A sort of incredible thing about markets is that they aggregate information and coordinate behavior, often without any participant really seeing/understanding the "big picture"
In this case, if Xinjiang miners were sufficiently decentralized, perhaps even they were surprised that they collectively made up 35% of hash rate!
I speculate this is true of most markets: nobody really understands, for example, the exact breakdown of who is doing what in cases like GME, treasury market craziness of March, etc
Markets are kind of perpetually shrouded in an N-party "fog of war" so to speak. Each market participant sees a small ball around their own behavior, speculates what the broader landscape looks like
but it's entirely possible everyone - even the eventual winners! - is basically wrong about the "big picture"
Market participants also tend to really like "narratives", discussion of the big picture, etc. But anecdotally, I think like such macro-understanding is only weakly correlated with performance as a market participant
Being successful in the market requires being very good at one particular niche, and sufficiently insulated against happenings in other niches, but one can do this tolerably well without understanding other niches extremely well
At some level this is pretty elementary - Hayek's point is that the pencil lead factory just needs to be very good at producing pencil lead, doesn't particularly need to understand what the wood and rubber companies are doing
What motivated me to write this is observing finance twitter, reading a few hedge fund memoirs, etc. Most people in the markets are very opinionated, but the correctness of one's opinions seem at best weakly correlated with perf
For some this seems to be because their job is essentially marketing rather than perf, and their stories are really designed to be convincing rather than correct
But for others it seems their outperformance is based more on "execution" in some sense, and being able to "dodge" macro risk, rather than getting macro right
This kind of makes sense: "macro" v generally defined involves a relatively small # of events, which is hard to juice a very high Sharpe ratio out of. If your goal is a high Sharpe, better to try to hedge out macro risk and focus on something relatively diversifiable
I mean this not only about trading, etc. but also if you are, e.g. a tech company, a clothes factory, etc. If your business involves essentially winning a big "sector bet", there's just not enough N to juice out a very high Sharpe ratio
I think there is a Soros quote that's somewhat related: "It's not whether you're right or wrong, but how much money you make when you're right and how much you lose when you're wrong"
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Thesis: crypto developers should explore the broad idea of "non-transferable tokens" (NTTs)
In the classical economy, there are many instances where firms attempt to limit the transferability of products they sell
A few examples:
- Tickets (concert, airlines)
- Video games (steam), other e-content (e-books, movies)
- Parking spaces
There's a couple motives for this.
- Price discrimination: firms may want to charge diff prices to diff parties. Transferrability makes this impossible
- Limiting speculation/bubbles: ticket "scalpers" buy tickets to flip, which may crowd out fundamental buyers
This is interesting as it raises a fun "boundaries of the firm" question. In the past, writing, editing, publishing, graphics, etc. were organized within firm boundaries. Substack makes writers mini-entrepreneurs
At the moment they do their own editing/etc., but you can imagine industries will pop up to provide editing, etc. services for creator economies.
But if these are organized as arms-length transactions rather than within a firm, how does this affect moral hazard/agency issues?
In a big firm, proofreaders/researcher/editors have reputational incentives to get things right. If you hire these Upwork-style, do they have similar incentives? Presumably ratings matter, so that's one thing
The recent Archegos saga is a fun piece of game theory. Stylized model:
- Suppose Hwang is long $50bil of, e.g. Tencent on 5x leverage. Technically this is being held by a few banks on his behalf, say, Goldman, Morgan Stanley, Nomura
Suppose Hwang runs out of margin and the banks decide to sell off his position. They realize they are stuck in a kind of prisoner's dilemma.
Trades move markets. If you try to sell $50bil of Tencent into the markets in a day, well, there aren't many buyers so you'll end up with much less than $50bil.
One thing ex-post kind of puzzling. In the US, there seems to have been much more public skepticism/outrage/distrust about mask-wearing, compared to vaccination
This is interesting because:
1. Masks, whether they are useful or not, are not new, and obviously not harmful 2. The vaccine is totally new science
You might think the American population's opposition towards 1. is driven by anti-science sentiment or something. It is then puzzling that ppl have been so willing to embrace 2., with (afaik) so little backlash/conspiracy theory/etc surrounding it
Cool idea. Somewhat related: there seems to be a difference between "specialist" and "generalist" kind of roles in general in industry, government, etc
"Technology" being inherent highly "specialized". "Technocracy" might be defined as "we should run society by dividing social problems into specialized chunks and assigning the best specialist to each chunk"
Other social roles such as being a CEO, news reporter, <others?> are inherently "generalist" in the sense that they require interfacing with lots of different specialist areas. Role perhaps is somewhat more about understanding emergent system-level properties/drawing connections
If you're in a top-10-ish US undergrad, there is a playbook which still gets you a good shot at top-10 econ PhD programs straight out of undergrad
(I think it's extremely unfair that this essentially only works for top US programs, but, info is info)
1. Major in math, or any major that lets you take hard math classes. Definitely take real analysis, and if possible a couple higher level classes (e.g. measure theory, stochastics, functional analysis, etc.)
2. Skip most of undergrad econ. Take a few classes in the PhD first year and get A's
3. RA for econ faculty, starting from around 2nd or 3rd year
4. Aim to have all this done by end of 3rd year/start of 4th year