Giuseppe Paleologo on Quant Investing at Multi-Strat Hedge Funds

Summary

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.

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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]).

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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])

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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]).

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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])

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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])

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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])

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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])

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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])

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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])

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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])

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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])

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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.

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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.

Transcript

[Mark Reape]

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[Jill Weisenthal]

News hello and welcome to another episode of the Odd Lots Podcast. I'm Jill Wiesenthal, normally joined by my co host Tracey Alloway, but she's on vacation today, so it's just me in this intro. But in today's episode you will hear a conversation taped live at Bloomberg's Reimagining Information Forum. On June 12th we spoke with Gappy Pagliologo, Global head of Quantitative research at Balliesny Asset Management. He has a new book out. It's called the Elements of Quantitative Investing. Neither of us have read it because it would go way over our heads because we're not quants. We don't know how to read that stuff. But Gapi is great at explaining all of this stuff in clear English. So we had a great conversation and we hope you enjoy listening to it.

[Tracey Alloway]

So just to begin, I'm going to start with a really, really dumb question. Possibly, but isn't all investing quant investing nowadays? I mean every investor has access to some form of quantitative girl using numbers?

[Mark Reape]

Yeah, I guess. Yes. End of answer. Yeah, I think so. I mean pretty much everybody uses some kind of quantitative overlay, but to different Degrees. So I have a friend who worked for one of the Tiger cubs and they refused to use Sharpe. They refused to use logs in a spreadsheet because they said that they were dangerous. Probably they took the log of a negative number. And so yeah, no, to different degrees, but yes, there is some quantitative culture seeping through.

[Tracey Alloway]

Okay, so what defines quantitative investing? How would you differentiate that from, I don't know, value investing, discretionary investing.

[Mark Reape]

Okay. I think that there are several possible answers. I'm going to go with one answer that I read in my life as a quant. I think it's a wily book. It's a very good book. By the way. I think Cliff Asness defined quantitative investing as basically investing in, in a large cross section of assets having a relatively low edge, low expected return in all of them. And so that's his definition, but it's not quite, I think, complete enough at this point. Because you can also be a quantitative investor trading a relatively narrow cross section of assets, but with high, high frequency. Right. So what matters really is the number of bets in a sense that you are going to take. Right. So I think that probably is if you have a large number of independent bets or quasi independent bets. This means that you need to be able to scale your method to a large number of independent bets. And this means that you are in some way a quantitative investor.

[Jill Weisenthal]

Speaking of roles and jobs. Role, what do you. Global head of quantitative research at Balliasney. What's your job? You've been there about six months. What does the job entail at a fund? At a firm like B.

[Mark Reape]

Okay, global head of quantitative research. Okay, so basically I am the head of quantitative research for equities. And maybe one day in the future I will do, you know, some commodities or fixed income. But I'm perfectly happy to, to serve equities both discretionary and systematic. What we do is, I mean, my group mostly, I mean, I am in meetings, so I don't do any work. So we in a sense provide centralized quantitative services for the firm. So the first backbone thing that we do is you develop factor models wherever you can. Right. So for equities at different horizons, ideally you would like to develop them for other asset classes. But you know, factor models are the backbone of a lot of quantitative investing nowadays. And then hedging at the firm level and at the individual PM levels, which is apparently very simple, but actually it's very deep as a problem. And then we do portfolio advisory services, which is basically you go to PMs, you help them construct better Portfolios, you help them understand their performance, which is extremely important, Manage their risk, manage their drawdown. On occasion, be their therapist. But this is what, what we do.

[Tracey Alloway]

I know you're in meetings all day, but, you know, if you were someone on your team, how would you be coming up with actual ideas for factors? I hear people who sometimes come up with ideas from odd lots episodes. Some of them have even turned out reasonably okay. But how does idea generation work? You sit down, you're like, I need to come up with a new factor today. What are you doing? What are you looking at?

