1. Determinants of Performance: A Case of Life Insurance Sector of Pakistan NAVEED AHMED Hailey College of Commerce, University of the Punjab, Lahore

2. INTRODUCTION

3. The performance of any firm not only plays the role to increase the market value of that specific firm but also leads toward the growth of the whole industry which ultimately leads towards the overall prosperity of the economy.

4. Measuring the performance of insurers has gained the importance in the corporate finance literature because as intermediaries, these companies are not only providing the mechanism of risk transfer but also helps to channelizing the funds in an appropriate way to support the business activities in the economy.

5. Insurance companies have importance both for businesses and individuals as they indemnify the losses and put them in the same positions as they were before the occurrence of the loss. In addition, insurers provide economic and social benefits in the society i.e. prevention of losses, reduction in fear and increasing employment.

6. Therefore, the current business world without insurance companies is unsustainable because risky businesses have not a capacity to retain all types of risk in current extremely uncertain environment.

7. Financial statistics reported the phenomenal growth of Pakistani life insurance companies as these companies comprise 52% and 69% share of entire (life plus non-life) insurance market in terms of net premiums and assets (Insurance Year Book, 2007). In addition, the premium of these life insurers increased by 36% in 2007 (Insurance Year Book, 2007) shows the remarkable progress of life insurance sector of Pakistan

8. Therefore, what determines the performance of the life insurance industry is an important discussion for the regulators and policy makers to support the sector in achieving the excellence so that desirable economic fruits could be reaped from the help of the life insurance sector of Pakistan

9. LITERATURE REVIEW

10. Wessels (1988) Chiarella et al. (1991) Kjellman and Hansen (1995) Rajan (1995) Wiwattanakantang (1999) Chen and Jiang (2001) Miguel and Pindado (2001) Nivorozhkin (2002) Frank and Goyal (2003)

11. Cassar and Holmes (2003) Low and Chen (2004) Buferna et al. (2005) Huang and Song (2006) Daskalakis and Psillaki (2007) Cheng and Weiss (2008) Bhaird and Lucey (2008) Li et al. (2009) Chang et al. (2009)

12. RESEARCH METHODOLOGY

13.  Sample and Data Currently, there are five life insurance companies operating in Pakistan and all these five companies are selected to measuring their performance over the period of seven years from 2001 to 2007. For this purpose, financial data has been collected from financial statements (Balance Sheets and Profit and Loss a/c) of insurance companies and “Insurance Year Book” which is published by Insurance Association of Pakistan.

14.  The following statistical analysis have been used to deduce the results of present study:  Descriptive Analysis  Correlation Analysis  Regression Analysis

15. Regression Model PR = β0 + β1 (LG) + β2 (TA) + β3 (SZ) + β4 (LQ) + β5 (AG) + β6 (RK) + β7 (GR) + ε Where: PR = Performance (Net income before interest and tax divided by total assets) LG = Leverage (Total debts divided by total assets) SZ = Size (Log of premiums) GR =Growth (Percentage change in premiums) TA = Tangibility of assets (Fixed assets divided by total assets) LQ = Liquidity (Current assets divided by current liabilities) AG = Age (Difference b/w observation year and establishment year) RK = Risk (standard deviation of ratio of total claims to total premiums) ε = the error term

16. EMPIRICAL FINDINGS Descriptive Statistics

17. YearsLeverageSize MeanSDMinMaxMeanSDMinMax 2001 0.800.210.450.996.022.123.068.93 2002 0.810.200.470.996.212.113.299.07 2003 0.820.190.510.996.502.083.579.20 2004 0.790.240.380.996.682.093.569.31 2005 0.830.210.470.996.952.033.969.53 2006 0.840.200.490.997.212.024.249.68 2007 0.790.300.261.007.512.064.5010.03

18. YearsGrowthPerformance MeanSDMinMaxMeanSDMinMax 2001 11.5311.903.2232.390.020.010.000.03 2002 22.2123.523.6860.990.020.010.000.03 2003 37.1832.628.3090.710.020.010.000.03 2004 22.2027.93-1.7861.160.030.020.000.05 2005 31.1810.3024.9748.980.02 0.000.05 2006 31.7926.143.7472.780.030.020.000.06 2007 34.829.2522.4445.660.07 0.000.17

19. YearsTangibilityLiquidity MeanSDMinMaxMeanSDMinMax 2001 0.030.020.000.061.700.761.072.65 2002 0.030.020.000.061.730.861.143.01 2003 0.030.020.000.052.181.111.223.72 2004 0.02 0.000.042.241.771.094.85 2005 0.02 0.000.043.022.261.155.94 2006 0.020.010.000.033.982.721.367.37 2007 0.02 0.000.056.368.631.3316.33

20. YearsAgeRisk MeanSDMinMaxMeanSDMinMax 2001 16.6020.406.0053.001.921.330.703.94 2002 17.6020.407.0054.000.830.470.401.34 2003 18.6020.408.0055.000.580.450.181.34 2004 19.6020.409.0056.003.343.080.007.23 2005 20.6020.4010.0057.004.702.151.236.36 2006 21.6020.4011.0058.003.603.860.519.72 2007 22.6020.4012.0059.006.356.511.7816.00

21. CORRELATION ANALYSIS

22. LeverageSizeGrowthTangibilityLiquidityAge LeveragePearson Correlation Sig. (2-tailed) SizePearson Correlation.374 ** Sig. (2-tailed).000 GrowthPearson Correlation.077.072 Sig. (2-tailed).661.680 TangibilityPearson Correlation -.476 ** -.142 **.051 Sig. (2-tailed).000.771 LiquidityPearson Correlation -.225 ** -.429 *.052.229 Sig. (2-tailed).000.025.796.250 AgePearson Correlation.415 *.401 ** -.153-.356 **.491 ** Sig. (2-tailed).013.000.379.000.009 RiskPearson Correlation -.427 * -.200-.060.084.364 ** -.071 Sig. (2-tailed).012.256.334.538.000.771

23. REGRESSION ANALYSIS

24. Model Unstandardized Coefficients Standard ized Coefficie nts tSig. BStd. ErrorBeta (Constant).010.051.204.841 Leverage -.265.090-1.579-2.940.008* Size.038.0091.7224.120.001* Growth -4.69.000-.032-.245.809 Tangibility.507.367.1831.382.183 Liquidity.001.003.058.205.840 Age -.003.003-.235-1.169.257 Risk -.004.002-.3741.903.072**

25. CONCLUSION

26. The results reveal that leverage, size and risk are most important determinant of performance of life insurance sector whereas ROA has statistically insignificant relationship with age, growth, tangibility and liquidity.

27. FUTURE RESEARCH

28. Thanks