Automobile Market Analysis

Same as the results of 10-year period, the healthcare excess return is positively correlated with the utility excess return while negatively correlated with the material excess return. Compared to the 10-year results, it is found that beta of X1 decreases from 0.2750 to 0.2045 and beta of X2 decreases from -0.3165 to -0.3382. Also, beta of X0 drops which indicates without incorporating the event risk, more other variable is explained by the explanatory variables. As the p-value is less than 5% significance level, the relationships are both statistically significant. The standard errors of X1 and X2 are both small enough to indicate that the observations are close to the fitted

The null hypothesis is true and for some reason I rejected it then I made type I error. (Single error, rejection of a true hypothesis). There is no significance difference in olive oil quality but I rejected.

“The significance level associated with a statistical test indicates the probability with which this error may be made” (Iacobucci, 2009). In this case the significance level is .05. The probability of occurrence is .001, which means that it is lower and there is a lower chance of the statement not being true. In this case the null hypothesis should be rejected because it is lower than the significance level.

“Variables not in the Equation” is the title for the final table of output. The importance of this table is that it reports that the residual chi-square statistic as 26.103, which is highly significant at p < .001. The value of this statistic is that is demonstrates that the addition of

Both of these values, along with the test statistic, are provided in the Excel regression output. An online F-value calculator1 produced a Critical F-Value of 2.704. As outlined in Appendix C, we reject the null hypothesis and conclude that the regression is in fact useful. Using the same null and alternative hypotheses, the same test can be performed for the regression between Décor and Cost. In this case, we reject the null hypothesis and confirm the alternative, which states that Décor (being the only independent variable) does in fact affect the Cost. So there is a direct relationship between the Décor and Cost. The multiple regression model does a good bob of predicting the Cost. As seen in Part II of the case study, the regression produced a relatively high Adjusted R Square value of .76406, which means that about 76% of the variation in Cost can be explained by the other variables (Appendix D). We can also see from this output that each variable is given a t-Stat value. All of these values are higher than the Critical T-Value (from T-distribution table) for 99 degrees of freedom all the way up to the significance level of 1% (t = 2.364). And, as shown before, the F-Value hypothesis testing for the regression as a whole proved that the regression was in fact useful. So all of these claims when used in concert provide strong evidence that this model has a high predictive value.

Continuing with the global test, the decision tool is not to reject the null hypothesis. The computed value of ‘f’ is less than or equal 5.41. If the computed value is more than 5.41 then we reject the Ho.

H21: The risk adjusted return of the stock price of the sample of investment firms is significantly negatively affected around the event date as defined by the event period This study uses the standard risk adjusted event study methodology to test the stock market’s response to the Lehman Brothers Bankruptcy on September 15, 2008. Using Yahoo Finance, I found the historical stock prices for the 15 firms and the S&P 500 index during the event study period. The event study period involved 180 days prior to the event and 30 days after, using day 0 as the event date. Using those prices, I calculated the holding period returns for the companies (R) and the corresponding S&P 500 index (Rm) for each day using the formula: Current daily stock return= (current day close price – previous day close price) previous day close price Current daily index return= (S&P current close- S&P previous close) S&P previous close A regression analysis was then performed using the actual daily return of each company (dependent variable) and the corresponding S&P500 index daily return (independent variable) over the pre-event period day -180 to -31 period prior to the event period of day –30 to day +30) to obtain the alpha (the intercept) and the beta (standardized coefficient). Table 1 shows alphas and betas for each firm.

The null hypothesis for this F test says that all slopes of each and every predictor variable in the regression model equal to 0 – this basically means that each of the predictor variable has no linear relationship with the outcome variable.

(c) t = 3.24 / 1.634 = 1.9829. Since car sales are expected to be positively related to real disposable income, the null and alternative hypotheses should be: [pic] and [pic]. Therefore, an one-tailed t test is appropriate in this case. The 5% one-tailed t value for 14 d.f. is 1.761. Since the computed t value of 1.9829 exceeds the critical value, reject the null hypothesis (one- and two tail tests sometimes give different results).

The company belongs to a Commercial Services and Supplies (Industrial) group. There was no steadiness in the GICS within the 12 month period, the GICS was decreasing and increasing throughout the year, around march the GICS was beginning to rise and around early June it had its first major increase, which made the company rocket up the chart but soon after had a huge decrease around July but later managed to recover, then started to decline within the next two months, but later ascended a little up then dropped but managed to rocket

〖FDI〗_. represents the foreign direct investment inflow into partner countries (in current USD). An inflow of cash will lead to a temporary financial solvency in recipient countries, which enables the consumers of those countries to buy more differentiated products. However, because it is a temporary cash inflow, the FDI inflow might have an inverse effect as well and therefore the predicted sign for this variable was indeterminate. The results of regression analysis indicate that the FDI coefficient is -0.03 which indicates a negative relationship with the logistic transformation of IIT, but the parameter estimate is statistically insignificant.

To modify the regression model before running the results, I do some diagnostic tests for the obtained series. Table A2 in the appendix I shows some descriptive statistics and diagnostic tests of data. First, the series average of these three areas is all strictly positive and greater than 1.5. The standard error of JPY series is 0.907 which is close to 1 while others are a long way from unity (0.284 and 0.331). In addition, the Jarque-Bera test statistics of all series are too large, which shows that all EUR, GBP and JPY series are not normally distributed. Second, p-values of Ljung-Box Q-statistic tests for level and squared residual in all series are 0. LB(Q) presents that all series have an autocorrelation for residuals and LB(Q2) shows that all series are conditionally heteroskedastic. In this way, ARCH or GARCH model can be utilized to improve the original model (3). Third, the unit root test. On 23

For the firm specific return prediction, a firm level monthly share prices, turnover and paid-up value data on KSE over eleven year period – January 2002 through December 2012 is collected from the websites of “Business Recorder and Karachi Stock Exchange” that are authentic sources of information. The data consists of monthly share prices of a large sample of 300 firms of 132 months. For risk free rate of return interest rate on 6-month Treasury bond is used. Accounting data has been collected from various bulletins of “balance sheet analysis” published by State Bank of Pakistan (SBP) Data set on “close prices” does not contain information on dividends. Mills and Coutts (1995) find that the exclusion of dividend payments is not a significant problem. This idea is, again supported by both Lakonishok and Smidt (1988) and fishe et al. (1993), who concludes that any dividend bias is relatively small and will not impact on the statistical significance of any results evidence from Draper and Paudyal (1997) adds further to this statement. However, it is noteworthy that Philips- Patrick and Schneeweis (1988) found conflicting evidence.

From the analysis we can say that return on assets and net profit margin has increased marginally. But on the other hands total asset turnover has declined from 1.15 to 1.14 from year 2013 to 2014. The reason is the company is not using its assets properly because of the poor infrastructure and light population. Probably they are having more stock at hand than they actually need. That extra stock can be used in north segment. Inventory turnover has decreased from 2.74 to 2.68 from year 2013 to 2014. The increase in the ration that have been calculate in the Appendix 1 is very marginal as compare to the above two segments. This means that the segment’s performance is bad.

28 5. Financial performances of NBE WSACA ........................................................................ 29 5.1 Adjustments of Financial Data ....................................................................................... 33 5.2 In-kind subsidy adjustment........................................................................................... 34