After 2016 saw the first decline in global revenues and profits since the 2008 crisis, 2017 rebounded with record-breaking performance backed by bull markets. This momentum carried into 2018. In fact, Edouard Leveque, head of Bloomberg buy-side order manageament sales in EMEA, notes that the asset management industry has a CAGR hovering between 6 and 7 percent and remains on track to exceed $120 trillion by 2020.
At the same time, margins are under extreme pressure, especially for firms in the middle of the AUM spectrum; this margin pressure will only become more challenging when strong equity markets eventually slow down. The nature of the industry is shifting as net flows to core active management are expected to decrease 8 percent during the next two years, with inflows favoring non-core investment strategies such as passives, ETFs, specialties and alternatives.
All of this may be why, on the back of its annual benchmark survey of leading asset managers, Boston Consulting Group sees 2018 as an “inflection point” in the industry’s ongoing transformation. The group predicts that in five years, asset managers will look very different than they do today due to a combination of structural shifts in the market and digital innovation.
They further predict that to succeed, firms will need to make profound changes in their technology. This makes intuitive sense. By increasing operational efficiency, or “doing more with less,” firms can ease margin pressures. This often starts with a broader effort to optimize the target operating model (TOM) — including how it is organized, how it processes data and how it utilizes technology. BCG notes that asset managers that optimize their TOM can expect cost savings of 10 to 20 percent.
The challenge, however, is what comes next. Once the TOM is optimized, where do asset managers invest in technology? Just as not every firm has the same business model, not every firm needs to adopt the same solutions. Setting priorities will likely depend on which buy-side trends affect your business the most.
Competitive differentiation
Firms that have addressed operational efficiency still find themselves in a difficult environment for alpha generation. To stand out from competitors, asset managers should consider investing in cutting-edge technologies, including artificial intelligence (AI) and machine learning (ML), to uncover opportunities, process more structured and unstructured data, and potentially gain an edge in predicting risk and price. Another way to differentiate is through specialization, which means asset managers should focus on technologies that provide advantages in niche markets. Of course, buy-side firms could choose both, using AI and ML to strengthen offerings in specialized regions, sectors or asset classes.
Data and analytics
The BCG survey says that most asset managers are pursuing digital strategies, whether that involves new analytics, digital labs, hiring data scientists or exploring alternative data sets. Much of this is designed to demonstrate value to clients that are quickly realizing the transformative power of data. Investing in new analytics technology can give asset managers a way to make data-driven recommendations based on a specific client’s history, thus allowing firms to couple stronger investment performance with value that exceeds clients’ basic expectations.
Active vs. passive
The “race to zero” in passives makes fees in active funds look even higher by comparison. In an environment where passive funds continue to see strong flows and outperform actives due to strong equity market performance, active managers should invest in technology that reduces costs and allows greater operational efficiency. As we will see in the following case study, trade automation is an increasingly popular choice, as are data management solutions that eliminate redundancies in data import, cleansing, distribution and consumption, as well as tools that predict trade failures.
The journey continues
The technology and data transformation journey is never truly complete. All organizations evolve, so operating models must change, too. But this journey has to start somewhere. Otherwise, firms will continue to rely on fragmented systems, inconsistent data and inefficient operations. Buy-side leaders understand this isn’t an option. They know that remaining competitive means establishing a future operating model, leveraging data as a strategic asset and partnering with an experienced buy-side technology provider that can help them implement their TOM effectively.
Bloomberg’s trade execution and order management solutions provide multi-asset order and execution management solutions and investment cycle analytics that enable buy-side firms to turn their trade and order data into a competitive advantage. As a result, firms can create more efficient workflows, connect to the global capital markets, drive regulatory compliance and lower their total cost of ownership. Learn more.
Case study: trade automation
As just one example of how the buy side is adopting new technology, consider the appeal of trade automation. These products are designed to help execution traders boost productivity, managing more orders during the daily trading window, as well as separating incoming order flow into “high-touch” and “low-touch” functions. Low-touch orders are those in liquid benchmark products, such as on-the-run USTs, while high-touch orders are larger in size and involve difficult-to-find names, such as a high-yield corporate where the PM wants to discretely trade in block size.
“Forward-thinking traders understand automation is not a threat,” says Ravi Sawhney, global head of trade automation at Bloomberg. “The idea is, how can we free up the bandwidth of the human trader to focus on more profitable activities? It makes traders more productive if they spend their time on more complex orders while automation takes care of the rest.”
Sawhney adds that automation allows the desk to scale through technology, not just headcount. For example, assume that under conventional circumstances an asset manager with $1 billion AUM and 10 traders would need to add another 10 traders to double AUM. With the help of automation, however, the same asset manager could scale to $2 billion AUM but only need to add five traders, achieving a built-in competitive advantage.
Today, traders need access to fragmented pools of liquidity, an intuitive execution platform, automated analytics and workflows, as well as high-quality data.
Bloomberg's Electronic Trading Solutions help buy-side professionals strive to achieve best execution across multiple asset classes with advanced trading solutions and sophisticated analytics in one a single platform.
Learn more about Bloomberg's Fixed Income Trading.
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