Capital Budget Model - Term Paper Example

Capital Budget Model Introduction Capital budgeting is considered a planning process utilized in determining whether the long-term investments of an organization, such as research development projects, new machinery, new products, new plants, and replacement machinery are worth getting funds through the organization’s capital structure (Baker, Singleton &Veit, 2010). This planning process involves using past long-term investments of an organization in order to forecast the types of investments that are worth investing to achieve business objectives and goals. Capital budgeting is important for several reasons. First, when making the decision to procure assets, managers are required to forecast the revenue of the assets over their lifetime. Second, provided with the duration of the organization’s projects, capital budgeting decisions eventually define the organization’s strategic plan in the future through forecasting. The importance of capital budgeting shows that forecasting plays an important role in determining future investments. Thus, it is important to understand forecasting in finance. Forecasting in business is considered an important tool for planning that assists management in its efforts to cope with the uncertainty of the future, depending mostly on historical and current data to determine the future direction of an organization (Abbott, 2014). While, forecasting is used by business organizations to determine the way budgets are allocated in the future, it is used by investors to determine whether events affecting a business, such as expectations of sales will decrease or increase the share price of that business. Since forecasting in business requires the use of historical and current data to determine the future direction of an organization, it needs a powerful software to analyze large historical and current data. Popular software for modeling different financial scenarios, such as Microsoft Excel and SPSS are used to analyze data for forecasting. These financial modeling software model scenarios that represent a real financial situation in the world, such as modeling the financial implication of increasing the price of products in the United States market for a period of two years or modeling the financial implication of financing growth using profits for a period of five years. Let us further explore financial modeling in business. Financial Modeling in Business Financial modeling as explained above is the task of creating a representation of a real financial situation in the world. It consists of the process of forecasting the performance of a financial portfolio or asset of a business through the use of a mathematical or scientific model in order to provide management of a business with adequate information to make informed decisions on the types of portfolio and assets to finance in the future. To understand more about financial models, it is important to critically review them and understand why they are important in today’s financial world. Let us begin with a critical review of financial models. Critical Reviews of Financial Models Financial modeling consists of the process of forecasting the performance of organizations and assets, through the use of relationships among a number of variables (Sengupta, 2004). This provides the management of an organization with insight into the short-term and long-term environment of business in order summarize specific events to provide direction concerning possible alternatives or actions management can take to achieve set goals and objectives. Financial models can be created in numerous ways, either by the use of a pen and a paper, or computer software. What is most significant, however, is not the type of user interface that is used, but the fundamental logic that incorporates the model. For example, a model can summarize the returns of an investment, such as Sortino Ratio, or it may assist approximate the direction of the market, such as Fed Model. In our critical review of financial models, we shall concentrate on Sortino ratio and Fed Model. Let us begin with the Fed Model. Fed Model is a methodology of valuation that hypothesizes there is a relationship between market return on equities and the long-term Treasury notes (Bekaert & Engstrom, 2009). This model states that the return on 10 year Treasury notes are expected to be comparable to the S&P 500 earnings yield. The model is usually used to measure the attractiveness of equity and assist the allocation of funds between bonds and equity. For example, when the equity earnings yield goes beyond the government bond yield, the investors move funds from bonds into equity. Fed Model is important in financial modeling because influential market players and analysts from ING, Prudential to JP Morgan embrace it in their calculations (Das, 2013). The influential market players and analysts use Fed Model as a valuation tool to evaluate whether they should purchase bonds or stock. While the Fed Model is used as a valuation tool, it lacks the power of prediction because it has no ability to forecast or predict long-term stock returns. This makes Fed Model reliable in forecasting or predicting whether to purchase bonds or stock in the market in the short-term and not in the long-term. Named after Frank K. Sortino, Sortino ratio is used to as a financial model to measure the adjustment risk of return of an investment portfolio, strategy, or asset (Ridley, 2004). However, the ratio commonly is concerned with returns that have fallen below a required rate of return set by a user. It penalizes those returns that have fallen below a specified required rate of return or target of a user. To put it in simple terms, it measures the extra return in a business organization against the risk of the minimum return not being met. Therefore, a large Sortino ratio implies a low probability of a large loss. The Sortino ratio does not penalize a manager of a portfolio for volatility and rather focuses on whether the returns are below a certain set threshold (Ridley, 2004). This is to say that it measures an asset downside