Cash flow

Finance academics and practitioners have pointed out distinctions of the use of cash flows with that of accounting numbers for valuation of equity with their use of the of the former for valuation, and the latter for reporting and controlling purposes. They have justified the use of the methods and approaches under the principle that cash flows consider the time value of money while the accounting numbers do not.

On the premise that the Cash flow has been the basic building block of the Discounted Cash Flow (DCF) model used for valuation, practitioners of the field and even some theorist displayed the obvious flexibility and versatility although not without limitations. One limitation perceived from the use of cash flow is the discipline imposed on the person estimating the cash flow especially those cash flows in the distant future. One would readily see that given the difficulty of forecasting far into future, a declining rate of growth in cash flows must be assumed beyond some future year to address the uncertainty. Given the therefore that valuations are extremely sensitive to these growth rate which is required to be assumed to go indefinitely, such exercise could become a good ground for patently optimistic analyst to say that the later growth rates could still be reliable. On the other hand therefore, the choice of the growth rate can therefore be made for personal reasons beyond what reasonable assurance dictates under the circumstances. Along this line therefore the search for better alternative for stock or equity valuation would always be wonderful and profitable areas for researchers given the possible dent of the credibility of DCF valuations.

Having recently reassessed the role of cash flow in valuation, academics and practitioners have recognized the Residual Income Model (RIM), an accounting based valuation technique, as an alternative technique or method to discounted cash flow methods. This RIV does offer many advantages over the cash flow methods.

Investment TIPS

Most of the people in stocks are not saying anything about the companies failure but instead their strength are being introduce in an investors like saying the big volume of the stock, the close price of the stocks well this is not the correct manner in investment. Here are some of the tips to be successful Investors.

1. Checked the company's profile. The companies profile will lead you on how the company succeeds in their business and also you can see their failure but you have to be careful because some company hides it. To be more safe go to their competitive firm then the other company will say their weakness.

2. Checked their chart for 5 to 10 years. The Chart tells you on how the company performance during the year. If the charts went down go and run away. And if the chart is going up then continue on checking.

3. Get a copy of their financial statement. Before investing get a copy of the company’s financial statement. Don’t invest yet! Tell the company that you going to study first if you going to invest or not tell them the truth that you are going to checked their performance.

4. Compute their performance. The performance can be computed using the formulas on finance book. You can use foundation of finance to get the performance.

Another tip if you don’t like to compute check their assets and liabilities but be careful because the company financial statements are somewhat manipulated but if you compute it thoroughly they can not escape.

Hurricane Analysis

The analysis of the effect on time series and US gas , Gulf gas, Cap, stocks in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data

Data and methodology

Data

This paper used randomization processes in publishing statistical abstract from the TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006.

The US gas is average price of wholesale gasoline in US, gulfgas is price of wholesale gas in Gulf Coast, Cap is refinery capacity utilization, Stock is total US gasoline stocks, and katdummy and ivandummy are dummy variables for the time period where Hurricanes Katrina and Ivan occurred was used for the computation of the effect.

From Jan 7, 2000 to Dec 29, 2006 the researcher examined the value of crude ratio. Approximately the number of weeks were calculated as 365. The researcher cluster the variables based on US and Gulf Coast gasoline price. The ratio of the different variables were calculated to suit the model.

Methodology

The researcher investigated if there is no significant effect on crude ratio and US gas , Gulf gas, Cap, stocks in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006. The researcher also used Frequency distribution and analysis of variance (ANOVA) . The Multiple Regression was to test heteroskedasticity of the elastic variables. The computation of the analysis of variance of US and Gulf Coast was also used for differentiation and analysis of the data. The researcher also computed the Mean Square Error, Standard Error of Estimate and the Multiple coefficient of determination. The result of this findings will testify the goodness of the predictors of this study.

RESULTS

This section presents the analysis of the data on the study to find out if there is no significant effect on time series and US gas , Gulf gas, Cap, stocks in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data.

The descriptive statistics was also used in this study for the presentation purposes. Tables 1 to 2 show the significant effect on time series and US gas , Gulf gas, Cap, stocks in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006.

From Graph 1 , the researcher found out that majority of the elasticity have no changes except on IVAN elasticity and US gas ratio. US gas ratio has slightly affected by the hurricane , this can be seen in week 301. It was notable that the Ivan data has remarkable change in terms of time series under weeks 241 to 271 and 301. This implies that there is a small probability of risk for the investors to invest in the gasoline stock market.

It can be gleaned in Graph 2 that Gulf gas ratio has slightly affected by the hurricane, this can be seen in weeks 291 to 320. It shows that the Ivan data has remarkable change in terms of time series under weeks 233 to 262 and 291 to 320. This reveals that there is a slight effect on time series and Gulf gas, in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006. This factor has slight effect on the economic process of the country. This leads the other businessmen to transfer for another countries or ship for another business.

This paper examined the analysis of variance for US ratio and Gulf Coast ratio to identify the elasticity of the other variables in relation with the crude ratio.

