Valuation Master Class: Q&A with Partha Mohanram

Partha Mohanram, Professor of Accounting at Rotman School of Management, U of T. answers a few questions on business valuation.

In the world of mergers and acquisitions, few issues are more vexing than valuation. M&A professionals know all too well that valuing an asset is one of the most common obstacles in negotiating an M&A deal. It’s a topic that Firmex, in partnership with MergerMarket, tackled in the report M&A Valuation: Trends, Challenges and Horror Stories. We sat down with Partha Mohanram, Professor of Accounting and the John H. Watson Chair in Value Investing at Rotman School of Management, U of T. to ask a few questions about the pitfalls of business valuation.

You teach business analysis and valuation at Rotman. What’s the hardest concept for Business School students to grasp about valuing companies?

I think the most important concept for anyone to understand is that valuation is not about knowing skills in finance, accounting or strategy separately, but rather in integrating all of these skills in understanding firms, their business models, their financial performance and prospects. All these insights are essential and need to be integrated to value firms appropriately.

In your experience, what is the number one hurdle M&A practitioners face when determining a valuation?

If it is ok, I will list a few hurdles.

At a philosophical level, the big problem is “anchoring”. A sense that valuation ought to be X or Y, and then reverse engineering the inputs or the analyses to justify that valuation.

At a practical level, I see two major problems. The first is that when one uses a method such as DCF, the emphasis on terminal value becomes inordinate, especially for fast-growing firms. This problem is exacerbated because of ad-hoc assumptions used for the terminal growth rate as well as cost of capital

The second problem is the valuation of synergy. Very often, the presence of, and extent of, synergy is crucial for valuation. However, it is unclear whether synergy exists in the first place, and if it does, the quantum is overestimated (i.e. the numerator effect), and the riskiness is underestimated (the denominator effect).

Everyone in the industry has a valuation horror story. What’s an example of a recent valuation disaster, in your opinion, and what can it teach us?

To me, HP’s acquisition of Autonomy in 2011 is probably one of the most egregious examples of unwarranted overpayment. I believe HP paid something like over $11 Billion for Autonomy in 2011 and ended up writing off almost $9 Billion.

There were telltale signs that this was bound to happen. Firstly, HP paid considerably more than what other bidders were willing to pay. Rumor has it that Cisco was willing to pay $6 Billion or so, while HP paid almost twice that. I teach a case on HP in my course and I compare this acquisition to the episode of Seinfeld where Elaine overpays like crazy to acquire JFK’s golf clubs for her boss, J. Peterman, mainly because she gets into a bidding war and has to win.  Second, a considerable part of the $11 Billion ended up as goodwill. Goodwill is one of those lovely accounting words that probably means the opposite of what it sounds like. In my book, the definition of goodwill should be “There’s a darn good reason why I paid so much, but right now, I can’t think of it.”

M&A professionals have access to better analytical technologies than ever before. Has technology helped make valuation more precise and prevent common blunders?

I don’t think valuation in the context of M&A is that precise for technology to make much of a difference. Very often, people use complicated analyses to hide the fact that they have no idea what is really going to happen in the future. If you don’t know, whether the value ought to be 20 or 30 or 40, you can get a false sense of precision by using a very complex model which gives you an answer of 26.43. You can make it seem even more analytical when you throw in some nice words like neural networks and Latent Dirichlet analysis.

I am not saying that using complicated models and sophisticated techniques is wrong. But when you have no idea what the correct inputs are, it may just be a waste of time. Remember – Garbage in Garbage out.

M&A practitioners know that it isn’t all about the numbers: mental biases can impact a target’s valuation. In your opinion, what is the most dangerous of these human or cognitive biases, and how can it be overcome?

I am going to rephrase this question into asking the following “Why do many M&As fail to generate value?”

Many M&A deals fail because they were never meant to work in the first place – i.e. the merger made no economic sense. These are deals that are made not for any underlying economic motivation, but for other reasons – everyone else is doing it, the valuation is attractive, the acquiring firm had excess cash.

