This seems like too general a principle. I agree that in many circumstances, public knowledge of a pattern in pricing will lead to effects causing that pattern to disappear. However, it is not clear to me that this is always to case, or that the size of the effect will be sufficient to complete cancel out the original observation.
For example, I observe that two different units of Google stock have prices that are highly correlated with each other. I doubt that this observation will cause separate markets to spring up giving wildly divergent prices to different shares of the same stock. I also note that stock prices are always non-negative. I also doubt that this will cease to be the case any time soon.
Although these are somewhat tautological, one can imagine non-tautological observations that will not disappear. If stocks A and B are known to be highly correlated, this may well lead to a larger gap as hedge funds who predict a small difference in expected returns will buy one and short the other. However, if they are correlated for structural reasons part of this might be that it is hard to detect effects that will cause their prices to diverge significantly, so the observation of the effect will likely not be enough to actually remove all of the correlation.
One can also imagine general observations about the market itself, like the approximate frequency of crashes, or log normality of price changes that might not disappear simply because they are known. In order for an effect to disappear there needs to be a way to make a profit off of it.
Is it unfair to say that prediction markets will deal with all of these cases?
I understand that’s like responding to “This is a complicated problem that may remain unsolved, it is not clear that we will be able to invent the appropriate math to deal with this.” with “But Church-Turing thesis!”.
But all I’m saying is that it does apply generally, given the right apparatus.
Unless you can explain to me how prediction markets are going to break the pattern that two different shares of the same stock have correlated prices.
I’m actually not sure how prediction markets are supposed to have an effect on this issue. My issue is not that people have too much difficulty recognizing patterns. My issue is that some patterns once recognized do not provide incentives to make that pattern disappear. Unless you can tell me how prediction markets might fix this problem, your response seems like a bit of a non-sequitur.
This seems like too general a principle. I agree that in many circumstances, public knowledge of a pattern in pricing will lead to effects causing that pattern to disappear. However, it is not clear to me that this is always to case, or that the size of the effect will be sufficient to complete cancel out the original observation.
For example, I observe that two different units of Google stock have prices that are highly correlated with each other. I doubt that this observation will cause separate markets to spring up giving wildly divergent prices to different shares of the same stock. I also note that stock prices are always non-negative. I also doubt that this will cease to be the case any time soon.
Although these are somewhat tautological, one can imagine non-tautological observations that will not disappear. If stocks A and B are known to be highly correlated, this may well lead to a larger gap as hedge funds who predict a small difference in expected returns will buy one and short the other. However, if they are correlated for structural reasons part of this might be that it is hard to detect effects that will cause their prices to diverge significantly, so the observation of the effect will likely not be enough to actually remove all of the correlation.
One can also imagine general observations about the market itself, like the approximate frequency of crashes, or log normality of price changes that might not disappear simply because they are known. In order for an effect to disappear there needs to be a way to make a profit off of it.
Is it unfair to say that prediction markets will deal with all of these cases?
I understand that’s like responding to “This is a complicated problem that may remain unsolved, it is not clear that we will be able to invent the appropriate math to deal with this.” with “But Church-Turing thesis!”.
But all I’m saying is that it does apply generally, given the right apparatus.
Unless you can explain to me how prediction markets are going to break the pattern that two different shares of the same stock have correlated prices.
I’m actually not sure how prediction markets are supposed to have an effect on this issue. My issue is not that people have too much difficulty recognizing patterns. My issue is that some patterns once recognized do not provide incentives to make that pattern disappear. Unless you can tell me how prediction markets might fix this problem, your response seems like a bit of a non-sequitur.