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Can someone point me to a more technical description of the methods he uses?
Was disappointed because title is misleading; I had hoped the fund was using actual Algae (i.e. computation in biological medium) to produce market decisions. Instead it is just biologists that are creating algos with their existing machine-learning knowledge. Apparently deep-learning and algae are the same thing.
Wait a second. The average hedge funds is underperforming the SP500 index every year and by important returns? How are they still in business?
If we all planted trees and algae then we'd increase carbon sinks and solve the greenhouse gas problem in a sustainable way.
I am surprised more money - or at least ink - isn't funneled into this!
Earlier quoted context omitted.
> If you have enough different attempts, one of them will beat the market by sheer game of chance. How do you define an "attempt", and how many "attempts" do you need to beat the market (consistently) by chance?
Get 25 monkeys to throw darts at S&P 500 stock symbols on January 1st of each year. The chance that at least one monkey outperforms 5 years in a row is about 55%. (The target areas need to be proportional to market capitalization etc.)
If you're going to quantify survivorship bias, you can't use entire years as data points, because that doesn't properly represent the amount of activity that occurs. We should reason about each event, because if consistency emerges on an event basis we might not even need more than one year for our sample. The decision-making that is being empirically examined here (i.e. acumen capable of beating the market beyond chance) ostensibly functions on trading events, which means years are not the correct data point to use (and will provide an incorrectly pessimistic sample).