It is time to revisit a basic but fundamental aspect of financial modeling: The Big Picture. What exactly is The Big Picture ("TBP")? TBP refers to the overall goal of the financial model and what answer the model is specifically being developed to answer.
Many times in my career I have been asked questions about financial modeling in Excel, questions that really indicated that TBP was not yet established. This is not a knock on anyone's intellectual capacity but rather, it confirms what I have always believed: people cannot sit down in front of a computer and miraculously create a financial model that provides the answers to relevant questions without first addressing the overall scope and purpose of the exercise.
Let us assume, for the moment, that you are a budding young financial analyst at Toyota and you have assigned to analyze domestic (U.S.) cars sales. If we assume that Toyota, being a global entity, has all of the reporting and data readily available, there are no information glitches in the exercise and it is all up to you, the financial analyst, to develop this model. So, how would you begin?
If the corporate office was interested in number of Camrys versus Tauruses sold in the state of Michigan, the exercise is straightforward. You could easily pull information on total car sales by make and model and have the answer. If the analysis is, however, to determine how much of an advertising campaign is required to increase market share by 3.0%, there will be more thought required to create the model. In this case, TBP can be thought of in a somewhat formulaic relationship: TBP = advertising impact on market share. This becomes the basis of the model.
Given the way that the formula is presented, this may be an opportunity to use a data analysis function incorporated in Excel like the regression analysis. Whenever an analyst is trying to determine relationships, taking historical advertising expenditures and running that against changes in market share may provide the appropriate regression equation from which the forecast can be created. For example, you may take 5 years of monthly data and determine that increases in advertising translates directly into increases in market share (all other variables held constant), and this will allow you to understand what the future market share over the next year might be based on those relationships. If the model displays profit margins, return on invested capital or reductions in debt, than TBP was not addressed. Those items reflect supporting data, but remember TBP: impact of advertising on market share.
The purpose of this brief article is really to beat into your head the need to focus on TBP. Part of being an effective analyst, consultant or advisor is listening to the problem and providing the relevant solution. If you need 10 apples for a pie, finding 12 oranges is not helpful. Focus on the purpose of the model before you start creating spreadsheets and you will find your efficiency in financial model development increasing significantly.
Wednesday, August 5, 2009
Friday, July 3, 2009
Sensitivity and Scenario Analyses in Financial Modeling
Microsoft Excel allows great flexibility when developing financial models, which is important given the level of subjectivity involved with determining variable inputs. Whenever a financial modeler prepares a set of projections or is doing an analysis that requires several inputs that are estimated, that value of the analysis becomes based on subjective values used to drive the model. In these situations, there are methods to provide ranges of possible outcomes. These ranges are crucial given the subjectivity of the inputs. The two main approaches are sensitivity analysis and scenario analysis.
Sensitivity analysis can be thought of as taking a particular variable, like price per pound, number of units sold or interest rate, and changing it to see the impact on the overall analysis. For example, assume that an analyst forecast assumes that there will be a 5% increase in the unit prices and a 5% increase in overall sales demand. One way to determine the impact of each assumption, one could keep unit prices constant while leaving the overall demand the same, or vice versa. Under this method, the analyst can see what impact changing one variable has on the overall assessment. This can be applied to any number of variables used in the model, and in general, this is referred to as “stress testing” the model. Under a worst case scenario analysis, one would take a variable like sales growth and assume 0% or negative, or take a cost assumption and increases it significantly. By taking this approach, one can see what might happen if the current product or service provided by a company was suddenly significantly altered one variable at a time and represents the ultimate stress impact.
Scenario analysis can be thought of as multiple sensitivities performed simultaneously. For example, an automobile manufacture has several things that affect the ability to sell cars and make a profit, like foreign competition, union wages and escalations, increasing costs of inputs and assumptions regarding postretirement benefits and pensions. Instead of assuming merely a 0% sales growth or increasing costs, managers may want to know what the combination of several effects might be and label different scenarios. One might assume 1) an increase in foreign car presence that would decrease domestic demand for cars; 2) higher union wages driven my contracts approaching expiration; and 3) recessionary impact on disposable incomes. Changes in these three variables might constitute on scenario, and the multiple variable changes can be assessed.
Normally, using data tables in Excel is an excellent method by which to assess stressing of variables. Of course, data tables at most can provide up to two variables simultaneously. Because of this, it is common to have three or four data tables in a row to get an overall assessment of changing more than two variables at a time. An example of this would be calculation of investor returns. One could change the exit multiples and year of exit in one, the exit multiple and sales growth in another, the amount of total debt and year of exit in another, and so on. The series of these analyses provides an overall picture of the changes in variables that may be very helpful in determining the factors that affect the outcome of the financial model the most.
