April was the stock market's best month in 30 years, which is not really what you expect during a global pandemic.
Historically the biggest short-term gains have been disproportionately amidst or immediately following bear markets, when volatility is highest.
Sure, it's part of how they earn money, but competition between them limits what's left, since they're bidding against each other to take the other side from the retail investor, who buys from or sells to the hedge fund offering the best deal at the time (made somewhat worse by deadweight losses from investing in speed).
It doesn't suggest that. Factually, we know that a majority of investors underperform indexes.
Absolutely, I mean that when you break out the causes of the underperformance, you can see how much is from spending time out of the market, from paying high fees, from excessive trading to pay spreads and capital gains taxes repeatedly, from retail investors not starting with all their future earnings invested (e.g. often a huge factor in the Dalbar studies commonly cited to sell high fee mutual funds to retail investors), and how much from unwittingly identifying overpriced securities and buying them. And the last chunk is small relative to the rest.
When there's an event that will cause retail investors to predictively make bad investments some hedge fund will do high frequency trades as soon the event becomes known to be able to trade the opposite site of the trade.
I agree, active investors correcting retail investors can earn normal profits on the EMH, and certainly market makers get spreads. But competition is strong, and spreads have been shrinking, so that's much less damaging than identifying seriously overpriced stocks and buying them.
Thank you, I enjoyed this post.
One thing I would add is that the EMH also suggests one can make deviations that don't have very high EMH-predicted costs. Small investors do underperform indexes a lot by paying extra fees, churning with losses to spreads and capital gains taxes, spending time out of the market, and taking too much or too little over risk (and especially too much uncompensated risk from under diversification). But given the EMH they also can't actively pick equities with large expected underperformance. Otherwise, a hedge fund could make huge profits by just doing the opposite (they compete the mispricing down to a level where they earn normal profits). Reversed stupidity is not intelligence. [Edited paragraph to be clear that typical retail investors do severely underperform, just mainly for reasons other than uncanny ability to find overpriced securities and buy them).]
That consideration makes it more attractive, if one is uncertain about an edge, to consider investments that the EMH would predict should be have very modest underperformance, but some unusual information would suggest would outperform a lot. I was persuaded to deviate from indexing after seeing high returns across several 'would-have-invested in' (or did invest a little in, registered predictions on, etc) cases of the sort Wei Dai discusses. So far doing so has been kind to my IRR vs benchmarks, but because I've only seen results across a handful of deviations (one was coronavirus-inspired market puts, inspired in part by Wei Dai and held until late March based on a prior plan of letting clear community transmission in the US become visible), and my understanding from colleagues in the pandemic space), the likelihood ratio is weak between the bottom two quadrants of your figure. I might fill in 'deluded lucky fool' in your poll. Yet I don't demand a very high credence in the good quadrant to outweigh the underdiversification costs of using these deviations as a stock-picking random number generator. That said, the bar for even that much credence in a purported edge is still very demanding.
I'd also flag that going all-in on EMH and modern financial theory still leads to fairly unusual investing behavior for a retail investor, moreso than I had thought before delving into it. E.g. taking human capital into account in portfolio design, or really understanding the utility functions and beliefs required to justify standard asset allocation advice (vs something like maximizing expected growth rate/log utility of income/Kelly criterion, without a 0 leverage constraint), or just figuring out all the tax optimization (and investment choice interactions with tax law), like the Mega Backdoor Roth, donating appreciated stock, tax loss harvesting, or personal defined benefit pension plans. So there's a lot more to doing EMH investing right than just buying a Vanguard target date fund, and I would want to encourage people to do that work regardless.
I agree human maturation time is enough on its own to rule out a human reproductive biotech 'fast takeoff,' but also:
All of those factors would smooth out any such application to spread out expected impacts over a number of decades, on top of the minimum from maturation times.
MIRI researchers contributed to the following research led by other organisations
MacAskill & Demski's A Critique of Functional Decision Theory
This seems like a pretty weird description of Demski replying to MacAskill's draft.
The interesting content kept me reading, but it would help the reader to have lines between paragraphs in the post.
I have launch codes and don't think this is good. Specifically, I think it's bad.
A mouse brain has ~75 million neurons, a human brain ~85 billion neurons. The standard deviation of human brain size is ~10%. If we think of that as a proportional increase rather than an absolute increase in the # of neurons, that's ~74 standard deviations of difference. The correlation between # of neurons and IQ in humans is ~0.3, but that's still a massive difference. Total neurons/computational capacity does show a pattern somewhat like that in the figure. Chimps' brains are a factor of ~3x smaller than humans, ~12 standard deviations.
Selection can cumulatively produce gaps that are large relative to intraspecific variation (one can see the same relationships even more blatantly considering total body mass). Mice do show substantial variation in maze performance, etc.
And the cumulative cognitive work that has gone into optimizing the language, technical toolkit, norms, and other factors involved in human culture and training into are immensely beyond those of mice (and note that human training of animals can greatly expand the set of tasks they can perform, especially with some breeding to adjust their personalities to be more enthusiastic about training). Humans with their language abilities can properly interface with that culture, dwarfing the capabilities both of small animals and people in smaller earlier human cultures with less accumulated technology or economies of scale.
Hominid culture took off enabled by human capabilities [so we are not incredibly far from the minimum need for strongly accumulating culture, the selection effect you reference in the post], and kept rising over hundreds of thousands and millions of years, at accelerating pace as the population grew with new tech, expediting further technical advance. Different regions advanced at different rates (generally larger connected regions grew faster, with more innovators to accumulate innovations), but all but the smallest advanced. So if humans overall had lower cognitive abilities there would be slack for technological advance to have happened anyway, just at slower rates (perhaps manyfold), accumulating more by trial and error.
Human individual differences are also amplified by individual control over environments, e.g. people who find studying more congenial or fruitful study more and learn more.
Survey and other data indicate that in these fields most people were doing p-hacking/QRPs (running tests selected ex post, optional stopping, reporting and publication bias, etc), but a substantial minority weren't, with individual, subfield, and field variation. Some people produced ~100% bogus work while others were ~0%. So it was possible to have a career without the bad practices Yarkoni criticizes, aggregating across many practices to look at overall reproducibility of research.
And he is now talking about people who have been informed about the severe effects of the QRPs (that they result in largely bogus research at large cost to science compared to reproducible alternatives that many of their colleagues are now using and working to reward) but choose to continue the bad practices. That group is also disproportionately tenured, so it's not a question of not getting a place in academia now, but of giving up on false claims they built their reputation around and reduced grants and speaking fees.
I think the core issue is that even though the QRPs that lead to mostly bogus research in fields such as social psych and neuroimaging often started off without intentional bad conduct, their bad effects have now become public knowledge, and Yarkoni is right to call out those people on continuing them and defending continuing them.