Global Institutional Investors To Increase Alternative Data Usage, Proficiency In Machine Learning Set For Growth
The majority of institutional investors globally, including pension funds, family offices, asset managers, private banks and hedge funds, are to increase their alternative data usage over the next 12 months, according to machineByte's White Paper in to Machine Learning & Alternative In Asset Management. Institutional investors are also expanding their proficiency in python and their use of machine learning tools and platforms.
NEW YORK - Sept. 16, 2019 - PRLog -- The majority of institutional investors globally are to increase their alternative data usage over the next 12 months, according to machineByte's White Paper in to Machine Learning & Alternative In Asset Management. Institutional investors are also expanding their proficiency in python and their use of machine learning tools and platforms.
The White paper collected data points from more than 100 institutional investors globally from North America, Latin America, EMEA, Asia & Australia that are active or currently exploring alternative data or machine learning - more than 50% of those institutional investors from whom data points were collected have more than US$15 billion in AUM. More than 80% of institutional investors where data points were collected expect to increase their use of alternative data over the next 12 months. More than 65% of institutional investors are applying machine learning across a specific area of the firm.
"Through case studies as to how institutional investors apply machine learning and alternative data, to profiles, rankings, data and breakout sections on overcoming issues and taping in to opportunities, the machineByte White Paper provides a roadmap for those institutional investors beginning their exploration of alternative data, AI and machine learning in finance," said Robert McGlinchey, director & co-founder at machineByte. "We find that the majority of institutional investors globally are seeking to tap in to alternative data and apply machine learning over the next 12 months, yet barriers exist to growth, particularly around cost, proficiency, the risk of overfitting and an unwillingness from the wider financial services community to embrace open source."
Some of the findings of the machineByte White Paper, include:
machineByte, a sister platform of EQDerivatives, provides education, content and forums for investment professionals who are currently exploring or active in machine learning, AI and alternative data in financial markets.