What Quants Want: Using AI and Data to Seize the Informational Advantage

Successful investing relies on the exploitation of information, which sounds slightly nefarious, but it isn’t. Quantitative (quant) strategies rely on the exploitation of numerical information, but we’re talking about hard data instead of qualitative measures like a trader’s mood. An estimated 1 in 5 hedge funds employ quantitative methods and 86% expected to increase use of those methods in the next five years.ⁱ Moreover, with agentic AI technology maturing at a furious pace, quants have joined an AI model arms race, a race to win the informational advantage. 

Quant strategies produced some eye-popping returns in 2024. Business Insider reported that Renaissance Technologies' Medallion Fund returned 30% for the year and Marshall Wace's TOPS fund recorded 22.7%.ⁱⁱ Quants have a huge thirst for new categories of data that might give them an investment edge, so the handling and modeling of that data becomes a mission-critical daily challenge. Numerous quant sub-strategies and fund structures also result in challenges on the middle- and back-office operations side. In this article, we will explore the state of quant strategies and ways hedge funds can optimize quant trading strategies with superior data management and agentic AI. 

Rise of the quants 

Once upon a time, quants managers set up shop in one corner of the investment ecosystem. Quants have since insinuated themselves into most managers and investors’ business. With so much pricing pressure on traditional public classes, most institutional asset managers (IAM) are embracing structured products, SMAs, private market assets, systematic strategies, quant businesses, and multiple quant funds. The days of the asset manager focused solely on mutual funds represent a bygone era. I can think of two IAMs right now that were once considered traditional mutual fund shops and now trade 25% of their business in quant strategies. You don’t like the cold calculation of quant trading? Tell your IAM to pop in some quantamental strategies, an active, hybrid strategy that fuses algorithmic analysis with, well, a human analyst’s instinct. 

Leading quant sub-strategies of 2024-2025 

BNP Paribas reported that in 2024, the top quant sub-strategy was Quant Equity with 14.6% returns, including sub-sub strategies Quant Equity Directional at 19% and Statistical Arbitrage at 17%. Quant, aka rules-based investing or systematic investing, are black box strategies, particularly when they’re proprietary. High-frequency trading, quant macro, risk premia, and factor-based investing all pose unique data demands and operational concerns. 

Overall, hedge funds captured over 2.5% more returns in 2024 than in 2023.ⁱⁱⁱ But that was last year, a year in which US policy was strikingly different. After two months of struggles in 2025 against the tectonic trade policy changes, hedge funds bounced back to chart a profitable April, including Citadel’s firm's fundamental equity and quant strategies fund which is up 3.2% in 2025 after a 1.9% gain in April.^iv One of the most successful has been the AQR Apex fund logging a +8% for the year (Jan-April), which, as all multi-strats do, includes quant. 

Rise of retail-ready quant investing 

American consumers are passionate about not be considered merely a number to businesses is a society-wide trend. Millennial and Gen Z cohorts famously want personalized service and more control. Retail investors have increasingly sought access to diversification and high-growth sectors often unavailable in public markets. Quant fund sleeves have found their way into retail portfolios. Correction, IAMs, hedge funds, and family offices have actively slid quant into various public-facing instruments like direct indexing platforms to reach high-net-worth and ultra-high-net-worth retail investors. The popular Smart Beta ETFs use a rules-based, systematic approach to choosing stocks from a particular index. They employ strategies that include equal weighting, fundamental weighting, minimum variance, and low volatility. Just like Factor ETFs, Smart Betas combine active and passive investing, supporting a trend towards more active consumer retirement planning. 

Firms dealing in asset tokenization, private credit, and quant strategies that lead the charge in bringing retail investors into the fold will inspire — and compel other firms to do so. IAMs’ foray into retail has only just begun to unfurl the opportunity. After all, there is a Great Generation Wealth Transfer underway in which Boomers and the Silent Generation will be sliding thick envelopes (~$84 trillion) to their children and grandchildren in staggering numbers.v  

Data quality demands of quant strategies 

Three clichéd phrases sum up quantitative investing strategies: Knowledge is power; time is money; and data is the new oil. Quant strategies have proven to be successful in recent years mainly due to the increased access to data. Data quality has become the 11th commandment, and it bears repeating. Garbage in... garbage out. The deluge of data flowing into our systems in the past two decades has created the roles of chief data officer, data scientist, and data analysts – even in inherently non-technical industries.  

