Totally agree with your analysis, especially that large firms and individuals within them are quite clever and operating as described in your sophisticated version. They are the very people who put the “efficient” in the efficient market hypothesis! And I agree that every trade is essentially a statement that “I know better than the entire world...”, with real money on the line. Yet the combined actions of these clever people, who have huge incentives to beat the market and are used to trading with confidence that they know better than the entire world, seemingly did not move the market quickly enough, and at least some individuals were able to take advantage of this.
And my intuition-that-I-can’t-justify is that: if people couldn’t make trades in this specific case due to social reality within the firms being out of sync with actual observable reality, then that’s probably not confined to this one particular case; and we’d see individuals beating the market a lot more often than we do.
This gets to the crux of the argument—under what circumstances would this disconnect occur, and can others outside the industry recognize when it may be happening? It must be quite rare, otherwise as you mention we would see more evidence of individuals regularly beating the market. It might require several factors to come together at the same time, without which the usual efficiency will be maintained. Some thoughts along these lines, expanding on my earlier comment:
1. Traders might only be willing to bet confidently against the entire world in their field of expertise (analyzing financial data), but not based on data from other fields (pandemic predictions). They may think: there are likely others who know more about this topic than me, and the market hasn’t moved yet (or, the knowledge is priced in already), so how can I justify betting big on my hunch? Especially when I would have to explain it to my manager (or the board of directors) if I was wrong?
2. Related to the last point—loss aversion bias regarding one’s social status with the organization. If someone bets big based on pandemic data, and is right, they will gain some social status and probably a financial reward. But if they’re wrong, they will lose social status and might be at higher risk of being fired. If the potential loss outweighs the potential gain, the safe option would be to not bet on pandemic data, and only trade based on financial data (as would be socially expected within the organization) and face little or no social status penalty.
3. Algorithmic trading was probably based initially only on financial information, which had not turned for the worse yet, so any pandemic prediction-based trades would be overwhelmed by algorithmic trades as far as moving stock prices. This would last until algorithms started taking into account pandemic data and/or the actual financial data got worse due to pandemic effects.
I’m sure others could identify additional relevant factors at play here.
One thing I don’t have any calibration on is how big a trade would have to be to overcome this neutralizing effect of algorithmic trading on overall stock prices. For example, say there is an employee of a large firm who has the authority to risk 1% of the firm’s overall portfolio. If they switched that 1% from a fully long position to fully short, would the market stay lower, or would algorithmic trading revert it to the previous equilibrium? What if the CEO of the firm switched their entire portfolio from long to short? What if the CEO did that, and also announced publicly they had done so and their reasons why? At what point would we expect markets to actually change course?
This quote from Moral Mazes seems relevant to this earlier discussion, and may provide further understanding for why markets were slow to respond to the pandemic (emphasis mine):
115. This explains why the chemical company managers kept putting off a decision about major reinvestment. After the battery collapsed in 1979, however, the decision facing them was simple and posed little risk. The corporation had to meet its legal obligations; also, it had either to repair the battery the way the EPA demanded or shut down the plant and lose several hundred million dollars. Since there were no real choices, everyone could agree on a course of action because everyone could appeal to inevitability. This is the nub of managerial decision making. As one manager says: Decisions are made only when they are inevitable. To make a decision ahead of the time it has to be made risks political catastrophe. People can always interpret the decision as an unwise one even if it seems to be correct on other grounds. (Location 1886)
In Feb/March, if the relevant financial institutions were going through such a behind-the-scenes process of “establishing the inevitability” of the pandemic before large market-moving decisions could be made, this could explain the apparent delay (and corresponding opportunity for the rational individual investor). One can imagine individuals within these firms feeling each other out—“This pandemic might turn into a big deal, huh?” “Yeah, but the boss hasn’t seemed too concerned yet, let’s give it another few days before we bring it up again”—before the consensus grew large enough where the decision became inevitable.
If this model is accurate, when would we expect to see these kinds of delays (and opportunities) in other situations? Here are some factors that may have contributed:
The early pandemic required integrating a lot of information outside the core areas of expertise of firms and their traders, leading to more uncertainty and a longer delay to reach consensus.
