This is going to be a LONG thread on how I think about active investing.
They say all good investing is value investing. This is true in the sense that you're buying into a situation where the sellers are underpricing potential outcome. But…
Value investing doesn't = valuing income flows at $x and buying them at $x/n.
The essence of investing is in variant perception of probability distributions. Let me explain:
Market price is the result of expected value of potential future outcomes. So you could draw out a "market" probability distribution. For most stocks it would look bell-curvish. The price represents the $ value of area under the curve.
When you're betting on a stock, you're implicitly saying your probability curve is not the same as the markets (hopefully skewed to the right).
***All investing is about probabilities.*** Any B-school student can add up asset values or do a DCF. Mr. Market only gets the *expected* value wrong, not the value in a given scenario.
Large margin of safety
= big edge
= low overlap in your estimated probability distribution vs. the markets
= your expectations - market expectations
Take a binary outcome to keep things simple (like bankrupt/not-bankrupt). $$ outcomes for each scenario are probably obvious to most market participants. You are betting on odds of outcome.
If market thinks odds are 20%, you don't want to make a bet that odds are really 23% b/c you likely could be wrong -- you want to bet when you think odds are 60%. This is "value investing".
The Nassim Taleb's of the world aren't capital V value investors, but they're doing the same thing. They think the tails of the curve are fatter than the market expects. Odds of extreme event higher than people think.
Same with momentum traders. What are odds crowd psychology will drive market to pay more for shares in near future? This is a historically reliable bias that is more predictable than many in value camp think.
Wide difference in odds can only come from breakdown in wisdom of crowds.
Crowd accuracy = size of crowd + diversity/independence of views + accuracy of individual views.
All interplay off each other: larger size or more diversity allows for less individual accuracy.
Each of these components can break down. Informational and Analytical disadvantages effect accuracy of individuals. Common psych biases effect diversity of views. Structural market aspects effect size of participants.
Doesn't matter the "market": businesses, debt, startups, land, currencies, political outcomes, horse races… If you can forecast odds better than average, you can make money in long-term.
Some markets are easier than others to find wide variant perceptions (less efficient), or get privileged access to info.
Productive assets have historically been easier to predict b/c there's a base "average" probability distribution that doesn't change radically. Average left tail risk also not as bad.
Similarly longer-term investing generally easier b/c odds are more driven by true underlying performance vs. short-term supply & demand.
My rule of thumb: If you hold a stock 10+yrs, ~70% of returns will come from reality of biz performance, 25% from difference in market odds at time of purchase, 5% random.
If you hold for only a few months however, at least 50% of return is going to be random supply/demand noise.
(This is for typical biz equities… in some very short-term trades returns can be driven mostly by variance in your vs. market odds. Like $GME recently both long+short.)
What about allocation and risk minimization? (aka bankroll management)
Kelly Criterion is the best way to allocate capital & manage risk. Full stop. Any other successful allocation method is either random or from simple heuristics that could be derived from Kelly (ie Buffett).
No, Buffett doesn't do the linear algebra required for advanced Kelly calculations. He has a neural net in his head trained from a million+ observations from age of 14 that does a rough version for him.
Those rules of thumb are likely related to: perceived margin of safety (compensation for uncertainty), liquidity, downside if wrong, upside if right, ability to influence outcome, etc.
All probability predictions have to be examined through lens of process, not outcome. If I predict an event has 80% odds & it happens, did I do well? I'll never know. If true odds were 50/50, I got it wrong. Outcome can only be judged over a LOT of bets.
And even after a lot of bets, you have to subtract performance of average investor in the primary area(s) you bet in. You invest primarily in tech cos? Subtract avg tech performance over period.
Buffett Partnership compounded at 29.5% for 12 years. Small company stocks averaged 19% for same period. So Buffett's 12 year edge was 10%/year. This is very good.
Annual return =
return of comparable index
+ compounded average edge
- fees
This is another reason why "stocks" are a good: it's a non-zero sum game, especially keeping fees low. Sports betting on other hand, 0% base - 5% vig = much larger edge needed. Negative sum games are hard.
And with that, I conclude by echoing Buffett: Skip active investing and just put $$ in an low-cost index fund :) Don't be fooled by your performance over past 5 years.
/end for now
And a follow up with more on how $GME can be viewed this way:
More on the $GME/short-squeeze saga using this framework.
The investment thesis here in the weeks leading up to the major squeeze was pretty sound -- especially if you understand general human psychology & how market structure can provide an edge.
No good trader would fool themselves into thinking investing in $GME at $50 or $AMC at $20 was based on business fundamentals. But it doesn't need to be. Again, investing is about the odds, and only betting when you have an edge.
What was the edge here? A complete break down of collective accuracy of the market, driven by herd mentality + forced structural buying.
1/ One of the models I use most in business analysis is tech stack trees 🌳
Every product is built on and enabled by 1 or more technologies.
Understanding where a product fits on its higher-level tech stack is an important part of any long-term strategy or investment thesis.
2/ A tech stack "tree" is higher-level version of a traditional tech stack. It shows not only the tech something is built on, but what's built on it. A typical stack tree looks something like this:
3/ Here's a few examples of stack trees from the tech industry, although they can be drawn out for products *any* industry. #AMZN#NVDA#TWLO
🧵 1/n A quick primer on GPT-3 for anyone who's heard about it but doesn't know what it is.
Why? GPT is a game-changer in AI that has the potential to disrupt a huge amount of areas, potentially leading to truly generalized AI problem solvers.
2/ GPT is a series of language-based machine learning models built by @OpenAI. The goal of language models is essentially text generation: look at a sentence → predict the next word(s).
3/ The premise behind the GPT models: how much data & computing power can you throw at an unsupervised deep learning model? What are the performance limits before you start getting diminishing returns?
Why can't you manage a research lab the same way as a construction project? How were we able to accomplish large scale collaborative efforts such as the Apollo program or Manhattan Project, but can't do the same thing for curing cancer?
2/ Efforts (pursuits of some objective) can be classified based on the certainty their means and ends.
This can help us guide management methods and understand why some efforts are harder than others.
3/ How do we classify efforts into modes?
The best paradigm I've come across is the How/What quadrants. In 1994 Eddie Obeng described 4 types of projects: quests, movies, painting by numbers, and fog.
How will the virus affect attendance of venues in the upcoming years (not just the next few months)? Valuations for companies like Six Flags $SIX looking enticing. Esp. when majority of attendance is in summer months, and there's some viral seasonality.
For $SIX looks like SG&A margins will matter a lot if rev drops. Quick & dirty valuation:
FCF@ Avg pretax margins of 27%: $400M
* 14x multiple
= $5.7B
- $2.1B net debt
- $0.6B noncontrolling interest
= $3B equity value
/ 84.6M shares
= $36 per share vs. $21 now
If they can hold pretax margins at 32% (2018:34%, 2017:33%), value/share ~= $50.
If 3-5yr margins drop to 24% (12 year avg), value/share ~= $28.
Their worst recent sales year, 2009, saw sales drop 11% and margins fall to 13% before recovering in 2010.
1/ A recent lightbulb moment of mine was that competitive advantage can be represented visually as 1 or more feedback loops. These create an advantage "flywheel" that maintain and grow a moat over time.
2/ Here's a few archetypical examples of common advantages represented as feedback loops:
3/ And some real-world examples I sketched out that combine multiple advantages into the flywheel engines driving growth: