Corporate Finance by Aswath Damodaran - Youtube lectures

Corporate Finance by Aswath Damodaran

https://www.youtube.com/playlist?list=PLJCQlCCSYx7uscvh-nnJ5X3jE9CgSzWfC

Valuation: Four Lessons to Take Away

Published on 17 Feb 2015

The tools and practice of valuation is intimidating to most laymen, who assume that they do not have the skills and the capability to value companies. In this talk, I propose to lay out four simple propositions about valuation. 

The first is that valuation is not an extension of accounting, insofar as it is not about recording the past but forecasting the future. 

The second is that valuation is not just modeling, where people put numbers into Excel spreadsheets and pump out values. A good valuation requires a narrative that binds the numbers together. 

The third is that valuing an asset or business is very different from pricing that asset or business, a difference that is often blurred in practice. 

The fourth is that luck plays a disproportionate role in whether you make money off your valuations. Put differently, you can do everything right and still walk away with nothing or worse at the end.

About the Author
I view myself, first and foremost, as a teacher. I do teach valuation and corporate finance not only to MBAs at Stern but to anyone who will listen (on iTunes U, online and on YouTube). I love to write books, teaching material and blog posts. After 30 years of teaching finance, I still find it fascinating as an interplay of economics, psychology and number crunching.

Q&A starts @ 56 minutes of the video.

As an investor, you will believe price will ultimately moves to value.

Price can deviate from value for an extended period.

It is all a matter of perception, like pricing a painting by Picasso.

Sometimes it takes so long to happen, there is no point in waiting especially for a trader.

It is almost a given if you are an investor, you have a longer time horizon.

An interesting answer by the Professor to a question at 60 minutes of the video.  

"Investing takes work.  If you don't have the time, don't over reach.  You don't get rich through investing.  You get rich by doing whatever you are doing. Investing is about preserving what you made elsewhere and growing it."

An Exclusive Interview with Professor Aswath Damodaran

Most valuations (even good ones) are wrong

Now this can be shocking to you if you spend a lot of time arriving at that magical number (intrinsic value) that helps you ascertain whether you must buy a stock or not.

Damodaran talks about three kinds of errors that cause most valuations – even the ones “meticulously” calculated – to go wrong:

1. Estimation error…that occurs while converting raw information into forecasts.
2. Firm-specific uncertainty…as the firm may do much better or worse than you expected it to perform, resulting in earnings and cash flows to be quite different from your estimates.
3. Macro uncertainty…which can be a result of drastic shifts in the macro-economic conditions that can also impact your company.

The year 2008 is one classic example when most valuations – even the good ones – went horribly wrong owing to the last two factors – firm-specific and macro uncertainties.

As Damodaran writes…

While precision is a good measure of process in mathematics or physics, it is a poor measure of quality in valuation.

So, to value or not value?
Knowing that your valuation could be wrong (and in most cases, it would be) despite any kind of precision you employ in your calculations, it should not lead you to a refusal to value a business at all.

This makes no sense, since everyone else looking at the business faces the same uncertainty.

Instead what you must do to increase the probability of getting your valuations right is…

1. Stay within your circle of competence and study businesses you understand. Simply exclude everything that you can’t understand in 30 minutes.
2. Write down your initial view on the business – what you like and not like about it – even before you start your analysis. This should help you in dealing with the “I love this company” bias.
3. Run your analysis through your investment checklist. A checklist saves life…during surgery and in investing.
4. Avoid “analysis paralysis”. If you are looking for a lot of reasons to support your argument for the company, you are anyways suffering from the bias mentioned above.
5. Calculate your intrinsic values using simple models, and avoid using too many input variables. In fact, use the simplest model that you can while valuing a stock. If you can value a stock with three inputs, don’t use five. Remember, less is more.
6. Use the most important concept in value investing – ‘margin of safety’. Without this, any valuation calculation you perform will be useless.

At the end of it, Damodaran writes…

Will you be wrong sometimes? Of course, but so will everyone else. Success in investing comes not from being right but from being wrong less often than everyone else.

So don’t justify the purchase of a company just because it fits your valuation. Don’t fool yourself into believing that every cheap stock will yield good returns. A bad company is a bad investment no matter what price it is.

Charlie Munger explains that – “a piece of turd in a bowl of raisins is still a piece of turd”…and…“there is no greater fool than yourself, and you are the easiest person to fool.”

So, get going on valuing stocks…but when you find that the business is bad, exercise your options.

