153: Xiao Qiao – Research Analyst Shares Practical Ways to do Better Research

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Hey folks, how you doing? Welcome once again to Chatwith Traders Podcast. The feature on this episode is someone who I had the great pleasure of meeting and speaking with at QuankCon 2017 in New York City. Xiao Chow is a research analyst for a Connecticut-based hedge fund focused on trading commodity futures. Prior to this point, he completed a PhD in finance.

and was a teaching assistant to renowned economist Eugene Famer, and notably Schau has also worked directly with trading legend Blair Hull on two quantitative research projects, which concern market timing and return predictability. White papers for both of these can be found in the show notes at chatwithraders.com slash one five three.

The main objective of this episode with Xiao is to learn how a research analyst thinks about things directly related to research and ways that you can do better market research for yourself. On top of this, Xiao, based on his experience, shares a few tips for those who have an urge to study something but are unsure about what to study.

and some of the differences he's observed between the world of academia and working as a practitioner. Please welcome Xiao Chao for episode one hundred and fifty three.

So one of the first things I wanted to ask you, Xiao, is um What pushed you in the direction or at least attracted you to finance? Like before grad school and before you decided what you were gonna study, all of that, what actually turned you on to finance? So I first started uh in college I I did a variety of things. Um I studied a little bit of uh engineering, a little bit of math, um, finance and statistics um because I wasn't sure exactly what I wanted to do.

Uh you know and I realized what I wanted or what I liked rather um is applied mathematics. And so I always thought I was gonna be an engineer. Both my parents are engineers. Um so I did some material science engineering. Uh I enjoy that, but I didn't enjoy working in a lab. But I did enjoy the applied mathematics side and I wanted to not work in a lab, um, still get good data and uh keep on using the the math background I had.

So finance is a natural uh laboratory for that in that um the data for finance is very, very good. Stock return data is is uh very precise and um easily available. Um and it would allow me to continue to to apply the same tools I have been applying in uh engineering or statistics. Um and also I enjoyed um some of the core finance idea like the

time value, money, or um risk neutral pricing. Some of these ideas just really appealed to me. So I thought I wanna dig a little bit deeper. So that's how I decided I would go to grad school in finance. Okay. And when we talk about applied math, what sort of things does that cover? So what I have in mind in particular are um I guess probability theory, um applied statistics, things like uh linear algebra, um real analysis, these sort of things. Right.

And then when you went to grad school, what was it that you actually studied there? Um so the program name I suppose is uh um is a PhD in finance. Um And uh So the initial training is broadly similar and you actually end up taking the same classes as the economics students in the economics department at the University of Chicago.

Um so which means uh microeconomics, macroeconomics and econometrics, um, in the first year and then you take some finance specific classes like asset pricing or corporate finance. Then afterwards you start to um uh drill down a little bit narrower into the research area you're most interested in and um That for me meant asset pricing. So that means working with uh specific asset pricing researchers and try to really hone your skills there.

Okay, so I wanna ask you this question. I feel like you're a good person who might be able to shine a little bit of light on the subject, but I've had a few emails from people lately. Seems to be a few more than than normal. Uh specifically asking about or seeing a f you know, asking me for advice, I don't think I'm really the right person to give it but You know, considering you've taken this path You know, people are a little bit unsure about

what sort of things they might want to study. Like how did you know that you were making the right choice? And did you know going into your grad program what you wanted to do coming out the other side of it? Sure, that's a that's a great question. Um and I I'm not sure if I'm entirely qualified uh to speak broadly about that question, but I can share my experience.

Um so in terms of deciding um what to study, I think the best thing to do is just try to explore early on, uh as early as you can. Um Because it's not It's not always clear what you like today is gonna be what you like tomorrow. Um, especially in your late teens or early twenties, I I think uh um a lot of your ideas become solidified and and your thoughts change quite a bit.

So that that was certainly true for me. I I went to college thinking I was gonna be some sort of engineer. Um my dad's a mechanical engineer, so I thought maybe I wanna do something like that. But once once I got to college I I realized

you know, maybe I want to do something similar to engineering, but not exactly engineering. So then I explored a little bit more in statistics and finance and found that I actually enjoy finance more than um the engineering that I was studying, material science. Um So I I think uh what worked for me is that uh really just trying to take classes in in different fields and and try to um you know talk to faculty or or talk to um maybe slightly older people who have uh chosen

um who have spent who have had some experience in in their respective fields to try to get their perspective what what the field is about. Um just try to gather information in that sense and then try to make a more informed decision. Okay, and we'll get to this in a bit, but you are a research analyst today.

When you were going through this grad program, is that what like, is that the specific thing that you wanted to do when you came out of it? Or even going through the program were you still a little bit unsure about what role you might slot into afterwards? You know, uh I'm not sure, um Even now if th this is, you know, you if you if I made the right choices, I I think we all sort of have to self doubt uh continuously, um, no no matter w where you are in and I'm not sure if there is any way to um

to necessarily absolutely get around that. Um so I I would maintain the same advice is just try to explore your options. So going into a grad school, typically

um at least in the US, what uh um what grad school entails is they try to train you to become an academic. So going to grad school, the expectation for both the program um director and for the students um Me in this case, the expectation for for for everybody is that you're gonna be an academic or you're gonna be trained to become an academic. So that's what I thought it was gonna be co going into uh the finance PhD program at the University of Chicago.

as I explored more of the program and and more aspects of academia, I found that perhaps I would enjoy uh working in industry as a practitioner. Um more compared to life as an academic and that was more of a personal choice than than anything, is out of preference um to have some more freedom. Um so so then I decided I would uh get a job um in industry rather than get an academic job, get a faculty position somewhere.

