Case study: AI boosts quant research, overcoming alpha erosion

When expertise meets operational bottlenecks

One of EMEA's most successful quantitative asset managers had built an impressive reputation managing multi-billion-dollar portfolios across global markets. Their foundation rested on three pillars: precision trading, advanced modeling, and cutting-edge technology. Yet even world-class quantitative expertise couldn't eliminate several persistent operational challenges.

The firm, which began as a high-performing division within a major hedge fund before spinning out as an independent entity in the mid-2010s, had always distinguished itself through disciplined, data-driven strategies. Their combination of machine learning, statistical modeling, and systematic trading approaches consistently identified market inefficiencies. But success brought its own complications.

Unstructured content bottleneck

Large volumes of proprietary sell-side and broker research remained locked in unstructured formats, requiring significant manual effort before integration into their sophisticated models.

Resource-intensive event detection

Identifying market-moving events and accurately assessing sentiment within internal content consumed valuable analyst time that could be better spent on higher-level strategy work.

Fragmented narrative creation

Creating coherent, insight-rich market narratives required painstakingly combining multiple fragmented data points, a process that simply couldn't scale with their growing research volumes.

The firm had evaluated multiple technology solutions over the years, but none aligned with their specific workflow requirements or met their rigorous cost-benefit standards. Then, in December 2024, everything changed.

The solution

Bigdata.com API delivered exactly what the firm needed: immediate operational integration without the burden of extensive annotation projects or system overhauls. The deployment focused on three core capabilities that directly addressed their pain points.

Automated entity recognition

The API instantly began recognizing and linking companies, sectors, geographies, and financial entities within their proprietary research, eliminating manual tagging work and ensuring consistency across datasets.

Intelligent event extraction

Corporate actions, macroeconomic announcements, and other market-relevant events buried deep within research content were automatically surfaced, giving analysts immediate access to actionable intelligence.

Financial-context sentiment analysis

Domain-specific sentiment scoring provided nuanced interpretations specifically calibrated for financial market contexts, moving beyond generic sentiment tools to deliver insights that actually mattered for trading decisions.

Transformational results

The impact was felt immediately across three key dimensions:

Operational efficiency

Time previously spent manually preparing research for modeling and analysis dropped significantly, freeing analysts to focus on strategy and alpha generation rather than data preparation.

Enhanced research quality

The firm began generating richer, context-aware narratives at unprecedented speed, improving both the depth and timeliness of their market analysis.

Future-ready infrastructure

The API-first architecture positioned them to seamlessly integrate new datasets without major re-engineering efforts, creating a scalable foundation for continued growth.

What's coming next

Success with the initial rollout opened up bigger possibilities. Here's what they're planning:

Going full stream

Instead of processing research selectively, they're moving to continuous ingestion. Real-time analysis of everything coming through their research pipeline.

More data, more insights

They're adding earnings transcripts and other structured datasets to get an even fuller picture of market movements.

Next-level narrative generation

The enriched data will power more sophisticated macro analysis and automated research workflows. Think less manual report writing, more AI-assisted strategy development.

The fund didn't just solve their immediate research bottleneck. They built a foundation for the next generation of quantitative analysis. And they did it without the typical enterprise software headaches - no lengthy implementations, no consultant armies, no six-month training programs.

Sometimes the best solutions are the ones that just work.