Not a call or a put option, but a “No” option.

Have you ever avoided buying a stock you “loved” because its valuations were not right? 

http://www.safalniveshak.com/avoid-2-bitter-truths-of-stock-valuations/

2 Bitter Truths of Stock Valuation

1. All valuations are biased

2. Most valuations (even good ones) are wrong

Characteristics of Young Companies and their Value Drivers

Characteristics of young companies

            Young companies are diverse, but they share some common characteristics. In this section, we will consider these shared attributes, with an eye on the valuation problems/issues that they create.

1.     No history: At the risk of stating the obvious, young companies have very limited histories. Many of them have only one or two years of data available on operations and financing and some have financials for only a portion of a year, for instance.

2.     Small or no revenues, operating losses: The limited history that is available for young companies is rendered even less useful by the fact that there is little operating detail in them. Revenues are small or non-existent for idea companies and the expenses often are associated with getting the business established, rather than generating revenues. In combination, they result in significant operating losses.

3.     Dependent on private equity: While there are a few exceptions, young businesses are dependent upon equity from private sources, rather than public markets. At the earlier stages, the equity is provided almost entirely by the founder (and friends and family). As the promise of future success increases, and with it the need for more capital, venture capitalists become a source of equity capital, in return for a share of the ownership in the firm.

4.     Many don't survive: Most young companies don't survive the test of commercial success and fail. There are several studies that back up this statement, though they vary in the failure rates that they find. A study of 5196 start-ups in Australia found that the annual failure rate was in excess of 9% and that 64% of the businesses failed in a 10-year period.  Knaupand Piazza (2005,2008) used data from the Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) to compute survival statistics across firms.  This census contains information on more than 8.9 million U.S. businesses in both the public and private sector. Using a seven-year database from 1998 to 2005, the authors concluded that only 44% of all businesses that were founded in 1998 survived at least 4 years and only 31% made it through all seven years. In addition, they categorized firms into ten sectors and estimated survival rates for each one. Table 9.1 presents their findings on the proportion of firms that made it through each year for each sector and for the entire sample:

Table 9.1: Survival of new establishments founded in 1998

	Proportion of firms that were started in 1998 that survived through
	Year 1	Year 2	Year 3	Year 4	Year 5	Year 6	Year 7
Natural resources	82.33%	69.54%	59.41%	49.56%	43.43%	39.96%	36.68%
Construction	80.69%	65.73%	53.56%	42.59%	36.96%	33.36%	29.96%
Manufacturing	84.19%	68.67%	56.98%	47.41%	40.88%	37.03%	33.91%
Transportation	82.58%	66.82%	54.70%	44.68%	38.21%	34.12%	31.02%
Information	80.75%	62.85%	49.49%	37.70%	31.24%	28.29%	24.78%
Financial activities	84.09%	69.57%	58.56%	49.24%	43.93%	40.34%	36.90%
Business services	82.32%	66.82%	55.13%	44.28%	38.11%	34.46%	31.08%
Health services	85.59%	72.83%	63.73%	55.37%	50.09%	46.47%	43.71%
Leisure	81.15%	64.99%	53.61%	43.76%	38.11%	34.54%	31.40%
Other services	80.72%	64.81%	53.32%	43.88%	37.05%	32.33%	28.77%
All firms	81.24%	65.77%	54.29%	44.36%	38.29%	34.44%	31.18%

Note that survival rates vary across sectors, with only 25% of firms in the information sector (which includes technology) surviving 7 years, whereas almost 44% of health service businesses make it through that period.

5.     Multiple claims on equity: The repeated forays made by young companies to raise equity does expose equity investors, who invested earlier in the process, to the possibility that their value can be reduced by deals offered to subsequent equity investors. To protect their interests, equity investors in young companies often demand and get protection against this eventuality in the form of first claims on cash flows from operations and in liquidation and with control or veto rights, allowing them to have a say in the firm's actions. As a result, different equity claims in a young company can vary on many dimensions that can affect their value.

6.     Investments are illiquid: Since equity investments in young firms tend to be privately held and in non-standardized units, they are also much more illiquid than investments in their publicly traded counterparts.

Young growth companies – Value Drivers

Revenue Growth

For a young, growth company to become valuable, small revenues have to become big revenues. To make judgments on revenue growth in the future, we have to assess two variables:

a.     Potential market for the product/service:  The first step in deriving the revenues for the firm is estimating the total potential market for its products and services. There are two challenges we face at this juncture.

