Are you sure you're not comparing liquid vs illiquid assets? I.e. public shares that you can sell at a stock exchange vs. a highly speculative valuation that may pop >10x like WeWork did.
There's also this wonderful disclaimer:
>Summary statistics from the AngelList dataset of 684 nonnegative investments that we consider in this paper.
Given that 90% of startups fail, they are are looking at the top 10% investments. I am pretty certain if you hand-pick the 10% top-performing public stocks, you will also get great numbers. The problem is, this only works in retrospective.
So their conclusion is based on just looking at non negative investments? If so how useful is this data? Because there is no way to practically know what investment will be a positive or negative investment. In other words, there is no way to broadly index among all non-negative investments.
The paper should have compared brodly indexing among all startups vs hand picking possible winners. This is practical and avoid any survivorship bias.
Of course there is a way to broadly index among all non-negative seed investments: by broadly indexing among all seed investments.
The fraction of money-losing investments in a population will affect, for instance, whether we would expect the typical investor making five investments at random to make or lose money. But regardless of whether losers are 10%, 50%, or 90% of the investment pool, if the winners are drawing from an unbounded mean power law then broadly indexing raises an investor's expected return.
There's also this wonderful disclaimer:
>Summary statistics from the AngelList dataset of 684 nonnegative investments that we consider in this paper.
Given that 90% of startups fail, they are are looking at the top 10% investments. I am pretty certain if you hand-pick the 10% top-performing public stocks, you will also get great numbers. The problem is, this only works in retrospective.