| For instance the ISMIS 2017 Data Mining Competition: Trading Based on Recommendations https://link.springer.com/chapter/10.1007/978-3-319-60438-1_... And I think it is enough to claim that the average of the recommendations was better than any individual stock picker. > The problem here is that all that information is public and the amount of alpha is scarce. Not insurmountable, no? I feel these objections are often busied by finance professors, after a student rudely assumes they wouldn't be teaching if they knew how to make money. RenTech was doing speech recognition on foreign TV broadcasts in the 90s. Can't you think of similar features you could whip up in a 100 lines of Python and the YouTube API? Some very profitable companies hire very smart PhDs to that for them. Could you bootstrap this? Work harder? Extend to some new hip platform a well-paid quant has never heard of? > trying to get a piece of the action, diluting or even eliminating the benefit for everyone. So join in. You are aware of the action. It's not like we are doing this for a bit of fun. The crowd wisdom is there, the hedge funds don't have a monopoly on polling it or analyzing it. Then redistribute the wealth for the benefit of everyone. Those very hard in on the action are not going to. > but doesn't work if they're clueless and are all trying to buy into the same bubble. Yes, this is correct, and a big problem in crowd analytics. Or at least, it has a big negative effect (you ideally want everyone to make decisions of their own accord, using their own information). But you can also again harnass this with counter trading strategies. Over the years, it has been fairly easy to call the top of a hype, and predict the obvious correction. So for instance, if the Teletubbies twitter is tweeting about Bitcoin, you know that maybe now is time to sell some Bitcoin, and rebuy back in 6 months when all newspapers are writing about how Bitcoin is a scam and a world-wide crypto ransom attack just occured. The problem can be overcome in a couple of ways. An interesting one is: "skin-in-the-game". If your wrong hype predictions damages your reputation or causes money loss, you are more serious about it. Another is to ask: What percentage of other people do you think got this question wrong? People who answer Sydney as the capitol of Australia, think that few got it wrong. People who give the correct answer think that many will get it wrong. > You do realize that the stock market is exactly that, right? Partly yes. The difference with crowdsourcing is that you are building a model on top of the other participants. Many day traders with bots do not know that hedge funds have models more complex tracking what they are doing and going to do, than the bot is complex. But many day traders also underestimate how they'd stack up against an office of suits, if they worked together, and ramen-noodle hacked it. |