Tag: DCF Analysis

Drowning in Data

If you agree with the proposition that more information leads to more efficiency in pricing in the markets, that should probably lead to the conclusion that markets have never been as efficient. We’ve simply never had this much data and data has never been as accessible as it is now.

Gone are the days when Warren Buffett could comb through Moody’s manuals to find net-nets. Now, data-rich stock screeners are readily available to anyone. On top of that, there are armies of hedge funds other quantitative investment shops out there, crunching data and trying to advantage of any arbitrage opportunity they can find.

A Valuable Lesson of VAR

Had you argued this to me about a year ago, I would have wholeheartedly agreed. Today I’m not so sure. And here’s the reason why.

You see, I’m a football fan (soccer) and recently they have implemented something called VAR into the game. VAR stands for Video Assistant Referee. It basically means that during a game there is now an additional assistant referee who reviews decisions made by the head referee with the use of video footage and analytical technology in real-time. He is then able to communicate with the head referee during the game.

The objective of the VAR implementation is to minimize human errors causing substantial influence on match results. Previously, the referee had to make split second decisions on incidents. Now he or she can utilize VAR, which means better data. The VAR can analyse incidents by replaying it from different vantage points and use graphics to determine rulings such as offside. Sounds great, doesn’t it.

The Interpretation of Data

The really interesting thing about VAR, is that after its implementation there is still a fair amount of dispute regarding key referee decisions. Even with the additional data provided by VAR, pundits are still arguing whether decisions on offsides, penalties and such where correct or not.

It seems that more accurate data by itself doesn’t necessary lead to better decision making. The data still needs to be interpreted. In that sense, it’s not just a question of decision being subject to human error or not. Sometimes, different people will perceive the same data differently. It is in some way a matter of opinion.

Financial Data and Insights

If we apply this to the investing world, it is safe to say the following:

If you would show two analysts the same financial and operational data two competing companies, it is entirely plausible that the conclusions that those two analysts might draw from the data would be diametrically opposed.

The interpretation of the data will be subject to frameworks the analysts used to draw insights out of the data. Insight, per definition, is the power or act of seeing into a situation. But insight, is in the analyst, not insight the data.

The Primacy of the Income Account

Have you ever listened to an earnings conference call or read a transcript from one of those calls? If you have, you will know that these calls usually have a question and answer session following the prepared remarks, In the Q&A sessions, sell-side analysts that cover these stocks can ask management about anything that is on their mind. 

I remember when I started following conference calls, how weird I thought the questions posed were. To me, the questions were unusually specific. It wasn’t until I realized what a sell-side analyst does, that the questions started to make sense. The analysts are simply trying to fish for inputs into their valuation models. They build these models, primarily by using discounted cash flow analysis, to come up with price targets for the stocks that they employed to cover. 

The Problem with DCF-Analysis

When you build a Discounted Cash Flow Model, you need to make a bunch of assumptions. By how much will the company grow its revenues in the next few years? How much capital expenditure will it require to maintain that growth? What is the cost of capital? Etc, etc, etc. 

DCF models can be very useful and it is imperative for business analysts to understand the possibilities as well as limitations of a DCF analysis. DCF analysis is useful when cash flows are stable and relatively predictable. DCF analysis gets difficult to use if the companies that are being analysed have extremely high growth rates or if they create value by other means than by consuming cash to generate earnings. 

Capital Allocation and Balance Sheets

The late Marty Whitman, a legendary value investor, often talked about the Primacy of the Income Account. In his opinion, analysts and other investors where too preoccupied with the income statement and earnings of companies. As a result, the wealth creation that happen through the balance sheet was often overlooked. 

I heard a great example of this the other day. I don’t remember which podcast it was, but the interviewee gave the following example:

Imagine if you had run a discounted cash flow analysis of Berkshire Hathaway shortly after Warren Buffett took over as CEO. You would have totally missed the point, since Buffett created value through capital allocation and by utilizing the balance sheet. 

A normal DCF model would nerver have captured this.