Cindicator’s Hybrid Intelligence hacks the prediction markets: +228.8% annualised profit

This article was first published on Stories by Cindicator on Medium
-----

Decentralised prediction markets are one of the best-known concepts of blockchain technology, allowing anyone to bet on anything.

Yet since the launch of Augur in 2018, it appears that adoption has been slow. Augur is essentially a betting tool. We decided to use Cindicator’s Hybrid Intelligence indicators to trade this instrument with other participants in prediction markets. The results are promising (for us and our token holders): +88% in 20 weeks, suggesting that it’s possible to profit from the current inefficiencies with Hybrid Intelligence.

What are prediction markets?

Prediction markets are markets where participants trade ‘shares’ in the outcomes of events. Market prices could be interpreted as the expected probability of the outcome. Traders who think the event will happen can buy shares (i.e. bet on 1), while traders who think the opposite can sell shares (i.e. bet on 0). In theory, prediction markets should aggregate information and reflect the consensus view on the event.

While Augur is the best known blockchain-based prediction market protocol, there are others such as Veil, an Augur fork that sadly closed down in July 2019.

What is Hybrid Intelligence?

Cindicator’s Hybrid Intelligence (collective + artificial intelligence) is sometimes incorrectly described as a type of prediction market. While it is also rooted in the wisdom of the crowd hypothesis, it’s actually very different.

In fact, Hybrid Intelligence is based on collecting forecasts from a large, decentralised group of analysts. They are incentivised to make correct forecasts through the possibility of winning rewards at the end of month (currently, the monthly prize fund is 1.25 BTC and USD 7,500). Yet unlike in prediction markets, the analysts don’t risk losing money if their forecast proves to be wrong — they only lose points.

After collecting the forecasts, Cindicator employs machine learning models to enhance the final indicator. The ...

-----
To keep reading, please go to the original article at:
Stories by Cindicator on Medium

Comments (No)

Leave a Reply