The most common reason for failed M&A is empire-building – managers who are making deals to grow the size of the firm at the expense of “quality” or profitability. M&A can also fail if the acquiring company is not resource constrained. A firm that either has piles of excess cash, or an artificially inflated stock price, is far more likely to make acquisitions and far more likely to make bad acquisitions.

The other reason why mergers fail is that managers often underestimate the challenges the deal will impose on the combined firm, or overestimate their own ability to solve potential problems. This “hubris” hypothesis has been extensively studied by the academic literature (e.g. the groundbreaking work by the economist Richard Roll).

Mergers that make economic sense can also fail if they do not make financial sense. Very often, the only parties gaining from deals are target firm shareholders and the firms providing support services (investment banks, lawyers etc).

Finally, mergers that make economic sense and financial sense can also fail because of other intangible factors such as lack of cultural fit. A merger between two companies that are inherently different in rigid and inflexible ways is almost always doomed to fail. An example would be the “merger” of Daimler and Chrysler in the late 1990s.

Recently, we’ve seen tech valuations go through the roof. Why do you think this is happening and is the hype in this hot sector warranted?

I think there are a few reasons why valuations in the tech sector often get out of hand. First – the market behaves like venture capital. They know that the average firm is probably overvalued but the select few that will succeed will be so successful, that they are willing to overpay. Second – there is often a big mismatch in size between a relatively small target and a large acquirer. So, whether a target is worth $500 million or $ 1 billion might not matter very much to an acquirer worth in excess of, say, $20 billion. Third, and this applies only to stock-based mergers, if the acquiring firm is itself overvalued, it might be a good time to go shopping with your overvalued currency. The target might be overvalued but not as much as the acquiring firm.

Is it more difficult to arrive at a valuation in a strategic deal rather than in a strictly financial one?

I don’t like this classification of deals into strategic and financial. Every deal should have both a strategic and financial aspect to it.

The deal should make sense strategically. If the deal makes no strategic sense, no price will make it right. If you buy something you did not need for 30% off, you did not save 30%; rather you just blew 70%.

If a deal is at a wrong price, i.e. the buyer overpays, the strategic value might not matter – it is still a negative NPV project. The biggest reason why many deals fail to generate value for acquirers is overpayment. There are of course myriad reasons for this – winner’s curse, sellers holding out for all the synergy/value creation, hubris on the part of the buyer etc.

Bottom line: Don’t buy something you don’t need, no matter what the price. If a deal makes sense, decide on what the “walk away” price is and if that is crossed, walk away. Most buyers don’t – they start questioning assumptions. “Maybe our discount rate was too high. Maybe our synergy estimates were too conservative.” This gets back to the reverse engineering I talked about earlier.

Valuation – Art or Science?

Honestly, I think it is neither. It is more like educated guesswork. So the aim of the game should be to increase the “education” component so you’re not guessing as much.

Partha Mohanram is the John H. Watson Chair in Value Investing at Rotman. He has published extensively in the areas of financial statement analysis, valuation of growth firms, implied cost of capital and executive compensation. He is an editor of Contemporary Accounting Research and serves on the editorial board of The Accounting Review and Review of Accounting Studies. His research work has won numerous awards including the Haim Falk award from the CAAA (2017) and the Rotman research impact award (2018). Professor Mohanram teaches advanced electives on business analysis and valuation. He joined Rotman after serving on the faculty of Columbia and New York University. He obtained his PhD from Harvard, MBA from IIM – Ahmedabad, and B.Tech from IIT-Madras.

Further Reading

Although not directly related to M&A, Professor Mohanram has co-authored two recent papers that may be of interest to valuation practitioners (PDF downloads):

Fundamental Analysis: Combining the Search for Quality with the Search for Value, Li, K., Mohanram, P. Contemporary Accounting Research (Forthcoming 2018).

Fundamental Analysis of Banks: The Use of Financial Statement Information to Screen Winners from Losers Mohanram, P.; Saiy, S., Vyas, D. Review of Accounting Studies (Issue:23. 2018).


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