risk with no desire to penalize a strategy or asset with huge positive performance deviations. While the Sortino ratio and Fed Model are suitable for modeling different financial scenarios for the purpose of forecasting potential future returns of investments, more businesses are now exploring the technology of modeling using computer software. Let us critically explore the technology of modeling. Critical Exploration of Technology of Modeling The use of technology of modeling in business is increasing rapidly because of emerging software that can forecast how a business will perform in a specified period of time, allowing management to make informed decisions concerning launching of new products in specific markets or deciding which projects to proceed with or terminate (Baden-Fuller & Haefliger, 2013). Two of the emerging software that are employed in financial modeling include Microsoft Excel and SPSS (Abbott, 2014). These modeling software are mostly used by financial institutions, students and researchers in colleges and universities to conduct research in various fields of education, such as mathematics and sciences. Microsoft Excel is a financial modeling software from Microsoft Inc. that is used to model financial scenarios such as profit and loss a company may expect in the next week, month, or year (Abbott, 2014). Similarly, SPSS is a financial modeling software from IBM that is used to analyze and model financial information in order to predict with confidence what will happen in a business so that a manager can make informed decisions to solve problems (IBM, 2015). Let us look at these two modeling software in detail and determine the principle of the logic underlying them when using to predict or forecast. Microsoft Excel financial modeling software uses several principles of logic or features that make it one of the best modeling software for prediction and forecasting. These features are as follows. First, the Excel modeling software can be used to add massive amounts of data that could take a human being an entire day (Proctor, 2004). The data being added could provide important financial information, such as the number of consumers who made product purchases over the past five years. This information can help management of a business forecast the number of consumers who will purchase the same products in the following year. Second, the Excel modeling software can be used to find the average age of the people purchasing specific products (Proctor, 2004). For instance, in a shoe store, the established average age of people purchasing shoes may be between 25 and 30 years. Due to this, the owner can predict that this age group will visit the shop in the next month. Therefore, the owner can stock the shoe store in the next month with shoes for people who are between the age of 25 and 30 years. Similarly, SPSS financial modeling software utilizes the various principles of logic also known as features that make it a modeling software capable of forecasting and predicting events or scenarios from a lot of data. These features are explained as follows. First, the modeling software uses Spatio-Temporal Prediction technique to find linear models for taking measurements over time in a specific location to predict the way that location will change over time (IBM, 2015). For instance, a business operating in a small town may want to predict or forecast the growth of the town in the next five years in order to able to make informed decisions on whether to use their profits to expand in that town. Second, the modeling software can find relationships between non-spatial and spatial attributes using GSAR (General Search and Replace) (IBM, 2015). GSAR uses historical data, such as the type of event, the location of the event, and the time an event took place to predict the occurrences of an event, such as outbreaks of diseases or crimes. A business that forecast a town in the next five years as being vulnerable to crimes and outbreak of diseases may avoid doing business in that town because of the fear of making losses. Therefore, in the discussion above, it can be seen that the use of technology of modeling in a business is important in assisting businesses to forecast or make a prediction concerning different issues. For instance, the Excel modeling software that is mostly used by college and university students can help shop owners to predict the age range of the people purchasing shoes in a period of one week, one month or one year. Similarly, SPSS (Statistical Package for the Social Sciences) modeling software has been seen to help a business to predict the occurrences of events, such as outbreaks of diseases or crimes using historical data, in order to give business an idea of where to invest in the future. Thus, it can be deduced that technology of modeling can be used for predicting and forecasting in capital budgeting. Because capital budgeting is considered a planning process utilized in determining whether the long-term investments of a business are worth getting funds, using the technology of modeling, such as Microsoft Excel and SPSS, can assist a business to make informed decisions from analysis of predicted data. While technology of modeling is becoming the norm in today’s world, issues materialize concerning their benefits and drawbacks in business. Let us critically evaluate how modeling is employed in business. Critical Evaluation of Analysis and Modeling Employed There are financial modeling software that are employed in the financial management of specific industries, such as airline industry. One of the software for forecasting that is used in the airline industry is the Profit Manager (Sabre Airline Solutions, 2015). This software for forecasting utilizes the huge pile of data that airlines receive from passengers to model various financial situations, such as the price of the airline ticket, the most popular destinations, and the low and high season for airline ticket booking. In light of this, it is important to look at the benefits and drawbacks of such a modeling software in the airline industry. First, the number of passengers