Reimagining alpha with data and AI

Key takeaways

• Persistent market volatility calls for an investment approach that’s dynamic and data-driven.
• As an AI first-mover, we seek to transform massive sets of financial and alternative data into consistent alpha.
• Our systematic strategies are built for client portfolio needs, including outperformance, income, and diversification.

Investors today are navigating a market defined by persistent volatility, shifting narratives, and a constant stream of new information.

Systematic investing, while not new, is built for moments like these. It draws on the explosion of data and technologies like artificial intelligence (AI) to cut through the noise in complex markets. An estimated 90% of the world’s data has been generated in just the past two years—a staggering volume, full of potential for those equipped to make sense of it.¹ That’s where AI plays a critical role, helping us stay ahead and transform data into consistent alpha.

Adding depth with data-driven insights

Big data and AI have been part of our process long before they became industry buzzwords. Over the past four decades, we’ve built a library of more than 1,000 data-driven investment signals—each designed to capture a distinct information edge.

Even a single data source can uncover a wide range of timely, granular insights. Online job postings, for example, can reveal everything from a company’s strategic direction and hiring needs to broader shifts in the economy and labor markets. The chart below highlights one way we used this data during the COVID reopening period. By tracking dog walking job postings across the country, we identified early signs of shifting mobility and local economic activity as restrictions eased and return-to-office mandates took effect. While just one of many inputs we relied on, it provided a real-time view of the pace and impact of the reopening across sectors and regions.

As investment questions grow more complex, we’ve continued to evolve our tools. We’ve been using AI and machine learning for more than 15 years to analyze large and complex datasets. Today’s large language models (LLMs), like those behind tools such as ChatGPT, represent the latest advancement in AI’s ability to interpret language and uncover deeper insights from data.

But not all models are built the same. General-purpose tools like ChatGPT are designed to handle a wide range of tasks, while our models are specifically trained for investment applications, such as forecasting how markets may react to company earnings. The chart below compares our earnings call model with recent GPT models in predicting post-earnings stock performance. Trained on 400,000 earnings call transcripts and over 20 years of market data, our model showed stronger accuracy on this specific task.

Even small improvements in forecasting accuracy can lead to meaningful results when compounded over time. Take tennis legend Roger Federer, who once noted that he won just 54% of the points he played in his career.² That narrow margin translated into more than 80% of his matches won and 20 Grand Slam titles. In investing, a similar dynamic plays out: a consistent edge, applied day after day, can compound through time and contribute to better long-term outcomes.

Data and technology have expanded what’s possible in investing, but human insight is key to putting it into action. At the heart of that insight is a global team of more than 220 individuals including portfolio managers, researchers, and technologists. This diversity of perspectives helps us tackle new challenges with innovation and creativity. While we can measure more today than ever before, true value lies in knowing how to interpret the data and adapt to circumstances our models haven’t yet seen.

Innovation for better portfolio outcomes

We combine data, AI, and human insight with one goal in mind: delivering the outcomes that matter most to client portfolios. These include alpha, income, and diversification with consistency over time.

Reimagining alpha in a new era

In a market defined by constant change, consistent results require more than chance. They come from a disciplined process grounded in data, powered by technology, and guided by human insight. That’s the foundation of our systematic approach, designed to help build portfolios for what’s ahead.

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