[Mark Reape]

Okay, I want to specify a little bit more. What's a factor? Because otherwise gets a little bit too vague. So there are factors and factors. There are some factors that are real factors. What are those? Those are essentially attributes of some kind that you can assign to your investable universe. And there are sources of returns that affect the individual securities through this characteristic. And they are pervasive. So every asset is in some form affected by this systematic source of return, number one. So they've got to be pervasive. The second thing is they got to be persistent, right? So it's not the. The case that I have a lot of factor returns for two months and then nothing for 10 months, right? So that's not really a factor. And then possibly the third characteristic is that they have to be interesting, so they have to be in some way vaguely interpretable. So when you match these requirements, it's a factor. Now imagine that you have the Trump factor. Let's say if Trump wins, a few stocks will definitely benefit. A few stocks will definitely not benefit from the election of Trump versus Kamala Harris. Another source could be, well, tariffs, right? Another source could be AI. Okay, AI definitely doesn't fit the characteristic of being pervasive because there is a relatively small universe that's affected by the AI. Theme is likely not going to be persistent. So it wasn't here like a few years ago, and it will probably not be here in five years because everything will be to some extent, AI. It's interesting, but that's a theme, it's not a factor. That's what I would call a theme. And there are also some mathematical characteristic of a factor versus a theme.

[Tracey Alloway]

Like what?

[Mark Reape]

So basically, you can create a portfolio that, that tracks a factor, and this portfolio will have a relatively small idiosyncratic risk. So it will be truly a reproduction of the systematic source of return that you were observing through the assets. So imagine that this systematic source exists, but you do not observe it directly. It's latent. It's out there, but you can actually reconstruct it. With a portfolio, a theme is, let's say 10 assets. You cannot really reconstruct it the same way because 10 assets are just too few to diversify away the idiosyncratic source of returns of the individual assets.

[Jill Weisenthal]

So when you're thinking about factor identification, how much of the money that you make, the actual returns come from essentially factor identification or being able to identify a factor before other measure, identify a factor that exists before other competitors out there in the market.

[Mark Reape]

Okay, that's a great question because I know think, I think I know the answer.

[Jill Weisenthal]

Okay, great.

[Mark Reape]

But the reality is this, I think you know, somebody else's factor is my alpha and vice versa.

[Jill Weisenthal]

Right, so say more.

[Mark Reape]

There are well known factors, let's say some variety of value and momentum or reversion, and you can bet on those. And you diversify away everything else. And what you get is basically you get some returns that are priced, priced in the sense that as you know, you pay basically some risk for that. So this is priced return. And that's great. But once upon a time these were not public knowledge. If you were lucky enough to be a hedge fund in the 80s, and I've, I've met a few, you know, and you are maybe also investing in Europe, these factors were really working very well and they were alpha. They were not called factors. You know, the first I think published paper is probably 89 for, for momentum right now. There is alpha. And alpha is basically, ideally would be a return that has no associated risk to it. It hardly ever exists. So what you really have are factors that exist at some frequency or in some universe, or with some characteristic that nobody else has found yet, and so they can be exploited more.

[Tracey Alloway]

How do you make sure that when you're isolating a particular factor, you're not accidentally taking into account some other dynamic? So, you know, maybe you want to invest in a bunch of companies with like pricing power during the tariffs, but actually your cohort of companies ends up just looking like a bunch of big tech companies or something like that.

[Mark Reape]

I mean, the short answer without explanation is that you can, but the long answer is a little bit more involved. If you have true characteristics like, I don't know, a tariff and a tech classification that are 100% correlated, well, then you really have only one. You don't need both. Right? So, okay, but if I have, in my, let's say arsenal of factors, if I 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.

[Tracey Alloway]

So you try to isolate them.

[Mark Reape]

You can isolate them, you can kind of purify them. Now there is also the scenario where there are factors that are not in the model and they should be, and basically those complicate the picture a little bit. But otherwise, if you have a reasonable model, you're going to be able to separate them. To understand what's the relationship, you can create a portfolio that exploits the first one and then create a second portfolio that is uncorrelated to the first one that exploits the second one. Example.

