Finance Topic #3 Responses

· Respond to 3 posts listed below. Advance the conversation; provide a real-world application and experiential examples;

· Conceptually discuss your key [most significant] learning insight or take-away from the selected forum topic comments.

· Responses should be a minimum of 150-250 words , supported by at least one reference outside of the textbook (use academic journals), either supporting or refuting the position of the author of the forum topic response or peer response.

Topic #1: The Cost of Capital

The cost of capital refers to the “cost” a company must incur in order to use funds towards a new project or investment. The funds may come from lenders by borrowing the funds, by financing equity, or by selling bonds or assets. The cost of capital is expressed as an annual interest rate that the company will be charged on the capital funds. Therefore, this is the minimum return a company must be striving for when undertaking a new project, making a purchase, or making an investment. Otherwise, they are simply losing money.

The cost of capital is used as the minimum rate of return that the project must achieve and is also the rate that is used in a discounted cash flow analysis. If the return of future net cash flows on an investment or project are greater than the cost of capital, then the investment or project is worthwhile to the business. For example, if a project generates a return of 20% and the cost of capital was estimated at 15%, then this project has added value to the business.

In addition, the NPV formula can be used to compare multiple projects using different costs of capital. For instance, Project A and Project B compared by using 10%, 15%, and 20% costs of capital. This can allow businesses to see how investing more or less capital would affect the outcome of the NPV. Just as you can compare these different costs of capital, you can also add the element of risk into your calculations by increasing the cost of capital to reflect a riskier project or investment. Very simply put, if investment B is riskier than investment A, then you could compare them with B having a cost of capital at r = 15% and A having a cost of capital at  r= 10% - therefore adjusting for one project/investment being riskier than the other.

Interestingly, there has been much discussion and debate over how companies set their cost of capital rates. In the case of large Fortune 500 companies, they spend hundreds of billions of dollars per year. If they miscalculate the cost of capital rate on an investment or project with a difference of even 1%, this could mean a gain or loss of billions of dollars depending on which way the value was incorrectly estimated. For example, if a company plans to invest $52 million dollars into a new project that is estimated to bring in $10.5 million per year over the next seven years, and the company incorrectly estimates the cost of capital by 1%, then you can see by the table below the affect this small percentage potentially has on the resulting NPV. If the company assumes a 9% cost of capital rate incorrectly and it should have been estimated at 10%, then the company may proceed with a bad investment or project. If they assume the cost of capital at 10% and in fact it should have been 9%, then they potentially miss out on a profitable investment opportunity. When NPV’s are being calculated with incorrect variables, then it affects the reliability of this decision-making tool. This is not to say that one improper or missed investment will significantly impact a business (although it may), but that repeated errors in calculating the cost of capital could result in an enormous cumulative loss in potential investment returns over time.

Project A Yields Cash Flow-In of $73.5 million over 7yrs and has a cost of $52 million

at 10% the NPV = -880,000

at 9% the NPV = +850,000 Therefore, how do companies accurately determine the cost of capital? Published surveys reveal varying information as to the preferred method of computing cost of capital. Some surveys showed greater than 90% of Fortune 100 companies preferred using “a weighted-average cost of capital” and others revealed that over 30% of Fortune 500 companies used the “capital asset pricing model (CAPM)” (Bruner, 1998). However, one particular survey differed from the previous paper surveys in that they were conducted by phone and questions/responses were focused specifically on how cost of capital was estimated by each of these firms (Bruner, 1998). This survey concluded that weighted average cost of capital (WACC) based on market value (instead of book value that looks at debt vs. equity) was the most popular method and the capital asset pricing model (CAPM) was the most used model for estimating cost of equity (Bruner, 1998).

