Factors Influencing the Profitability and Loss of Chinese Insurance

Companies Based on Factor Analysis

Zhenjie Wang

School of Mathematics, Hohai University, Nanjing, Jiangsu, 211106, China

Keywords: Factor Analysis, Insurance, Ridge Regression, Balance Sheet

Abstract: The insurance industry has a significant impact on economic development. This study selects the balance

sheets of 55 Chinese insurance companies as samples for the year 2021. Selecting multiple items from

the balance sheet for factor analysis. The factor analysis of Chinese insurance companies' balance sheets

has identified three crucial factors: Financial Activity Diversity, Insurance Liabilities and Investment

Strategy, and Financial Risk Management. Ridge regression analysis revealed that these three factors have

a positive impact on the net profits of insurance companies. These factors collectively contribute

positively to company operations, underscoring the significance of diversifying financial activities,

aligning insurance liabilities with investment decisions, and strategically managing financial soundness

and risk. The findings highlight key considerations for insurance companies seeking to enhance their

financial resilience, optimize investment strategies, and ensure effective risk management. The study aims

to uncover the relationship between the balance sheet and company profits, further assisting in enhancing

the market value of insurance companies.

1 INTRODUCTION

Insurance is crucial in people's lives, offering both

economic and social security by reducing various risks

and uncertainties. Ilhan's analysis highlighted a

positive link between the insurance industry and

economic growth (Ege and Bahadir 2011). Studying

the profitability of insurance companies reflects the

industry's development and provides insights into the

objective state of the economy. The study of factors

influencing the profitability and loss of insurance

companies has made extensive and deep progress

globally. The main analysis factors include company

size, leverage ratio, liquidity, capital adequacy ratio,

premium growth rate, market share, and so on.

However, the impact of these factors varies in

different countries.

Berhe studied the factors influencing the

profitability of insurance companies in Ethiopia. The

regression analysis results showed that the

profitability of insurance companies is notably

affected by factors like insurance size, capital

adequacy, liquidity ratio, and GDP growth rate.

Conversely, the leverage ratio, loss ratio, market

share, and inflation rate were found to have an

insignificant impact on insurance companies'

profitability (Berhe and Kaur 2017). In Saudi Arabia,

Dhiab's empirical findings indicated that the

profitability of Saudi insurance companies was

positively influenced by the written premium growth

rate, tangible asset ratio, and fixed asset ratio.

Although company size and liquidity ratio showed a

positive correlation with profitability, they lacked

statistical significance. On the other hand, loss ratio,

liability ratio, insurance leverage ratio, and, to a lesser

extent, company age, had a negative impact on the

profitability of Saudi insurance companies (Dhiab

2021). Kulustayeva argued that in Kazakhstan, the

most significant impact on the profitability of

insurance companies was financial leverage

(Kulustayeva et al 2020). In general, the role of

leverage ratio varies significantly across different

countries. Besides, according to Tegegn's results, the

key determinants of profitability were size, premium

growth rate, liquidity, and age. Specifically, premium

growth rate and size exhibited a positive correlation,

while liquidity and age were negatively and

significantly associated with profitability (Tegegn et

al 2020). Similarly, in Malaysia, Alarussi's research

results indicated a significant positive correlation

between company size (total sales), working capital,

company efficiency (asset turnover ratio), and

profitability (Alarussi and Alhaderi 2018). Likewise,

Wang, Z.

Factors Inﬂuencing the Proﬁtability and Loss of Chinese Insurance Companies Based on Factor Analysis.

DOI: 10.5220/0012821800004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 64-69

ISBN: 978-989-758-690-3

Orty's results indicated a positive correlation between

the size of the company and its profitability (Ortyński

2016). Hence, it can be observed that the company size

has a positive impact in the majority of studies.

Regarding liquidity and growth rate, Kripa found

that factors such as growth rate, liabilities, liquidity,

and fixed assets play a crucial role in influencing the

profitability of insurance companies. Specifically,

profitability is positively correlated with the growth

rate, while liabilities, liquidity, and fixed assets are

negatively correlated (Kripa 2016). Daar found that

in India, the capital adequacy ratio and GDP have a

positive impact on profits, while liquidity and

inflation have a negative impact on the profitability

of general insurance companies (Daare 2016).

In addition to these market indicators, many

scholars have also analyzed more detailed factors.

Ullah identified a strong inverse link between

Underwriting Risk and Size in relation to Profitability

(ROA). It also found a noteworthy positive association

between Expense Ratio, Solvency Margin, and ROA

(Ullah et al 2016). Datu's research results indicated

that profitability was positively influenced by factors

such as low underwriting risk, low reinsurance

utilization rate, low input costs, and a smaller

company size (Datu 2016).

