The accessibility of data is greater than we have ever experienced. Today, data is indeed easy to come by, however, knowing how to gather, organise and use it, is an entirely different story. Let’s take a detailed look into data from both a buy- and sell-side perspective—the strategies, challenges, trends and future of data management in the journey to transform from where we are into a data-driven enterprise.
What it is and how to use it
Data comes in all shapes and sizes—from survey statistics, exchange and dark pool trading data, to geo-spatial data and any financial company information. It can all be used to develop a clearer picture of an industry or company and therefore facilitate more accurate trading decisions.
External, unstructured and proprietary data can be used in conjunction with one another, leveraged and then combined to generate unique insights for alpha trading signals. Thematic trading often uses data points that show the market cap of a company and AI data generated from business activities, whereas impact investing focuses more on ESG metrics where the focus is on wanting to make an investment count.
In 2019, there was a noticeable upward trend in ESG conversations with clients, however, they were simply just conversations. There was no real appetite for progress because the more traditional financial analysis and portfolio management took precedence—often leaving ESG analysis by the wayside or considered much later in the piece.
The focus for 2020 has now shifted from conversation to implementation with an increase in awareness surrounding sustainability and its considerable fiscal impact on companies. We are seeing a trend in which high net worth individuals are approaching their asset managers requesting a lower percent return for the peace of mind in knowing all of their investments are in environmentally friendly and ethical companies. This increase in desire to ethically invest actually assists in driving stock prices up, so after a period of time it’s possible that the returns are equal to or higher than regular investments.
Both the US and Australia have experienced large AUM growth in ESG via passive investments like ETFs. In Asia however, investors still fall behind their global counterparts when comparing the uptake in ESG investing. The low engagement in the Asian markets indeed points to an immature ESG investing environment. It can also be attributed to legacy issues, short-termism, lack of awareness, talent gaps and a lack of access to quality ESG data.
And yet, there is still potential for change.
ESG investing is a two-way street, involving both the buy- and sell-sides knowing how to put their most sustainable foot forward. For the buy-side it’s about understanding pricing while the sell-side is expected to provide clients with the right data taken from quality research to make an appropriate choice in ESG investments.
It’s about enablement, using the right software and tools to ensure you have real-time market data, news, and company reports to facilitate sophisticated analysis. It doesn’t just stop at one piece of software, however—it’s about easy and open integration with other software to create a powerful partnership of data and software, together.
So what are the main challenges for the buy-side?
Currently across the buy-side, there’s a heavy reliance on in-house and third-party research providers to help validate the buy-side investment model. This model has a laser focus on investments in tech to enhance cloud and data infrastructure—such as a regional data warehouse. Without a warehouse, data is just sitting there unhoused—which means data may not be correctly gathered, cleaned, stored or shared. So, without access to the right data, ESG investing for example, becomes much more difficult to navigate, since the basis of ESG investing is to understand how a company scores against another based on ESG ratings.
And what about the sell-side?
Currently across the Asia Pacific market there’s a big focus on near real-time client segmentation data analysis. In a perfect world, access to this data—coupled with enhanced client insights—are absolutely instrumental in creating value for clients and subsequently driving up revenue. However, building the necessary infrastructure, normalising the data across multiple sources, and developing analytical tools to build those insights, still remains a significant hurdle in progress.
The ability for us to deliver value across all client segments using a combination of internal and external data is now table stakes for all sell-side brokers.
Regional Head of Research Technology and Innovation, Maybank Kim Eng
When looking at gaining a deeper understanding of clients trading behaviour and how to tailor personalised content, ideally the sell-side should generate targeted content and trading recommendations. In reality, however, they are simply pushing this information to their entire client base without precision or the ability to measure monetisation.
Another area very much still in its infancy is digital client engagement. We are seeing brokers with access to digital engagement metrics who aren’t actively using this data to its full advantage. Currently it is difficult to extract and analyse audit trail data and therefore virtually impossible to proactively predict client behaviour and deliver targeted retention and growth sales and marketing campaigns.
What does the future of buy-side data management and utilisation look like?
When it comes to data management and utilisation, the buy-side has to be willing to invest in data analytics capabilities—on an agile basis—and pay a premium for alternative data sources to deliver portfolio performance.
The sell-side is required to create value for the buy-side through investing in capabilities to predict client intention and deliver recommendations on the best next steps forward.
At Iress, we understand that consistent and accurate data is the building block of smart and sound investing. Truthfully it’s the missing link and a powerful asset in the trading equation. We are committed to this journey and we believe the right data strategy backed with the right set of priorities will differentiate the business leaders against the rest.
So, what does the future look like?
As stated in S&P Global, ‘Mikkel Larsen, chief sustainability officer at Singapore's DBS Group Holdings Ltd., expects rapid growth in ESG investing in Asia—partly due to growing investor demand and partly because of rising pressure from regulators in the region.
Larsen says: "You've seen around Asia a number of regulators stepping up, and they're not deterred particularly by the COVID-19 crisis.”
Research out of KPMG demonstrates that ‘as the impact on portfolios of COVID-19 becomes clearer, the asset management industry itself will undergo profound changes—from how securities are selected through, how portfolios are managed, to how asset allocation decisions are made.
‘Some governments in the Asia Pacific region are taking the lead in promoting ESG as part of their agendas for economic growth, with Hong Kong (SAR) and Singapore chief among them.’
We’re encouraged by these governments who are keen to support financial institutions in their transformation to a data-driven enterprise. Singapore, for example, has launched the Artificial Intelligence and Data Analytics Grant (AIDA) to provide support for the strengthening of its AIDA ecosystem.
An increase in these sorts of grants means increased capital flows are funnelled toward a greener economy for Asia Pacific. This isn’t an automatic opt-in however—the industries and businesses who will benefit from this funding, will be the ones who are ready to go.
Now is the perfect opportunity to get in and get ahead. So the only question left is—are you in?
Minimal upfront costs to kickstart new software projects
While we can’t change this new world we’re in, it’s vital to consider the long term and adapt. So what does this look like for Singapore buy- and sell-side organisations wanting to implement new financial technology software?
Co-funding through the MAS Digital Acceleration Grant means more bang for your buck.
