eToro, one of the industry’s paramount social trading networks, has just unveiled its CopyFunds service, supporting machine learning offering to help advance and offer algo-funds for investors, per an eToro statement.
CopyFunds is the group’s latest foray into innovative trading, which will see a duality of traders – more specifically, traders will be disaggregated into Top Trader CopyFunds and Market CopyFunds. The former will be composed of the top performing and standout traders on the network.
By extension, Market CopyFunds will be derived from selected instruments, ranging from stocks, commodities, or exchange-traded-funds (ETFs), whereby allowing investors to track a pantheon of sectors around a defined market strategy.
TrioMarkets Partners with HokoCloud, Expands its Portfolio with Social TradingGo to article >>
Broadly speaking, the CopyFunds service suite will help coalesce financial assets of any type, under a singular chosen strategy or theme, created and traded on eToro’s platform. The initial iteration of CopyFunds will be available to invest in starting from $5,000, while a full composite of funds, including factsheets, will be available at launch.
CopyFunds is important for investors as it will help grant them invest in a group of high-performing traders as well as targeted market strategies. As such, the service will also seek to help investors mitigate long-term risk, simultaneously advancing opportunities for growth via diversified investments.
CopyFunds is specifically targeting a group of individuals that address future trading, i.e. millennials. Recent studies have shown the reliance that these traders place on social trading strategies, making the initiative a very conducive option for this demographic.
According to Yoni Assia, Chief Executive Officer (CEO) and Founder of eToro, in a recent statement on the launch, “We have dedicated the last decade to shaking up the world of trading. With the launch of CopyFunds™, we’re taking aim at a closed and traditional fund management industry by combining the wisdom of the crowd and machine learning.”