Business valuation and small company risk premium

The debate about how to assess company risk and calculate the discount and cap rates rages on. While the CAPM and Build Up cost of capital models remain widely accepted, the devil, as usual, is in the details.

None as obvious as when using the risk premia for building up the equity discount rates for valuation. There seems no argument among the industry mavens that the risk free rate of return and so-called equity risk premium, or ERP for short, are the gold standard.

Company size premium – does size matter?

With this pretty much settled, the lively debate ensues as soon as you venture into the murky world of company size risk premia. The idea behind this additional element of discount rates is that smaller, less capitalized companies are inherently more risky than the larger, more established competitors.

Big business advantages and size effect on company’s value

Other things being equal, a large competitor is likely to enjoy better access to capital investment, greater acceptance of their products and services by customers, more flexibility in dealing with regulatory compliance (yes, the lobbying does pay off quite often), handsome war chests for fending off legal challenges, and much more.

Market acceptance of established companies is well known. Consider the power of a brand from a major competitor and compare it to a functionally equivalent product from a less known, smaller company. Study after study shows that customers prefer to go with the better known brands from larger companies.

In the business to business space, customers may hesitate to commit to purchases from a new vendor for fear of the company failing and leaving them high and dry without a critical supply. Often the procurement managers would demand to see the start-up’s balance sheet just to make sure they are not going belly up any time soon. The old adage of ‘show me the money’ plays right into the hands of larger, established deep pockets competitors.

Bigger companies also tend to enjoy the economies of scale, unavailable to their smaller industry peers. Ever wonder how come a large software company can come up with a working prototype by lunch time, while a smaller company has to invest ‘all-nighters’ to show similar progress?

That’s because the big company has plenty of talent on board with many pre-existing solutions lying around ready for reuse. They don’t need to reinvent the proverbial wheel, plenty of spare parts ready to be plugged into a new solution.

Smaller companies – trail blazers often fly under the radar screen

As a result, smaller companies often thrive in the netherworld of emerging markets. Big companies are generally not interested in such opportunities until they become large enough to warrant their attention. If and when that time comes, the bigger company usually has the luxury of either making their own competitive offering or buying a start-up’s technology and bringing the key talent on board.

All these and many other factors add up to making the larger companies a safer place to conceive and bring to market new products and services. Common sense tells us that this lower level of operational and financial risk should translate into measurable evidence of lower cost of capital.

Dilemma – when data analysis fails to back the company risk premium

Some financial analysts focus on data evidence exclusively to validate this idea. Smaller companies historic returns must be higher in order to demonstrate their higher cost of capital. The problem with this approach is that if your data analysis fails to show up the difference in returns between companies of various sizes, you would be tempted to assume that smaller companies are about as risky as their larger competitors.

The mindset behind this is ‘if I can’t see it, it does not exist’.

To a seasoned business person this would sound like being asked to suspend disbelief. A pragmatic response would be, ‘if you don’t see evidence of an obvious trait, your analysis is flawed, or you are not looking hard enough in your data collection’.

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