The next frontier of systematic investing: data, infrastructure, and expanding asset classes Systematic investing continues to evolve rapidly as firms seek new sources of alpha and greater operational efficiency. From extracting signals in unstructured data to building infrastructure that connects trading workflows, technology is reshaping how quantitative teams operate. At the same time, systematic strategies are expanding beyond traditional equity markets into fixed income, volatility, and structured products.

Matthew Healy and Erin Holbrook from talent advisory firm, Maven Partnership, explored three key themes shaping the next phase of quant investing.

Generating alpha from alternative and unstructured data

One of the most significant developments in quantitative investing is the growing ability to extract signals from alternative and unstructured data sources. Techniques such as artificial intelligence and natural language processing (NLP) are now being applied to vast pools of publicly available information - from social media conversations to consumer sentiment data - to identify patterns that may translate into investment insights.

While these technologies are not entirely new, their application in finance has become far more sophisticated. NLP models are increasingly used to scrape, structure, and analyze large volumes of text-based data to inform systematic trading strategies.

As Matt explains:

“Artificial intelligence and natural language processing are probably the primary techniques we’re seeing used to generate alpha from alternative data sources. What’s changed is the scale and depth of how firms are applying them - scraping and parsing everything from consumer sentiment to Reddit discussions to drive systematic investment decisions.”

Beyond the technical tools themselves, the talent landscape is also evolving. Firms are increasingly seeking professionals who can bridge the gap between advanced technology and financial application.

Erin notes that this shift is particularly visible in recruiting:

“Across the forums we’re involved in, there’s a huge uptick in interest around upskilling in AI and technology. But the most desirable candidates aren’t just technologists - they’re people who can apply those technologies in a financial context to enhance alpha generation.”

In other words, success increasingly depends not just on building sophisticated models, but on deploying them in ways that directly impact investment performance.

Connecting front, mid, and back office workflows

As systematic strategies grow more complex, firms must also address the challenge of fragmented technology infrastructure. Connecting front, mid, and back office systems has become critical to reducing friction, latency, and reconciliation risk.

According to Matt, the most successful teams solve this challenge by investing heavily in engineering and data infrastructure.

“Infrastructure and engineering effectively create the highway through which information flows. Real-time data drives investment decisions and risk methodologies - and without the infrastructure supporting that flow, the system breaks down.”

This infrastructure enables the seamless movement of data across the organization - from research and model development to trading and risk management. Without it, even the most sophisticated quantitative research cannot be fully monetized.

As Matt puts it: “Research cannot be monetized without the infrastructure around it. The front office depends on data storage, model development platforms, and execution systems - but those capabilities also rely on the underlying engineering foundation.”

From an operational perspective, the benefits of integrated workflows are substantial. When data flows seamlessly between departments, firms can eliminate bottlenecks and reduce operational risk.

Erin highlights how critical this efficiency is in a competitive industry:

“When front, mid, and back office channels flow seamlessly and communication lines are clear, firms gain a real efficiency advantage. In an industry where everyone is competing for small pieces of alpha, those efficiency gains can ultimately translate into meaningful differences in returns.”

In short, technology infrastructure is no longer just operational support - it is a competitive differentiator.

Expanding systematic strategies beyond equities

While equities have traditionally been the dominant arena for systematic strategies, firms are increasingly expanding these approaches into other asset classes, including fixed income, volatility, and structured derivatives.

Supporting these strategies requires significant adaptation in both tools and infrastructure. Markets such as fixed income and derivatives present different liquidity dynamics, data challenges, and modeling complexities compared to equities.

As systematic investing expands into these areas, firms are investing in tools capable of handling more complex data sets, modeling frameworks, and risk analytics. This includes platforms that can support large-scale data ingestion, flexible model development, and integrated risk management across multiple asset classes.

At the same time, success in these markets still comes back to the same foundational elements discussed earlier: robust infrastructure, integrated workflows, and talent capable of bridging technology and finance.

Looking ahead

The next generation of systematic investing will be defined by three converging forces:

• Advanced data extraction techniques, particularly AI and NLP applied to unstructured information

• Integrated engineering infrastructure that connects research, trading, and operations

• Expansion into new asset classes that require increasingly sophisticated tools and expertise

Firms that successfully combine these elements will be best positioned to capture the next wave of alpha.

As Erin summarizes:

“The firms that stand out are the ones that can connect the technology with the investment outcome - whether that’s through better data, better infrastructure, or people who understand how to turn those tools into real investment performance.”