How AI Is Transforming Quantitative Finance

(upbeat music) - Welcome, everyone. This is season three of the Future of Finance Podcast here at the Walton School. I am Etae Goldstein, a finance professor and currently the chair of the finance department. We devote the third season of the Future of Finance Podcast to this very exciting new topic of AI in finance. And today we are gonna dive deep into the topic of how AI is reshaping quantitative finance. Quantitative finance has been one of the fast growing areas of finance both in research and in teaching. We just launched the new Bruce Jacobs masters in quantitative finance program in order to help our students get more familiar with some of these new techniques in quantitative finance and in particular AI in finance. And we're gonna try to get an understanding from both the academic perspective and the practitioner perspective on how AI is used in quantitative finance. And for this, we have two perfect guests. First, let me introduce my colleague here at the finance department, Nick Rosanov. Nick Rosanov is the Moise Safra professor of finance, and he has been with us for about 20 years now. He is also the MBA major quantitative finance advisor, Nick, it's great to have you. - Always happy to be here. Thank you for bringing me on. - Thank you. And together with Nick, we are very happy to have Ingrid Tyranns, who is the head of the data strategy team in the global investment research division at Goldman Sachs. She is also a part of the Walton community as a member of the advisory board for Walton's Jacobs Levy equity management center for quantitative financial research. This is where a lot of the activity on quantitative finance here at Walton is taking place. Hello, Ingrid. - Thank you, and great pleasure to be here. - So, let's dive right in. And I'm gonna start with you, Nick, for a little bit of an academic perspective. So I just said that you've been with us for about 20 years now here at Walton. And you have been heavily involved, I would say, in quantitative finance research. So you have seen a lot of the changes in this area. So can you maybe take us here on a little bit of an overview of how quantitative finance has changed in the last 20 years and how AI is shaping up to change it further? - All right, of course, thanks a tie. When we say quantitative finance historically, this has meant different things. Originally, kind of following the breakthroughs in the finance theory in particular, option pricing and so on in the 1970s, quantitative finance typically referred to a quantitative mathematical modeling of complicated financial assets in particular derivatives or bond pricing, understanding the structure, the terms structure of interest rates, the yield curve, and then various derivatives associated with interest rates and so on and so forth. So for many years, that was the view that the quantitative finance is basically using mathematical models to value complicated securities, the securities that have complicated payoffs and need some structures of mathematical model to understand them. Now, with the advent of even larger data sets, and now we talk about alternative data ranging from things like credit card transactions or satellite imaging and so on, as well as more powerful statistical tools at machine learning, this quantitative investing has grown beyond what would traditionally business credit as systematic and even some of the discretionary portfolio managers will use quantitative tools and data. And of course, risk management underpins everything that financial industry does is also, of course, very heavily quantitative. And so when we think about how is this being applied to quantitative finance, well there's several ways in which AI is revolutionizing quantitative finance. The most basic one is the same in which AI is being used elsewhere, which is just a productivity tool for model builders, for risk kind of researchers on the quantitative finance side, but as well as portfolio managers, that basically makes their more workflow a lot more efficient in terms of writing code. So as we know, that AI has revolutionized or maybe appended the software, software industry, and of course, quantitative finance is built on a lot of computer code and to the extent that AI is helping may have increased productivity and the production of that computer code that is obviously affecting that field. As far as kind of systematic quantitative investing, AI is represented by these very large multi-parameter models that can now use directly to, again, predict returns even better than what was done using earlier methods. And finally, people are using the large language models themselves without necessarily tinkering when the underlying architecture, but using the large language models themselves to analyze vast quantities of textual data, which they can do much better and much faster than human analysts to produce signals for a fundamental portfolio managers, including discretionary ones. - Okay, very good. So Ingrid, from where you see it in the industry, and obviously Goldman is a very important player in the industry, I'm trying to get a sense of how much of a game changer AI is. Is it just helping analysts do their job faster or is it doing completely different things from your point of view? - So I think the answer is of course, you know, everything, and I'm gonna expand a little bit on what was said before here. We talk about quantitative finance. It almost sounds like there's quantitative finance and there is