Welcome to the first edition of my “I learned this week” newsletter.
When to average down
An insightful framework by John Hempton of Bronte Capital on when to average down on your losing positions (2017).
Don’t average down on (operationally|financially) leveraged companies. If the fundamentals move further against them, they can drop very rapidly, even if they might have recovered fine in the long term (if they were not leveraged).
Some cited examples are: banks (during a financial crisis, or if defaults spike) and miners (eg: gold/oil prices falling below mining costs) as forms of financial leverage. Another one could be user/revenue growth expectations that might are priced in.Don’t average down on companies that are facing obsolescence (technological, regulatory), even if their current financials look great. Kodak, Blackberry, Nokia are good examples, and this might be the biggest risk of investing in Intel. Have TSMC, Apple, NVidia and Amazon (AWS Graviton) unbundled and disrupted Intel so much that they are too far behind to catch up?
Write ex-ante descriptions on why you would want to average down. Verify that the fundamentals haven’t changed before actually executing. Actively seek contrarian opinions; why does the market disagree with you?
Have a total max loss limit on any position, and never exceed that.
Minsky moments
Byrne Hobart periodically references this, so it’s worth remembering.
As per the WSJ, it refers “to the time when over-indebted investors are forced to sell even their solid investments to make good on their loans, sparking sharp declines in financial markets and demand for cash that can force central bankers to lend a hand.”
In other words, it’s the starting point of a chain reaction in complex, tightly coupled, over-leveraged systems. The over-leverage (standard leverage in finance when volatility and returns drop, or lack of lag when it comes to supply chains) accumulates over time as demand and return curves smoothen during the stable part of the economic curve. And then some (small set of) event(s) perturbs the system enough to make it all crash down.
Here’s Byrne’s article on applying this concept to supply chains.
…Minsky Moments don’t just happen in financial markets. They happen in any scenario with liquidity and maturity mismatches—any time an activity needs a constant supply of short-term inputs to fund something with longer-term, potentially-uncertain outputs.
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Just-in-time is all well and good, but it comes at the expense of just-in-case.
Self-taught lesson from poker
In poker, the money you save by folding a losing hand is the same money that you’ll have to win later to break even. It’s okay to get bluffed out (with smaller net losses) sometimes, than to lose big (if our read was wrong).
The goal isn’t to win all hands, or even to win all hands when you were ahead. The goal is to play a +EV game, and let Central Limit theorem handle the long term results.
Each poker hand should be considered as an independent even in our “portfolio”, and the overall return distribution of our strategy is what matters, not a single hand.
All of this is easier said than done.
Crazy new ideas
If someone I knew to be both a domain expert and a reasonable person proposed an idea that sounded preposterous, I'd be very reluctant to say "That will never work."
Source: Crazy New Ideas, by Paul Graham
PG’s reputation on HN is on a bit of a downtrend in the recent past, and this post is also being criticized for being too defensive about Mighty. To be fair, I am quite skeptical about Mighty too; I am not saying that it can’t work as a technology, it’s more that it’s a solved problem for the users who have the means to pay for it. That said, my critique is so obvious that certainly Suhail, PG et al must have already considered it, so I am genuinely interested in learning why it might work.
Focusing on whether “PG has too much skin in the game to be credibly neutral” is choosing to focus on the medium rather than the message. PG might be completely biased, and also be completely right, and the latter is what matters.
Especially with the following disclaimers:
Such ideas are not guaranteed to work. But they don't have to be. They just have to be sufficiently good bets — to have sufficiently high expected value.
I'm not claiming this principle extends much beyond math, engineering, and the hard sciences. In politics, for example, crazy-sounding ideas generally are as bad as they sound.
I also liked:
Few understand how feeble new ideas look when they first appear. So if you want to have new ideas yourself, one of the most valuable things you can do is to learn what they look like when they're born. Read about how new ideas happened, and try to get yourself into the heads of people at the time.
Update: Looks like the tone of the top comments has changed to become more positive :)