                                               i.     Defining the product/service offered by the firm: If the product or service offered by the firm is defined narrowly, the potential market will be circumscribed by that definition and will be smaller. If we use a broader definition, the market will expand to fit that definition. For example, defining Amazon.com as a book retailer, which is what it was in 1998, would have yielded a total market of less than $ 10 billion in that year, representing total book retailing sales in 1998. Categorizing Amazon.com as a general retailer would have yielded a much larger potential market. While that might have been difficult to defend in 1998, it did become more plausible as Amazon expanded its offerings in 1999 and 2000.

                                              ii.     Estimating the market size: Having defined the market, we face the challenge of estimating the size of that market. For a product or service that is entering an established market, the best sources of data tend to be trade publications and professional forecasting services. Almost every business has a trade group that tracks the operating details of that business; there are almost 7600 trade groups just in the United States, tracking everything from aerospace to telecom.  In many businesses, there are firms that specialize in collecting information about the businesses for commercial and consulting purposes. For instance, the Gartner Group collects and provides data on different types of information technology business, including software.

                                            iii.     Evolution in total market over time: Since we have to forecast revenues into the future, it would be useful to get a sense of how the total market is expected to change or grow over time. This information is usually also usually available from the same sources that provide the numbers for the current market size.

b.     Market share: Once we have a sense of the overall market size and how it will changeover time, we have to estimate the share of that market that will be captured by the firm being analyzed, both in the long term and in the time periods leading up to steady state. Clearly, these estimates will depend both on the quality of the product or service that is being offered and how well it measures up against the competition. A useful exercise in estimation is to list the largest players in the targeted market currently and to visualize where the firm being valued will end up, once it has an established market. However, there are two other variables that have to be concurrently considered. One is the capacity of the management of the young company to deliver on its promises; many entrepreneurs have brilliant ideas but do not have the management and business skills to take it to commercial fruition. That is part of the reason that venture capitalists look for entrepreneurs who have had a track record of success in the past. The other is the resources that the young company can draw on to get its product/service to the desired market share.  Optimistic forecasts for market share have to be coupled with large investments in both capacity and marketing; products usually don't produce and sell themselves.

Target Operating Margin

Revenues may be the top line but as investors, but a firm can have value only if it ultimately delivers earnings. Consequently, the next step is estimating the operating expenses associated with the estimated revenues. We are stymied in this process, with young companies, both by the absence of history and the fact that these firms usually have very large operating losses at the time of the estimate. Again, we would separate the estimation process into two parts. In the first part, we would focus on estimating the operating margin in steady state, primarily by looking at more established companies in the business. Once we have the target margin, we can then look at how we expect the margin to evolve over time; this 'pathway to profitability' can be rockier for some firms than others, with fixed costs and competition playing significant roles in the estimation. One final issue that has to be confronted at this stage is the level of detail that we want to build into our forecasts. In other words, should we just estimate the operating margin and profit or should we try to forecast individual operating expense items  such as labor, materials, selling and advertising expenses? As a general rule, the level of detail should decrease as we become more uncertain about a firm's future. While this may seem counter intuitive, detail in forecasts leads to better estimates of value, if an only if we bring some information into that detail that otherwise would be missed. An analyst who has a tough time forecasting revenues in year 1 really is in no position to estimate labor or advertising costs in year 5 and should not even try. In valuing young companies, less (detail) is often more (precision).

Survival

Many young firms succumb to the competitive pressures of the market place and don't make it. The probability of failure can be assessed in one of three ways.

c.      Sector averages: Earlier in the chapter we noted a study by Knaup and Piazza (2007) that used data from the Bureau of Labor Statistics to estimate the probability of survival for firms in different sectors from 1998 to 2005. We could use the sector averages from this study as the probability of survival for individual firms in the sector.

d.     Probits: A more sophisticated way to estimate the probability of failure is to look at firms that have succeeded and failed over a time period (say, the last 10 years) and to then try to build a model that can predict the probability of a firm failing as a function of firm specific characteristics – the cash holdings of the firm, the age and history of its founders, the business it is in and the debt that it owes.

e.     Simulations: In chapter 3, we noted that simulations can be put to good use, when confronted with uncertainty. If we can specify probability distributions (rather than just expected values) for revenues, margins and costs, we may be able to specify the conditions under which the firm will face failure (costs exceed revenues by more than 30% and debt payments coming due, for example) and estimate the probability of failure.

The Little Book of Valuation

Aswath Damodaran