Effect of Sales Growth Rate Changes and Corporate Tax Rate Changes on the Share Price of Cook PLC - Assignment Example

SVA Report Name: Students ID: Unit Code: Instructor: a. Using SVA, calculate the estimated current share price of Cook PLC on a spreadsheet. The estimated current share price of Cook PLC is 0.44. b. From the following mutually exclusive scenarios using a spreadsheet, measure and comment upon each one’s sensitivity to the share price. Table 1: Effect of Sales Growth Rate Changes on the Share Price of Cook PLC Sales growth (%) Share price of Cook PLC 2 0.36 3 0.39 4 0.44 5 0.47 6 0.52 7 0.57 The results in table1 above indicates that the share price is directly related to the sales growth rate. Therefore, an increase in the sales growth rate will boost the share price of Cook PLC. On the other hand, if the sales growth rate declines, the share price of Cook PLC will also drop. This is because a higher share growth rate increases the value of the company. Potential investors will be attracted thereby the share price of Cook PLC will go up. Table 2: Effect of Corporate Tax Rate Changes on the Share Price of Cook PLC Corporate tax rate (%) Share price of Cook PLC 10 0.70 20 0.57 30 0.44 40 0.30 50 0.17 60 0.03 The results in table 2 above indicates that the share price is inversely related to the corporate tax rate. An increase in the corporate tax rate drives the share price of Cook PLC down. On the other hand, if the corporate tax rate is reduced, the share price of Cook PLC will rise. This is because a higher corporate tax rate eats into the earnings of the company. This effectively reduces the amount of retained earnings or dividends to be paid that directly impact the investor’s amounts. Investors would not want to put their money if they do not realise an above average return for the sums invested. Table 3: Effect of Constant Profit Margins on the Share Price of Cook PLC Year Sales (£) Profit Margin Operating Profit (£) share price 2015 416,000,000.00 7% 29,120,000.00 -0.04 2016 432,640,000.00 7% 30,284,800.00 -0.04 2017 449,945,600.00 7% 31,496,192.00 -0.04 2018 467,943,424.00 7% 32,756,039.68 -0.04 2019 486,661,160.96 7% 34,066,281.27 -0.04 The results in table 3 above indicates that the share price remains at 7 per cent the same just as the profit margin remains the same. This simply indicates that investors would hold on to their shares in anticipation of improving profit margins. The share price entirely reflects on the profit margin parameter. c. It has been argued that SVA is flawed in that there are too many steps in the model and too many variables attached to them. Analyse this statement and in doing so evaluate the need for each step in the model. Every firm seeks to create value for its shareholders. Creating shareholder value can be justified as the over-riding goal of the firm principally because there is an implied theory in most of the finance works that all the markets in which the firm functions are perfectly competitive. Shareholder Value Analysis (SVA) is used today as non-traditional accounting method to approximate the value of the shareholders’ stake in the company and forms a critical basis for conveying and assessing strategic decisions. It was developed by Alfred Rappaport in the 1980s. SVA helps the management to maximise shareholder wealth of the firm. Investors generally invest cash in the firm because they are concerned with getting their money back. The market value of their investment is measured by the future cash flows. The actual value of a business operations is established by discounting expected future operating “free cash flows” at the most suitable cost of capital. Free cash flow method is used because it captures whether or not the firm really generated cash from its operations. To arrive at the acceptable shareholder value, the value of “marketable securities and other investments” should be added and the value of debt deducted from the business valuation. Free cash flow indicates the cash flow from the company’s operations for a period ahead of considering any financing-related cash flows, such as those involving dividend, share or debt issues, and interest payments, among others. For the value of the business to be precisely established, free cash flow for all upcoming years must be estimated [Rog10]. The operating free cash flows in a clear planning period can be established by approximating future values for every one component individually on a year-by-year basis. However, the application of this detailed approach possibly will not be necessary at all times. There are various different models presented by consultants for predicting future cash flows, all of which emphasise somewhat different “value drivers.” The SVA value driver method offers an easy substitute approach that possibly will give an appropriately dependable estimate in several circumstances. The simplified SVA approach takes in “seven value drivers” to approximate the value of the business operations all through the planning horizon [Bus97]. This “seven value drivers” are: Percentage yearly sales growth rate; Cash income tax rate (minus deferred tax); Operating profit margin (before non-operating items such as tax and interest payable); Investment in working capital rate; Incremental fixed capital investment rate; Planning horizon; and Cost of capital. The first five “value drivers” are used to compute the free cash flow for every year all through the planning horizon. The figure arrived at is then reduced at the company’s cost of capital. The correct discount rate to use is the weighted average cost of capital (WACC). The WACC weights the proceeds of equity and debt investors in line with the comparative amounts of equity and debt capitalised in the company [Bus97]. The last two “value drivers” are also important. Investors put their money in a company with a desired return in mind. However, the cost of capital is an opportunity cost that makes it very hard