non-quantitative finance. And I think, you know, if we look at the world right now, like all finance by definition is really quantitative and every single investment strategy or process, if you're not using data analytics, technology models, I don't really think, you know, that's 2026 anymore. And so for me, it's kind of, you know, similar, that's what we were saying before here, distinction that is more meaningful to me is there are different investment approaches, there is systematic and there is primarily discretionary. And so if you think about systematic to me, that is like a mix of, you know, very well-diverse fight portfolios, the bets are typically, you know, more limited discretionary side, obviously people have more conviction, human judgment still plays a larger role, and the portfolios tend to be more concentrated, but I think it's consistent across the board is AI is reshaping every single investment approach, whether that's systematic or whether that's discretionary. And if I look at what's going on, we talk to a lot of clients as well, they're kind of like three categories that I would look at. Like number one, AI is turning a lot of previously inaccessible information into data. So I think that's super important to think about. So again, if you kind of roll the clock back a little bit, typically investors were looking at structured data. So like, very simplistic example, like prices, earnings, economic releases. And even, you know, think about a simple example of where in the midst right now, earnings season, so you look at an earnings call, many years ago, a quant signal could simply be an earning surprise. There are people who build businesses around, earning surprises as their main quantitative signal. Right now, roll forward with the advent of AI, you can be analyzing every single sentence spoken by management companies, could be across thousands of companies, you can do it going back many, many years. Same is, you know, the case for filings, research reports, images, videos, you name it. And so from that perspective, AI is really turning an increasing share of human activity into data. And it's kind of interesting to think about it, right? Like if you think about digitization, we started with transactions and, you know, communications. And now we're actually, thanks to AI, we're starting to digitize reasoning itself, right? Like every single time when any of us are interacting with a chatbot, you have AI now capturing how you think about things. It's probably maybe the first time in human history that the thought process itself is being digitized and just think about, you know, the possibilities from that perspective. So the second thing I think that's important is AI allows people to connect information across silos, right? And that's, I think, a little bit coming to the scalability thing, the productivity thing you were talking about. So, you know, in the past, again, you know, time is limited, human capacity is limited. You would look at, like, predefined variables, you know, you would potentially construct strategies around that. AI can do a much better job at starting to shift to diverse sources of information and see if they can, you know, identify additional insight from that. And so again, you know, concrete example, an analyst at PM, you know, looking at, I can investigate 20 reports, all the stocks that I really care about now, you know, 20,000, you know, related documents, et cetera, and then a third one, which I think is very important as well going back to more the quantitative aspect, AI is also democratizing quantitative analysis itself, right? Like in the past, the people who could do quantitative analysis, you know, really taking advantage of sophisticated risk management tools, portfolio analysis tools, et cetera, tend to be the people who could code who are really the power users of these tools. Now you put the interaction with natural language in the mix, and I think, again, people like one or two steps we moved from that can potentially get way more use out of a lot of that tooling, the applications that may have been built many, many years ago, but the applicability is just, you know, going to become much broader. So kind of, you know, like addressing the question that you were asking here about, you know, does that mean that it's productivity play or is it, you know, beyond that, there is definitely productivity again, we just spoke about it, I mean, just think about it, like searching, reading, summarizing, translating, all of that, you know, it's complete table stakes right now. But I think what is really more exciting is the fact that the scope of what you can analyze and the opportunity set being much broader, it opens a lot of doors, right? Like again, going back to its earnings season, you know, you can kind of, you know, in the past, okay, what did management say about, you know, whatever, you know, some example of spending plans, now you can go back like, you know, did a market detect that, we want to analysis over multiple companies going back multiple decades, the other thing that I've realized from like talking to analysts as well is, you always have, as research, you're the same thing, you always have tons of ideas of things that you might want to analyze, we're all self-selecting if we have limited time and, you know, limited tools available and you're going to go like meh, low-probability event, I'm not going to do all of the work. AI again opens the door that you might start to analyze lower probability scenarios that