The Analysis of Variance was used to determine if there is no significant effect on crude ratio and US gas ratio, Gulf gas ratio, Cap ratio, stocks ratio ,Hurricane Katrina and Ivan TSP_Sample_econometric_1informational data. The result of the “Analysis of Variance” (ANOVA) shows that the value of compute F is greater than the tabulated F under US gasoline price. The computed value of F is 51.989 while the tabulated F is 2.21. This implies that there is a significant effect on Gulf Coast gasoline stock price in the existence of Katrina and Ivan Hurricanes because null hypothesis is rejected. And the result of the “Analysis of Variance” (ANOVA) of Gulf Coast gasoline also shows that the value of compute F is greater than the tabulated F . The computed value of F is 50.156 while the tabulated F is 2.21. This implies that there is a significant effect on US gasoline stock price in the existence of Katrina and Ivan Hurricanes because null hypothesis is rejected.

The Regression results are as follows: the unbiased estimator of the variance of the error in the multiple regression model is equal to .001. There is small value of MSE so the estimator is a good fit of the regression. Standard error of estimate is equal to 0.03145. Multiple coefficient of determination is .420 and an adjusted multiple coefficient of determination is equal to .412 showed that the data produced a good predictions.

The unbiased estimator of the variance of the error in the multiple regression model is equal to 0.001. There is a small value of MSE so the estimator of elasticity is a good fit of the regression. Standard error of estimate is equal to 0.3169. Multiple coefficient of determination is 0.411 and adjusted multiple coefficient of determination is equal to .403 showed that the data produced a good predictions.

Summary of the two multiple regression, the two findings reveals that the variables present in this study were good predictors of the gasoline stock price. This implies that even though there is a hurricane in the said countries the value of elasticity will remain controlled.

conclusion:

In this paper, the researcher empirically examined the success of the comparison between US and Gulf Coast gasoline stock price and the researcher investigated if there is no significant effect on crude ratio and US gas , Gulf gas, Cap, stocks in term of the Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006. The researcher also used Frequency distribution and analysis of variance (ANOVA). The computation of the analysis of variance of US and Gulf Coast was also used for differentiation and analysis of the data.

This paper examined the two countries that probably affected by the hurricane Katrina and Ivan. The two countries that are under this study were US and Gulf Coast.

The researcher discovered that there if there is a significant effect on crude ratio and US gas ratio , Gulf gas ratio, Cap ratio, stocks ratio, Hurricane Katrina and Ivan TSP_Sample_econometric_1 informational data under Jan 7, 2000 to Dec 29, 2006.

This means that other variables can affect the flaws of the gasoline stock price. The researcher suggested the additional parameters for the greater accuracy of the model.

Appendices

GRAPH 1

GRAPH 2

US Regression

Variables Entered/Removed(b)

Model	Variables Entered	Variables Removed	Method
1	IVA DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, USGAS RATIO (a)	.	Enter

a All requested variables entered.

b Dependent Variable: CRUDE

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.648(a)	.420	.412	.03145

a Predictors: (Constant), IVA DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, USGAS RATIO

ANOVA(b)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.257	5	.051	51.989	.000(a)
	Residual	.355	359	.001
	Total	.612	364

a Predictors: (Constant), IVA DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, USGAS RATIO

b Dependent Variable: CRUDE RATIO

Coefficients(a)

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	.150	.172		.869	.385
	USGAS RATIO	.469	.030	.674	15.692	.000
	CAP RATIO	.077	.079	.041	.979	.328
	STOCK RATIO	.305	.145	.088	2.111	.035
	KAT DUMMY	-.008	.011	-.027	-.669	.504
	IVAN DUMMY	.009	.011	.031	.777	.438

a Dependent Variable: CRUDE RATIO

Gulf Coast Regression

Variables Entered/Removed(b)

Model	Variables Entered	Variables Removed	Method
1	IVAN DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, GULFGAS RATIO (a)	.	Enter

a All requested variables entered.

b Dependent Variable: CRUDE RATIO

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.641(a)	.411	.403	.03169

a Predictors: (Constant), IVA DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, GULFGAS RATIO

ANOVA(b)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.252	5	.050	50.156	.000(a)
	Residual	.360	359	.001
	Total	.612	364

a Predictors: (Constant), IVAN DUMMY, STOCK RATIO, KAT DUMMY, CAP RATIO, GULFGAS RATIO

b Dependent Variable: CRUDE RATIO

Coefficients(a)

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	.158	.174		.907	.365
	GULFGAS RATIO	.435	.028	.678	15.404	.000
	CAP RATIO	.187	.082	.099	2.288	.023
	STOCK RATIO	.222	.145	.064	1.534	.126
	KAT DUMMY	-.011	.011	-.038	-.919	.359
	IVAN DUMMY	.008	.011	.029	.725	.469

a Dependent Variable: CRUDE RATIO

Note : This part was done by the used of SPSS program

REFERNCES:

Aczel,Amir D., Complete Business Statistics, 3^rd Ed., Irwin/ McGraw-Hill

Companies.,USA. , co. 1996
Bordens, Kenneth S. and Abbott, Bruce B. Research Design and Methods, A process Approach 4^th Ed. Mayfield Publishing Company USA. co. 1999

Colander, David C., Economics, 3^rd Ed. Irwin/ McGraw-Hill Companies.,USA. , co. 1998

Geroski, P. A. and Gregg, G., Coping with Regression: Company Performance in

Adversity, Oxford University Press, co. 1997.

Larson, K, Miller, P., Financial Accounting, Irwin Press, London & N.Y., 1995

Sander, D. and Smidt, R., Statistics, A first course, sixth Ed. McGraw-Hill Higher Eduction, co. 2000

Thomas, R.L. Modern Econometrics, An introduction Addison Wesley Longman co.1997

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