A WACC is a standardized method for evaluating a company’s cost of capital. It is a “weighted average of individual sources of capital” that are used for a project or investment (Bruner, 1998). It is expressed by the formula: WACC = Wdebt (1-t)Kdebt) + (Wpreferred Kpreferred) + (WequityKequity)  (Bruner, 1998) K = component cost of capital W = weight of each component as percent of total capital t = marginal corporate tax rate

The above formula shows three sources of capital, but more may be added. The capital costs must be those under current market conditions; the costs should equal the expected internal rate of return for each source of capital, and the cost of debt value should be “after tax” to reflect tax deductible interest (Luehrman, 1997). However, difficulties still arise when calculating the cost of equity. As stated, the CAPM was most commonly used, but there were differing opinions on the variables used in this formula (risk-free rate of return, equity beta estimates, and market risk premium). This component of determining equity had the most debate and therefore affects the results of the WACC depending on which variables were selected to calculate the CAPM (Dempsey, 2013). The impact of using differing rates to calculate the WACC mean that there could potentially result in an unnecessarily wide range from minimum WACC to maximum WACC having huge potential economic impact on decision making.

Given the enormous amount of capital used for projects and investments each year by corporations, the proper assessment of cost of capital rates is an extremely important concept for financial analysts and managers. Where there is fairly standard use of the WACC, the method and rates used to determine cost of equity using CAPM varies greatly and can cause large differences in cost of capital estimates (Bruner, 1998). Therefore, it seems that there should be better guidelines for calculating this where, based upon industry, standardized, current averages may be widely used.

References Bruner, R. F., Eades, K. M., Harris, R. S., & Higgins, R. C. (1998). Best practices in estimating the cost of capital: survey and synthesis. Financial Practice and Education, 8, 13-28. Dempsey, M. (2013). The Capital Asset Pricing Model (CAPM): The History of a Failed Revolutionary Idea in Finance?. Abacus, 49(S1), 7-23. Luehrman, T. A. (1997). What’s it worth. Harvard Business Review, 75(4), 132-142.

Topic #2: Risk and Diversification

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In the realm of investments and financial management, risk is an important variable that cannot be neglected.  There are many sources of risk in the financial variables of any corporation and investments.  Risk can be political in nature, where the investments in a specific country can be threatened by political turmoil or changes.  Risk can be market driven, where the new product lunch may be unsuccessful due to a current competition [Microsoft surface tablets for example, never captured any market share and the company was forced to sell the product below production costs at a loss] (Arthur, 2014).  There is also a risk involved when a new product is introduced to the market despite previous risk analysis done.  

 

The risk can also stem from investments.  In general there are ten main types of risk associated with investments (Brown, 2008).  They are risks associated with interest rates, business, credit, taxability, call, inflation, liquidity, market, reinvestment and currency/exchange rate.  Business risk is associated with a specific security which is also known as the unsystematic risk.  In credit risk, the issue is with the bonds issuer, who may not be able to make the expected payments.  In taxability risk, a security was issued with a tax-exempt status by a municipal office, however, it may lose that status prior to its maturity.  In inflation risk, the value of the asset may be lost to inflation, especially if the currency in which it was issued has lost value since.  In liquidity risk, the investor may not be able to purchase or sell a specific investment in a timely manner or the optimum time.  Market risk or systematic risk [as opposed to the business risk] is a risk that cannot be controlled by diversification of the assets.  Currency or exchange risk occurs when the currency in which the assets are purchased may have lost value, or the desired final currency has increased in value since the initial purchase.  This risk has been common in the past year.  For example Euro was valued as 1.4885USD in May of 2011 and today it is 1.1734USD.  Although the difference appears to be small [0.31], but when it is measured in millions of euros the difference is significant.

 

As Brigham and Houston (2004) explain, diversification is the single best weapon against risk.  They further explain that “global diversification offers investors an opportunity to raise returns and at the same time reduce risk” (Brigham, 2004, p. 231).  “The process of spreading an investment across assets [and thereby forming a portfolio] is called diversification” (Ross, 2000, p. 413).  Therefore, we can conclude that spreading of the investments across many forms of assets will reduce some of the risks, but will not eliminate the risk all together.  Unsystematic risk [or business risk] is perhaps the only risk that can be completely eliminated with diversification of the assets (Ross, 2000, p. 415).  In contrast to the unsystematic risk, systematic risk cannot be eliminated because it affects all assets to some degree.  The systematic risk which is also known as non-diversifiable risk.  In the financial management, we calculate the total risk by adding systematic risk to the nonsystematic risk.  