With the rapid development of the Chinese

economy and the acceleration of globalization trends,

the role of the insurance industry in the national

economic system is becoming increasingly prominent

(Zhou 2023). As a vital component of economic risk

management, China's insurance sector not only plays

a crucial role in providing risk protection for

individuals and businesses but also exerts a profound

impact on capital markets and financial stability.

However, the fluctuation of profitability and loss in

insurance companies remains a focal point of market

attention. Therefore, in-depth research into the factors

influencing the profitability and loss of Chinese

insurance companies is significant.

2 METHODOLOGY

2.1 Data Source and Description

This study takes various insurance companies in

China as research samples and selecting data from

2021 for empirical research. A total of 55 insurance

companies were selected for analysis. Data

processing is carried out as follows: categories with

five or more years of data missing within the decade

are excluded. For categories with data missing in a

few years, this study uses the data from the previous

year or the second year for replacement. All the data

used in the empirical research in this paper are

sourced from the annual information disclosure of

various insurance companies (Table 1).

Table 1: Definition of variables

Abbreviation Variables Range

TFA Trading Financial Assets [0.63,123131]

CE Cash and Cash E

uivalents [3.71,526301]

L Loans [0,2599510]

RE Retained Earnin

s [-9816.97,521677]

LPP Loans Pledged by Policyholders [0,768975]

PR Premiums Receivable [0.17,94003]

RAs Repurchase Agreements [0,122765]

RAP Re

urchase A

reement Pa

ables [0,276602]

CS Capital Surplus [-405.25,134474]

CP Claims Pa

able [-0.12,65094]

HTM Hel

-to-Maturity Investments [0,1189369]

AFSA Available-fo

-Sale Financial Assets [182.03,1215603]

SR Surplus Reserve [2.7,86027]

TD Time De

osits [7.3,545667]

LIR Long-term Health Insurance Reserve [0,2768584]

PDP Polic

holder Dividends Pa

able [0,122510]

PDI Policyholders' Deposits and Investments [0,200730]

LHIR Lon

-term E

uit

Investments [-92.73,187019]

LR Loss Reserve [0,162022]

UPR Unearned Premium Reserve [0,177041]

PIC Pai

-in Capital [500,44224]

IPF Insurance Protection Fun

[-9.55,1008]

Factors Inﬂuencing the Proﬁtability and Loss of Chinese Insurance Companies Based on Factor Analysis

Selecting the balance sheet from the annual

financial reports of each company as a variable for

analysis. The balance sheet reflects a company's

assets, liabilities, and shareholders' equity, offering

an initial understanding of attributes such as debt-

paying ability, liquidity, net worth, and more.

Selecting variables as shown in the following table.

All units are in millions.

2.2 Method Introduction

Due to the multitude of variables and the high degree

of correlation among them, considering the use of

factor analysis for dimensionality reduction. Factor

analysis is a statistical method aimed at uncovering

the underlying structure or factors among observed

variables. It simplifies the data by interpreting the

observed variables as latent, unobservable factors.

Building upon this, Considering the use of ridge

regression for factor analysis to assess the correlation

with insurance company profits. Ridge regression

adds a regularization term to the loss function of

multiple linear regression to prevent overfitting and

enhance the model's generalization ability. The

expression of the loss function is as follows：

Loss =

∑



−ŷ



)



+α

∑













(1)

Regularization term α

∑









used to penalize

the magnitude of coefficients. It makes the absolute

values of the coefficients as small as possible. This

helps prevent the model from overfitting the training

data and, in the presence of multicollinearity,

stabilizes the estimates. The goal of ridge regression

is to minimize the aforementioned loss function. This

is achieved by solving the following optimization

problem:

β= argmin

∑



−ŷ



)



+α

∑













(2)

Its analytical solution can be expressed in matrix

form:

β=(X



X+αI)





y (3)

X is the feature matrix, y is the output vector, I

is the output vector.

3 RESULTS AND DISCUSSION

3.1 Model Results

Firstly, perform the Kaiser-Meyer-Olkin (KMO) and

Bartlett's tests to assess the suitability for factor

analysis. The results of the KMO test indicate a value

of 0.724, and concurrently, the results of Bartlett's

sphericity test reveal a significance p-value less than

0.0001, demonstrating statistical significance. There

is significant correlation among the variables,

suggesting the suitability of factor analysis with a

high degree of appropriateness (figure 1).

