Odd Lots Podcast Summary: Giuseppe Paleologo on Quant Investing at Multi-Strat Hedge Funds

Release Date: June 21, 2025

Bloomberg's Odd Lots podcast features insightful discussions on finance, markets, and economics. In this episode, host Jill Weisenthal engages with Mark Reape, the Global Head of Quantitative Research at Balliesny Asset Management (referred to as Gappy Pagliologo in the transcript), to delve into the intricacies of quantitative investing within multi-strategy hedge funds. The conversation, recorded live at Bloomberg's Reimagining Information Forum on June 12th, offers a comprehensive exploration of quantitative strategies, factor models, data utilization, and the evolving role of artificial intelligence (AI) in investment management.

1. Introduction to Quantitative Investing

Jill Weisenthal opens the discussion by prompting Mark Reape with a fundamental question: "Isn't all investing quant investing nowadays?" This sets the stage for a deep dive into what differentiates quantitative investing from other investment methodologies.

Mark Reape responds affirmatively, highlighting that while many investors incorporate quantitative elements, the degree and sophistication vary widely. He shares an anecdote about a friend at a Tiger Cub firm rejecting basic quantitative tools like Sharpe ratios, illustrating the spectrum of quantitative adoption in the investment community ([02:26]).

2. Defining Quantitative Investing

The conversation shifts to delineate what constitutes quantitative investing.

Mark Reape references Cliff Asness’s definition, describing quantitative investing as involving "a large cross-section of assets having a relatively low edge, low expected return in all of them" ([03:20]). He emphasizes that quantitative investors manage numerous independent or quasi-independent bets, requiring scalable methods to handle vast portfolios effectively.

Notable Quote:

"What matters really is the number of bets in a sense that you are going to take." – Mark Reape ([03:20])

3. Role and Responsibilities in Quantitative Research

Jill Weisenthel inquires about Reape’s role as the Global Head of Quantitative Research.

Mark Reape outlines his responsibilities, which include developing factor models for equities, hedging strategies, and providing portfolio advisory services. He humorously notes that his role involves being in meetings most of the time, offering centralized quantitative services across the firm ([04:48]).

4. Factor Models and Idea Generation

A significant portion of the discussion focuses on factor models—core components in quantitative investing.

Mark Reape defines a factor as an attribute assigned to securities that systematically influences their returns. Essential characteristics of a factor include:

1. Pervasiveness: Affecting a wide range of assets.
2. Persistence: Providing consistent returns over time.
3. Interpretability: Being logically understandable and meaningful.

He distinguishes factors from themes, which may not meet these criteria. For example, AI is labeled a theme rather than a factor due to its limited and fluctuating impact on the investable universe ([06:35]).

Notable Quote:

"Factor models are the backbone of a lot of quantitative investing nowadays." – Mark Reape ([06:35])

5. Alpha Generation and Factor Identification

Jill Weisenthel probes into the source of alpha in quantitative strategies, questioning whether identifying unique factors before competitors is the primary value driver.

Mark Reape explains the dynamic nature of alpha, stating, "somebody else's factor is my alpha and vice versa" ([09:57]). He emphasizes that well-known factors like value and momentum now offer "priced returns," meaning they come with associated risks and are no longer pure alpha sources. The true alpha lies in uncovering novel, less-exploited factors that can be effectively integrated into investment strategies.

Notable Quote:

"Factors that exist in some frequency or in some universe, or with some characteristic that nobody else has found yet, and so they can be exploited more." – Mark Reape ([10:08])

6. Isolating Factors and Managing Overlaps

The discussion addresses the methodological challenges in isolating specific factors without conflating them with others.

Mark Reape acknowledges the difficulty but suggests that with a robust model, it's feasible to separate overlapping factors. He illustrates this by explaining how portfolios can be constructed to exploit individual factors independently ([11:49]).

Notable Quote:

"If you have multiple factors, they’re somewhat overlapping, but not completely overlapping, then you can build a portfolio that separates the impact of one from the other." – Mark Reape ([12:31])

7. Execution Research in Quantitative Strategies

Tracey Alloway shifts the conversation to the less glamorous but critical aspect of execution research.