potentially can yield inside that otherwise you wouldn't have been our country. And so sitting in a, in a cell site research division where the mandate is, you know, our goal is really to come up with differentiated insights in timely manner. The productivity gets to the timeliness, the scope and the increased opportunity set gets to the, you know, can be surface things that are more differentiated and make a difference from that perspective. So you mentioned democratization of financial analysis. And I think it brings up a very interesting question. What is AI going to do to competition in financial markets? I guess one scenario is, you know, these tools are now going to be available to everyone so it really opens up the field and there will be many new players and it's not clear who is going to have the comparative advantage. But another scenario is the barriers to entry are going to increase because it's really difficult to have the capacity to use all these tools and so it's going to make it less competitive. Where do you think we're going to go on that? I think there's definitely going to be an arms race of sorts. With AI, we will see financial firms, I think, entering and to have the second or maybe another level of horse racing, just like the AI builders themselves are competing on who has a better model and we have, you know, the vast investments into data center capacity to train bigger and bigger models and we will see this happening among financial firms as well. They will invest more and more in the tokens of those AI models that people use, but also build their own models and their own data centers at which they are doing already. Let's say, you know, XTX is a good example, is a British high frequency trading firm that's building its own data centers in Finland, which is of course a cold place, keeping lots of hot servers. So I think there will be a degree to which, you know, the arms race is going to make things more and more competitive. It is true that in order to be able to invest in data centers, you need certain scale, right? So in that sense, maybe this will further lead to further consolidation in this industry and it will be harder for smaller players to make a difference, but from the standpoint of let's say retail investors or ultimate hand of users of capital firms, right, it could still help make markets more efficient even though that competition will be larger and larger players going forward. I mean, I'll add a little bit to that, right? Like, you know, obviously, like as we were discussing, data is becoming more accessible, you know, the tooling is becoming democratized, but then at the same time, I think the noise to information ratio is increasing, you know, dramatically as well. And so I think the edge is still going to be coming from, you know, to really understand what's going on, right, like, you know, when does this fail, you know, where is the domain expertise connecting with what the tools are producing, and then really like, you know, how is this related to an investment thesis? And I think those are all, you know, still very much open questions, and so you can kind of think about it in a simple example, right? You know, very topical tool like, you know, suppose some, you know, AI tool finds that companies that are discussing AI are outperforming. That might be interesting, but is that causal, you know, is that a temporary phenomenon? Is that already priced into the market, does it persist, et cetera? And so I think the humans still have an important role to play. The other thing I would, you know, put here as well is like, like I always try to think about is a bit of an analogies of what has been going on with the asset management industry and of itself. And so if you kind of think about what happened with the introduction of index funds before index funds, any manager who could construct a broadly diversified portfolio could call themselves, you know, someone who's running an active strategy. So index funds completely change that ball game, and then if you kind of think, you know, you're all forward, since the advent of index funds, a whole lot of stuff that was called alpha has become completely, you know, become beta products, and the benchmark moved higher and higher. And so I draw the analogy here with AI doing the same thing with information. So gathering information now is becoming incredibly cheap. And so if your value was, I was the person who was sitting in the midst of information and could gather it, and that was your value add, you're probably going to have to rethink a little bit, you know, what you're doing here. And so it also starts pausing the question of like, what is your own personal alpha? Like what is the alpha of your business? And it's just, you know, a trademark of innovation that as things, you know, get entered, obviously we all need to move forward and hopefully, you know, we keep capturing or creating our own differentiated alpha and we keep staying ahead of the benchmark. Right. And I think that this touches on a very important question that we have with AI, which is what is AI and what is human? If AI can do all these wonderful things, then what is left for humans to do? And you know, connecting to some of the things you both said, I guess one possibility is that we still need humans to kind of put it all together and be a check on AI and make sure that it all makes sense and that there is an underlying intuition and underlying thesis. Or maybe another possibility is that there are still some pieces