5. Akpan, S.S.& Etukafi, N.I. (2017). Risk-taking propensity, capital structure and performance ofInsurers
in post-risk based capital regime in Nigeria. Journal of Finance and Business Policy (JOFIBP), 4(1), 82-
90
6. Akpan, S. S. Mahat, F. Nordin, B. A., & Nassir, A.A. (2017). Revisiting Insurance Capital Structure,
Risk-Taking Behaviour and Performance between 1995 – 2002. Asian Social Science, 13,(11),128-141
7. Akpan, S.S., Mahat, F., Noordin, B. & Nassir A (2017b). Contrasting the Effect of Risk- and Non-Risk-
Based Capital Structure on Insurers’ Performance in Nigeria. Journal of Social Science, 6, 143; 1-17
8. Akpan, S. S. Mahat, F. Nordin, B. A., & Nassir, A.A. (2017c). Another look at risk-basedcapital regime,
capital structure, insurer’s risk profile and performance: A conceptual paper. Paper presented at Global
Conference on Business and Economics Research (GCBER), Universiti Putra Malaysia, Serdang,
Malaysia, August 14–15.
9. Amraoui, M., Jianmu, Y., & Bouarara, K. (2018). Firm’s capital structure determinants and financing
choice by industry in Morocco. International Journal of Management Science and Business
Administration, 4(3), 41-51.
10. Alonge, O. F., Adebayo, O. A. & Idowu M. A. (2024). Risk Based Capital and the Development of
Insurance Companies in Nigeria. International Journal of Financial Research and Management Science
Vol. 6 No. 7 E-ISSN 3027-2866 P-ISSN 3027-1495
https://www.researchgate.net/publication/386593126_RISK-
BASED_CAPITAL_RBC_AND_THE_DEVELOPMENT_OF_INSURANCE_COMPANY_IN_NIGE
RIA
11. Anafo,S. A., Amponteng, E & Yin, L (2015). Impact of Capital Structure on Profitability of Banks Listed
on the Ghana Stock Exchange. Research Journal of Finance and Accounting, 3(8), 115–130.
12. Avci, Emin. (2016). Capital structure and firm performance: An application on manufacturing industry.
˙Iktisadi ve˙Idari Bilimler Dergisi, 38, 15–30.
13. Berger, A. N., & Emilia B. D. 2006. Capital structure and firm performance: A new approach to testing
agency theory and an application to the banking industry. Journal of Banking & Finance, 30, 1065–1102.
14. Bishnu P. B. (2016). Capital structure and firm performance: Evidence from Nepalese manufacturing
companies. Journal for Studies in Management and Planning, 2(3), 138-150.
15. Bonaccolto, G., Borri, N., Consiglio, A., & Di Giorgio, G. (2025). Systemic risk in the European
insurance sector. arXiv. https://arxiv.org/abs/2505.02635
16. Burkhanova, A., Enkov, V., Korotchenko, D., Kichkaylo, M., Marchenko, K., Rozhdestvenskaya, A. &
Ulugova, A. (2012). Dynamic Trade-off Theory of Capital Structure: an Overview of Recent Research.
Journal of Corporate Finance Research, 3 (23), 70-86.
17. Chadha, S.& Sharma. A.K. (2015). Capital structure and firm performance: Empirical evidence
fromIndia. Vision, 19, 295–302.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1592
18. Chaudhuri, Kausik, Subal C. Kumbhakar, & Lavanya Sundaram (2016). Estimation of firm performance
from a MIMIC model. European Journal of Operational Research, 255, 298–307.
19. Cheng, J., &Weiss. M.A. (2012a). The Role of RBC, Hurricane Exposure, Bond Portfolio Duration, and
Macroeconomic and Industry-wide Factors in Property–Liability Insolvency Prediction. Journal of Risk
and Insurance, 79, 723–750.
20. Cheng, Jiang, & Mary A.Weiss (2012b). Capital Structure in the property-liability insurance industry:
Tests of the trade-off and pecking order theories. Journal of Insurance Issues, 35, 31–43.
21. Chukwuma, J. E., & Uche, C. I. (2019). Risk-based capital regulation in West Africa: Implications for
insurance performance. West African Journal of Insurance, 7(1), 23–37.
22. Dang, V. A., Kim, M., & Shin, Y. (2012). Asymmetric capital structure adjustments: New Evidence from
dynamic panel threshold models. Journal of Empirical Finance, 19(4), 465-482.
23. De Haan, Leo, & Jan Kakes. 2010. Are non-risk-based capital requirements for insurance companies
binding? Journal of Banking & Finance, 34, 1618–1627.
