Cluster analysis is a process which use to group a set of items that are similar with each other than the items of other groups. In this report, it has been to estimate cost of capital of a firm. It is very evident that for many firms, such as mining companies, cost of capital, calculated under general and popular methods, is not quite relevant and realistic. Moreover, many big companies, like Google Inc., do not pay dividends. In that case, ascertaining cost of capital by using CAPM model may not be possible (Core et al. 2015).
The report describes that divisional cost of capital and cost of capital from accounting data can be used as the required rate of return for such occasions. However, both the methods have some limitations (Kim and Sohn 2013). Cluster analysis method, on the other hand, relies on the accounting data and market data of the proxy firms, which are similar to the actual firm in terms of activity. As the method includes a group of similar firms, the outcomes become more authenticate in context to the market valuation.
The cluster analysis method selects the proxy firms based on the accounting data. It can be described as very practical selection process, as market data of the actual firm is not available, but it would have the accounting data surely. Moreover, the proxy firms are incorporated on the basis of the close resemblance with the accounting data of the actual firm. Thus, it reduce possibility of inaccurate outcomes to the minimum extent.
The accounting data, used for the analysis process are ROE and cash flow from operations. For deeper analysis, ROE is calculated by using DuPont Formula. Thus, the analysis process covers the profitability, leverage and asset turnover of the firms within a single method and can work on more detailed data. Cash flow from operations depicts the liquidity aspect and working capital structure of the firms. Therefore, by incorporating the operational cash flow, the method can correlate the outcomes with the liquidity position of the company also (Healy and Palepu 2012).
The correlation of variation is calculated for both ROE and cash flow of all the firms to synchronize with the risk factors and compare the accounting data of the firm in more accurate manner. Moreover, the outcomes for each firm are compared with balance group to ensure the accuracy of the process. It also ensures that main outcome should not be affected by any under or over weighted data of any specific firm.
Finally, , the cost of capital of the selected firm is calculated by using regression analysis on the basis of the calculated CVs of the all firms and the market CAPM, risk-free rate of market, expected market return. As the cost of capital is based on the data of similar firms, regression analysis helps to set a range of the cost (Berger et al. 2012).
From the above discussion, it is clear that cluster analysis incorporates and uses all the elements quite efficiently to determine the cost of capital of a firm. Though, it is a quite lengthy process, the delivered outcomes can be considered as more accurate than other unconventional methods of cost of capital evaluation.
However, the process is better for evaluation of any business or firm. It cannot be used for stock trading purpose, as stock trading requires data that are more accurate. Moreover, it cannot provide proper results for any specific firm, which is unique within the industry or owns some unique property. For example, a pharmaceutical company may invent a revolutionary drug. In that case, the accounting data of other similar type of drug companies will be insufficient to measure its cost of capital, as the company will be ahead of others for a certain period of time.
Berger, P.G., Chen, H.J. and Li, F., 2012, September. Firm specific information and the cost of equity capital. In EFA 2006 Zurich Meetings
Core, J.E., Hail, L. and Verdi, R.S., 2015. Mandatory disclosure quality, inside ownership, and cost of capital. European Accounting Review, 24(1), pp.1-29
Healy, P.M. and Palepu, K.G., 2012. Business Analysis Valuation: Using Financial Statements. Cengage Learning
Kim, J.B. and Sohn, B.C., 2013. Real earnings management and cost of capital. Journal of Accounting and Public Policy, 32(6), pp.518-543
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