money & markets

Once a prediction market becomes large enough, it transitions from being truth-seeking to being tautology-seeking.

It's worth noting that we already have a term for continuous-valued prediction markets: cash-settled futures.

They're approximately impossible to hedge, so liquidity providers hate them. Liquidity providers try to minimize risk. With a contract that settles to a continuous number, you can almost always find an instrument (usually the underyling security the derivative settles to) to hedge your risk. This makes providing liquidity relatively safe, so more... See more

People doing -EV trades, willingly, because those trades are positive utility for them. What's great is that they're positive utility for the other side too because the two sides have different risk preferences.

ES trades so much because people use it to hedge. They're transferring risk they don't want, and they're perfectly willing to pay a (small) price for the service the market provides.

Megafunds don’t have to win on distributions. Venture perhaps evolves to a point where mathematical return optimization is no longer the edge. When you can own the cap table, capture optionality across product cycles (YC’s obvious play), and crown the kings in a space, you become more than a fund - you are now a meta allocator. Speed, access, and... See more

But the strategy required reimagining traditional venture capital portfolio construction. Abstract’s model focused on “relative ownership” rather than absolute ownership: If he could get 5% ownership in deals where Andreessen Horowitz got 15%, he might have one-third their ownership, but out of a fund that was one-fifteenth the size, his LPs got 5x... See more

In statistical distributions with thick tails, as Nassim Taleb says, the tail wags the dog . The relevant information is all in the tails, not in the body. Why would an allocator want statistical estimators to ignore the tail and instead overweight the center of the distribution, which is predominantly noise?

Statistical robustness is about finding metrics that can handle tail events, large deviations, without changing much. And, for measuring venture returns, the median is used vs. the mean because it is more robust as a statistical estimator. It represents the typical outcome (i.e., middle-of-the-road funds) vs. the mean that is sensitive to outliers... See more