purchasing airline tickets has increased in the market and management needs to use financial modeling software for forecasting in order to anticipate the number of passengers who will be traveling in the next week, month or year. Second, passengers are constantly changing their traveling behavior because of competition from other airlines offering the same ticket price at an affordable price or offering better onboard services at an affordable price. This means that the management of the airline needs to be prepared to meet ticket demands in the next week, month or year. This can only be done through the use of financial modeling software for forecasting the behavior of passengers based on the past behavior. Finally, some airlines are reporting losses because they always make the same mistake of overspending when it comes to introducing new traveling routes. These airlines need to prevent losses by using financial modeling software for forecasting in order to use past traveling routes to determine the new routes that would be used in the next week, month or year. The above benefits of software for modeling in a business have provided us with insight into how forecasting using software for forecasting can ensure an airline business is prepared in the future to meet its goals and objectives. While the software for forecasting in the airline industry has benefits as shown above, it also has drawbacks. First, the software depends on passenger data that is fed into the computer by airline employees to show information, such as the best routes to introduce that can ensure the airline makes a profit. This is a huge drawback because in case the airline employees feed the software for forecasting with bad information, the airline stands to introduce new traveling routes that may lead to losses. Second, using software for forecasting may not be the most reliable way to use to introduce new traveling routes to destinations. While passengers have predictable traveling behaviors, they may not follow the same pattern. This implies that the past data that has been collected concerning the traveling patterns of passengers may result in an airline purchasing new airplanes to meet the growing demand of passengers. The airline may have to declare bankruptcy because of losses. In light of the above, let us look at the implications of using software for forecasting. Implications of Software for Forecasting The benefits and drawbacks of software for forecasting above have highlighted that financial modeling in business is important in forecasting how a business can take advantage of past information to predict the future in order to achieve business objectives and goals. This means a business can both benefit and lose from using software for forecasting. For instance, in the discussion, the Profit Manager software for forecasting showed that passengers are constantly changing their traveling behavior because of competition from other airlines offering the same ticket price at an affordable price or offering better onboard services at an affordable price. The implication as seen in the discussion was that the management of the airline would not be able to meet ticket demands in the next week, month, or year. Therefore, through the use of financial modeling software for forecasting the behavior of passengers based on the past behavior could be known and the airline would be able to meet the ticket demand for passengers. However, even with the benefits of software for forecasting, it was seen that the airline employees may feed the software with wrong information, which may lead to the airline introducing new traveling routes that may result in huge losses. Therefore, a business intending to use software for forecasting in planning through forecasting needs to look at capital budgeting. Since capital budgeting is considered a planning process utilized in determining whether the long-term investments of an organization, such as introducing new traveling routes are worth getting funds through the airline’s capital structure, the use of software for forecasting will be important in using past data in determining the way forward. Conclusion In the discussion, capital budgeting has been seen to be a planning process utilized in determining whether the long-term investments of an organization, such as new products and replacement machinery, are worth getting funds through the organization’s capital structure. Since capital budgeting requires a planning process to forecast, for instance, the number of customers who will purchase a new product in a particular area, financial models, such as Excel, SPSS and Fed Model can be used to assists in forecasting. Therefore, forecast data from financial models will help businesses to make informed decisions in order to achieve their business objectives and goals. References Abbott, M. (2014). Understanding educational statistics using Microsoft Excel® and SPSS®. Hoboken, New Jersey: John Wiley & Sons. Baden-Fuller, C., &Haefliger, S. (2013). Business models and technological innovation. Long Range Planning, 46(6), 419-426. http://dx.doi.org/10.1016/j.lrp.2013.08.023 Baker, H., Singleton, J., &Veit, E. (2010). Survey research in corporate finance. Oxford: Oxford University Press. Bekaert, G., &Engstrom, E. (2009). Inflation and the Stock Market: Understanding the 'Fed Model'. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.1319307 Das, A. (2013). Financial time series- recent trends in econometrics. Global Journal of Management and Business Research Finance, 13 (5), 13-18. IBM (2015). IBM SPSS statistics features. Retrieved from https://www- 01.ibm.com/software/analytics/spss/products/statistics/features.html Proctor, K. (2004). Building financial models with Microsoft Excel. Hoboken, NJ: Wiley. Ridley, M. (2004). How to invest in hedge funds. London: Kogan Page Ltd. Sabre Airline Solutions (2015). Route forecasting: Powerful decision support for airline route planning. Retrieved from http://www.sabreairlinesolutions.com/home/software_solutions/product/route_forecasting Sengupta, C. (2004). Financial modeling. Hoboken, N.J.: Wiley. Read More