From the course: Financial Modeling and Forecasting Financial Statements

Use sensitivity analysis to minimize the costs of being wrong

From the course: Financial Modeling and Forecasting Financial Statements

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Use sensitivity analysis to minimize the costs of being wrong

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Sales forecasting is not an exact science. Lots of things can go wrong. For example, sometimes data are misinterpreted. Also, it's often the case that the future just doesn't turn out according to the forecast. The future, it turns out, is an uncertain place. An example of data misinterpretation arose in the acquisition of autonomy by Hewlett Packard, or HP. HP is a hardware and software company based in Palo Alto, California. In October of 2011, HP purchased Autonomy, an enterprise software company founded in Cambridge, England. The purchase price was $11 billion. Just over one year later, in November of 2012, HP announced that it was recording a $9 billion impairment loss related to the autonomy acquisition. This means that HP had determined the value of autonomy wasn't $11 billion. It was just $2 billion. So to the courts we go. HP alleged fraudulent financial reporting and autonomy countered by saying that this is a simple matter of the people at HP not understanding international financial reporting standards. Now, autonomy's CEO was cleared of any wrongdoing in a U.S. criminal trial but was convicted in a civil case in the UK. So it's not entirely clear who got it wrong with this acquisition. What is clear is that bad data or data misinterpreted caused a company to look like it was worth 11 billion dollars when it was actually worth only 2 billion dollars. But here's my favorite sales forecasting fiasco, the AOL acquisition of Time Warner. In January of 2001, AOL, originally known as America Online, acquired Time Warner. The combined value of the company was valued at over $150 billion. Now, this was at the height of the internet bubble. The market was crazy in terms of assumptions about how much money would be made through internet commerce. The AOL-Time Warner merger seemed like a match made in heaven. Pairing the online presence of AOL with the vast media library of Time Warner. The expectation was that the obvious synergies would generate astronomical future sales and profits. Well, not long after this deal, a wave of reality swept over the entire world. People came to their senses, and it was realized that the future sales growth expectations for internet-related companies were wildly overoptimistic. Worldwide, the values of tech companies dropped by an average of 50 percent. Because the $150 billion AOL Time Warner valuation had been based on future sales projections that were just too high, more realistic assumptions were applied, and it was realized that the combined company was worth much less, over $100 billion less. But errors also happen in the other direction. Sometimes things turn out much better than had been forecasted, and if the initial forecast was pessimistic enough, the forecast itself might have turned the company away from something that ultimately turned out to be very profitable. Recall that in the early 1980s, IBM made two fateful decisions to outsource microprocessor production to Intel and operating systems software to Microsoft. Presumably IBM executives had forecast that future sales and profits from personal computer microprocessors and operating systems wasn't large enough to pursue. Well, as of August 2025, the combined market values of Intel and Microsoft were over 17 times as much as the market value of IBM. Oops. Sometimes data are faulty or misinterpreted, as with autonomy. But the most common reason for significant errors in forecasting is that the future just doesn't turn out to be as expected or hoped as with AOL and Time Warner and with IBM, Intel, and Microsoft. As we learn the basics of financial modeling and forecasting, keep in mind that this is not an exact science. Prudence, caution, and healthy skepticism are in order in selecting the data to be input into a forecasting model.

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