Mark Reape explains that execution research involves understanding and mitigating trading costs, such as liquidity impacts and market microstructure effects. He distinguishes between high-frequency trading firms, which operate on micro-level data without traditional market impact models, and hedge funds that require sophisticated market impact models for portfolio optimization ([18:54]).

Notable Quote:

"Market impact is a very, very sizable fraction of the lost P and L of a firm." – Mark Reape ([18:54])

8. The Role of Artificial Intelligence and Machine Learning

The advent of generative AI and large language models (LLMs) is another focal point.

Mark Reape expresses cautious optimism about AI’s role in enhancing productivity, such as automating document handling and information retrieval. However, he notes that in investment strategies, AI's application is more complex. He mentions advanced machine learning algorithms being employed in data-rich environments but remains uncertain about AI's long-term impact on slower investment styles.

Notable Quote:

"Everybody is trying to be more productive with AI, right?" – Mark Reape ([20:31])

9. Data as a Competitive Advantage

The conversation underscores the importance of proprietary data sets in maintaining a competitive edge.

Mark Reape contends that unique data sources, like observing portfolio manager behaviors or having access to extensive historical and proprietary data, are invaluable. He suggests that large firms with substantial data repositories and multiple portfolio managers are better positioned to leverage this advantage, though he remains skeptical about the ease with which others can replicate such benefits ([26:10]).

Notable Quote:

"Maybe that will work out." – Mark Reape ([26:32])

10. Challenges: Regime Changes and Backtesting

Quantitative strategies often rely heavily on historical data, making them vulnerable to unforeseen regime changes.

Mark Reape acknowledges the difficulty in detecting and adapting to regime changes using quantitative methods. He shares his experience that algorithms for regime detection rarely work effectively. Instead, he suggests monitoring changes in portfolio manager behavior as a more reliable indicator of shifting market conditions ([30:27]).

Notable Quote:

"Regime change is very difficult to detect and to act on in an effective manner." – Mark Reape ([30:27])

11. Evaluating Traditional Factors in the Modern Market

The persistence of traditional factors like size, momentum, and value is debated.

Mark Reape observes that some factors, such as size, have lost explanatory power when combined with other characteristics. Others, like medium-term momentum, still offer positive Sharpe ratios and thus merit inclusion in portfolios. He notes that while some factors have become commoditized, others retain their effectiveness, albeit often with lower returns.

Notable Quote:

"Medium term momentum is tradable and it’s relatively high capacity." – Mark Reape ([33:27])

12. Conclusion and Final Thoughts

Wrapping up the conversation, Mark Reape emphasizes that in the realm of quantitative investing, there is no such thing as "bad alpha." Every alpha signal represents a unique opportunity, and it's the quant’s role to discern and exploit these signals effectively.

Notable Quote:

"No, every alpha signal is, you know, God's little child. There is no bad alpha." – Mark Reape ([34:53])

Jill Weisenthel thanks Mark Reape for his insights, concluding the episode on a reflective note about the evolving landscape of quantitative investing.

Key Takeaways

• Quantitative Investing Defined: Involves managing numerous small, independently identified factors across a broad asset base.

• Factor Models: Essential for systematic investing; must be pervasive, persistent, and interpretable.

• Alpha Generation: Shifts from well-known factors to novel, less-exploited ones to maintain competitive advantage.

• Execution Research: Critical for minimizing trading costs and understanding market microstructures.

• AI and Machine Learning: Offer productivity enhancements but present complex challenges in strategy development.

• Data as a Cornerstone: Proprietary and unique data sets are paramount for sustaining edge in quantitative strategies.

• Challenges: Regime changes and the limitations of backtesting necessitate adaptive and flexible approaches.

• Traditional Factors: Some retain value, while others have diminished, requiring continuous reevaluation and adjustment.

This episode provides a nuanced understanding of the current state and future directions of quantitative investing within multi-strategy hedge funds, highlighting both the opportunities and challenges faced by professionals in the field.