People are bad at extrapolating exponential growth (citation needed), and while some individuals within firms may have realized the implications right away, others may have thought their concerns were way overblown, again prolonging the time to reach consensus.
This was a rare event that had not occurred within anyone’s living memory, so there was no good frame of reference to fall back on, also increasing uncertainty.
I feel like there’s something here worth investigating more closely, although I’m still having trouble understanding it as well as I would like to. For now I’ll note that these three factors also seem very applicable to the current state of AGI development, and so may tie in with previous discussions such as this one.
Totally agree with your analysis, especially that large firms and individuals within them are quite clever and operating as described in your sophisticated version. They are the very people who put the “efficient” in the efficient market hypothesis! And I agree that every trade is essentially a statement that “I know better than the entire world...”, with real money on the line. Yet the combined actions of these clever people, who have huge incentives to beat the market and are used to trading with confidence that they know better than the entire world, seemingly did not move the market quickly enough, and at least some individuals were able to take advantage of this.
This gets to the crux of the argument—under what circumstances would this disconnect occur, and can others outside the industry recognize when it may be happening? It must be quite rare, otherwise as you mention we would see more evidence of individuals regularly beating the market. It might require several factors to come together at the same time, without which the usual efficiency will be maintained. Some thoughts along these lines, expanding on my earlier comment:
1. Traders might only be willing to bet confidently against the entire world in their field of expertise (analyzing financial data), but not based on data from other fields (pandemic predictions). They may think: there are likely others who know more about this topic than me, and the market hasn’t moved yet (or, the knowledge is priced in already), so how can I justify betting big on my hunch? Especially when I would have to explain it to my manager (or the board of directors) if I was wrong?
2. Related to the last point—loss aversion bias regarding one’s social status with the organization. If someone bets big based on pandemic data, and is right, they will gain some social status and probably a financial reward. But if they’re wrong, they will lose social status and might be at higher risk of being fired. If the potential loss outweighs the potential gain, the safe option would be to not bet on pandemic data, and only trade based on financial data (as would be socially expected within the organization) and face little or no social status penalty.
3. Algorithmic trading was probably based initially only on financial information, which had not turned for the worse yet, so any pandemic prediction-based trades would be overwhelmed by algorithmic trades as far as moving stock prices. This would last until algorithms started taking into account pandemic data and/or the actual financial data got worse due to pandemic effects.
I’m sure others could identify additional relevant factors at play here.
One thing I don’t have any calibration on is how big a trade would have to be to overcome this neutralizing effect of algorithmic trading on overall stock prices. For example, say there is an employee of a large firm who has the authority to risk 1% of the firm’s overall portfolio. If they switched that 1% from a fully long position to fully short, would the market stay lower, or would algorithmic trading revert it to the previous equilibrium? What if the CEO of the firm switched their entire portfolio from long to short? What if the CEO did that, and also announced publicly they had done so and their reasons why? At what point would we expect markets to actually change course?
This quote from Moral Mazes seems relevant to this earlier discussion, and may provide further understanding for why markets were slow to respond to the pandemic (emphasis mine):
In Feb/March, if the relevant financial institutions were going through such a behind-the-scenes process of “establishing the inevitability” of the pandemic before large market-moving decisions could be made, this could explain the apparent delay (and corresponding opportunity for the rational individual investor). One can imagine individuals within these firms feeling each other out—“This pandemic might turn into a big deal, huh?” “Yeah, but the boss hasn’t seemed too concerned yet, let’s give it another few days before we bring it up again”—before the consensus grew large enough where the decision became inevitable.
If this model is accurate, when would we expect to see these kinds of delays (and opportunities) in other situations? Here are some factors that may have contributed:
The early pandemic required integrating a lot of information outside the core areas of expertise of firms and their traders, leading to more uncertainty and a longer delay to reach consensus.
People are bad at extrapolating exponential growth (citation needed), and while some individuals within firms may have realized the implications right away, others may have thought their concerns were way overblown, again prolonging the time to reach consensus.
This was a rare event that had not occurred within anyone’s living memory, so there was no good frame of reference to fall back on, also increasing uncertainty.
I feel like there’s something here worth investigating more closely, although I’m still having trouble understanding it as well as I would like to. For now I’ll note that these three factors also seem very applicable to the current state of AGI development, and so may tie in with previous discussions such as this one.