The availability and use of data in today’s investment world
Gaining a competitive edge from today’s incredibly broad data universe is not without its challenges. The Science of Investment talks to Founder and CEO of Hivemind, Dan Mitchell, about how investors can use datasets accurately and in a manner appropriate to their research.
The practical application of AI
The use of machine learning algorithms is enabling highly efficient securities processing for investment managers, without the need for internal development.
The Science of Investment spoke with Andreas Burner, chief innovation officer at SmartStream, about the incentives and barriers to adoption of artificial intelligence (AI) and machine learning (ML) applications. Continue reading “Viewpoint : AI development : Andreas Burner”
Lessons from Quantmageddon
A review of portfolio construction could help investors who got burned tracking the ‘Momentum’ factor in 2019.
Momentum investors took a massive hit in September 2019, which analysts at Nordic bank SEB dubbed ‘Quantmageddon’. Continue reading “Lead : The Momentum strategy : Dan Barnes”
How to spot a bad IPO
Can data be used to sort the wheat from the chaff when a new listing is being sold? Pádraig Floyd reports.
The year 2019 showed so much promise, with high profile New York listings planned for Uber, Lyft and WeWork. Continue reading “Data : Primary markets : Pádraig Floyd”
Managing risk diversification as the 60/40 rule breaks down
The changing correlation between bonds and equities creates new challenges for diversifying risk. Chris Hall reports.
“If you had tried to predict the 1970s and 1980s based on data from the previous three decades, you’d have been badly wrong.” Continue reading “Unconventional wisdom : The death of 60/40 : Chris Hall”
Data wrangling crucial to AI development
Seeing data through the same lens as AI creates the transparency needed for trust. Dan Barnes reports.
Data wrangling and governance will be key to optimising the use of artificial intelligence (AI) within investment management. Continue reading “AI development : Data wrangling : Dan Barnes”
In January, the European Banking Authority (EBA) published a report identifying four key pillars for the development, implementation and adoption of big data and advanced analytics (BD&AA) Continue reading “EBA highlights four pillars to developing analytics”
Refinitiv has launched its Tick History dataset on Google Cloud Platform (GCP), letting customers access, query and analyse Refinitiv’s extensive archive of pricing and trading data Continue reading “Refinitiv puts Tick History data on Google Cloud”
Analyst firm Greenwich Associates has predicted that data scientists are the next evolutionary step Continue reading “Greenwich Associates: Data scientists to become new quants”