The Biggest Lesson from Earnings Season
Here is a fresh take on earnings season. No matter how many sources you followed, you have not seen this before.
Let me start with what we did not hear.
- This stock is a poor investment because it has a bad Tobin's Q ratio. Readers are invited to correct me if someone is offering an opinion that Google has a poor replacement value. How do you even think in these terms? This method is an outdated approach in search of a modern critic.
- This stock is over-valued based upon the Shiller CAPE method. Readers are invited to correct me with examples of specific stocks where people think that the P/E ratio should be based on the earnings over the last decade, adjusted for inflation and divided by 10 (or some close variant). Has anyone ever made any money using this method?
- The earnings of the company should be ignored because it is happening in the midst of a counter-trend rally in the midst of a cyclical bear market. Readers are invited to correct me with examples of analysts on specific stocks who thought this was relevant.
- The current value of the company should be compared to its earnings in 1870. In 1910. In 1935. In 1950. In 1970….and so forth. Readers are invited to submit examples of recent earnings analyses where anyone thought this past history was relevant to the current stock price.
Stocks react dramatically to earnings reports. None of the price changes had anything to do with the factors above. None. No one ever makes a dime from applying these methods in real time.
What did we actually hear?
- Did earnings meet the Street expectations? How about earnings quality?
- Did revenues meet expectations?
- What is the outlook for the upcoming year – with special emphasis on macro concerns like Europe, recession chances, and China.
- Are there special factors affecting the outlook?
Every major stock move is explained in these terms, or a variant thereof. The ability to understand and answer these questions is the key to investment success.
The Overall Investment Implication
Anyone who thinks objectively about these questions is driven to a rather obvious conclusion about market valuation:
How can a method advertised as a good measure for the overall market fail to explain any of the components?
As corporate earnings move higher and expectations improve, most analysts conclude that the stocks in question are worth more. The market seems to agree.
There is another group of pundits who embrace Tobin's Q, CAPE, and cycles extending farther back than the dead-ball era in baseball. The enthusiasm of their followers approaches cult status. For these analysts the market seems to be in a permanent state of over-valuation. The followers are not investors, but merely spectators. They never get a "buy" signal.
A Better Method
Is there any better method? What if we could somehow create a network of sources that monitored all of the major companies? The sources would have to be professionals whose entire work would be devoted to studying specific companies. None would have to work with more than a few similar companies. These workers would question companies at a level impossible for most of us, challenging the assumptions and outlook, using detailed earnings models.
The sum of their conclusions would represent a comprehensive look at current earnings, revenue, challenges, and the outlook for the next year. Briefly put, it would aggregate all of the things that constitute the focus for each earnings season.
Ben Graham was brilliant. If such information been available to him, he would have found a way to use it. If only there was some way — somehow – that modern investors could find and use such a source.
Some would probably ignore the information, finding an excuse to validate their predisposition, whether supported by data or not. Those of us who embrace the best data and evaluate everything objectively would have an advantage.
If only we could hire such a work force? How much would it cost?
Where can we find such information? Any ideas? The answer might require some real contrarian thinking.