Charlie Munger held court at the DJCO annual meeting a few weeks ago. One issue with Munger’s (and Buffett’s) popularity is the Q&A session at these meetings get repetitive. Anyone watching past sessions or reading through old transcripts will hear the same questions and answers annually.
Now, every so often, Munger changes up the answers.
This year, he expanded on a trick he calls the inversion process. It’s the idea of flipping a problem (or question) on its head. It has wide uses, including in investing:
One of my favorite tricks is the inversion process. I’ll give you an example. When I was a meteorologist in World War II, they told me how to draw weather maps and predict the weather. But what I was actually doing is clearing pilots to take flights.
And I just reversed the problem. I inverted. I said, “Suppose I wanted to kill a lot of pilots. What would be the easy way to do it?” And I soon concluded that the only easy way to do it, would be to get the planes into icing the planes couldn’t handle or to get the pilot to a place where he’d run out of fuel before he could safely land.
So, I made up my mind I was going to stay miles away from killing pilots, by either icing or getting them into socked-in conditions when they couldn’t land. I think that helped me be a better meteorologist in World War II. I just reversed the problem.
And if somebody hired me to fix India, I would immediately say, “What could I do if I really wanted to hurt India?” And I’d figure out all the things that could most easily hurt India, and then I’d figure out how to avoid them.
Now you’d say, “It’s the same thing. It’s just in reverse.” But it works better to, frequently, invert the problem.
If you’re a meteorologist, it really helps if you really know how to avoid something, which is the only thing that’s going to kill your pilots. And you can help India best if you understand what will really hurt India the easiest and worst.
Algebra works the same way. Every great algebraist inverts all the time because the problems are solved easier.
Human beings should do the same thing in the ordinary walks of life. Just constantly invert. You don’t think of what you want. You think of what you want to avoid. When you’re thinking of what you want to avoid, you also think about what you want. And you just go back and forth all the time.
Investors can benefit a lot from the idea of flipping a question on its head.
Most people, when they first start investing, ask how do I make the most money possible. So they look for the highest quality, best-performing stocks with the best returns, add in some leverage, and so on.
Really, they should ask the inverse — how do I lose the most money possible? Then avoid those things. Once you get past the easier answers to that question, like not going all-in with borrowed money on a long shot, then things get interesting.
Instead of asking, how do I find the best-performing stocks, ask how do I find the worst?
A pile of research highlights what the worst performers look like. For instance, we know high multiple stocks (based on any number of value metrics) not only underperform low multiple stocks, but also the market. So the highest multiple stocks should be avoided.
Here are a few more:
- Companies in financial distress or a high risk of bankruptcy.
- Companies with a high risk of fraud or earnings manipulation.
- Companies with a high (and rising) debt load (High Debt-to-Equity Ratio).
- Companies that are serial issuers of stocks (Negative Buyback Yield).
- Lowest quality companies (Low Gross Profitability or F-score).
- Companies with weak or negative operating income and cash flows.
- Stocks with a high short interest ratio (30+%).
- Stocks exhibiting negative momentum.
…to name a few. Some of the criteria overlap, but that should help sort the qualifying companies.
Now imagine an index fund that invests in the universe of U.S. stocks minus the worst offenders.
The authors of Quantitative Value actually tested a similar idea. They started with the universe then removed 5% of the worst stocks based on earnings manipulation, fraud, and financial distress. Removing the 5% showed an improvement in returns and drawdowns.
M. D. Beneish also did something similar with his O-Score. He looked for companies based on the potential for fraud, high sales growth, low operating cash flow, recent acquisitions, and stock issuance. Stocks of qualifying companies saw average losses of roughly 25% over the next year.
By cutting out the worst offenders, you avoid companies that destroy capital. It’s a simple way to lower the chance of failure in a portfolio.
The natural next step is to add qualifiers of winning stocks and create a more concentrated portfolio. Or just raise the limits on the above criteria — only accept low multiple stocks, look for a positive buyback yield, a low and/or falling debt load, etc.
But why not stop there, since not everyone can handle the added volatility that comes with concentration. I bet a similar fund that removes 10-20% of the worst offenders would outperform a total index fund by 1-2% a year.
A market-cap weighted version would allow for scale and lower cost. An equal-weighted version would allow for random factor premia to seep into returns. And the same could be done with a global index fund.
It’s not sexy and it may not sound like much it certainly sounds better than “owning the market.”
DJCO Annual Meeting 2020 (video)
- A Viral Market Meltdown: Fear or Fundamentals? – Musings on Markets
- Controlling the Pendulum of Emotions – MicroCapClub
- Crisis Investing: How to Maximize Returns During Market Panics – Verdad
- Credit Suisse Global Investment Returns Yearbook 2020 Summary Edition – Credit Suisse
- Top Ten Behavioral Biases, Illustrated – Above the Market
- The Big List of Behavioral Nudges – D. Crosby
- Avoid the Zeros – Of Dollars and Data
- This is Not 1999 – E. Cinnamond
- 100 Little Ideas – M. Housel
- Markets Have ALWAYS Been Rigged, Broken & Manipulated – A Wealth of Common Sense
- Equity Returns Don’t Compensate for Downside Risks – Klement on Investing
- Your Company is Too Risk Averse – HBR
- The Perils of Survivorship Bias – Scientific American
- The Great Buenos Aires Bank Heist – GQ