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[Jill Weisenthal]

Just zooming out for a second again, and this sort of relates to Tracy's first question, but also I guess relates to my first question. If you have a fund and it has various PMs and analysts in there, is there a difference between quant at your level, which is at the fund level, versus say a POD or a PM whose specialty is quant trading? And there are different definitions or different senses in which that term can apply?

[Mark Reape]

Yeah. The fact is that quant is a very generic label nowadays. So there are many, many quants and they do all sorts of very interesting jobs. Some of them are just differentiated because they live in different constructs. So nowadays in a platform, especially in a quantitative one, it's not impossible to see pods and center groups. Okay, so that's one distinction. So what's the difference? In a pod, you typically have a siloed group. I'm probably stating the obvious, but you know, you have a siloed group, they don't communicate with other pods. You want at the firm level to have independent sources of alphas and their payout typically is a percentage of their P and L after costs. Okay. And then you're a quant in a pod. In a center group, typically you are part of a larger group and the group will hopefully have large capacity. So these have a larger program, like a larger research program. Their compensation tends to be more discretionary and that's a center group. Then you have all sorts of other quants. So you have people like me who serve the firm at the center level. I also serve the leadership of the firm. And then you have people who doing, for example, execution research, which is extremely complex and interesting. So it's not black and white. You can do execution research and be responsible for some P and L. It's very, very rich nowadays and very specialized.

[Tracey Alloway]

I was actually going to ask about execution because when we're talking about quant investing, I think a lot of questions are around factors and Idea generation. But you have all the, I would assume, boring stuff like liquidity trading costs that you also have to think about. How do you actually incorporate those into your strategies?

[Mark Reape]

So you can do it in a variety of ways. It depends first of all what position the firm occupies in the ecosystem. So if you are a high frequency trading company, most likely you are using your own capital because you are capacity constrained. So you don't need a lot of capital. So those firms exploit market microstructure level information. So in a sense, a high frequency trading firm does not have a market impact model in the traditional sense. They don't see parent orders. Right. They execute at the microscopic level. If you are a hedge fund, typically you trade a lot. You have your own data set of orders. These data sets differ a lot. So you could have a market impact model for a quantitative trading group or a strategy. And you could have a different market impact model for hedging and a different market impact model for fundamental investing. And then what you get is basically a term, a function that you place in your optimization problem that hopefully helps you size the portfolio or trade the portfolio optimally. And this is extremely important. You know, market impact is a very, very sizable fraction of the lost P and L of a firm.

[Jill Weisenthal]

As of today. What value is there in your world of specifically generative AI, LLMs, et cetera? How do you currently or not currently get actual value out of them?

[Mark Reape]

Okay, so on this I have really relatively little to say that's original.

[Tracey Alloway]

But tell us everything your employer is doing with AI.

[Mark Reape]

Yes, they'll send you the resume.

[Tracey Alloway]

Thank you.

[Mark Reape]

But I think. Okay, just let's recap the basics. Right? So the basics are, at least for the time being, everybody is trying to be more productive with AI, right? So you want to have all your documents, you want to have now, you know what? Perplexity has a finance module. I think one day soon maybe Bloomberg will not have the keywords any longer. You just give Bloomberg a task and it will grab all the pieces of information and hand it over to you and maybe you can schedule it. All of this is relatively table stakes. I mean the, the agentic aspect is not yet, but it will become pretty soon. I think it's going to be very hard to compute with the likes of maybe Bloomberg. But for sure, let's say, you know, the big hyperscalers. So that's one at the investment level, it's, it's much more complicated. So in, in strategies where there is a natural richness in data, you can definitely use, if not deep learning or AI, but you can definitely use very advanced machine learning algorithms and you do not have a data snooping problem, you do not have a backtesting problem. And so you are in a data rich environment and you can do that. And it's not a secret that for example XTX has a very large on prem, you know, number of Nvidia cards. I don't remember H100 or something like that. So that's one thing, right? The question is really what's going to happen to the slower investment styles. And my view is that hopefully large firms like mine will have an advantage. But we'll see, right? Why? Because we do have the scale, we have a large number of PMs who we have a lot of historical data, we have a lot of proprietary data that nobody else has. So maybe that that will work out. But how to make it happen I don't know because things are changing so fast and also I'm relatively a tourist in the area so I'm trying to learn a little bit more about foreign.

