In 2019, the global financial services industry is set to spend an estimated USD 50bn on the raw, historical markets and transactions data inputs required to fuel a broad spectrum of daily trading activities across all major asset classes.
GreySpark Partners presents an insight paper to inform CTOs of the potential cost savings that can be made by engaging a near-shore data centre provider for non-latency sensitive high-performance computing (HPC) services.
Prior to the onset of the financial crisis and the subsequent wave of resulting global re-regulation, the majority of bonds and swaps trading activity within small-to-medium-sized asset management firms, hedge funds and wealth management firms was a game of dependencies.
In 2018, from an asset management firm or long-only institutional investor perspective, the time to await change in the fixed income market has passed; not only has significant change occurred, but it continues to change at a rapid pace.
Gold-i CEO Tom Higgins recently claimed in Finance Magnates Magazine (“Cryptocurrency liquidity, past, present, future” article) that “the most challenging factor continues to remain the access to and quality of liquidity.”
Since the 1980s, the electronification of financial markets trading resulted in innovations in computer hardware and software design that frequently tested the limits of what the technology that is utilised by markets participants – specifically, asset managers, hedge funds, institutional investors and investment banks – in their every-day operations can achieve.
As the efficacy of long-established cost-savings and efficiency efforts dry up, financial institutions seeking to transform their business models are increasingly looking to automation technologies to support process workflow optimisation.
The surging value of cryptocurrencies has featured in many media reports over the summer of 2017. Specifically, the value of Bitcoin quintupled between January 2017 and September 2017, when it reached a valuation of just under USD 5,000 per Bitcoin.
Predictive analytics is a branch of advanced analytics wherein a variety of different types of software tools can be used to make predictions about future events.