Sensitivity analysis can be thought of as taking a particular variable, like price per pound, number of units sold or interest rate, and changing it to see the impact on the overall analysis. For example, assume that an analyst forecast assumes that there will be a 5% increase in the unit prices and a 5% increase in overall sales demand. One way to determine the impact of each assumption, one could keep unit prices constant while leaving the overall demand the same, or vice versa. Under this method, the analyst can see what impact changing one variable has on the overall assessment. This can be applied to any number of variables used in the model, and in general, this is referred to as “stress testing” the model. Under a worst case scenario analysis, one would take a variable like sales growth and assume 0% or negative, or take a cost assumption and increases it significantly. By taking this approach, one can see what might happen if the current product or service provided by a company was suddenly significantly altered one variable at a time and represents the ultimate stress impact.
Scenario analysis can be thought of as multiple sensitivities performed simultaneously. For example, an automobile manufacture has several things that affect the ability to sell cars and make a profit, like foreign competition, union wages and escalations, increasing costs of inputs and assumptions regarding postretirement benefits and pensions. Instead of assuming merely a 0% sales growth or increasing costs, managers may want to know what the combination of several effects might be and label different scenarios. One might assume 1) an increase in foreign car presence that would decrease domestic demand for cars; 2) higher union wages driven my contracts approaching expiration; and 3) recessionary impact on disposable incomes. Changes in these three variables might constitute on scenario, and the multiple variable changes can be assessed.
Normally, using data tables in Excel is an excellent method by which to assess stressing of variables. Of course, data tables at most can provide up to two variables simultaneously. Because of this, it is common to have three or four data tables in a row to get an overall assessment of changing more than two variables at a time. An example of this would be calculation of investor returns. One could change the exit multiples and year of exit in one, the exit multiple and sales growth in another, the amount of total debt and year of exit in another, and so on. The series of these analyses provides an overall picture of the changes in variables that may be very helpful in determining the factors that affect the outcome of the financial model the most.
Wednesday, May 13, 2009
A Simple Model Concept to Assess Leverage Capabilities in Microsoft Excel
In this article, I break away a bit from Excel specific information to address a fundamental concept related to corporate finance when companies want to issue debt or a private equity firm wants to acquire a company and use leverage. Even in the current environment, there are still some willing traditional and alternative lenders, but it is certainly not as robust as it had been 5 years ago. In any event, the method by which to quickly assess whether or not a company can handle debt is useful in determining which ideas can be acted upon and which ideas should be shelved.
Many years ago in my early days of investment banking, I developed a Quick Look Model for refinancing for our group to assess the capability of companies to issue debt. It was a simple model that required few inputs but could quickly evaluate whether a debt transaction was viable. Beginning with the latest calculated EBITDA, I constructed a forecast based on EBITDA change for a five-year period. Once you have depreciation, capital expenditures, non-cash working capital changes and a tax rate, the model can be constructed.
Staring with the recent history, grow EBITDA by whatever amount is necessary that fits with the latest news and/or understanding of a company's prospects. For example, if EBITDA for the last fiscal year was $100 and the business prospects are steady but not spectacular, you may assume a simple 5% growth rate per annum, so $105 in year 1, $110 in year 2, $116 in year 3 and so on. The next step is to understand depreciation, along with capital expenditures. In the simplest of models, and without detailed information on expenditure plans, analysts will sometimes have depreciation and capital expenditures as offsetting items. In other words, a company will spend enough each year merely to replace the amount of asset depreciation. In this fashion, the net plant, property and equipment will stay the same for the forecasting period. Finally, you need to make some assumption abut working capital uses. For a quickly growing company, it is very likely that cash will be tied up in working capital as inventories and accounts receivables build. For more mature companies, working capital may be consistent over time as the established entities tend to have long-term relationships with suppliers and customers and the swings in net working capital may be less volatile. Whether you use a percentage of revenue approach or other method, this is another subjective input requiring thought by the financial modeler.
One thing to keep in mind is the iteration concept in Excel. In my opinion, it is more appropriate to use the iteration style of modeling for financial purposes to more accurately reflect the inflows and outflows of cash. For example, you could make an assumption regarding the payment of debt that allows the beginning cash balance (say January 1, 2009) to determine the total amount of interest an entity would pay for the calendar year 2009. This is only appropriate if the type of debt is either a non-principal paying loan or one in which only year-end amortizations occur. In the real world, companies borrow from institutions that may impose quarterly or monthly interest and principal payments. By allowing for iteration in your model, you capture interest paid over the course of the year by taking the beginning and ending debt balances and averaging the numbers. You could increase the realism by using monthly or quarterly forecasting periods, but for the most part, the simple average with iteration approach will provide enough of a first cut to make a decision to do further research on a potential project or not.
Whereas in a standard valuation model in which EBITDA is reduced by depreciation to arrive at NOPAT (net operating profit after taxes, or sometimes used interchangeably with EBIT, which is not exactly correct) and then calculate free cash flow, we are looking at this from a debt perspective. As such, the way to quickly determine how much cash is left to repay borrowing, one must subtract interest expense. Again, we are not calculating the value of the firm, but rather, we are analyzing a company's ability to repay debt. EBITDA minus depreciation minus interest expense provides a pretax number that is then taxed at a specified corporate rate. To reiterate, this interest expense will be based on average balances and thus, the iteration box must be clicked in Excel.
