Evolutionary Awards 2025 – Stanlib: evolution in asset management

Category: Evolutions in investment

Describe your evolution:

The evolution of STANLIB Asset Management’s Systematic Solutions team has been a methodical transformation anchored in building a scalable, research-driven, and technology-enabled investment process.

We began with a fundamentally driven equity approach, using spreadsheets to screen and rank South African stocks based on value, quality, and growth. While effective in a narrow context, this process was limited in scale, speed, and adaptability.

Recognising the need for a more robust framework, we transitioned to a coded environment using R. This shift allowed us to automate workflows, reduce manual errors, and test investment ideas more rigorously. It marked the beginning of a disciplined, repeatable, and transparent investment process.

As our ambitions expanded to global markets and our data needs swelled, we encountered infrastructure constraints. Migrating to the cloud was a pivotal step. It enabled us to store and process large datasets, run simulations at scale, and significantly accelerate research turnaround.

This infrastructure also laid the foundation for integrating machine learning and artificial intelligence into our workflows. In parallel, we adopted software engineering best practices, including version control, modular coding, and collaborative platforms like GitHub.

These tools have made our work more secure, auditable, and team-oriented. These practices enhanced the security, auditability, and scalability of our process while fostering team collaboration and reducing key-person risk.

More recently, we have integrated AI tools to streamline time-intensive tasks such as summarising earnings calls, analysing broker reports, and troubleshooting code.

This has freed up our team to focus on high-value research and strategic decision-making. Our process evolution reflects a commitment to continuous improvement. By combining human insight with the power of data and technology, we have built a future-fit investment capability that is agile, transparent, and scalable.

Describe the impact your evolution has had in response to its identified challenges and targeted outcomes.

The evolution of STANLIB Asset Management’s Systematic Solutions team has delivered measurable impact across performance, productivity, and client outcomes. What began as a spreadsheet-based, locally focused equity process has become a globally capable, technology-powered investment engine.

By transitioning to a coded environment, we reduced operational risk and improved the speed and accuracy of our research. This allowed us to respond more quickly to market opportunities and deliver more disciplined portfolio construction. Our migration to the cloud unlocked the ability to run global simulations, store vast datasets, and scale our research infrastructure.

As a result, we have launched new global strategies, shortened research cycles, and improved time-to-market for new ideas. The adoption of software engineering principles has made our process more resilient and transparent. This has enhanced governance, reduced errors, and improved collaboration across the team. Artificial intelligence has further amplified our capabilities. Tasks that once took hours, such as summarising earnings calls or analysing broker reports, can now be completed in minutes. This has increased our research productivity and allowed us to focus on higher-order investment decisions.

The impact of our evolution is evident in several key areas: – Improved consistency and repeatability in portfolio construction

• Faster research turnaround and product development cycles
• Enhanced transparency and auditability for clients and regulators
• Greater scalability across both local and global mandates
• A more agile, collaborative, and future-ready investment team

This transformation has not only modernised our investment process. It has materially improved our ability to deliver better outcomes for our clients, more consistently and at scale.

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