Scale, Safety, and Scalability—Raffaele Savi on the Future of Systematic Investing at BlackRock
In July 2025, BlackRock's Systematic division celebrated its 40th anniversary. Raffaele Savi leads this division, which is responsible for quantitative investment strategies within BlackRock, one of the world's largest asset managers. He oversees approximately $317 billion in assets and has been a driving force behind quantitative management in equities, fixed income, and factor investing. This article provides a detailed look at Savi's career, investment philosophy, AI strategy, risk management approach, and the future of the quantitative investment industry, based on his interview on the Goldman Sachs podcast 'Great Investors'.
1. Origins and Turning Points in a Career
1-1. From Engineering to Finance
Savi majored in electrical engineering and conducted research on remote sensing during his university years. A turning point came in the early 1990s when he took a course on derivatives and became fascinated by the mathematics of financial engineering. For his graduation thesis, he chose the mathematical modeling of derivatives and incorporated programming, which was rare at the time. This experience marked the beginning of his career as a 'quant'.
1-2. Experience in Italy and the Move to BGI
Savi spent the first decade of his career at the Italian investment management firm Capitalia, eventually rising to the position of CEO. At 35, he moved to London and, through a chance connection, joined Barclays Global Investors (BGI). BGI was a pioneer in factor investing and quantitative strategies, and it was a company Savi had long admired.
1-3. Integration with BlackRock
In 2009, BGI was integrated into BlackRock. Savi remained after the merger, drawn to the company's unique ability to blend diverse investment approaches, such as 'active vs. passive' and 'fundamental vs. systematic'.
2. The Evolution of Quantitative Investing
2-1. The Era of Factor Investing
BGI's early strategies were centered on what we now call factor investing. Strategies designed to capture market risk premiums over the long term performed well after the dot-com bubble burst and saw rapid growth in the mid-2000s.
2-2. The Second Wave: Big Data and Machine Learning
Since 2009, BlackRock has introduced big data and machine learning. The firm shifted its focus beyond simply improving traditional factor models to developing entirely new predictive models. This led to the creation of more dynamic strategies capable of adapting to market environments.
3. Three Transformations Brought by AI
Savi categorizes the impact of AI into the following three points.
3-1. Scale
As noted in 'The Bitter Lesson,' the expansion of data volume and computational resources is the primary driver of improved model accuracy. He points out that the investment industry has yet to fully grasp this importance compared to other sectors.
3-2. Generality
Large Language Models (LLMs) are groundbreaking in that they can be used intuitively by non-experts. This allows domain experts outside of quantitative teams to contribute to the investment process.
3-3. Safety Engineering
Just as in the automotive and aviation industries, it is becoming possible to use AI to enhance the safety and durability of portfolios. He notes that it is not just about pursuing alpha, but also about improving shock resistance and the certainty of achieving goals.
4. Expanding Quantitative Investing to Private Markets
4-1. Changes in the Data Environment
Traditional quantitative models relied on time-series data such as stock prices and trading volume. However, it is now possible to analyze companies using unstructured data such as social media posts, product reviews, and job postings, expanding the scope of application to private companies as well.
4-2. Public-Private Hybrid Portfolios
In the future, building optimal portfolios and risk models that span both public and private markets will become crucial. AI will serve as a bridge through the analysis of unstructured data.
5. Recent Market Volatility and Lessons Learned
Between June and July 2025, systematic strategies experienced drawdowns of up to 40% in some areas. The factors included losses on short positions due to the surge in high-volatility stocks and a chain reaction of position liquidations. Savi calls this a 'prime example of why the industry must never let its guard down.'
6. Risk Management Philosophy
6-1. The Dual Pillars of Systems and Philosophy
Risk management requires not only sophisticated numerical models but also philosophical judgment regarding 'what to prioritize.' He emphasizes the teaching that avoiding small losses prevents large ones.
6-2. Safety First
Citing the aviation adage, 'It is better to be on the ground wishing you were in the air than in the air wishing you were on the ground,' he emphasizes a stance of not forcing positions when one lacks conviction.
7. Success Factors for Quantitative Investors
Successful systematic investors possess not only high intelligence but also curiosity and openness. The flexibility to accept new opinions, even within one's own area of expertise, leads to long-term results.
8. BlackRock's Strength: An East-West Hybrid Approach
The lineage of quantitative investing includes the 'East Coast style' originating from investment bank trading desks and the 'West Coast style' of University of Chicago-style factor investing. BlackRock has built a unique model that fuses the talent and culture of both at a 50:50 ratio, capable of handling everything from short-term strategies to long-term, illiquid assets.
9. Summary and Outlook
Savi is convinced that the universality and scalability of AI will expand quantitative investing into areas previously unreachable. In particular, applications in private markets and long-term investing will be the greatest growth areas over the next decade. On the other hand, the unpredictability of the market and the importance of risk management remain unchanged. Future success will come to investors who utilize data and technology while never forgetting human judgment and philosophy.
