From the course: Finance Foundations: Business Valuation

Why business valuation matters

From the course: Finance Foundations: Business Valuation

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Why business valuation matters

- Business valuation is not an exact science. - Lots of things can go wrong. - For example, sometimes data are hidden or distorted. Other times data are just badly misinterpreted. Finally, it's often the case that the future just doesn't turn out according to the forecast. Let's look at some famous examples of each of these business valuation problems. - You may have heard of Enron. Although the Enron debacle is getting far enough in the past that it's fading in into historical memory. - Briefly stated, Enron started out as an energy trading firm, matching sellers of energy, and buyers of energy, and providing price insurance to each, a perfectly fine business. - But, as is often the case with high flying companies, real growth just couldn't keep up with expected growth. So, Enron started using accounting tricks to conceal losses, in order to meet the profit expectations set by the market. - [Narrator] At its peak, Enron had a total market value or market capitalization of about $80 billion. This valuation was based on the data available to the public. - [Illustrator] When the concealed losses were revealed, and all the smoke had cleared, Enron declared bankruptcy and was worth zero. So this is a case where the business valuation was off, by $80 billion, because it was based on bad data. - Another valuation misadventure arose in the acquisition of Autonomy by Hewlett Packard or HP. HP is a hardware and software company based in Palo Alto, California. - [Illustrator] In October 2011, HP purchased Autonomy, an enterprise software company founded in Cambridge, England. The purchase price was $11 billion. - [Narrator] 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 that the value of Autonomy wasn't 11 billion, it was just $2 billion. - HP has accused Autonomy of manipulating the recording of its sales, to boost the apparent value of the company in advance of the acquisition. Autonomy on the other hand, says that this is a simple matter, of the people at HP not understanding, International Financial Reporting Standards. - What is clear is that bad data either manipulated or misunderstood, caused the company to look like it was worth $11 billion, when it was actually worth only $2 billion. - Now, here's my favorite business valuation fiasco. The AOL acquisition of Time Warner. In January 2001, AOL originally known as America Online, acquired Time Warner. The combined 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 profits. - Well, not long after this deal, a wave of reality swept over the world. People came to their senses, and it was realized that the profitability and future growth expectations for all internet related companies were wildly over optimistic. Worldwide, the value of tech companies dropped by an average of 50%. - [Narrator] Because the $150 billion AOL, Time Warner valuation had been based on future projections that were too high, when more realistic assumptions were applied, it was realized that the combined company was worth much less, over 100 billion dollars less. - Sometimes data are hidden as in the Enron case. Other times data are badly misinterpreted, as with Autonomy. But the most common reason for significant misvaluation, is that the future just doesn't turn out as expected or hoped, as was the case with AOL and Time Warner. - As we learn the basics of business valuation, keep in mind that this is not an exact science. Prudence, caution, and healthy skepticism are in order, in selecting the data to input into a valuation model

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