#39 Sean Adler - Exploring Quantitative Investment Strategies: From Smart Beta to AI Integration

The Nalu Finance Podcast

Join us in this Nalu Finance podcast episode as we speak with Sean Adler, CEO of GZI, about the practical aspects of quantitative investment strategies and alternative financial data.

In this episode, Sean demystifies smart beta, factor investing, and quantitative finance, explaining their real-world applications. He emphasizes the importance of not relying solely on backtests and discusses the balanced integration of AI in financial markets, highlighting both its potential and pitfalls. Sean also shares insights on risk management, the use of derivatives, and the continuous optimization needed to stay competitive. 

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🎙️ Transcript:

Stefan (00:00:45) - I'm here with Sean Adler, CEO and founder GZI. GZI empowers finance investors with alternative financial data engineering. I particularly would like to talk to you a little bit today about quantitative investment strategies and other related topics. but I think it might be worthwhile to start initially to understand the key terms that we're going to be using in our conversation. Is it possible for you to define and distinguish between smart beta factor investing and what I already mentioned, quantitative investment strategies?

Sean (00:01:23) - Okay.

Sean (00:01:24) - Smart beta refers to portfolio volatility in terms of beta weighing and variance. so there's dynamically updating that factor investing. You have micro macro. This is more on the econ and business, fundamental analysis and so on. Then on quantitative finance, which is probably more where we specialize, there's no shortage of things. Everything from analysis of econometric time series to jump diffusion or various algo trading strategies, ie so on and so forth. They're all sort of enmeshed at this point in time.

Stefan (00:02:10) - You mention it, a lot of words, big words for some people. Is there a common myth about that kind of investing in our industry that you believe needs to be debunked?

Sean (00:02:25) - I firmly believe that there is no one true way when it comes to investments. And this is coming from somebody on the quantitative and AI side. What you hear from a lot of people who are experienced quants is not to wholeheartedly trust your back tests.

Sean (00:02:42) - Everybody who's not on the technical end is like, yeah, of course. But when you deal with people who are strongly based in computer programming, they trust them wholeheartedly, and that creates a huge number of issues. So I think that's one of the biggest myths. There are plenty of successful managers who don't do anything with quant finance. And then of course, there are legends like Jim Simons who are straight quant finance, and there's a good balance in between where everything is enmeshed. I think that that's probably the biggest myth out there, and that's should be dispelled.

Stefan (00:03:18) – Backtests can be a joke, show me a backtest that doesn't look good. Basically. There's a negative selection process. Basically, I would say, you know, you mentioned investing and there's no right way pure and only one way to invest right now. There's also, I think, different perceptions of risk. And what is your risk perception and how do you define it.

Sean (00:03:50) - I honestly fall more into the heavily balanced, high risk category. I'm a huge fan of derivatives baskets and more precise strategies. And also dabbled in, the venture side. Of course, everybody in, in private investment banking is interested in these obscenely high risk deals with large payouts.

Stefan (00:04:15) - But these are sort of more asymmetric payouts in a sense.

Sean (00:04:19) - Yes. So, anything in the private side is much more asymmetric. When you start dealing with derivatives trading, it's much easier to model dynamically and come up with symmetric returns. I think that is more in the balanced high risk category. But the thing is that when the strategies and the models are right, a lot of the high risk is, is mitigated. And so it becomes more of a balanced risk. When you start doing applications of smart data to basket and rainbow options and everything else, you factor them into a model that's based on equilibrium.

Sean (00:04:58) - You know, a lot of that gets mitigated. And it's about scaling things onto themselves.

Stefan (00:05:04) - And how do you measure risk in your investments when you look at it?

Sean (00:05:09) - A lot of data weighing obviously everybody checks profit and loss beta delta dollars. Various volatility ratios for options. I use a lot of standard metrics for basic risk gauging and more non-standard things for, you know, actual modeling.

Stefan (00:05:32) - I mean, some people look at, you know, loss of capital, Sharpe ratios, you know, volatility by itself.

Sean (00:05:42) - So Sharpe ratios are pretty much everybody's go to you know. With volatility ratios a lot of that can be gauged from various portfolio betas. and whether it's one relating to the instrument or the overall portfolio or an individual basket of derivatives, I think that that's important. If a model is bleeding money, that's a giant red flag. But before that happens, a lot of metrics usually start going haywire.

Stefan (00:06:15) - Understood. Now, you mentioned AI earlier. how is this sort of being integrated now in investment?

