You are using a framework that assumes strongly efficient markets with respect to private information, and where most private information is of the sort that has a clear impact with respect to the priors implied on the market. I am using a framework of limited market efficiency, where only information that can be profitably exploited, because it e.g. provides a high enough Sharpe ratio, will be reflected in market prices, and where private information can often have an ambiguous relationship to the current odds implied by market prices. Note that my example was based on information of a probabilistic nature, analogous to using a novel statistical model or the like, whereas your example was based on discrete information.
OK. And I think you are wrong in thinking that markets are limited in efficiency and that clearly relevant private information nevertheless has ambiguous implications.
I agree that there is not a bright line separating possible and impossible when it comes to whether information can be profitably raised in the market, but there are clearly things that fall on one side or the other of the fuzzy line.
Transaction costs, available capital, risk, market efficiency, and other factors set a fuzzy line for what kind of information and how large a change in probability will be profitable, yes. We’re dealing with real world markets and events, after all.
Technically, you only need enough expertise to correctly estimate the impact of your information, but realistically you will in most cases need to become an expert on the asset (and likely multiple assets, since you will want to long some and short some in order to extract as much value as possible) in order to create those estimates
I think most news is fairly easily evaluated. The Russian atomic bomb example may be too clean an example, but it doesn’t seem terribly hard to guess whether Steve Jobs unexpectedly going to a hospital is bad or good for APL.
Is it important who becomes the next president of the United States? Many would say that it is, and it is a perennial favorite of prediction markets. Could you build a profitable investment strategy if you ONLY knew who was going to become the president a day ahead of time? You better sit down now and do a lot more work (i.e. invest in a tremendous amount of information cost, i.e. become an expert in the relevant assets), because the impact that will have on the markets is by no means unambiguous.
I’ve already linked to papers which have done the footwork for one interested in that question. It’d take maybe an hour to read them. Is that ‘a lot more work’? And how hard would a finance professional with access to the relevant databases find to replicate the analysis, even if they had no a priori beliefs about how a Democratic victory might affect various stocks?
It could easily be the case that despite having good information and the optimal strategy, the residual volatility between your hedge positions makes this a bad strategy on a risk-reward basis, because it overrides the return differential, particularly after costs. This makes it possible but unprofitable to reflect your views in the financial markets, and is a fairly fundamental issue with Lumifer’s idea, since in general we expect our views to be only marginally more accurate than the market, but we observe fairly large volatility in the differences of even correlated assets. For example, correlations between US and European stock markets are on the order of .6, which leaves a substantial amount of residual volatility if you are hedging them.
I don’t follow this point. Why can’t I find an appropriate set of hedges? Doesn’t that imply inefficiencies?
Fundamentally, a perfect prediction market would require two assets which are perfectly anti-correlated EXCEPT for the scenario in question, and the further you get from that ideal, the harder it is to create profitable predictions on a risk-reward basis.
I assume you mean by ‘perfect’ a prediction market in which even the slightest bit of new evidence can be profitably exploited because there are no kinds of transaction costs or other friction? That may be true, but I don’t think it meaningful refutes Lumifer’s observation that markets allow for expression of views on “something important”.
OK. And I think you are wrong in thinking that markets are limited in efficiency and that clearly relevant private information nevertheless has ambiguous implications.
Transaction costs, available capital, risk, market efficiency, and other factors set a fuzzy line for what kind of information and how large a change in probability will be profitable, yes. We’re dealing with real world markets and events, after all.
I think most news is fairly easily evaluated. The Russian atomic bomb example may be too clean an example, but it doesn’t seem terribly hard to guess whether Steve Jobs unexpectedly going to a hospital is bad or good for APL.
I’ve already linked to papers which have done the footwork for one interested in that question. It’d take maybe an hour to read them. Is that ‘a lot more work’? And how hard would a finance professional with access to the relevant databases find to replicate the analysis, even if they had no a priori beliefs about how a Democratic victory might affect various stocks?
I don’t follow this point. Why can’t I find an appropriate set of hedges? Doesn’t that imply inefficiencies?
I assume you mean by ‘perfect’ a prediction market in which even the slightest bit of new evidence can be profitably exploited because there are no kinds of transaction costs or other friction? That may be true, but I don’t think it meaningful refutes Lumifer’s observation that markets allow for expression of views on “something important”.