This book is the definitive text on DCF valuation, combining theory, practice, and clear presentation as McKinsey should. Its focus on core value drivers (growth, margin, and capital efficiency) is seminal.
Some reviewers claim the book focuses excessively on cash flow modeling to the exclusion of important considerations such as the skill of the management team, the company's product lineup, etc. I think this criticism is off base:
For example, a great management team should lead to a more valuable business. Mechanically however, this works because a great management team will be better at driving growth, margins, capital efficiency, or all three -- in other words, cash flows. In fact, all of these broader issues can and should be assessed in terms of the ways in which they influence the expected future cash flows of the business.
For example, if you believe a management team is unusually strong in operational cost control, you can adjust your forecasted cost structure, but if you think they're far better in marketing, be more aggressive on the top line while potentially modeling a fatter cost structure. This way you force yourself to be specific about the impact of these factors, and rely on the mechanics of the DCF model to translate this impact into a number. The model - and the value of a company in general - will be more sensitive to some variables than to others. By driving your assumptions through the model, you ensure the different factors' effects are properly scaled in your final value number.
To criticisms about using past cash flows to help forecast the future, it is certainly true that to use a model that blithely carries past growth into future years is to develop a valuation based on fantasy. The authors state as much. However, a company's history is full of learnings that are key to assessing its future prospects. Historical financials show what has been accomplished given the constraints of industry, business model, technology, management team etc. To imagine the future, start by really understanding the past, then methodically think about how each variable will most likely change going forward. Some historical variables, of course, won't show up in the financials at all, for example a pharmaceutical company's pipeline of drugs. With good reason, investors look beyond the books to assess such non-financial assets.
Some point out that most M&A fails. What the data shows in fact is that, in general, public companies that buy other public companies do not see an increase in value after the transaction. However, the shareholders of the company being acquired usually sell for a premium over where their stock had traded before any deal announcement. This implies that the market fully prices any expected transaction synergies into the shares. So why do managers buy other companies? Sometimes there is a valid strategic rationale for future synergies that the market doesn't see and doesn't know to give them credit for. And sometimes it's all about misaligned incentives, ego, and empire building. It is not, however, about a methodological flaw in using cash flows for valuation.
Ultimately, when you make an investment, there are only two ways to make money. One is to extract cash while you own the business (i.e., dividends), and the other is to sell the investment to someone else for a higher price. A cash flow forecast is the best way to estimate the first of these. Estimating the second depends on how you think potential buyers (i.e., the "market") will estimate the cash flows during their own hold time as well as their own future prospects for ext. Most institutional investors at some point use a DCF model, so viewing the world through your potential buyer's lens is a useful exercise.
Of course investors can also be irrational (viz. "New Economy" valuations in 1999). Quantifying that irrationality remains one of the biggest gaps in the literature.