Sean (00:06:25) - So AI is everywhere. There are a lot of ways that's being applied in finance. There are things in natural language processing where people will essentially screen pools of SEC filings or they'll go over social media data on Twitter to see if the next GameStop jump happens and so on and so forth. There's the language processing side. On the quantitative side, there is a lot of modeling in terms of how you would select securities or how you would place options and when. That's more heavily rooted in modeling and time series, things like Kalman filters. So that's a different aspect. Somewhere in between there is alternative data. That's anything from measuring traffic from satellites to the number of cars in a target parking lot, to R&D reports from biotech companies.

Sean (00:07:29) - And so it's a little more new. I think it's also a little more iffy. but the number of funds are using it.

Stefan (00:07:38) - Cynics could say, you know, for example, I what you see sort of in GGP right now is like it's just a pattern recognition tool. If if you feed it with enough data, it will recognize the pattern.

Sean (00:07:50) - Yeah. That's also why AI becomes dangerous in finance. Is that everybody everybody who's spent enough time trading knows that things happen, period. Something unexpected happens. There's always some random jump. And the models don't always account for that. Even if you've got something that's screening every single news update and is gauging risk sentiment from that, there's always an X factor in the financial markets. That's why I think the balanced approach is important because strictly relying on AI for trading is is somewhat dangerous. When you start dealing with more of the traditional aspects like key financial ratios and so on and so forth, when you understand what you're trading, you can mitigate any kind of risk from, overfitting models or computerized prediction.

Stefan (00:08:52) - I mean, you mentioned already overfitting models. The other challenges I have found often with that one is sort of how good is the data quality or and how much quantity you have, or even you might not even have enough and, or some data is missing. And that poses a certain risk of making the wrong conclusions.

Sean (00:09:11) - Yeah. So a lot of the financial data is readily available. There are different ways to mix things in. If you are a developer, you can build AI models from scratch by just running an equation and then feeding the outputs into a model. That's probably overkill for most people who aren't in development, but there's everything from web scraping to additional API feeds. There's really no shortage of data that can be used.

Stefan (00:09:47) - But how do you make sure that that data is actually meaningful or for better word correct?

Sean (00:09:55) - Test it and get results. My honest opinion about about any kind of data is that.

Sean (00:10:04) - It's as good as the results. Everybody likes to talk about how phenomenal the data is. If you can demonstrate reliance from the data, that's how you really gauge the results. Because at the end of the day, that's what people are really about in this industry.

Stefan (00:10:23) - And reliance. How would you measure that one? Call it the hit ratio or your return side the return.

Sean (00:10:33) - You could do everything from hit ratios to things from standard ROC curves like sensitivity or specificity, etc., etc.. If you want to get more into the esoteric side of AI, you could look at entropy and everything else. But I think that a safe bet is is generally hit ratios, profit and loss and, and the basic statistics and like sensitivity and specificity because it gets really easy to overanalyze things. You honestly just want to simplify things and see what works.

Stefan (00:11:11) - My field was always a little bit with AI. I mean, if AI is just rational for a better world, it will at one point find a way of manipulate how it can manipulate the market. Like spoof the market fake is certain liquidity. How do you prevent AI from doing that?

Sean (00:11:31) - At this point AI is almost just as good as the people who create the models. The accuracy and the design of the model is really going to be determined by the people building it at this stage. Obviously you can start getting into these theories where eventually, there'll be like the Terminator and it will just operate on its own. And yeah, in 20 years maybe. But I think that for actual market manipulation to happen, people have to allow it within the models. There are some things where ChatGPT has engaged in insider trading, The extent at which that affects the market is largely dependent on frequency and volume and everything else.

Sean (00:12:21) - There's definitely potential for market manipulation. But a lot of the regulations within trading will just remain intact regardless of what the AI does.

Stefan (00:12:35) - Now, when you go about in designing, say, an investment strategy using AI or something, what are sort of the most significant challenges that you face when designing and implementing it?

Sean (00:12:50) - Probably updating at this point. It reaches a point where people become very comfortable with the design, the efficacy of a strategy. Then it becomes about maintaining that as you continuously update and add new things to it. The continuous optimization side is probably the most challenging.

Stefan (00:13:14) - And to know when to do it. Or is it just a constant? You just you do it even before something happens. Just to stay on top of it.