Figure 1: Scree Plot (Picture credit: Original)

To draw a scree plot based on the explanatory

power of each principal component regarding data

variability. Its purpose is to confirm the number of

factor principal components to be selected by

examining the slope of the eigenvalues' descent,

combined with the variance explained table, which

can be used to confirm or adjust the number of factor

principal components:

At Principal Component 4, the eigenvalue

explaining the total variance falls below 1.0, with a

contribution rate to the variable explanation reaching

96.736%. Selecting 3 factors for analysis (Table 2).

Table 2: Factor Weight Analysis

Rotated

Variance

Explained

(%)

Rotated

Cumulative

Variance

Explained (%)

Weight

(%)

Factor 1 0.422 42.193 44.462

Factor 2 0.342 76.352 35.996

Factor 3 0.185 94.896 19.542

The results of factor analysis weight calculations

show that the weight for Factor 1 is 44.462%, for

Factor 2 is 35.996%, and for Factor 3 is 19.542%.

ICDSE 2024 - International Conference on Data Science and Engineering

Table 3: Factor Loading Coefficient

Factor 1 Factor 2 Factor 3

TFA 0.969 0.088 0.188

CE 0.968 0.07 0.214

L 0.938 0.238 0.237

RE 0.88 0.324 0.328

LPP 0.872 0.342 0.167

PR 0.811 0.187 0.543

RAs 0.787 0.056 0.553

RAP 0.772 0.455 0.379

CS 0.808 0.408 0.241

CP 0.701 0.661 0.247

HTM -0.015 0.989 0.102

AFSA -0.028 0.945 0.21

SR 0.089 0.917 0.159

TD 0.366 0.893 0.202

LIR 0.479 0.862 0.147

PDP 0.448 0.86 0.074

PDI 0.584 0.785 0.107

LHIR 0.594 0.706 0.226

LR 0.377 0.104 0.899

UPR 0.561 0.063 0.811

PIC 0.055 0.412 0.809

IPF 0.5 0.286 0.783

From the table 3, it can be observed that TFA, CE,

L, RE, LPP, PR, RAs, RAP, CS, CP exhibit high

factor loadings in Factor 1. These financial items can

be considered to belong to a category with similar

characteristics. These factors all involve the

company's management of cash flow, optimization of

capital structure, and investment through buying and

selling financial assets. This factor encompasses

various financial activities, including financial

investments, insurance operations, and financial

management. Therefore, Factor 1 is referred to as the

Financial Activity Diversity Factor.

HTM, AFSA, SR, TD, LIR, PDP, PDI, LHIR exhibit

high factor loadings in Factor 2. These characteristics

are related to insurance liabilities, investments, assets,

financial reserves, and accumulations. These factors

reflect some common features in the company's

management of its investment portfolio, financial

risks, and future preparations. Factor 2 is termed as

the Insurance Liabilities and Investment Strategy

Factor.

LR, UPR, PIC, and IPF exhibit high factor

loadings in Factor 3. These characteristics are related

to the financial health of the company, reserve

management, as well as risk management and the

provision of insurance protection funds. They reflect

some common features in the company's efforts to

maintain financial strength, manage risks, and

provide safeguards. Factor 3 is termed as the

Financial Risk Management Factor.

Table 4: Component Matrix

Component1 Component2 Component3

TFA 0.181 -0.065 -0.099

CE 0.177 -0.069 -0.086

L 0.153 -0.035 -0.077

RE 0.116 -0.018 -0.028

LPP 0.143 -0.007 -0.102

PR 0.07 -0.05 0.092

RAs 0.073 -0.074 0.107

RAP 0.072 0.013 0.011

CS 0.11 0.007 -0.058

CP 0.065 0.065 -0.047

HTM -0.098 0.189 0.006

AFSA -0.118 0.177 0.061

SR -0.079 0.165 0.016

TD -0.021 0.137 -0.015

LIR 0.017 0.125 -0.061

PDP 0.024 0.13 -0.089

PDI 0.055 0.104 -0.096

LHIR 0.041 0.082 -0.038

LR -0.091 -0.049 0.345

UPR -0.029 -0.067 0.271

PIC -0.171 0.039 0.349

IPF -0.054 -0.018 0.259

Based on the matrix composition table 4, factor

scores for each factor can be calculated, thereby

reducing variables. By calculating the Variance

Inflation Factor (VIF), it was observed that there is

multicollinearity between Factor 1 and Factor 3.

