Testing A Series Of 2 Simples T Tests

* Test the utility of this regression model (use a two tail test with α =.05).

Bjerk used regression techniques to test his assumptions. Several different models, including a series of formulas were used. The tables were well organized and the dependent variable was clearly stated in all tables.

The aim of this report is to value the equity of Genworth Mortgage Insurance Australia (GMA). With the lenders mortgage insurance industry being largely affected by macro-economic factors like population growth, interest and cash rates, unemployment, we have explored the influence of these factors on GMA in the past, and the influence they may have on the growth of the company in the future.

Therefore, two-tail hypothesis testing has applied to test the effect of both variables, the null and alternative hypothesis are below:

1. The discriminant function is fitted (that is, the values of a, b1, and b2 are obtained) using historical data for a sample of firms that either went bankrupt or did not go bankrupt during some past period. When the data in the lower part of Figure 22A-1 were fed into a “canned” discriminant analysis program (the computing centers of most universities and large corporations have such programs), the following discriminant function was obtained:

This variable was regressed against dividend pay-out. Correlation analysis result will be provided first and later that of regression. Pearson correlation is used for the correlation analysis, Pearson’s’ correlation examines the strength of a relationship between variables. The results of correlation test have been summarised in Table 6. The Pearson correlation between dividend pay-out ratio and profitability seem to have worsened as time evolves, from a positive 0.010 pre-crisis decreasing to negative -0.019 during the crisis with an increase to positive 0.007 post crisis. With total period at positive 0.004, however, this overall positive relationship was not found to be significant as the p-value was 0.931 which is higher than the 0.05 threshold. The regression results in Table 7 show that profitability has an unstandardized coefficient of 0.041 for the total sample period, indicating a positive relationship. Thus, one percent increase in profitability would lead to 0.041% increase in dividend pay-out ratio. Which implies an increase in profitability does lead to an increase in dividend pay-out, likewise a decrease in profitability does lead to decrease in dividend pay-out. However, the p-value of regression is 0.878, which is again far above the 0.05 threshold. Overall although a positive relationship between dividend pay-out ratio and profitability was found in this study, as expected from reviewing existing literature and projected hypothesis. This

According to the result in Table 11, the CET1 ratio is statistical significant in the model under 10% level of significance and has a positive relationship with the crisis, although it is not as expected. If simply analyze from the empirical result, 1 percentage point increase of the common equity core tier 1 ratio would increase the probability of the financial crisis by 11.23833 percentage point when all other variables remain unchanged.

The t-test p-value of individual MU, namely, muGDP; muIPI; muCPI; muVNI depicts the level of significance respectively at 95%; 90%; 95%; 95%. These levels empirically portray the significant impact of MU on the outcome of cumulative abnormal returns associated with M&A. Simultaneously, other variables along with MU significantly determine CAR. For instance, cvFS (firm size) registers at 90%-95%, cvETD logs the level at 90% across all models, while cvMTB varies between 90%-95% and insignificance. Additionally, the outcome of a CAR also is significantly determined by whether it is attributed to acquirers. The null hypothesis is further rejected by the significance levels of MU.

Credit risk has been the subject of considerable research interest in Banking and Financial communities, and has recently drawn the attention of statistical researchers. The exposure to credit risk continues to be the leading source of problems in the banking industry and as a result needs to be managed. Credit risk is identified as a core pillar for the viability of banks and credit institutions (Michael et al., 2011)

Over the period of 1996-2005, accounting data based researches showed that the universal banks can only increase the volatility of accounting profits by diversifying non-interest income and have no effect on raising their average profits. For American banks, there may be some benefits by introducing new business differing from traditional interest-income based derivatives, the profits are offset by the increasing exposure to the systemic risk that institutions may suffer. The similar result raised in Stiroh 's (2006) research in American equity market during 1996-2005. He concluded that, on the one hand, there is no apparent link between non-interest income exposure and average profits. On the other hand, the diversification of non-interest income has a strong and positive correlation with the volatility of equity return.

The financial sector has been hit badly by the financial crisis in 2008. The increased competition between banks induced executives to take excessive risk to maximize bank’s profit, as their performance compared to other competitors is measured by the revenues they achieve to financial institutions and its stock value (Kashyap, Rajan et al. 2008).

Insurance and risk management make up an immense global industry. According to a survey conducted by a leading global insurance firm, Swiss Re, worldwide insurance premiums totaled $4.270 trillion in 2008 (the latest data available), up about 6.3% from $4.061 trillion in 2007. This was equal to about 6.18% of global GDP. Global life insurance premiums were $2.79 trillion during 2007, while all other types of insurance totaled $1.78 trillion.

Research from Ahmed, Ahmed and Ahmed (2010) implemented the ordinary least square regression model to measure seven explanatory variables and expected their relationship with capital structure of insurance companies from 2001 to 2007 in Pakistan. They concluded that profitability, liquidity and age had negative relationships with leverage inflecting Pecking Order Theory, size, growth, tangibility and risk had positive relationships with leverage, but only growth and tangibility has statistically significant relationship with leverage in 5% alpha level. Najjar and Petrov (2011) examined the relationship in similar methodology between firm characteristics and capital structure based on the relative data of life insurers in Bahrain during 2005 and 2009. They found that tangibility of assets, firm size and liquidity had positive relationships with the capital structure while profitability and revenue growth had negatively related to the capital structure, but only profitability and growth were significantly correlated to debt

The global insurance industry seems to have escaped the worst of the financial crisis in comparison to other financial institutions. Day to day business has been relatively unaffected but the area that gave the most cause for concern has arisen from exposures to risky financial instruments. “The Reactions Magazine’s Global Insurance Conference 2009” was held in Swissotel, Zurich, Switzerland. At this conference the financial services industry tried to re set itself following the worst crisis for years. (www.euromoneyseminars.com). Insurance is an established industry. Like other companies insurance is facing increased competition from global players. It has been difficult for insurers to accomplish profitable growth, so

“Variables Affecting Profitability & Growth in Insurance Industry-A case Study of HDFC Standard Life Insurance”