24. Devieux, D., Kovalerchik, O., & Ragusa, E. (2021). A very long engagement: U.S. life insurers’ response
to NAIC bond factor changes. MetLife Investment Management. https://investments.metlife.com
25. Dhaene, J., Hulle, C., Wuyts, G., Schoubben, F., & Schoutens, W. (2017). Is the capital structure logic
of corporate finance applicable to insurers? Review and analysis. Journal of Economic Surveys, 31, 169–
89.
26. Dierker, M. J., Kang, J. K., Lee, I., & Seo, S. W. (2015). Risk changes and the dynamic trade-off Theory
of capital structure. business.kaist.ac.kr
27. Dincer, H., Gencer, G., Orhan, N., & Şahinbaş, K. (2011). A comparative analysis on ranking insurance
firms using RBC and CAMELS. Business and Economics Research Journal, 2(4), 1–20.
28. Eling, M., & Marek, S. D. (2014). Corporate governance and risk taking: Evidence from the UK and
German insurance markets. Journal of Risk and Insurance, 81, 653–682
29. Erel, Isil, Stewart C. Myers, & James A. R. (2015). A theory of risk capital. Journal of Financial
Economic, 118, 620–635.
30. Ezekwesili, C. A., & Ojo, A. M. (2021). The impact of risk-based capital regulation on firm performance:
Evidence from Nigeria. Journal of African Financial Studies, 13(1), 88–105.
31. Falade, O. A. & Oyedokun, G. E. (2022). Claims Payment and Financial Performance of Listed Insurance
Companies in Nigeria. Hmlyan Jr EcoBus Mgn, 3(2), 37-48.
32. Fier, S. G., McCullough, K. A. & Carson, J. M. (2013) Internal capital markets and the partial Adjustment
of leverage. Journal of Banking and Finance, 37(3), 1029–1039.
33. Florio, C., & Giulia L. (2017). Enterprise risk management and firm performance: The Italian case.The
British Accounting Review, 49, 56–74.
34. Foo, V., Amer A. A. J., Karim, M. R. A. & Zatul, K. (2015).Capital structure and corporate performance:
panel evidence from oil and gas companies in Malaysia. International Journal of Business Management
and Economic Research, 6, 371–379.
35. Fosu, S. (2013). Capital structure, product market competition and firm performance: Evidence from
South Africa. The Quarterly Review of Economics and Finance, 53, 140–151.
36. Grace, M. F., Klein, R. W., & Phillips, R. D. (2004). Insurance company failures: Why do they cost so
much? Journal of Banking & Finance, 28(6), 1291–1315. https://doi.org/10.1016/S0378-4266(03)00121-
5
37. Hartman, D. G., Braithwaite, P., & Butsic, R. P. (1992). Property-casualty risk-based capital requirement
- A conceptual framework. The Forum Spring 1992, Casualty Actuarial Society, New York, 211-280.
38. Holmes Jr, R. M., Bromiley, P., Devers, C. E., Holcomb, T. R. & McGuire, J. B. (2011). Management
theory applications of prospect theory: Accomplishments, challenges, and opportunities. Journal of
Management,37(4), 1069-1107.
39. Heinrich, T., Sabuco, J., & Farmer, J. D. (2019). A simulation of the insurance industry: The problem of
risk model homogeneity. arXiv. https://arxiv.org/abs/1907.05954
40. International Monetary Fund. (2020). COVID-19 stress testing of insurance companies: Lessons from
the U.S. experience. VoxEU – Centre for Economic Policy Research.
https://cepr.org/voxeu/columns/impact-covid-19-insurers
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1593
41. Iroh, E. H. & Orobator, B. (2025) Risk Based Supervision and Financial Performance of Insurance
Companies in Nigeria. Vol 14 No 1 (2024): The Nigerian Journal of Risk and Insurance.
https://njri.unilag.edu.ng/article/view/2429?utm_source=chatgpt.com
42. Jaaman, S. H.,Ismail, N.& Majid, N. (2007). Assessing risk and financial strength of general insurers in
Malaysia. Journal of Quality Measurement and Analysis, 3, 65-73.
43. Lai, G. C. (2011). Regulatory capital and profitability in the property–liability insurance industry: A
dynamic approach. Journal of Risk and Insurance, 78(3), 611–641.
44. Lai, I. (2011). Malaysia Looks to Risk-Based Capital Model for Takaful. Best Week
Asia/Pacific.Availableonline:www3.ambest.com/ambv/bestnews/presscontent.aspx?altsrc=0&refnum=
17718
45. Lazam, N. Md, Tafri, F. H., Shima, S. N., & Shahruddin, S. M. (2012). Impact of the risk-based capital
implementation: A case study on an insurance company in Malaysia. In Statistics in Science, Business,
and Engineering (ICSSBE), 2012 International Conference, 1-6.