to express the desired return in simple terms. Owing to this complexity, it is unfortunate that it can be easily overlooked while making profit calculations. But it is no less ‘real’ since if the shareholders do not get the preferred return on their investment, after the lapse of their planning horizon, they will in the end devote their money somewhere else. The fundamental principle of managing for shareholder value is that the cost of debt must be considered when calculating value. That is, a company simply makes a genuine or monetary profit once it has settled up the cost of capital that was used to produce it. The simplified SVA approach gives emphasis to the “seven key value drivers” and therefore lends itself to “sensitivity analysis”. Furthermore, the “seven key value drivers” can be disintegrated into in depth and useful performance measures and targets such that managers are stimulated to act in a manner that is consistent with the vital objective of creating shareholder wealth. The weightiest difficult with this method is forecasting the prerequisite variables in the analysis [Rog10]. d. Analyse how genetic algorithms may improve managerial decision-making, highlighting both the benefits and weaknesses they possess. Genetic algorithm is an iterative, artificial intelligence trial and error system that mimics the evolutionary, survival-of-the-fittest course to produce ever more enhanced solutions to a problem. Generally, artificial intelligence systems upsurge the promptness and steadiness of decision making, solving problems with inadequate information, and decide complex matters that cannot be resolved by conventional calculation. A genetic algorithm is in essence an optimising system that finds the arrangement of inputs that gives the pre-eminent outputs. Genetic algorithms use the mutation process through arbitrarily testing combinations and assessing the success or failure of the end result. Genetic algorithms are well-matched to decision-making settings in which thousands, or maybe millions, of results are possible. Genetic algorithms were first used to simulate millions of years in biological, geological, and ecosystem evolution in just a few minutes on a computer. Now genetic algorithm software is being used to model a variety of scientific, technical, and business processes. Genetic algorithm uses software, randomisation, plus other mathematical functions to simulate an evolutionary process to produce more and more enhanced solutions to a problem [Meh05]. Genetic algorithms can find and evaluate solutions with many more possibilities, faster and more thoroughly than a human. Organizations face decision-making environments for all types of problems that require optimization techniques. For instance, business executives use genetic algorithms to help them decide which combination of projects a firm should invest in, taking complicated tax considerations into account. Investment companies also use genetic algorithms to help in trading decisions. Generic algorithms solve the problems for the management by reducing the complication or by using the correct knowledge on the variables in the shareholder value analysis. The generic algorithm are applied in virtually all areas of the managerial problems where decision making becomes difficult. It is helpful to solve the problems which are required to make the decision quickly and also reliable of the data. They interface on the existing models and can be applied on the sensitivity analysis of each variable (seven value drivers) listed above. Therefore, the genetic algorithm are considered to be robust where the situation/considerations of the variables are large, complex and they are bad performance” [ELó00]. The generic algorithm helps to detect the financial distress of the companies and the outcomes. The information obtained is valuable for all categories of the public associated with the organizations from the directors, top management, employees, shareholders, creditors, and investors. The generic algorithm help the companies to take up investment opportunities and avoid the wastage of resources. Moreover, the generic algorithm provide an alarm about the firms for shareholders and management thereby enabling them to act accordingly. The generic algorithm are used for bankruptcy prediction. They can also be used to calculate yield with different variables for different firms with a high level of accuracy. The generic algorithm also assist in making decisions involving takeovers and acquisitions. The method does not need knowledge or gradient information about the search space. Also, gaps present on the response surface have minute effect on the general optimisation performance and genetic algorithm are resilient to becoming confined in local optima [Shi02]. However, generic algorithm heavily rely on financial ratios from company statements. The variables are coded and the factors that are impacted by the financial or decision making variables are not analysed by factorial analysis. The models are designed and implemented in the multi-layer feed forward neural network. The selections of the variables are made through probability, that is, random sampling. The models can be used as feed forward control but within a limited duration of time on decision making, whereas shareholder value analysis is considered as long-term model. Other challenges facing the genetic algorithm is that it is hard to find the exact global optimum, large number of response (fitness) function evaluations are required, a coding of the problem is also required. In addition, more capable algorithms are presented for many precise problems, particularly when the problem is simple, lesser or statistically well behaved [Nas14]. Bibliography Rog10: , (Roger, Jan 2010), Bus97: , (Performance, 1997), Meh05: , (S, et al., 2005), ELó00: , (González, Rodríguez-Fernández, & C.Mendaña-Cuervo, 2000), Shi02: , (KS & YJ, 2002), Nas14: , (Sogh, et al., 2014), Read More