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UK Professor Develops Breakthrough Crypto Rating System Evai.io

Professor Andros Gregoriou has pioneered breakthrough research, leading to the creation of a decentralized and unbiased credit ratings system used to evaluate over 5,000 cryptocurrencies worldwide.

The system is underpinned with Artificial Intelligence (AI) and machine learning (ML) technology and will provide the crypto industry with an unbiased and ‘fit for purpose’ rating system. It will combine AI with a self-correcting protocol and governance mechanism executed by Turing complete smart contracts. This will allow Evai.io to harness elements of machine learning to manage rating modifications. In other words, the ratings will continuously learn, maintain, and decentralize ratings to produce the most unbiased recommendations on the market.

How are Evai ratings formed?

Evai‘s unique rating system dubbed “The Bridge” factors six elements to generate unbiased ratings. These include Liquidity, to measure the fungibility of an asset; Systematic Risk, or risk resulting from the collapse of an entire market; Profitability, the potential payoff of an investment; Momentum, the rate of change of an assets price; and Peak to End Value Demand, the last value and the peak of an asset’s price over a specific period; all of which combines to determine the final and most important facet: Investment.

Unlike other centralized rating services on the market, Evai embodies the core ideals of decentralization. Moving away from the central model remedies any intentional or unintentional bias—something integral to a fair rating system. Additionally, Evai is entirely independent; it will never accept compensation from companies and investments it rates

The cryptocurrency credit ratings theory is inspired by two of Professor Andros’ leading research papers*, which capture insights from the 2008 Great Recession and take learnings from the pitfalls of centralised credit ratings.

Professor Andros Gregoriou

Professor Andros Gregoriou, said: “The system we have developed at the University of Brighton is essential to the future of cryptocurrencies. It uses a combination of AI and traditional financial models to credit rate all cryptos and exchanges. This will ultimately provide investors of all levels with the confidence to make smart decisions about their asset allocations in cryptocurrencies.”

‘The Bridge’ ratings model based on the research framework is set to be launched later this year by UK based crypto firm Evai.io. Matthew Dixon, Evai.io Founder and CEO, said:

“Evai ratings will be groundbreaking, fully automated and will command respect and recognition within the crypto and wider financial industry. Creating a bridge between crypto and traditional finance leading to the adoption of crypto as a recognized asset class in the investment world.”

Andros Gregoriou is a Professor of Finance and the Research excellence lead for Brighton Business School. He has published over 70 research papers in internationally recognised journals and is a regular consultant for the CFA and the London Stock Exchange. His thought leadership on liquidity will now be applied to 5,000 cryptocurrencies, which are listed on leading exchange platforms around the world.

Evai.io

The EVAI token

The Evai platform token, aptly dubbed “EVAI,” is an ethereum-based ERC-20 standard token. EVAI acts as an incentive for users who wish to contribute to the development of the Evai rating system. Based on the development of a pending proof of stake (PoS) consensus mechanism, the token will encourage users to vote on protocol upgrades and thus the direction and development of the Evai platform.

Holders of EVAI are also incentivized to contribute ideas and market intelligence—aiding the continuous and progressive evolution of the Evai rating system.

Evai.io is currently completing its final funding rounds and plans to launch the ‘Bridge’ cryptocurrency ratings platform from October 2020.

References

*(2019) Prospect theory and stock returns – 7 factor pricing model and (2011) Trading frequency and asset pricing on the London Stock Exchange

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