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This is: Limits of Current US Prediction Markets (PredictIt Case Study), published by aphyer on the LessWrong.
(Disclaimers: I work in the financial industry, though not in a way related to prediction markets. Anything I write here is my opinion and not that of my employer.
This is a US-centric piece based on a case study of PredictIt: as some people have pointed out in the comments below, if you are outside the US you may have substantially better options.)
SECTION I: INTRODUCTION
So there's an argument that I've seen a lot over the past few years, particularly in LW-adjacent circles, that goes something like this:
You say you believe X is likely to happen. But prediction markets say X is likely not to happen. Since markets are efficient, you must be wrong. Or if you do know better than the market, why aren't you rich? Since you haven't bet on that market to make free money, you must be lying. Or stupid. Or both!
This post is dedicated to disagreeing with that argument, not from an anti-Efficient-Market Hypothesis position, but from a pro-Efficient-Market Hypothesis position. My position is:
The argument above is pretty much sound if we are discussing mainstream financial markets. If someone claims to have better information than a mainstream financial market on the value of Google stock, or of copper, they ought to either use this knowledge to make a huge amount of money or stop talking about it. However, it is not true if we are discussing prediction markets. Current prediction markets are so bad in so many different ways that it simply is not surprising for people to know better than them, and it often is not possible for people to make money from knowing better.
I've been meaning to write this for a while, but got tipped over the edge by the recent post here, which talks about the limitation of prediction markets being the correlation of the events they predict to other assets, and their consequent value as hedging instruments. That is...well...it's not wrong exactly, but there are so many other problems that are so much bigger that I felt it was worth laying (some of) them out.
Math follows. I will be focusing on PredictIt for this analysis. Other prediction markets may work a bit differently, but similar analysis is applicable to any of them. If you think the math is wrong I am happy to discuss/make changes, but I very much doubt any changes will materially alter the final message.
As of this writing PredictIt has Donald Trump at 40% to win the election (or, to put it another way, you can pay 40 cents for a share that pays out $1 if Trump wins). Suppose you think he is more/less likely to win. How likely/unlikely does it need to be for Trump to win for you to make money (in expectation)? Or, to put it another way, what range of probabilities for Trump to win are consistent with the prediction market values?
SECTION II: REASONABLY SIMPLE PROBLEMS
1: Spread.
This is only a small problem, but it is non-zero. PredictIt will sell me 'Donald Trump wins' shares for 40 cents, but will sell me 'Donald Trump loses' shares for 61 cents (which, from a finance perspective, works out very similarly to letting me sell 'Donald Trump wins' shares for 39 cents). So if I think there is a 39.5% chance of Trump winning, there is no way for me to make money off of it: I can buy 'Trump wins' shares for 40 cents, or sell them for 39 cents, and if the true value is 39.5 cents both of these will lose me money.
The range of possible probabilities for which you cannot make money starts at 39-40%.
2: Transaction Fees.
PredictIt charges a 10% fee on profits (see). As far as I can tell, it does not net profits against losses before calculating these fees. That is to say, if I make two $100 bets at even odds, win one, and lose the other, PredictIt will charge me a $10 fee on my winnings on the bet I ...
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