Sean (00:13:24) - Before any trades get placed, there has to be significant analysis beforehand. I think that everybody in this industry is constantly chasing more accurate or better returns.

Sean (00:13:37) - That's kind of the nature of the beast and how everything gets sized up. So the process of continuous optimization, in order to survive and compete in the industry is somewhat of a necessity. But, it's the balance between continuing to scale and pushing the risk too high that everybody is walking at this point.

Stefan (00:14:01) - For some people who already have a certain, let's say, certain reputation in the business, they can probably raise money for strategies like this. But the challenge I often I've seen in a great idea, it looks good. It might even have a real track record. Try it out in a managed account. Everything else. But how crucial is it if you want to raise and go up above money? The transparency in trying to be able to explain what the strategy actually does to your clients. And so, you know, how do you have to balance complexity versus comprehensibility?

Sean (00:14:37) - Decrease complexity when speaking to investors and in due diligence focus on comprehensibility. At the end of the day, what people really want in terms of initial meetings is just an overview of things.

Sean (00:14:52) - Once you're actually in due diligence and everything from background checks to trading logs, like a company returns everything else that's in question. At that point, you're dealing much more with the complexity of things. It takes a lot of comprehension before people are really interested in getting to that end, due to the degree of detail people have to go over before any of these transactions happen or even clear.

Stefan (00:15:28) - And I mean, that was a challenge I always found or still find when it comes, for example, with derivative strategies. And where do investors put this in their portfolio? You know, often they just stick it into what they call the alternative budget and ignore any effect it might actually have on the overall portfolio. Is it risk reducing, risk increasing, giving more exposure to equity or not? Is there a way how to how you can approach this with strategies like this.

Stefan (00:15:55) - Because they they can quickly change their exposure for a better world.

Sean (00:16:00) - I think that if you're managing your own capital, you could probably do something that's up to 20% derivatives. If you do that when you're managing other people's capital, I think a number of people would probably be somewhat uneasy about that. All these funds talk about maybe put 1% or like 0.5% in something like cryptocurrency futures. And you can balance it that way. In terms of incorporating derivatives, a lot of that depends on the overall composition of the portfolio, because everything is relative to itself. And so you can dynamically adjust the ratios. But in general, if you're dealing with other people's money, I wouldn't push it above 10% unless everybody's comfortable with it.

Stefan (00:16:57) - And when it comes to these let's say AI driven strategies. Same thing a little bit applies there as well that they.

Stefan (00:17:05) - Where do you put them in your portfolio or the exposure to them.

Sean (00:17:09) - There's a little bit of AI in everything. You need to be careful. You can use the AI for econometric time series. You can use it to screen financial ratios or coupon rates on bonds and everything else. It becomes useful in that regard. But AI is is never perfect. If the AI was truly perfect on the financial markets everybody on Wall Street in Silicon Valley would essentially be sitting back and kicking their feet up.

Stefan (00:17:38) - Correct? Yes, I agree. On the other hand, you could say it's just an arms race, basically who can who's faster and has a new idea of how looking at something else or analyzing potential investments in a different way.

Sean (00:17:52) - I think that's fair. In the corporate world and, hedge funds, startups, there's always sort of this, this arms race.

Sean (00:18:01) - The biggest issue with the arms race is that the people who actually win the race are usually something slightly different. People like Jim Simons and David Shore. Jim Simons had a PhD in mathematical geometry. David Shor was a physicist who ran a quant fund and does research and computational bioinformatics. So the people who win the race are not usually just a run of the mill MBA, although I think that's more of what the corporate world attracts.

Stefan (00:18:40) - If somebody you know would like to contact you and engage with you, interested parties is their is their best way to contact you.

Sean (00:18:49) - Absolutely, emails probably easiest on LinkedIn is is helpful if you send a message a corporate emails. Just Sean Adler at refinance if anyone's inclined. My personal as Sean Xu, SCA and Xu at ProtonMail. Com. Go for corporate first and then personal, but generally to happy to speak to anyone about this.

Stefan (00:19:12) - And my last question, which I always like to ask everybody, what's your favorite song right now that you listen to or music?

Sean (00:19:25) - Paul is dead by scooter. Everybody in Germany probably knows who scooter is. I'm not so sure about Switzerland. I've been a fan of scooter for almost a decade now.

Stefan (00:19:39) - Excellent. Thank you very much, Sean. Very much appreciate you took the time to speak with me.

Sean (00:19:44) - Anytime. Thanks for featuring me.