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[Tracey Alloway]

Mentioned proprietary data and this comes up a lot where people talk about, well, the competitive advantage nowadays really is that data set. I mean, is that, is that true? If I get something really cool and unique, I can automatically become, I don't know, a billionaire trader if I can figure out how to execute on it. Is that all there is?

[Mark Reape]

Maybe yes. I have very weak beliefs on this. I don't know. Maybe yes, we'll find out.

[Tracey Alloway]

Well, so where are people getting interesting data sets from?

[Mark Reape]

I mean, you get interesting data from observing human beings actually investing. And you don't get to see a great PM investing, but I do. That's the benefit.

[Jill Weisenthal]

So from your central position, you just get to see a lot of activity and you get to see novel data that other people don't get to see simply by being in the center of all of these different trades and everything. And that gives you a sort of higher abstraction layer or whatever it is that someone else in the market doesn't have.

[Mark Reape]

Yeah, and it's possible that not in the distant future good PMs will become good because they can improve on themselves by basically playing or training or having a baseline of an agent that reproduces their behavior. So there is an alter gap. Well, I'm not a pm, but an alter whatever who says what would you do? And you get a baseline behavior and then you can think about it and you could say, well, I would do something different. And then that becomes an example in a Reinforcement learning process. Process where the AI keeps learning from you and you keep improving because the baseline is changing.

[Tracey Alloway]

So before we came out here, I asked Perplexity to come up with a new factor, and it came up with something called the policy agility factor, which is supposed to be that countries that display policy flexibility have better outperformance over the longer term. Countries that are able to more quickly adapt to changing situations are out performers over the long run. Can you grade that factor? I didn't do a back test, but like, if someone brought you an idea like that, not me, Perplexity, I don't want you to insult me over the next five minutes. What would you say to them? What are the problems with this?

[Mark Reape]

I mean, no major problems. There are questions. So the first thing that you want to make sure is that if AI, whatever it means, brings to you a definition, Right. That definition should be at a point in time and should not be trained on all the past data. So, number one, you want to do that because if you backtest that feature, and in a way, Perplexity has already tested it, it's not a fair play. You know, the performance will. The back test will look great. So unfortunately, we live in a world where some factors will never be back testable. So you don't know whether they work or they don't work. Right. You just know that you cannot test them in advance, like policy agility. This seems to be a very low turnover factor. Right. And it seems to be probably a.

[Tracey Alloway]

Very low sharp factor and a low universe of.

[Mark Reape]

And a small universe. So how do you know? Well, probably you want to make sure that it makes sense and maybe you can start putting a small volatility allocation.

[Tracey Alloway]

To it and then you would build it up as you build it up.

[Mark Reape]

Build it up. Yes. Out of sample.

[Tracey Alloway]

Okay. So speaking of backtest, I have one more question, but it seems like quant investing, part of the issue with this is you are looking back at historical data. That's all you have. You don't have data about, about the future. Unfortunately, it strikes me as hard to deal with regime changes. So when you have a big break in how something works in finance or markets or the global economy, how does quant investing actually take into account those sorts of risks? Like say, you know, a lot of investing is based on the idea that bonds and stocks are going to move inversely to each other, and then in 2022, they started moving together.

[Mark Reape]

I think that most people with a quantitative background in finance will tell you that regime change is very difficult to Detect and to act on in an effective manner. So I think that's been my experience at least. Right. So in every possible application I've tried and it really never works for me. Maybe it works for somebody else. What I think it's a bit easier to do is to detect regime change in a human being. So instead of trying to use. There are many algorithms for regime change. There are Markov based qsum, completely non parametric. Instead of trying to act on regime changes in the environment, try to detect changes in the behavior of a portfolio manager and act on that. Because that works, I think, and usually jives with experience. So that is something that can be exploited.