of information that AI cannot pick up on its own and humans are those who are bringing those signals from outside the system. So where do you see this tension between humans and AI? I actually look at the mess as being very complimentary, not necessarily, you know, competing, hopefully, you know, reinforcing it short. And so probably like maybe three things I would bring up and you kind of already hint. a little bit to some of them, the context and framing the problem, right? Like you can ask AI tools a lot of questions and it's always going to give you an answer. But the human is still deciding what are the questions that are really worthwhile asking, right? And like again, the imagination of the human of what is worthwhile pursuing that creativity, I think that's going to remain. Then the second thing, you know, judgment, then especially judgment, when there is uncertainty, and that can go back to like earlier models, you know, like the whole quant history, right? Like the same thing, yeah, you stick numbers in an optimizer and even if the differences are tiny, the optimizer is going to, you know, take it very literal. So you've got to build in, you know, uncertainty around it, etc. And again, you know, same thing here, right? Like AI is learning very, very quickly about a whole lot of stuff, but it's still to a large extent based on situations that have occurred in the past with probabilities around it, etc. And so, you know, you kind of wonder about rollback the clock, how would AI have dealt with episodes like, you know, COVID, geopolitical shocks, like all of the things were, again, we're doing it to a, you know, a narrow quant concept, like momentum, momentum works really well until there's a break in the system. And like again, same thing here, how, how will AI deal with that? I think it's still a little bit of an open question. And then last but not least, responsibility and accountability, right? Like at the end of the day, AI can create a ton of possibilities. The humans are still going to decide which of these possibilities deserve capital to be committed to them. And then also, if the outcome isn't really what you wanted, I don't think an AI tool is going to volunteer to take the responsibility for that. And I think that's an open question, you know, whole host of other fields as well. Forget about finance, where, you know, it's an AI driven outcome that someone may be pursuing, it goes wrong, who ultimately will take the responsibility for it. Nick, do you want to add to that? I mean, I agree with everything that Ingrid said. I think the key kind of point is that ultimately asking the right questions is still, I think, fundamentally, kind of the advantage of the humans and ultimately judgment. I mean, we've now seen that one can, why can have AI write what looks like an academic paper in finance, let alone constructive trading strategy. But the question is, is that is that a good question to ask? Is this a meaningful meaningful strategy to use? Is it a paper good? Not so far, I don't think we're there yet, but there are people who are trying. Now, that's not to say that AI will not, at the same time, act as substitute for certain types of human intelligence, right? We already have seen that coding is going to be a lot more commoditized than coding skills. Not that they're going to be irrelevant. You still need to be able to make sure that whatever the code that AI wrote for you actually makes sense and is doing what you want. And this is going to be a big challenge for a lot of companies going forward. They're relying on AI to produce their code base. But we don't necessarily need as many entry-level workers and software and maybe not as many entry-level workers ultimately, even though maybe we've not seen it yet. And let's say investment banking, where we need analysts basically sifting through company reports or putting together PowerPoint presentations, things that are, you know, AI is pretty good at. And on the one hand, that means that this effort will be saved and economized in a way that would allow these smart people to do something else and maybe more interesting. But it's also possible that it will reduce the headcounts that is already reducing headcounts at various entry-level positions. And the downside of that, of course, is that to be able to progress to become a portfolio manager, you have to be mentored by somebody, right? But if there's nobody to be mentored, right? And who's going to step into those shoes? Eventually, if they can have the bottom rungs of the letter, the letter going to hollowed out by AI that is going to mean that it's going to potentially that letter itself is going to have to change. I don't think, I don't think we have quite yet figured out how to kind of rebuild the career ladder in quantitative finance as well as in other fields in a way that takes advantage of the democratization of, let's say, research tools that in the past were only available to maybe select you have gone down to graduate programs and had extensive training. But also, we're going to preserve these men, they're kind of mentoring stages of one's career. And I don't know if Ingrid has any views on that, by the way. Yeah, I was going to maybe follow up on that and ask, so now with the launch of our new masters in quantitative finance, what are the main skills we should try to get for our new graduates? What is the main thing that they need to have in quantitative finance going into the job market? So I'll answer it a little bit from the practitioner's