Quantitative Uptake Among Multi versus Single Managers

The Rise of Quantitative Frameworks Among Multi-Managers

As capital continues to consolidate within larger multi-managers, these firms have distinguished themselves by the superior quality of both talent and resources they can deploy. A prime example is their use of advanced quantitative frameworks for tasks such as idea generation, data scraping, and risk management. These sophisticated approaches come with substantial costs, both in terms of infrastructure and the specialised talent required to implement them. Similarly, multi-managers extensively leverage sell-side research and expert networks, employing these tools at a scale that smaller, leaner funds often find unsustainable.

“If it wasn’t for our quant team’s research, I’d feel like I was flying blind”

Proprietary Data and Sector-Specific Insight

Quantitative teams are often tasked with developing proprietary datasets for different investment teams using advanced scraping techniques. This enables firms like Millennium and Citadel to analyse precise real-time metrics, such as app downloads for tech companies or car sales for automotive firms.

At Millennium, it’s common for each team to have at least one dedicated data scientist focused on building research tools that provide a competitive edge in the market. Typically, these Analysts can gain a significant edge through a sector-specific utilisation of data, building dashboards which flag signals such as increased shorting activity on a name, divergence of names that typically trade together, and sector-specific signal generation detecting broader trends occurring across subsectors.

This information is often complemented by the alpha trackers utilised by Millennium and other funds, where sell-side analysts or others in the space recommend their top stock picks into the centre book. The Data Analysts on these teams can collate this data and represent top recommendation trends and analysis on the accuracy of these recommendations to Portfolio Managers across these subsectors, revealing deeper market insight and broader sentiment. Data Analysts are also able to build infrastructure to create automated modelling updates, supporting Analysts and saving them significant time, which can instead be used on fundamental analysis of their broad universe. These sector-specific analysts are also complemented by a centralised research team, which assists them in building more complex tools, also providing additional support in different coding languages should the team's Analyst be less well versed in the area. The specialisation of these analysts also means they can begin to more actively intuit the needs of their teams and proactively anticipate their needs, building tools to suit.

Data as a Competitive Edge

Highlighting the significance of these efforts, a senior analyst at Millennium remarked, “If it wasn’t for our quant team’s research, I’d feel like I was flying blind,” underscoring their value, particularly during earnings season. These insights allow these teams to understand how and where a business is growing in real time, adjusting their price targets accordingly. These data points are particularly valuable when paired with the opportunity to consult, and occasionally badger, the management of these companies at hand. It is not uncommon for analysts at multi-strategy funds like Millennium to be more up to date on real-time performance metric updates than representatives of these companies themselves. As a result, these analysts can keep these management teams on the back foot, which then allows them the upper hand when trying to extract extra points of information on their business process, which could also allow further insight. Thus, interestingly, the edge of this increased data-driven insight is not only helpful for the minute-by-minute trading that it allows, but also for an increased foothold and outlook gained through management.

The High Cost of Quantitative Infrastructure

This edge comes at a steep price, however, with a sector Data Analyst at Citadel making a total compensation of approximately £300k and the average multi-manager data analyst earning a base of £120-150k. These costs are then substantially increased by purchasing access to a range of different data sources, which are then utilised by these data analysts, typically prioritising non-public data which is more valuable as a result of its novelty. This is a cost that can quickly run away from many smaller funds without sufficient backing to afford these expenditures.

Single-Managers: Broader Roles, Fewer Resources

Single Managers, alternatively, tend only to have one Data Analyst, if any, which means they’re given a much broader responsibility set. These Analysts are typically expected to create tools and infrastructure for the entire team across different sector focuses and with responsibility for personally developing all of the infrastructure from scratch without central support. These Data Analysts at Single Managers are also typically less equipped with regard to their access to datasets, meaning that they spend more of their time web scraping data rather than generating dashboards. This means that while these Analysts might be able to provide a broader view from a data perspective, they don’t necessarily create the same tools which are typically the main alpha drivers of their competitors in the multi-manager space.

The Role of Alternative Data Vendors

Alternative data vendors like Nielsen and Consumer Edge offer more cost-effective options for firms that can’t afford to conduct their own scraping. However, they still come with high price tags—often running into thousands of dollars—and are criticised by many analysts for being too broadly available, meaning their insights are largely priced into the market and are less a competitive edge than a necessary data point. As a result, the insights from these providers have become less valuable and given rise to the use of data scrapers for hire, which, while less efficient than an in-house data analyst, can provide some of these firms with more bespoke datapoints than their competitors in the market, although often still at a hefty price tag.