[Jill Weisenthal]

I want to go back to an answer you gave early on, which is sort of like the old school factor investing and like the original versions. And maybe there was sort of a international factor or a liquidity factor, the small cap factor, the value factor. And it feels like a lot of these things haven't worked in ages. And there's this debate that seems like, okay, is this the long cycle and eventually it's going to come back, or is it that everybody not only knows about these factors that have discussed them to death, they're also extremely commodified in the sense that you could just buy an ETF of them.

[Tracey Alloway]

Right?

[Jill Weisenthal]

You could just buy a small cap etf, it's trivial to execute. You can just buy a momentum etf, it's trivial to execute, a value etf, et cetera. My intuition would be, since everyone knows about them and they're completely commodified technologically, they're just gone. But there is still debate. Some people think it's only a matter of time before these come back in vogue and that it's the long cycle, et cetera. I'm curious how you think about some of the original factors that people discuss in their prospects going forward.

[Mark Reape]

Well, so some factors were identified, but then somehow they got demoted. So famously size. Right. So conditional on having other characteristics of a stock size doesn't really explain much of your returns. And so it's a combination of other factors. Okay, well that's, that's one case. Then there are cases where it seems that some factors have been exploited. You know, their capacity has been exhausted and so you can't make an attractive return of them. There are some factors that still have a low Sharpe, but they still have a positive sharp. And so, you know, every positive Sharpe deserve however small an allocation.

[Jill Weisenthal]

What's an example of that?

[Mark Reape]

Medium term momentum. Right. Okay. Medium term momentum is tradable and it's relatively high capacity. Then you have the whole term structure of momentum. So there is a shorter horizon reversal and whatnot. Short interest worked great until it didn't really work so consistently any longer. And then they also assume different characteristics. Right. So you start having more crashes and the like.

[Jill Weisenthal]

Is there a meme factor?

[Mark Reape]

No, I don't think so. No. But has that changed anything or something like that? Sorry, it's a theme or.

[Jill Weisenthal]

It's a theme.

[Mark Reape]

Okay. Yeah, I don't know that ESG is a factor either. I don't think so.

[Jill Weisenthal]

Okay.

[Tracey Alloway]

Oh, why do you say that?

[Mark Reape]

Because I don't think it's really that persistent.

[Tracey Alloway]

You mean it doesn't affect human behavior?

[Mark Reape]

I think that just. I mean, there is also this feature, right. The moment that you say that a factor exists, it's reflexive, right? Yes, there is reflexive reflexivity in this. Right. But I. I don't know that it really explains much of the returns in recent times.

[Tracey Alloway]

So I'm going to ask one more question because I started with a dumb one and so I will finish with another dumb one. Is there good and bad alpha or is bad alpha just beta?

[Mark Reape]

No, every alpha signal is, you know, God's little child. There is no bad alpha.

[Tracey Alloway]

All right, Gabby, thank you so much for coming back on Odd Lots.

[Jill Weisenthal]

We're gonna leave it there. That was our conversation with Gappy Paleologo. I'm Jill Weisenthal. You can follow me at the Stalwart. Follow Tracy at Tracy Alloway. Follow our guest Gappy, he's Polliologo. Follow our producers Carmen Rodriguez, Erminarmond, Dashiell Bennett at dashbot and Cale Brooks at Kalebrooks. For more Odd Lots content, go to bloomberg.com oddlots where we have a daily newsletter and all of our episodes. And if you enjoy the show, please leave us a positive review on your favorite podcast platform. Remember, Bloomberg subscribers get to listen to Odd lot and free on Apple Podcasts. Just go to the Apple Podcasts app and follow the instructions there. Thanks for listening.

[Gappy Pagliologo]

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[Mark Reape]

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[Mark Reape]

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[Mark Reape]

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[Gappy Pagliologo]

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[Mark Reape]

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[Tracey Alloway]

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[Mark Reape]

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