46. Majumdar, S., & Kunal, S. (2010). Corporate borrowing and profitability in India. Managerial and
Decision Economics, 31, 33–45.
47. Margaritis, Dimitris, & Maria Psillaki. 2007. Capital structure and firm efficiency. Journal of Business
Finance & Accounting, 34, 1447–1469.
48. Matemilola, B. T., Bany-Ariffin, A. N., & McGowan Jr, C. B. (2012). Trade off theory against Pecking
order theory of capital structure in a nested model: Panel GMM Evidence from South Africa. The Global
Journal of Finance and Economics, 9(2), 133-147.
49. Maureen Nneka Nwala, & Danjuma Tusha Sukana. (2025). Effect of Capital Adequacy Regulation on
The Financial Performance of Insurance Firms in Nigeria. Vol 4 No 2 (2023): Lagos Journal of Banking,
Finance & Economic Issues.
https://journals.unilag.edu.ng/index.php/LJBFEI/article/view/2526?utm_source=chatgpt.com
50. Merton, R. C., & Perold, A. F. (1993). Theory of risk capital in financial firms. Journal of Applied
Corporate Finance, 6(3), 16–32
51. Okafor, T. C., & Onwumere, J. U. J. (2018). Capital adequacy and financial performance of insurance
firms: Empirical evidence from Nigeria. Nigerian Journal of Finance and Management, 23(2), 55–70.
52. Olaleye, J. O. (PhD) & Adeagbo, K.A. (2023). Capital Structure and Financial Performance of Quoted
Insurance Companies in Nigeria. International journal of research and innovation in social science
(IJRISS), 7(1), 939-949
53. Onaolapo, A. A. & Kajola, S. O. (2010). Capital structure and firm performance: evidence from Nigeria.
European Journal of Economics, Finance and Administrative Sciences, 25, 70-82.
54. Oyugi, M., & Mutuli, M. (2014). An approach to Risk Based Capital for African Life Insurers. A paper
to be presented to the International Congress of Actuaries, Washington DC Pervan
55. Perold, A. (2005). Capital allocation in financial firms. Journal of Applied Corporate Finance, 17, 110–
18.
56. Putra, N. D. (2017). Influence growth of income, assets, ratio of claim and risk based capital on the
profitability of life insurance companies in Indonesia. International Journal of Business and Commerce,
6(9), 24-40
57. PwC (2020). Africa insurance trends. www.pwc.com
58. Sarfaraz, A.B., Ikhtiar A.G., Zulfiqar A.R., & Saifullah, S. (2021). A conceptual review of capital
structure under risk-based capital regime, risk profile of insurers and performance. Sukkur IBA Journal
of Management and Business – SIJMB, 8(1) 15-27.
59. Shimizu, K. (2007). Prospect theory, behavioral theory, and the threat-rigidity thesis: Combinative
effects on organizational decisions to divest formerly acquired units. Academy of Management Journal,
50(6), 1495-1514.
60. Shim, J. (2010). Capital-based regulation, portfolio risk and capital determination: Empirical evidence
from the US property–liability insurers. Journal of Banking and Finance, 34, 2450–2461.
61. Shimpi, P. A., & S. Re (2002). Integrating risk management and capital management. Journal of Applied
Corporate Finance, 14, 27–40
62. Shyu, J. (2013). Ownership structure, capital structure, and performance of group affiliation: Evidence
from Taiwanese group-affiliated firms. Managerial Finance, 39(4), 404-420.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
Page 1594
63. Soumaré, I., & Tafolong, C. (2016). Pricing and capital allocation in credit insurance: A business cycle
approach. Quantitative Finance, 16(12), 1893–1907. https://doi.org/10.1080/14697688.2016.1206960
64. Toporowski, Jan. 2008. Excess Capital and Liquidity Management. Working Paper No. 549, Levy
Economics Institute of Bard College, Annandale-on-Hudson, NY, USA.
65. Van Bragt, D. (2021). A look across the border: The U.S. Risk-Based Capital framework for life
insurance companies. Aegon Asset Management. https://www.aegonam.com
66. Yaudil, Y., Achsani, N. A., & Maulana, T. N. A. (2023). The effect of capital structure and risk-based
capital on financial performance in Indonesia's insurance industry. International Journal of Economics,
Business and Accounting Research, 7(1), 122–132.