Financial Analysis and Forecasting - Coursework Example

? Financial Analysis and Forecasting Financial Analysis and Forecasting A) Does it appear that the proportionality assumption holds true? Explain. SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 1 Standard Error 1.03226E-15 Observations 5 ANOVA   df SS MS F Significance F Regression 1 160.1971 160.1971 1.5E+32 1.2E-48 Residual 3 3.2E-30 1.07E-30 Total 4 160.1971         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3.55271E-15 1.6E-15 2.216969 0.113363 -1.5E-15 8.65E-15 -1.5E-15 8.65E-15 X Variable 1 0.5 4.08E-17 1.23E+16 1.2E-48 0.5 0.5 0.5 0.5 From the results obtained above, the regression analysis shows that there exist a perfect relationship between sales and assets. The value of R-square is 1 this shows a perfect relationship that will produce a best-line-of fit that passes through the origin. The proportionality assumption that the value of assets increases proportionally with sales is therefore, holds, and is true. B) Repeat the part a regression analysis assuming the given data. Under these conditions, does it appear that the proportionality assumption holds true? Explain. From the results obtained below, the R squared value is 0.906304 this shows a good relationship because the R square value tends to 1, which is usually a perfect relation. SUMMARY OUTPUT Regression Statistics Multiple R 0.952 R Square 0.906304 Adjusted R Square 0.875073 Standard Error 4.495916 Observations 5 ANOVA   df SS MS F Significance F Regression 1 586.5602 586.5602 29.01858 0.012533 Residual 3 60.63978 20.21326 Total 4 647.2         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -50.9698 16.52736 -3.08396 0.053968 -103.567 1.627654 -103.567 1.62765 X Variable 1 3.246979 0.602756 5.38689 0.012533 1.328741 5.165216 1.328741 5.16522 From the above evaluation, it is clear that the best-line-of fit does not pass through the origin making the assumption not to hold true for sales and assets analyzed. This is because there is no close association between the dependent and independent variables in the study. C) Which of the preceding situations is likely to hold for most firms? What implications does your answer have for use of the percentage-of-sales-method? From the above situations, both cases have close range on R-square but the first is preferred most. The first situation is likely to hold for most firms because each firm will try as much as possible to ensure there is a good correlation between sales and assets. Any imbalance on these two variables may lead to collapse of the business because there might be too much expense in relation to company assets. Such a situation may result because of poor management and control of measures and standards. R is a measure of goodness of fit. Quantities neighboring 1 show a very suitable good fit. When the firm’s R is squared, it illustrates the percentage of changeability of y accounted for by x.In some other terms, most firms tend to ensure that their R-Square value stays or should not go below 0.95, as this will account for 95% of the changeability in y with respect to x. In business, usually an R-square values more than 0.9 are preferred, but it is essential to mark that even when a firm has an R-square value of 0.35, this implies that x is still demonstrating a considerable percentage of the y trait. Nevertheless, those below 0.5 are taken as somewhat inadequate for bivariate evaluation, since the related error is so wide. Multivariate analysis for firms is however, different. In addition, when applying mathematical associations to forecast y given x, then the pact is to present an error = 2 ? SSE, but this resolution is not often the case. Implications on use of the percentage-of-sales-method Percentage-of-sales-method is an approach of forecasting cash needs by stating revenues and costs as percentage of sales, and from these percentages to develop a pro forma income statement. While predicating financial information for strategic arrangement, budgeting, or for generation of pro forma financial reports, forecasters may apply the percent-of-sales method of approximating to generate considerable projections for given fundamental data. By choosing the first situation as an inclination in which most firms will tend to, it implies firms’ financial forecast will aimed at ensuring they meet the R-square value of 1. The desire to view how a financial statement account item associates factually to sales figures, and afterwards to apply that association to focus the amount of those financial statement account elements depended on future sales approximations. From my answer, this approach of prediction needs the materials to be forecasted depending on associations to sales values. It is crucial that transfers in the materials to be estimated are largely correlated with changes in the sales values. When there is no precise correlation between the materials to be estimated and sales values, then that material should be estimated by another mechanism. To ensure that firms meet the selected R-square value of 1 while relating variables of y and x, financial prediction is a fundamental section of all financial aspects of an organization because it is the foundation for budgeting practices and approximating future financing requirements of the firm. Financial predictions largely entail prediction of sales and expenses sustained to make those sales. Without the application of regression analysis when generating financial estimations, directors usually use an approximation of different sales, expenses, liabilities, and the highly extensively utilized method for ensuring such prognoses is the percent-of-sales approach. Under this method, liabilities, assets, and entire expenses are forecasted as a percentage of sales, which are afterwards related to the pre-determined sales. The figures are afterwards utilized to create a pro forma balance sheet. Reference: Expert Systems, Inc.(1996). Financial Analysis and Forecasting- Case 52, Directed. 1996. New York, NY: Cengage learning publishers. Read More