Thus, top of your quick look model (the mini income statement information) will have a net income number plus depreciation minus capital expenditures and changes in non-cash working capital to arrive at cash available to retire debt. The bottom should have the debt balances and cash balances, which will adjust over time depending on the forecasted performance of the company. The change in debt balance will be based on the cash available to retire debt and may or may not include a minimum cash balance constraint or other minor adjustments a financial modeler may incorporate. Once the assumptions are completed, you can quickly see if a company can support debt. Some additional calculations might include coverage ratios (EBITDA/interest, EBIT/ interest) and leverage ratios (total debt/EBITDA, total debt/(EBITDA – capex)). These are the types of ratios that lender will study before determining whether or not a company can support leverage.
The key here is that you can make it as simple or a bit more detailed as you would like, but this would likely take an intermediate financial modeler no more than an hour to setup, quickly research and analyze the merits or drawbacks of a potential project. Sometimes, the most effective approach is a simple one.
Many years ago in my early days of investment banking, I developed a Quick Look Model for refinancing for our group to assess the capability of companies to issue debt. It was a simple model that required few inputs but could quickly evaluate whether a debt transaction was viable. Beginning with the latest calculated EBITDA, I constructed a forecast based on EBITDA change for a five-year period. Once you have depreciation, capital expenditures, non-cash working capital changes and a tax rate, the model can be constructed.
Staring with the recent history, grow EBITDA by whatever amount is necessary that fits with the latest news and/or understanding of a company's prospects. For example, if EBITDA for the last fiscal year was $100 and the business prospects are steady but not spectacular, you may assume a simple 5% growth rate per annum, so $105 in year 1, $110 in year 2, $116 in year 3 and so on. The next step is to understand depreciation, along with capital expenditures. In the simplest of models, and without detailed information on expenditure plans, analysts will sometimes have depreciation and capital expenditures as offsetting items. In other words, a company will spend enough each year merely to replace the amount of asset depreciation. In this fashion, the net plant, property and equipment will stay the same for the forecasting period. Finally, you need to make some assumption abut working capital uses. For a quickly growing company, it is very likely that cash will be tied up in working capital as inventories and accounts receivables build. For more mature companies, working capital may be consistent over time as the established entities tend to have long-term relationships with suppliers and customers and the swings in net working capital may be less volatile. Whether you use a percentage of revenue approach or other method, this is another subjective input requiring thought by the financial modeler.
One thing to keep in mind is the iteration concept in Excel. In my opinion, it is more appropriate to use the iteration style of modeling for financial purposes to more accurately reflect the inflows and outflows of cash. For example, you could make an assumption regarding the payment of debt that allows the beginning cash balance (say January 1, 2009) to determine the total amount of interest an entity would pay for the calendar year 2009. This is only appropriate if the type of debt is either a non-principal paying loan or one in which only year-end amortizations occur. In the real world, companies borrow from institutions that may impose quarterly or monthly interest and principal payments. By allowing for iteration in your model, you capture interest paid over the course of the year by taking the beginning and ending debt balances and averaging the numbers. You could increase the realism by using monthly or quarterly forecasting periods, but for the most part, the simple average with iteration approach will provide enough of a first cut to make a decision to do further research on a potential project or not.
Whereas in a standard valuation model in which EBITDA is reduced by depreciation to arrive at NOPAT (net operating profit after taxes, or sometimes used interchangeably with EBIT, which is not exactly correct) and then calculate free cash flow, we are looking at this from a debt perspective. As such, the way to quickly determine how much cash is left to repay borrowing, one must subtract interest expense. Again, we are not calculating the value of the firm, but rather, we are analyzing a company's ability to repay debt. EBITDA minus depreciation minus interest expense provides a pretax number that is then taxed at a specified corporate rate. To reiterate, this interest expense will be based on average balances and thus, the iteration box must be clicked in Excel.
Thus, top of your quick look model (the mini income statement information) will have a net income number plus depreciation minus capital expenditures and changes in non-cash working capital to arrive at cash available to retire debt. The bottom should have the debt balances and cash balances, which will adjust over time depending on the forecasted performance of the company. The change in debt balance will be based on the cash available to retire debt and may or may not include a minimum cash balance constraint or other minor adjustments a financial modeler may incorporate. Once the assumptions are completed, you can quickly see if a company can support debt. Some additional calculations might include coverage ratios (EBITDA/interest, EBIT/ interest) and leverage ratios (total debt/EBITDA, total debt/(EBITDA – capex)). These are the types of ratios that lender will study before determining whether or not a company can support leverage.
The key here is that you can make it as simple or a bit more detailed as you would like, but this would likely take an intermediate financial modeler no more than an hour to setup, quickly research and analyze the merits or drawbacks of a potential project. Sometimes, the most effective approach is a simple one.
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