Average Reviews:
(More customer reviews)My friend Tom Brakke, liked this book and said I would too. He was right, and soon afterward, I heard the author speak at the Baltimore CFA Society. Hearing James Valentine speak is an advantage here. He summarized what is most important, which if you are reading the book, it would be chapter 20 (out of 27). It is his FaVeS framework: Forecast, Valuation, and Sentiment, in that order of importance. Remember that as a key to the book if you read it; it tells you what to focus on as an analyst.
Another key, since the book is long, is to look at the shaded summaries which are usually at the back of each chapter. If stretched for time, read those first, and then read the chapter if you didn't get it.
This book aims to focus analysts on information that matters. Aim for information that makes a difference, and that few others have. Create an information web that maximizes the value of your time, and creates value for your research.
This book covers both the buy-side and the sell-side, telling each how to best use the other side. As a former buy-side analyst, to me it means fewer analyses, and better analyses. Aside from that, it is a game: buy-side: identify the better sell-side analysts and listen to them. Sell-side: identify clients that will generate commissions and market their best insights to them.
Regardless, analysts must identify the few factors that account for 80% of the performance in a given industry, and focus on those intensely. It helps to get into the industry organizations, which can help drive insight into the industry as a whole, and provide a backdrop for questions to ask when talking with executives in the industry.
Learning this will give an analyst a leg up on other analysts. Analysts should also understand the basic accounting structures of their industry so that they can identify companies that are not playing fair -- over-reporting income. I would add don't get negative too quickly. Frauds can develop a momentum of their own. Wait until the fraud gets large relative to the size of the industry before issuing a sell call -- wait for price momentum to go to zero. (Note: for investigative journalists, this does not apply. Jump on early, so that you can say that you warned everyone.)
Basic forensic accounting skills help, as do modeling skills, and basic statistical skills. I was surprised to learn a bunch of Excel shortcuts that I haven't seen elsewhere, and I have used Excel for nineteen years at a high level. The summary of accounting deviations is cogent, as well as pointing readers to Mulford and Schilit.
One idea that I heartily agree with: set up your spreadsheets to differentiate data and formulas. Cells with data series should only contain data. Formulas should have no numbers in them, unless they are trivial. This makes analysis a lot easier and cleaner in the long run.
The book also brings out the need to consider multiple scenarios, which help an analyst to flesh out his analysis. Being willing to consider what can go wrong, or right, richens an analysis. Also, the book warns against common pathologies that overcome analysts, notably -- Confirmation bias, overconfidence, Self-Attribution-bias, Optimism, Recency, Momentum, Heuristics, Familiarity, Snakebite (won't go back to one that hurt you), Falling in love, anxiety, over-reaction, loss-aversion, etc. I have experienced a few of those myself, and would have benefited from thinking these through before becoming an analyst.
Quibbles
I would warn any analyst trying to use simple or multiple regression that they are playing with fire, unless they understand the weaknesses of the data, and the limitations of the general linear model. In twelve-plus years working on Wall Street, I never saw regression used right once.
The author seems to favor DCF over multiples. Truth, neither works well, and one must live with the weaknesses of any approach. DCF embeds a lot of assumptions that are known, though some may be wrong -- multiples embed unknown assumptions.
The author does not like price-to-sales. For industrials and utilities I would say look at a chart of price versus price-to-sales. In most cases, they track, because sales don't vary that much in the short run. If you know the high and low P/S ratios for companies in an industry (P/B for financials) you have valuable information. It gives you boundaries to look at in buy and sell decisions.
I would also warn analysts against using Damodaran and those like him. I don't think his models are wrong so much as impractical. I would rather use a simple model that catches 80-90% of the action, versus one that catches 100% of the action, bet cannot practically be calculated.
Who would benefit from this book:
All equity analysts would benefit from this book. It is detailed, and yet practical. Some of our competitors will benefit from it, and if you don't read it, you will wonder why.
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