perspective. I'm going to keep this fairly simple, like one, learn data. And, you know, there is no AI without data. And I think with AI, sometimes people seem to think that that's going to solve all of the data problems magically, like get some experience getting your hands dirty with data because then you'll have a much better understanding of what potential shortcuts AI may or may not be making. It's a little bit a self-serving answer as well because my background and, you know, my PhD degree and my career here. If I need to reduce that to one sentence, it's like, I've spent my entire life trying to make sense of data. Right? The second one is obviously learn AI that doesn't mean that people need to know all of the ins and the outs of the large language models, but you do need to understand where they do well, where they fail, how you actually validate the outputs. Because this is again, you know, situation where you answer, you ask a question, you get an answer and the output looks so convincing that, you know, people sometimes forget to do their due diligence on this. And then last but not least, which goes back to there's an important role for humans to be played here, you need to learn judgment and communication. The people who can, you know, connect the dots and connect the technical pieces with the investment decisions, I think those are really going to be the people who are going to have the legs up. So for any student who's listening here, I would say, like, you know, learning of finance to ask or start asking the right questions and then learning of technology to be able to use the tools and then learning of communications so you can actually persuade people. Okay, so we're coming close to the end of our time here. So I just want to have each one of you maybe take 30 seconds here at the end telling us, where do you think the field is going? Because one thing about AI is that it is moving very fast and we're seeing the changes happening in real time very fast. So if we are trying to sit here and project what's going to happen in 10 years, how the field is going to look like in 10 years, what do you see? So Nick, maybe you can get started. I do think that the field of quantitative finance will be alive and well and perhaps will be more all-encompassing as a subset of finance. Again, it has been historically somewhat silent, but as Ingrid said, ultimately, all of finance is quantitative. All of finance using data, thinking about risk return trade-off is fundamentally quantitative task and so I think it is just going to be permeating more of the financial industry and its skills and tools of quantitative finance will be a lot more universally applied in part because AI drew in democratization again that Ingrid talked about. So I think AI will certainly be ubiquitous as a tool in getting answers. Humans will still be central, of course, in asking the questions as well as, again, Ingrid said validating those answers because we know already from our experience with chat bots, AI will always produce an answer and it will learn what answer pleases you. So the person who is asking the question, right, it certainly aims to please more than more than anything else. And of course, that's not the way to produce something that is kind of scientifically accurate. And as far as quantitative finance, I think the traditional tools are still going to be relevant, ultimately, back testing, let's say the output of large language models is impossible because large language models have been trained on everything out there and have learned everything. So there's no way that we can validate out of sample that their predictions actually work. So there's a lot of challenges that are remaining with the implementation of AI. But I think it will be ubiquitous, the field will will adapt and will use it with some limitations. Again, their privacy concerns, people are worried about their code base, about their proprietary models being learned by the AI tools that they use. So a lot of more of the AI models will be trained internally, a lot of money will be spent on that. And so it will be central, but I don't think the field of quantitative finance itself will be gone. It will probably take over the rest of the rest of finance precisely for the reason we discussed. >> And so from that perspective, my prediction is going to be, by the way, 10 years it's way too long because the field is narrowing way too fast, but I completely agree with it's going to be so ubiquitous that I think we're not even going to talk about quantitative anymore. We're just going to say it's investing. So that's prediction number one, we don't need the word quantitative anymore. Number two, I do think the human is going to remain from 10th century to this. Again, just my earlier comment on the benchmark is going to keep moving, but I do think the field is going to become, by definition, also more conversational. I, less away of life, do I have a particular skill set? But can I just use all of the tooling that's out there? And again, the issue of, yeah, information is all out there, but the distinction between information and two inside, I think will become even more pronounced than it already may be the case. >> Okay, thank you very much. This was a fascinating conversation about AI and quantitative finance. Thank you, Nick. Thank you, Ingrid. This is Etai Goldstein here at the Wharton School, and this is our third season of the Future of Finance, podcast dedicated to the very fascinating developments of AI and finance.