Not all fund managers are the same and neither is their need for data.
There’s a big difference between a quant shop or a high-frequency trader, versus a fundamental, bottom-up stock picker.
The former is a voracious devourer of every smidgen of data, raw and in real time. The later might be content with basic filing and feeds from a trading terminal.
This divide is narrowing, however, as traditional houses and asset owners that rely on fundamental research find they too need to modernize their data intake.
The international, Hong Kong-based arm of ChinaAMC, for example, a $200 billion asset manager based in Beijing, has just started to make use of quantitative analysis.
“We’re becoming more automated, with our financial-data models in one place, to make it easy for everyone to access,” said Courtney Wei Xiaopeng Wei, ChinaAMC’s Hong Kong managing director.
Better with data
Companies have to adapt to the times, but they also face their own unique circumstances that prompt change. Many Asian fund management companies in Hong Kong have found retaining talent difficult – the city is known for a job-hopping culture.
“There’s a lot of turnover among Chinese fund houses,” Wei said. “We’re one of the more stable houses. We have a lot of good research and information in-house, but without continued investment in technology, when people leave the company, we lose valuable institutional knowledge.”
The company has been integrating research and investment decision histories. Initially bringing data together was about how to give analysts, portfolio managers and salespeople a record of what the firm has done with clients and their money.
But it has become a way to improve performance.
“Our true DNA is in our history: what investment calls worked, what sectors we’re good at researching,” Wei said. Establishing that history and making it accessible is a way to learn.
Fundamental shops are also increasing their use of quantitative investing in certain new areas, such as ESG or impact investing.
There are challenges with data, however, that fundamental buy sides are beginning to grapple with. ChinaAMC is the first Chinese asset manager to sign the U.N.’s Principles for Responsible Investment. It needs to incorporate external data with its own research process to manage ESG-themed portfolios.
First is internal management. When a company introduces new tools and processes, it has to convince its teams to use them, through revised performance criteria or upskilling.
Second is how to integrate alternative data sources with processes built around traditional feeds. Wei says firms are now constantly hunting for new sources of data, because they have a limited shelf life in providing alpha – the advantage soon gets arbitraged away.
Data is expensive, and requires resources to integrate into an investment process – as well as on the operational side.
Technology providers are aware of the challenges that fundamental buy sides face. They are adapting their own business models to help asset managers source and deploy data to the degree that suits their needs.
“We see clients struggle to build out an enterprise data function,” said Todd Hartmann, senior vice president for strategy, content and technology solutions at FactSet.
Managing the data flood
One factor driving up costs is the difficulty of managing the growing influx of new data sources. “Firms hire data scientists to gain insights, but these people end up spending most of their time wrangling with the data – onboarding and connecting it.”
FactSet aggregates data for financial institutions and provides enterprise data-management solutions. One of its initiatives is Open:FactSet, a marketplace designed to help fund managers access alternative data from a range of third-party vendors, such as satellite imagery for emissions or data on private companies.
FactSet connects all of these streams to its core structure, like adding spokes to a hub. The real work is in mapping these data sources to FactSet’s own way of categorizing and organizing data, so that clients who download a new data set via Open:FactSet will not have to spend yet more time configuring it for their own systems.
“The influx of data is at crazy levels,” Hartmann said. “We connect it and make it work together.”
Peter Davaney-Graham, head of content strategy for Open:FactSet, says the biggest demand still comes from systematic investors running quant strategies and feeding them into machine-learning programs. But it’s not just quants that want a solution like Open:FactSet.
“Even traditional fundamental research teams are using data scientists,” he said. “They recognize the power of having someone who can manipulate data and get conclusions from it.”
For many fundamental teams, they may not need a marketplace like Open:FactSet. It might be enough to use the traditional feeds from a FactSet terminal. But he says more asset managers are subscribing to the marketplace.
“Our biggest theme now is ESG,” Davaney-Graham said.
At ChinaAMC, Wei says the hunger for data isn’t new. But new sources of data mean more things that fund managers must analyze in order to keep their edge.
“The technology is really evolving fast,” she said.