 

Another aspect diversification is in the supply chain.  The management of the company must ensure sufficient supply of materials in-order to meet the demands of the market (Bowersox, Closs  & Cooper, 2002).  To ensure ample and un-disturbed supply chain of materials, financial managers and operations must strike a balance between multiple suppliers (Bowersox et al, 2002, p. 15).  An example of this would a company like Apple who uses many materials for its devices and hardwares.  If the company uses one supplier for its device displays [the same as investing in one form of asset] and should that supplier is unable to meet the demands or the promised screens for number of reasons [such as political turmoil, fire in the facility, worker strikes] then company [Apple] is unable to make its products.  The same is true in the case of the company’s product lines.  Apple has secured its market superiority with multiple safety nets [product lines] that can be viewed as “diversification”.  By having a diverse line of products within an eco-system, Apple has ensured a steady stream of income regardless of the market changes.  At the same time, Apple has diversified its suppliers across the globe.  Incidentally in 2009, Apple caused a shortage of flash-storages in Asia which caused other companies including Samsung itself to scramble to locate parts (Farrell, 2009).  

  

Risk in the business world is unavoidable.  Risk in involved in multiple aspect of the business from the supply chains to market and also investments.  Risk is so important to businesses that colleges and universities offer specific degrees in Risk Management and avoidance.  Along with sound investments and planning, risk can be reduced by diversifying the companies portfolio, assets, supply chains and product lines to name a few.  Diversification breaks down the risk associated buy reducing the overall probability of the risk (Su & Tsang, 2014).  Doing so ensures that investments the company has made is secure from risk, that even should a part of the operation is affected [from a given risk], the whole operation can still maintain a steady move forward.  A low risk enterprise will therefore becomes a sound investment which in return will increase the sources of investment in that enterprise [and therefore reducing yet another risk].

References:

Arthur, C. (2014, May 1). Microsoft Surface tablet is still losing money, figures show. Retrieved January 19, 2015, from http://www.theguardian.com/technology/2014/may/01/microsoft-surface-pc-tablet-losing-money-figures-show

Brigham, E., & Houston, J. (2004). Risk and Rates of Return. In Fundamentals of financial management (10th ed., p. 231). Mason, Ohio: Thomson/South-Western.

Brown, K. (2008, June 2). Types of Investment Risks - Series 6 | Investopedia. Retrieved January 19, 2015, from http://www.investopedia.com/exam-guide/finra-series-6/evaluation-customers/types-investment-risks.asp

Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York: McGraw-Hill.

Farrell, N. (2009, September 15). Apple causes NAND shortage in Asia. Retrieved January 19, 2015, from http://www.theinquirer.net/inquirer/news/1533511/apple-causes-nand-shortage-asia

Ross, S., & Westerfield, R. (2000). RETURN, RISK, AND THE SECURITY MARKET LINE. In Fundamentals of corporate finance (9th ed., p. 412). Boston: Irwin/McGraw-Hill.

Su, W., & Tsang, E. (2014). Product Diversification and Financial Performance: The Moderating Role of Secondary Stakeholders. Academy of Management Journal, amj-2013.

Topic #3: The Monte Carlo Simulation

The Monte Carlo simulation is a simulation that is used in order to understand the impact of risk and possible uncertainty in project, financial and cost management. It is also often referred to as a probability simulation. Though for the purpose of this paper I will talk about its applications in business and finance it also has strong applications in engineering, biology  and physical sciences. By definition the Monte Carlo simulation encompasses "any technique of statistical sampling employed to approximate solutions to quantitative problems" (Kwak,2007). A model or a real-life system or situation is developed, and this model contains certain variables. These variables have different possible values, represented by a probability distribution function of the values for each variable.    The first item that one needs to understand though is that when you use any forecasting model there is a relative uncertainty. This is due to the fact you are using a model that is planning ahead for the future. When doing this the analysis is forced to make assumptions that can range from a possible investment return on a portfolio to the possible cost of a project. There are also factors that have to be assumed in this equation such as time. In order to decide these values the financial manager must formulate guesses based on historical data and past experiences rather than actual hard numbers. This creates an overall uncertainty in the projection.

