We all know that Warren Buffett is a fan of Ted Williams, who outlined the following rule of hitting: “My first rule of hitting was to get a good ball to hit. I learned down to percentage points where those good balls were. The box shows my particular preferences, from what I considered my 'happy zone' –Â where I could hit .400 or better –Â to the low outside corner –Â where the most I could hope to bat was .230. Only when the situation demands it should a hitter go for the low-percentage pitch.”
Buffett extends this to investing: “In investments, there’s no such thing as a called strike. You can stand there at the plate, and the pitcher can throw the ball right down the middle, and if it’s General Motors at $47 and you don’t know enough to decide General Motors at $47, you let it go right on by, and no one’s going to call a strike. The only way you can have a strike is to swing and miss.”
It’s one thing to hear about the science of hitting; it’s another thing to practice it. As investors, shouldn’t we do the same exercise and find out our batting average in different zones and act accordingly?
I’m guilty of coming up with this exercise so late. But better late than never. What I did was compile a list of all the investments I have ever made and categorize them into different zones. Then I simply calculate my batting average in each zone. You can count the home runs or you can inverse by counting the % of losers then subtract it. There are different ways to divide the zones – you can use the Peter Lynch Categories:
- Slow Growers
- The Stalwarts
- The Fast Growers
- The Cyclicals
- Turnarounds
- The Asset Plays
Or you can make the zones by industries and market cap. There could be some overlap as well. The goal is to be as simple as possible, but each zone has to have clear distinctions.
I first performed this task by sectors. Below is a sample of my calculation (% of non-losers):
Consumer stables: >80%
Energy: 10%
Retail: 20%
Healthcare: 75%
Financial Services: >80%
Precious metal: 50% (small sample size)
This is getting very interesting. I knew retail and energy were my weak links, but I had no idea my batting average was so low. On the other hand, I was also pleasantly surprised by my batting average in consumer stables, healthcare and financial services. Here I noted that luck may have played some role, especially in my early years. But at least I have some statistics that I can do more analysis on.
Then I used the Peter Lynch Category (adding spinoff), except this time I added another statistic (number of home runs , meaning at least two-bagger over three years). Here I’ll list three categories:
- Slow Growers: 80%, zero home runs.
- Turnaround: 50%, two big home runs.
- Spin off: 100%, two home runs.
Again, I take the result with a grain of salt. One of the turnaround home runs was pure luck (Conn’s, which I bought for less than $5, and it became a multi-bagger when I sold it). Also I only invested in two spinoffs before so the sample size is too small to justify any conclusion.
You can tweak the exercise to be as detailed as you want to be but there’s definitely a bit of art involved (for instance, what categories you choose to use). The goal is not to be precise but roughly right. Once you have some results, you should spend some time figuring out the implications. I haven’t finished this exercise yet, but it has already become very interesting. If anyone has any suggestions or feedback, please feel free to comment.