Much of this data is poorly structured or unstructured, comes from disparate systems, is festooned with gaps that need smoothing out, and cannot be ingested, aggregated, or accessed by legacy systems. Quant managers use algorithmic trading platforms for real-time market data and order execution, so the external data must flow cleanly so it is immediately visible, easily accessed across the organization, and seamlessly consolidated for meaningful reporting and analysis.  

Gaining the informational advantage to drive excess returns 

According to quantitative research and investment expert Giuseppe Paleologo, risk, liquidity, flow predictability, funding restraints, and informational advantage (in predicting future returns) are the main factors for quants in driving the desired excess returns.vi Firms’ data leaders should adhere to the six dimensions of data quality to build their informational advantage. Instead of trying to make decisions based on a haphazard morass of data, analysts and portfolio managers should be making decisions based on reliable, trustworthy information.  

However, firms must have the plumbing to handle data flowing in on ratios, balance sheets, benchmarks, corporate actions, as well as alternative data, macro, and volatility. Alternative data includes information like satellite images, credit card receipts, NLP sentiment indices, and social media sentiment. It employs rigorous technical and mathematical analysis and econometrics, unique and diverse non-traditional economic data. Data scientists are tasked with transforming data into viable and cleaner datasets to feed into their quant models, including risk measurement, market impact, and expected return models.  

Additionally, these sophisticated strategies bring added complexities to middle- and back-office operations as much as data infrastructure. Quant strategies generally come with higher trading volumes, so achieving high straight-through-processing rates is essential. You want no or nearly no trade kickouts or reconciliation breaks. If you have hundreds of thousands of open positions, even a fraction of them having breaks would require a bigger Ops team to handle them. Firms must be able to scale along with their quant strategies. 

The quant arms race: AI and alpha 

And there is AI, no longer a postponable initiative. The race to pay tech debts has intensified as financial services firms move to integrate agentic AI tools into their tech stacks. Reuters reported in March that Chinese asset managers have leapt into an AI arms race, including funds like Baiont Quant, which uses machine learning to trade markets with no human intervention. U.S. systematic trading firms are doing likewise, exploring use cases across the board. Smaller asset managers could use AI to catch up to the big players using it for quant strategies, with faster trade execution and real-time responses to market fluctuations, thereby improving operational efficiency.  

“It's equal part engineering, equal part dev-ops, and equal part data science. All three are coming together to inform how AI agents work and perform. AI agents in finance need to have a certain level of observability, traceability, and fidelity in operation before pushing it into an investment lifecycle product.” – Arcesium Vice President of AI/ML Ashwin Swarup 

Chief digital officers are MVPs in driving algorithmic strategies success via data infrastructure, governance, and quality. Two thirds (66%) of CDOs say they are actively fixing their organizations data quality in order to better adopt AI.vii  

Mining orderly information from chaotic datasets 

In a digitally transforming world of capital markets where quality data is the key to driving risk-adjusted returns, quality data is even more key to driving returns when using systematic, algorithmic, or other quant strategies that in essence systemize data to bring order from chaos. Quant strategies are evolving fast, powered by AI, data analytics, and institutional adoption. This makes clean, scalable data systems indispensable for success as firms race to harness algorithmic insights and outperform in volatile markets. 

5 Key Takeaways  

Q1: Why are quant strategies gaining momentum in 2025? 

A1: Strong 2024 returns, AI-driven alpha, and increased institutional interest are fueling quant strategy growth. 

Q2: What are the top-performing quant sub-strategies? 

A2: Quant Equity, Statistical Arbitrage, and Multi-Strats were leaders, delivering double-digit returns in 2024. 

Q3: How is AI impacting quant investing? 

A3: AI enables faster trade execution, real-time market response, and efficient data processing, giving firms a competitive edge. 

Q4: What challenges do quants face with data? 

A4: Data quality, integration across legacy systems, and the handling of unstructured data are key obstacles. 

Q5: How is retail investment influencing quant adoption? 

A5: Direct indexing and smart beta ETFs are extending quant strategies into HNW and retail portfolios, driven by demand for personalization and access.