            The question then becomes how do financial managers estimate the proper ranges of values that can be used when projecting a project. These  ranges are typically based on experience in the field of typical cost and time frames of projects. Once this is established the Monte Carlo simulation can start to come into play. In the Monte Carlo simulation values are selected at random for each of the tasks to be analyzed. Based on then on the range of these estimates a model is calculated based on the random value. The results that are found are then recorded and the process is then repeated. The results are typically demonstrated by a graph that shows various points in a scatter plot format. They also can be displayed in tables but the graph is the typical model this is normally accepted and followed. These graphs can at some points have over 3000 data points to be taken into consideration. It is important though to note that there are situations where the numbers are not completely random but this is not always the case.  It is not atypical for a Monte Carlo simulation to be repeated hundreds or even thousands of times, each time using a different set of randomly selected values.

     Even though at first appearance the Monte Carlo simulation appears like a firm simulation there are various problems that a company must be aware of when using this simulation model. Nawrocki,2001 addressed that fact that the Monte Carlo simulation does prove very useful in cases where data and normal analytic models are not available. Outside of this though the model is flawed. The model often creates more work for the financial analyst and the results are not always as accurate as other techniques. The cost in this situation often outweighs the benefits of using the model. The major driving problem is that assumptions are made and very few financial managers have the operations background to make these assumptions. For example many times this model is used in building projects. It takes a person with a strong construction background to place proper cost and time frames. When the model is projected by someone lacking this background it often skews the numbers. The Monte Carlo simulation also makes the implication that a company is operating under conditions of risk and that the financial manager knows all of the underlying distributions. This is not typically the case as normally in uncertinatany the distributions are unknown. In other words the financial manager might be able to calculate the obvious risk such as broken equipment and employee sick days, but may not be able to factor in things such as natural disasters that cannot be predicted.  Another downside as referenced by Whiteside, 2008 is that analyst often take the Monte Carlo predictions over the historical data. The problem with this is that historical data is factual where the Monte Carlo simulation is a set of assumed numbers. It often presents a problem that people invest more faith in assumed numbers over actual historical data with true proven values.

   There are though positive points for the Monte Carlo method or else it would not be used in financial management today. It is a simulation that has lasted a long time in the financial and other worlds so it has its’ use and merits.  One of them addressed by Whiteside, 2008 is that even though the process is time consuming once the equations are entered in the computer system the analysis then becomes easy to run. It produced fast results that appeal to a broad audience that are capable of understanding the entire picture over just the individual elements that are required under most financial systems. This speed makes this method very attractive as in today’s business world time is often money.  The system also produces a wide range of numbers which adds strength to any analysis as the more data you have the stronger the data. The final positive aspect is the simulations use in other fields. Even though at first this may not appear to matter in reality it does. When a financial manager can explain to a biologist how they are finding their data and that the data shares the same type of model it becomes a point of similarity. This can help in creating a bridge between two very different business worlds which is always an attractive point.

 

References

Kwak, Y. H., & Ingall, L. (2007). Exploring monte carlo simulation applications for project management. Risk Management, 9(1), 44-57. doi:http://dx.doi.org/10.1057/palgrave.rm.8250017

 

Nawrocki, D. (2001). The problems with monte carlo simulation. Journal of Financial Planning, 14(11), 92-103+. Retrieved from http://search.proquest.com.proxy.davenport.edu/docview/217559045?accountid=40195

Whiteside, James D,I.I., P.E. (2008). A practical application of monte carlo simulation in forecasting. AACE International Transactions, , ES41-ES49,ES410-ES412. Retrieved from http://search.proquest.com.proxy.davenport.edu/docview/208193177?accountid=40195

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