Quantitative Capabilities as a Catalyst for Growth

These quantitative capabilities enable large funds to boast superior risk management and capitalise on market enthusiasm for data-driven strategies, which, in turn, drive accelerated capital inflows. Additionally, multi-managers benefit from integration across strategies, with quantitative and systematic teams that can generate additional foundational data points, and established trading teams which can bring technical analysis to the rapid trading that often accompanies this approach.

Fundamental Focus Among Single-Managers

In contrast, most UK single-manager funds have traditionally relied more heavily on fundamental analysis. These more fundamentally driven investors conduct longer-term, in-depth research on their potential investments, focusing on longer investment theses – trying to understand and predict how a company might evolve over the next 1-2 years rather than the next 1-2 quarters. These investors typically focus on forensic analysis of their potential investments, spending multiple weeks to months conducting due diligence on names which might end up in their portfolio. This is often done with a level of time commitment per name that larger firms with a broader coverage model can’t commit to. This approach also typically involves building their own models from scratch with which to value and understand their names under coverage rather than utilising sell-side models. This deeper dive fundamental analysis is typically complemented by a higher conviction approach, which allows these firms to hold tight through much of the volatility that drives alpha for faster-moving investors in the multi-manager space.

Data-Driven Single-Managers: The Hybrid Approach

However, while multi-managers might have an edge in the space, there is still competition. A growing number of Single Managers are developing innovative data-driven approaches which attempt to marry the fundamental with the data-driven. One example of this would be Fifthdelta, founded by former Citadel Portfolio Managers Tio Charbaghi and Niall O’Keefe. Fifthdelta was founded with a data-first mentality to their approach, launching after the founders spent over two years building their data infrastructure. The team then built their fundamental approach on top of this, rather than attempting to develop data tools on top of an existing fundamental framework, as is typically the case. Fifthdelta’s data-first approach means that the firm can increase the efficiency of its idea generation with the awareness that before launching into the fundamentals on a name, they are fully stocked with the data points to assist this. It is important, however, to note that this is not the tale of a poorly funded startup; Fifthdelta was able to fundraise significantly on the back of this infrastructure, launching with $1.3bn as the largest startup fund of 2021. The firm was also able to save considerably on its infrastructure costs by building a fundamental strategy around this foundation rather than vice versa, a luxury that established single managers in the market have less flexibility to deploy.

The Expanding U.S. Trend

More data-driven single managers as a trend are also gaining traction in the US, with firms like Chimera Capital and Taproot adopting quantitative frameworks to enhance idea generation and analysis. These funds increasingly leverage alternative data sources —non-traditional, often non-public datasets—to gain a competitive edge. Examples include scraping online reviews to gauge consumer sentiment or analysing GPS imagery of parking lots to track quarterly foot traffic. This has given these firms the ability to compete with the faster pace of trading and quarterly investment of their competitors in the multi-manager space.

Diminishing Returns and Strategic Recalibration

As these Alternative data strategies become increasingly prevalent, they are faced with diminishing marginal returns and broader saturation with the market. This saturation has led firms like Balyasny to reduce their reliance on the strategy and instead favour more traditional fundamental approaches. These firms have found that the success of their strategies has been challenged by the saturation in the space and has created a leverage requirement for substantial returns that outweighs their risk tolerance. Dmitry Balyasny, who described this approach as trying to “catch every wiggle” in the market for small slices of alpha, has also recently announced the firm’s intentions of shying away from this expensive and competitive approach.

The Quantamental Future

Rather than chase small and competitive intraday wins, some funds are seeking to use their quantitative approach to provide a touch point for longer-term insight. Marshall Wace, infamous for its approach, which focuses primarily on Fundamental Equities and Quantitative strategies as its two key approaches, has held the torch for many in the space. Marshall Wace notably utilises significant quantitative research while also investing in a long-biased manner, aiming to harvest insights from their quantitative edge to better fundamentally understand the companies in which they invest. This longer-term approach to a quantamental strategy is one that Marshall Wace was a true first mover in, with a data scientist on all their major teams supporting an approach that differs significantly from competitors with a similar structure. This is an approach that Balyasny is similarly seeking to emulate rather than “triangulating and calling quarters” as the firm's Senior Managing Director of Equities, Steve Schurr, said to describe their prior trading-driven strategy.