Considering ridge regression, the obtained results are

as follows (Table 5):

Table 5: Ridge Regression Analysis Results

K=0.021 Unstandardized

Coefficients

Standardized

Coefficients

t P R² Adjusted R² F

Standard

Erro

Beta

constant 674.898 512.088 - 1.318 0.194

0.979 0.978

775.366

(0.000*

**)

Factor1 0.179 0.008 1.014 21.731 0.000***

Factor2 0.072 0.005 0.423 15.157 0.000***

Factor3 0.044 0.016 0.144 2.836 0.007***

dependent variable: net profit

Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.

Factors Inﬂuencing the Proﬁtability and Loss of Chinese Insurance Companies Based on Factor Analysis

It is found that factors 1, 2, and 3 all have a

positive impact on the profits of the insurance

company, with factor 1 having a significant influence.

3.2 Discussion

The results indicate that the three factors derived from

the factor analysis all have a positive impact on the

profits of Chinese insurance companies.

Factor 1 is identified as the Financial Activity

Diversity factor. Through diversification of financial

activities, companies can better cope with claims

payments and other short-term liabilities, ensuring an

adequate supply of liquid assets. According to

modern financial theory, insurance companies can

enhance cash flow management by diversifying their

investment portfolios, encompassing both short-term

and long-term assets. Additionally, this approach

provides a more flexible capital structure and broader

investment opportunities. These practical

implications underscore the strategic significance of

financial activity diversity in the insurance industry.

Factor 2 is identified as the Insurance Liabilities

and Investment Strategy factor, holding significant

importance in the operations of insurance companies.

This factor is not only closely linked to the company's

insurance operations, ensuring adequate funds for

claims payments and policy dividends to maintain

financial health, but it also influences investment

decisions, requiring the company to consider the

structure of liabilities within its investment portfolio.

This association underscores the foundational

importance of elevated liability levels for insurance

companies in terms of compliance, customer trust,

and business continuity. Supported by financial

theory and industry practices, this factor highlights

the intimate interplay between insurance operations

and investment decisions, crucial for the long-term

robustness of the company.

Factor 3, the Financial Risk Management factor,

also exerts a positive impact on the operations of

insurance companies. The significance of this factor

lies in ensuring the company maintains a robust

financial foundation. Through prudent reserve

management strategies, it mitigates potential risks

and underscores the importance of risk management

and safeguarding funds to shield the company,

customers, and stakeholders from potential threats.

Supported by financial theory and industry practices,

this factor emphasizes the long-term strategic

importance of insurance companies in terms of

financial health, reserve management, and risk

mitigation. This not only aids in establishing a

reliable financial soundness but also lays a solid

foundation for attracting investors and customers

while ensuring business continuity.

Due to significant differences among samples, the

research results may deviate from reality. Further

analysis, such as exploring the mean values of assets

and liabilities over multiple years for a broader range

of companies, could be conducted to gain additional

insights.

4 CONCLUSION

In summary, the factor analysis of Chinese insurance

companies' balance sheets has revealed three pivotal

factors: Financial Activity Diversity Factor,

Insurance Liabilities and Investment Strategy Factor,

and Financial Risk Management Factor. These factors

demonstrate positive influences on company

operations, emphasizing the importance of financial

activity diversity, the close connection between

insurance liabilities and investment decisions, and the

strategic significance of financial soundness and risk

management.

It is recommended that insurance companies

adopt a series of strategies to optimize their

operational efficiency. Firstly, diversifying financial

activities to enhance liquidity, reduce short-term

liability risks, and adapt flexibly to market

fluctuations is advised. Secondly, there is a

suggestion to further integrate insurance operations

and investment decisions, ensuring coordination

between investment portfolios and liability structures

for maximizing returns and securing funds for claims

payments and policy dividends. Additionally, a

strong emphasis is placed on reinforcing financial

health management, especially in reserve

management and risk mitigation, to effectively

withstand potential risks. Furthermore, maintaining a

proactive stance towards learning and improvement

is advisable, involving the comparison and learning

from best practices of peer companies, with timely

adjustments of strategies to adapt to market changes.

These recommendations aim to assist insurance

companies in enhancing business resilience,

adaptability, and competitiveness, fostering

sustainable development.

Despite potential biases introduced by significant

differences among samples, further analysis

involving the mean values of assets and liabilities

over multiple years for a broader range of companies

could provide a more comprehensive understanding

of the universality and practical impact of these

factors. This study offers valuable insights into

ICDSE 2024 - International Conference on Data Science and Engineering

comprehending the financial characteristics of

Chinese insurance companies.

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Factors Inﬂuencing the Proﬁtability and Loss of Chinese Insurance Companies Based on Factor Analysis