SimonM

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An Introduction to Prediction Markets

Generally, running the Olympics comes with a lot of local economic activity to make the event happen and various actors benefit from being able to plan ahead. 

 

I agree with this, I just don't think hotel rooms are a particularly good example since supply is fixed there is little hotel operators can do with knowledge of the probability of the event. (They can "change" prices, but prices are effectively driven my a market equilibrium (which is going to effectively be a prediction market on the Olympics going ahead))

An Introduction to Prediction Markets

Having access to an accurate probability about whether the Olympics will tell local hotels about how important it is to have a lot of beds available

Aside from changing pricing on the rooms (which is already an implicit prediction market on the Olympics) I'm not really sure what the hotels are supposed to do. Individual hotels can't exactly increase supply overnight. (Unlikely your example with Airbnb)

An Introduction to Prediction Markets

To give one further example of uses of prediction markets, Scott Sumner has the idea of using NGDP futures as a way to have market driven monetary policy.

That said, whenever these things become more "useful" I can't help but worry that "information discovery" and "institutional hedging" act against each other. Ultimately if something becomes correlated to people's view of the world, pricing becomes less about "forecasting" and more about "risk premium". To take a concrete example, if there were NGDP futures, you should expect them to be biased low / you should earn a premium for being long GDP. Specifically because if GDP falls so will the rest of your assets, which makes it "painful" to hold - hence a premium to own it.

Can you improve your intelligence with these types of exercises?

I would do additional conditioning. So P(opera | farmer), P(museum | opera, farmer), P(chess | museum, opera, farmer), etc.

My guess would it would look something like:

P(opera | farmer) = 5% (does anyone actually like opera?)
P(museums | opera, farmer) = 95%
P(chess | m, o, f) = 40%

So 5% * 95% * 40% = 1.9% of farmers...

P(o | t) = 80%
P(m | o, t) = 50%
P(c | m, o, t) = 20%

So 80% * 50% * 20% = 8% of trumpet players...

Which is a likelihood ratio ~.25 so I end up with something like 125 to 1 that we're talking to a farmer.

Can you improve your intelligence with these types of exercises?

For farmers: 10% enjoy opera, 20% enjoy visiting museums, 5% grew up playing chess = 0.001

I doubt these are independent.

Total number of trumpets in a symphony orchestra ~500

I realise you have the math right further down, but this should be ~5000. (I assume typo)

The Reebok effect

Prior to carbon plates I would disagree with this. It would suggest the shoe manufacturer has the largest marketing spend. (In a post-carbon-plate world where people are covering over the swoosh so that (not-Nike) sponsored athletes can race in Nike shoes I think it's pretty clear which shoe is best)

Why quantitative finance is so hard

The title is "Why quantitative finance is so hard" but it misses the main reason why quantitative finance is hard:

The competition is brutal.

Why quantitative finance is so hard

Small nitpicks:

You should never make a trade with negative expected return.

No!

You explain why in your post, but let me spell it out more explicitly. Diversification means that adding a negative expected return trade to a portfolio can INCREASE the return by adding a negatively returning, negatively correlated asset. Lets say we have two assets: "market" and "insurance". Market returns 11%/year 9/10 years, down 50% the other year. Insurance returns -3^%/year 9/10 and up 22% the other year. Expected market returns are: 5%/2.6% (simple mean / compounded), insurance are: -0.5%/-.7% (mean / compounded). By your logic you should never buy the insurance, and yet if we have a portfolio which maintains a 15% allocation to our insurance asset our expected (compounded) returns increase.

Here is a concrete real-world example: (60/40 + tail hedge). 

Another way to reason about this is: there's nothing special about zero nominal returns. So if you shouldn't make a trade with negative expected return, you should be able to say the same thing about ~any return and by extension you should only put your $ in the highest returning asset... but that misses the whole value of diversification!

The only free lunch in finance is diversification.

(Emphasis mine) This is a strong claim, which I would dispute. Risk premiums (not in the sense you've used the word, but in the sense I understand it to mean) are an obvious example - some assets have a positive yield just for holding them, even after accounting for volatility... Leverage would be another example. 

This is the principle behind index funds.

Kinda, sorta, maybe. It's "a" principle behind them, but if diversification were the only concern, why would you want cap-weighted index funds? Why not equal-sector weight or equal-company weights or some other weights?

Being smart is cheap.

I can only assume you've never attempted to hire people to work for a quantitative hedge fund. If anything, this claim would undercut your main claim that QF is "so hard". Unfortunately (or fortunately for your thesis) being smart is really expensive.

Scott Alexander 2021 Predictions: Buy/Sell/Hold

It is possible that Scott believed that ETH is negatively-skewed (ie small chance of collapsing, large chance of small increase) but this would be inconsistent with his probability that ETH is going to 5k. 

I think the vast majority of people think crypto is positively-skewed.

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