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Determinants of Saudi Takaful Insurance Companies Profitability

Profile image of Said OUASSAFSaid OUASSAF

2018, Academy of Accounting and Financial Studies Journal

Abstract

The purpose of this paper is to investigate the main internal factors affecting the profitability of insurance Takaful companies in an Islamic insurance system. We collected the data from the quarterly reports of the six largest Saudi Takaful Insurance companies for the period 2010-2016, which represents more than 60% of the total assets of the Insurance market. Panel data techniques, namely, pooled ordinary least squares, fixed effects and random effects, were used to estimate the relationship between return on policyholders as a proxy of insurance company profitability and company-specific variables such as age, size, loss ratio, the rate of retention, risk level, and the written premium growth rate. The regression results indicate that age, size, written premium growth rate and loss ratio, have significant effects on the profitability of insurance Takaful companies. Many studies were conducted to determine variables affecting insurance Takaful companies profitability, but most of...

Figures (20)
Of the 33 insurance companies listed on the Tadawul Market (Saudi financial market), and after excluding companies that did not fit the criteria, only six insurance companies are considered in this research. The study is mainly conducted based on secondary data to evaluate the profitability of Takaful insurance companies in Saudi Arabia. It covers seven years period with quarterly data from Q1 2010 to Q4 2016.
Of the 33 insurance companies listed on the Tadawul Market (Saudi financial market), and after excluding companies that did not fit the criteria, only six insurance companies are considered in this research. The study is mainly conducted based on secondary data to evaluate the profitability of Takaful insurance companies in Saudi Arabia. It covers seven years period with quarterly data from Q1 2010 to Q4 2016.
To examine the impact of firm characteristics on profitability as measured by the Return on Assets (ROA), the following variables were chosen based on the existing literature and data availability. Table 2 provides the definitions and measurements of all variables used in this study.
To examine the impact of firm characteristics on profitability as measured by the Return on Assets (ROA), the following variables were chosen based on the existing literature and data availability. Table 2 provides the definitions and measurements of all variables used in this study.
Like any insurance company, the size of a Takaful insurance company has an important impact on profitability. However, as mentioned earlier, the Takaful insurance company has two balance sheets, one for policyholders, and the other for shareholders. In our study, we considered the return on policyholders as a proxy of the whole company profitability, as the major source of revenue of Takaful Company (Investment income and underwriting income) comes from policyholders’ assets.
Like any insurance company, the size of a Takaful insurance company has an important impact on profitability. However, as mentioned earlier, the Takaful insurance company has two balance sheets, one for policyholders, and the other for shareholders. In our study, we considered the return on policyholders as a proxy of the whole company profitability, as the major source of revenue of Takaful Company (Investment income and underwriting income) comes from policyholders’ assets.
Table 3 provides a summary of the descriptive statistical analysis for the sample study.
Table 3 provides a summary of the descriptive statistical analysis for the sample study.
Suggesting an amendment to this test is needed to include additional mutations for the dependent variable in order to eliminate the autocorrelation. The length of the decomposition in the three cases is determined by either the Akaika Information Criterion (AIC) or the Schwartz Bayesian Criterion (SBC) or by Lagrange Multiplier; the three possible cases are given by the following equations: Distribution of the Augmented Dicky-Fuller Test Assumptions that the error limit is Statistically independent and contains a constant variation. Therefore, using the Augmented Dicky- Fuller, we must make sure that the error limit is not correlated and that it contains a constant variation. A generalization of the Augmented Dicky-Fuller, Phillips-Perron allows for autocorrelation at the error limit. The method of Philip Peron is to modify the Dicky-Fuller t- Statistic to take into account the limitations to the extent of error. The ROA of each insurance company under study was drawn up during the period 2010-2016 as shown in Figure 1. It is clear that the return on assets of both Bupa and Allianz companies may have an
Suggesting an amendment to this test is needed to include additional mutations for the dependent variable in order to eliminate the autocorrelation. The length of the decomposition in the three cases is determined by either the Akaika Information Criterion (AIC) or the Schwartz Bayesian Criterion (SBC) or by Lagrange Multiplier; the three possible cases are given by the following equations: Distribution of the Augmented Dicky-Fuller Test Assumptions that the error limit is Statistically independent and contains a constant variation. Therefore, using the Augmented Dicky- Fuller, we must make sure that the error limit is not correlated and that it contains a constant variation. A generalization of the Augmented Dicky-Fuller, Phillips-Perron allows for autocorrelation at the error limit. The method of Philip Peron is to modify the Dicky-Fuller t- Statistic to take into account the limitations to the extent of error. The ROA of each insurance company under study was drawn up during the period 2010-2016 as shown in Figure 1. It is clear that the return on assets of both Bupa and Allianz companies may have an
Table 4
Table 4
Table 5 shows that the values of autocorrelation decrease over time while the partial autocorrelation values fade after the initial period, meaning that their model is autocorrelation from the first order AR (1). The study of statistical hypothesis is "There are no significant differences between the average return on assets among insurance companies". The average return on assets rate of insurance companies under study are can be represented by Table 6 and Figure 2:
Table 5 shows that the values of autocorrelation decrease over time while the partial autocorrelation values fade after the initial period, meaning that their model is autocorrelation from the first order AR (1). The study of statistical hypothesis is "There are no significant differences between the average return on assets among insurance companies". The average return on assets rate of insurance companies under study are can be represented by Table 6 and Figure 2:
Table 8
Table 8
are cere RELATIONSHIP BETWEEN ACTUAL VALUES AND PREDICTIVE VALUES OF DIFFERENT MODELS
are cere RELATIONSHIP BETWEEN ACTUAL VALUES AND PREDICTIVE VALUES OF DIFFERENT MODELS
REFERENCES
REFERENCES

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