Nội dung

CHAPTER 8 Modeling Financial Statements erhaps the most widely used financial models are the pro forma income statement and balance sheet. Most companies use deterministic versions of these models for planning, and they are a natural place to start when constructing a Crystal Ball model. Especially when decision makers are first exposed to risk analysis with Crystal Ball, it is best to make your stochastic models resemble as much as possible the deterministic models to which decision makers in your company are already accustomed. In this chapter, we start with deterministic pro forma statements from Chapter 6 of Sengupta (2004), which is an excellent source for more information on constructing deterministic financial models in Excel. In this chapter, we will focus on using Crystal Ball with an existing deterministic model, just as you might do on the job. We walk through use of the basic tools to get you started. As you gain experience, you will be tempted to make your models far more complex than those presented here. However, do not add complexity just for its own sake. It is far better to start with a simple model, and add complexity only if necessary to help make a sound decision. P DETERMINISTIC MODEL Figure 8.1 shows the historical income statement for 1999–2002 for the Vitex Corporation example from Chapter 6 of Sengupta (2004). Figure 8.2 shows the balance sheets for 1999–2002 for the Vitex Corporation. Our job is to project these measures forward using historical data and input from management about the future uncertainty. We begin thinking about the uncertain future by looking to the past. While it is always true that there are no guarantees the future will resemble the past, historical data are often the best information you will have available. Furthermore, if you do have available to you a better source of information about what portends for your company’s fortunes, then you can easily incorporate it in a Crystal Ball model using the methodology described here. Figure 8.3 shows the common size statements for 1999–2002 for the Vitex Corporation from Sengupta (2004). These are created by dividing each year’s 125 126 FINANCIAL MODELING WITH CRYSTAL BALL AND EXCEL FIGURE 8.1 Historical income statement in Sengupta.xls. income statement line items by the corresponding year’s sales so that each line item in the common size statement is expressed as a percentage of sales. By looking at the variability of common size line items over the years, we gain an idea of how much variability to expect in each line item. Then we can quantify the impact of each item on the bottom line. TORNADO CHART AND SENSITIVITY ANALYSIS Figure 8.4 shows a Crystal Ball model for 2002–2006 for the Vitex Corporation income statement from Sengupta (2004), which we use to create a tornado and sensitivity chart. Cell J6 models the average changes in sales across years as a uniform(1%,10%) Crystal Ball assumption based on information provided by management on the possible variation they see for sales. Cells J7, J10, and J12 are uniform(48%,52%), uniform(27%,30%), and uniform(−1%,0%) assumptions, respectively. These parameters were selected based on the common size statements, and were defined to facilitate use of the tornado chart and sensitivity chart to see which variables had the greatest impact on the forecast, EBIT in 2006. Figure 8.5 shows a tornado chart for the Crystal Ball model for 2002–2006 for the Vitex Corporation income statement from Sengupta (2004). This shows that the sales forecasting factor had the largest impact, then cost of sales, followed by sales, general and administrative (SG&A) expenses. Modeling Financial Statements FIGURE 8.2 Balance sheet in Sengupta.xls. FIGURE 8.3 Common-size statements in Sengupta.xls. 127 128 FINANCIAL MODELING WITH CRYSTAL BALL AND EXCEL FIGURE 8.4 Crystal Ball model for income statement in Sengupta2.xls. Cells J6, J7, J10, and J12 are Crystal Ball assumptions, and I3 is a forecast. FIGURE 8.5 Tornado chart for Crystal Ball model in Sengupta2.xls. The values of the Crystal Ball assumptions were varied from the 1st percentile to the 99th percentile in five equally spaced steps. Modeling Financial Statements 129 FIGURE 8.6 Sensitivity analysis chart for Crystal Ball model in Sengupta2.xls. CRYSTAL BALL SENSITIVITY CHART Figure 8.6 shows a sensitivity chart for the Crystal Ball model for 2002–2006 for the Vitex Corporation income statement from Sengupta (2004). This chart shows results similar to those obtained from the tornado chart, but indicates an even larger impact from the sales forecasting factor. See Chapter 2 for an explanation of the differences between these two types of charts. Figure 8.7 shows a Crystal Ball model for 2002–2006 for the Vitex Corporation income statement from Sengupta (2004). Note that this models the annual changes in sales and cost of sales as a percentage year-by-year as Crystal Ball assumptions. In Chapter 11, we look closer at modeling financial time series with this sort of multiplicative random walk model, and introduce more models for simulating financial time series. Figure 8.8 shows a forecast chart for the Crystal Ball model for 2002–2006 for the Vitex Corporation income statement from Sengupta (2004). A 95 percent certainty interval for earnings before interest and taxes (EBIT) in 2006 is from $201.5 to $309.2. CONCLUSION In modeling financial statements, our intent is to project financial measures into the future to help make informed decisions about the activities that result in these 130 FINANCIAL MODELING WITH CRYSTAL BALL AND EXCEL FIGURE 8.7 Crystal Ball model in Sengupta3.xls. FIGURE 8.8 Forecast chart for Crystal Ball model in Sengupta3.xls. measures. Deterministic versions of pro forma statements have long been used for answering ‘‘what-if’’ questions about the bottom-line impact of managerial decisions, and a Crystal Ball model can be viewed as a type of more-sophisticated what-if analysis from which decision makers can gain more insight than from a deterministic model. Modeling Financial Statements 131 The procedure for modeling financial statements is to identify the key inputs, as we did with the tornado chart, which varied many inputs one at a time to see the impact on the key output, EBIT in 2006. Any output may be specified depending on the decision maker’s interest. Because we used sales-driven forecasting, it is no surprise that we found sales growth to be the most important driver of 2006 EBIT. This emphasizes the need to come up with a good model for defining sales assumptions. We started with common-size statements to see which percentages are stable over time and which are variable. We usually base our projections on historical data where they are available, but can incorporate other educated guesses too, if they are encapsulated in probability distributions that are used to define Crystal Ball assumptions. When historical data are available, a popular technique is to obtain subjective estimates from subject matter experts for the parameters of Uniform or Triangular assumptions. The procedure for modeling financial statements is to identify the key inputs, as we did with the Tornado Chart, then observe key outputs like EBIT in 2006. Any output may be specified depending on the decision maker’s interest. Because we used sales-driven forecasting, it is no surprise that we found sales growth to be the most important driver of 2006 EBIT. This emphasizes the need to come up with a good model for defining sales assumptions.