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Episode 2: Numerai
This is an episode on the super cool Machine-Learning-powered hedge fund, Numerai.
Summary
Numerai is a global equity long-short hedge fund, which specializes in relatively long time-range investments, on the scale of 6-9 months. They’re interested in more finely granular moves, that are at the level of individual stocks; they aim to identify anomalously-priced stocks, and thus exploit price discrepancies. One might argue that their essential task is to restore market efficiency.
Collaboration in finance is largely uncommon and impractical due to the competitive edge that is characteristic to the kind of predictive modeling that is associated with the area. Numerai addresses this via running rounds of successive Machine Learning competitions for the purpose of crowdsourcing predictions, which then serve to form trading decisions on the traditional markets.
Key Insights
Exchanges
As of the time of recording this episode, Numeraire (NMR) was listed on the following exchanges:
Episode Notes
Auction Mechanism
The figures from the example, presented in the episode, were as follows:
c denotes confidence, s - stake, t - model result, d - tokens destroyed, w - actual winnings, and p = s/c - potential winnings.
The actual reward is calculated as R[i] = min(p[i], r), where i denotes the potential winnings of the ith participant (ranked in descending order of confidence); and r denotes the remaining prize pool amount.
The free electronic version of the book is, in fact, only available in HTML format, rather than PDF (the latter was originally indicated in the recording). ↩
View all episodes
This is an episode on the super cool Machine-Learning-powered hedge fund, Numerai.
Summary
Numerai is a global equity long-short hedge fund, which specializes in relatively long time-range investments, on the scale of 6-9 months. They’re interested in more finely granular moves, that are at the level of individual stocks; they aim to identify anomalously-priced stocks, and thus exploit price discrepancies. One might argue that their essential task is to restore market efficiency.
Collaboration in finance is largely uncommon and impractical due to the competitive edge that is characteristic to the kind of predictive modeling that is associated with the area. Numerai addresses this via running rounds of successive Machine Learning competitions for the purpose of crowdsourcing predictions, which then serve to form trading decisions on the traditional markets.
Key Insights
Exchanges
As of the time of recording this episode, Numeraire (NMR) was listed on the following exchanges:
Episode Notes
Auction Mechanism
The figures from the example, presented in the episode, were as follows:
c denotes confidence, s - stake, t - model result, d - tokens destroyed, w - actual winnings, and p = s/c - potential winnings.
The actual reward is calculated as R[i] = min(p[i], r), where i denotes the potential winnings of the ith participant (ranked in descending order of confidence); and r denotes the remaining prize pool amount.
The free electronic version of the book is, in fact, only available in HTML format, rather than PDF (the latter was originally indicated in the recording). ↩