There’s one big reason wealth managers have accelerated their move to the cloud in recent years: big data. So large is the sheer volume of data sets fund managers use every day to make their investment decisions that analytical solutions have become the obvious choice.
John Kain (pictured), Market and Business Development Director for Banking and Capital Markets in Financial Services at Amazon Web Services (AWS), knows the technology needs of the industry better than anyone. Since industry first seized the opportunity in the cloud -Technology for scalable and practically unlimited storage and computing that are available on demand, fund managers are increasingly turning to cloud-based solutions.
“The main trend driving cloud adoption has been the increased use of data, particularly for investment modeling and analysis,” said Kain, a Wall Street veteran who previously worked for JP Morgan. “We have not only seen the rapid growth in the volume of traditional financial data, be it quotes or news, but also the availability and use of alternative data sets in the investment process.”
In the four years since joining AWS, Kain has grown significantly in both the amount of data being put in the cloud and the sophistication of the cloud-based tools that fund managers use.
Machine learning
The rapid advancement of cloud technology means highly specialised services that were once the preserve of a handful of industry giants, with deep enough pockets to build their own AI and machine learning-based tools, are now being deployed by managers across the board.
“By the time I joined AWS, we saw that the more quantitative funds were using the computing power of the cloud to backtest and research the various business models they were building, ”he says.
“So whenever your researchers had an interesting thesis that they wanted to model, they could get it right away, and they could do it on a large scale. And since many of these financial models go faster the more computing power you put in, they can recover their investment research faster.”
Attractive economics
In addition to the attractiveness of a cloud-based solution, it also comes with the lowest cost. The extreme flexibility of the technology allows fund managers access to extreme computing power based on huge data sets as and when they need it, a system much preferable to the old days when fund managers stored most of their data on their own servers. “It doesn’t matter whether you run 1000 servers for an hour or one server for 1000 hours, the economy is the same in the cloud,” says Kain. “Most importantly, they can use some of the unused capacity. We have special pricing models to make this incredibly profitable.”
Kain says this approach, promoted by the largest quant funds, is now being used across the industry. “Many of the big quant companies have been doing research in the cloud for some time. What has been done has created enough magnet for the industry, which is increasingly seeing the availability of traditional and alternative data sets in the cloud, for asset managers to use.”
This means that you don’t have to create cross-vendor ingest pipelines and figure out how to pull data into your infrastructure, those datasets are now readily available in the cloud environment. To enable asset managers to take the obvious next step: automate other processes related to managing a fund to further streamline their operations.
Research environments
To work effectively with this growing volume of data, analysts need access to a secure, managed research environment that is connected to their data and provides self-service access to the computations needed to quickly perform their data sampling, preparation, and analysis. AWS recently announced FinSpace, a data management and analysis service that helps wealth managers set up a research environment for their analysts to store, catalog, prepare, and analyze financial industry data on a large scale in minutes. Kain said, “using FinSpace customers can reduce the time it takes for analysts need to find, prepare, and analyze data from months to minutes.”
Natural language processing
Kain also provides the example of natural language processing tools that use machine learning to select topics or trends that can be used in investment decisions. The reduced costs and easy access to these open up many new opportunities for asset managers. Previously, it was only available to the largest companies in the world. He said these tools give portfolio managers, for example, “the ability to do anything from using standard growth measures for China’s GDP to things like satellite imagery, counting the number of ships entering and leaving a port, to searching vacation websites to pass in Hong Kong , to measure consumer sentiment and find ways to incorporate this data into the investment process.”
He continues, “In the past, a company traditionally had to have your own machine learning capability and a dedicated team of data scientists to understand how to develop them.” Today, these advanced tools are increasingly available from cloud providers, including AWS, the world’s largest cloud service provider owned by Amazon. For example, AWS offers SageMaker, which enables companies to analyze data sets using advanced machine learning techniques without having to develop custom technologies.
Last year, AWS introduced a service called Comprehend Events, which is a natural language processing service to extract details about real-world events from unstructured text. “It can go through a news article to spot an acquisition; it can identify the acquirer as well as other parties mentioned in the story and filter them out automatically. In the past, that would have been a huge investment for any company to get these things. Getting this kind of information from unstructured data has just gotten a lot easier.”
High frequency holdouts
It’s true that a handful of niche areas in the fund management industry, like high-frequency trading, are reluctant to move everything to the cloud as their strategy relies on keeping the computers that make their business decisions as close to the place of performance as legally possible: a critical ability to reduce latency and allow them to perform operations ahead of competition. Kain admits that it is not easy for these types of players to see an obvious alternative that would work purely in the cloud.
“I think it’s an obstacle for the near future. When measuring nanoseconds it is challenging to do so in a dynamic on-demand environment.” However, this is still a niche area and in general there is little doubt that asset managers are increasingly turning to cloud-based solutions.
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