Historical Data. Entering historical data is often the first thing we do when building a financial model. Inputting this data allows us to conduct ratio analysis to understand the company's recent performance and business drivers. For example, analyzing historic revenue growth and margins, can help us to predict next year's revenue growth and margins. Also for certain line items such as revenue and long-term debt, these historic figures will be the basis of our forecast numbers. The first thing that we need to do when we're inputting the historical data is to reorganize the data that the company has provided in its own income statement and balance sheet to suit our analytical needs. For example, a company might include lots of small asset and liability balances in its balance sheet that we don't want to forecast separately. We might want to aggregate these in our model, and just describe them as other assets and other liabilities. Equally, I might want to include more information in my model that is included on the face of the income statement or balance sheet. For example, if I'm building a model for a company with lots of different business segments, with different business drivers, I might want to include the revenues and operating profit for all of these segments in my model, even if the face of the income statement shows just revenues and costs for the whole business. When we are inputting the historical data we should remember the following, if we have less time available for building our model, then aggregating line items will save time when building the model. So instead of including cost of good sold and selling general and admin costs separately, we could just include total operating costs in our model. If we have more time available for building our model, then we can disaggregate more line items. Although the model will take longer to build, this will generally result in more robust forecasts. When we're inputting historical data into our model, we always calculate our subtotals rather than hard coding them, this is a really good check of our numbers. For example, if we calculate gross profit, EBIT, and net income, and then check our subtotals to the financial statements, we ensure that the numbers that make up these subtotals have been input correctly. The good thing about calculating subtotals for historical numbers is that we can copy them forward into the forecast years, and we know that these subtotals are correctly calculated because the formulas worked for the historical data.

We can often create subtotals using the keyboard shortcut for the sum function, and that's ALT equals, this is a really quick way to create subtotals that when you're doing this in the income statement, you do need to make sure that costs are shown as a negative number. For example, if costs of goods sold are shown as a negative, you can auto sum revenues and cost of goods sold to give gross profit. When we're inputting the historical income statement information, we always want to check that net income in our model agrees the net income in the company's financial statements. Although it might sound super simple to input numbers from the financial statements into Excel, it's actually really easy to make errors, or to accidentally emit a whole line item. So these crosschecks make sure that we've input our numbers correctly. In terms of the balance sheet, a great crosscheck here is to make sure that assets equal liabilities and equity. If these don't balance your historical data, then you must have made an error, either when inputting the data, or when calculating your subtotals, and you'll need to go back and check these.