Um so I think it it comes down to accommodation of preferences and opportunities. Um and these things unfortunately are um always changing. So you really have to keep an open mind and and just try to explore what you can. No, I think that's really sound advice.

And during this time I don't know the proper word for the relationship but Uh, you either learn from these two people, they were lecturers, or I don't know, you can correct me here. Um, Fama and Hansen, I've just got their last names here on my notes. Um can you tell us a little bit about what it was like to learn from them? Like these are two, from my understanding, quote, well respected names in academia, right?

Yeah, absolutely. These are two absolute giants um in in finance, economics in general. And um I'm very fortunate to have had the opportunity to interact with them. Um so I've uh I've interacted with in in different capacities. So for Fama, I was actually his teaching assistant for the asset pricing class he's taught um for the past fifty years. Um so I got to know uh Fama a little bit better than Hansen.

And what I what I learned from Fama is you have to be very precise and concise in your exposition. When you talk about ideas, when you especially when you write academic research papers, um, you really don't wanna uh use more words than you have to. And you wanna be you wanna be very clear exactly what you mean.

And I I didn't really quite appreciate this, especially coming out of college. I thought maybe the content is more important than the presentation. But I think one of the main things I learned in grad school is being able to communicate your content is equally as important, if not more important. Uh another thing I learned from Fama is how to learn to read tables um very carefully. Um

I I've directly asked Fama this and I said, Gene, you know, how how come you're so sensitive to these numbers and tables? You go to a presentation and uh you immediately point out and say, Hey, this number doesn't make much sense. Um and the researcher goes back and and realizes maybe he's made a mistake. Uh how do you do that? And Fama said, Well, you really just have to uh think about these things carefully. Read the paper before you come, um and think through uh

You know, what what what are uh intuitively what what are some constraints on on what the possible numbers are maybe in in a regression table? Um so Since then I've taken um I've taken reading tables mu much more seriously and uh now I try to um really think deep about uh you know, both the economic meaning behind all the empirical work work we do, but also try to be sensitive to the numbers. Um for uh Lars Hansen, um I didn't quite interact with him in in uh research capacity.

I think our re research interests were a little bit different. Um, but I did take three classes with them. And what I got out of Hansen is to take economics seriously. The economics is not. is not just a fun uh set of abstractions for uh the ivory tower for for the academic researcher, but is actually an important tool that we can use to understand the world. I guess in in that sense we I try to apply economic thinking in everyday life try to you know try to apply what Hansen has uh taught us.

Okay. So as you mentioned there you work I guess more closely with Fama. How did that opportunity come about? Like how were you fortunate enough to get into that position? Did you just happen to be in the right class and build a bit of a relationship with him? Or um like how did that opportunity come about? So so Fama teaches this uh uh the first out of a s of a series of uh required um finance PhD classes actually.

And uh so e every finance student uh has to take it and and many economic students and some MBA students also take this class. So I took this class uh early on and every year um Pharma picks uh his TAs he picks two teaching assistants from the previous year who who has taken the class in the previous year. So I suppose I did reasonably well in this class and his uh the two TAs who I had recommended that uh um

I'd be chosen for to to serve as the teaching system for the following year and unfortunately um for me Fama agreed. Um so that that's how that opportunity came about. Makes sense. Very good, man. Now, how long was the grad programme that you went through?

You know, I I think the the length um differs by the individual, but typically um these days for the finance program it's about five years from uh the day you enter the program to the day you leave the program. Okay. And that's considered or classified as full time? Yes. Now

I'm not sure whereabouts this came into the picture, but you linked up with Blair Hull. How did this opportunity come about? Yeah, so I I think there is a lot of serendipity at work here. Um so I was working through my my uh PhD program. I was trying to write a uh doctoral dissertation to um to graduates, and then one day I I got an email from uh From this guy Blair Hall, um actually from from his uh personal assistant that said uh um he wants to write a white paper on on market timing.

Um so I I I think I've heard of the name Blair Hall at some point in in just my leisure readings. So I Googled uh who he was and uh and he's this uh big shot uh trader who who sold his company to Goldman Sachs in the late nineties for for a absurd amount of money. Um So that that was interesting and uh I also noted that he um Um he played blackjack and uh while in college I I actually enjoyed playing blackjack quite a bit.

Um so I emailed him back and said, Hey, uh I'm also uh I've also played some blackjack but uh I've also thought about market timing a little bit, so can we talk? Um so Uh uh so I went to Blair's office and we we chatted for an hour, hour and a half and uh we we just hit it off uh on both uh Mark Atime and Blackjack and uh at the end of that chat Blair said, Hey let's let's work together, let's write this paper and think about the research.

Just like that, eh? Yeah, uh I I th I think uh I was quite fortunate in that sense. So how did his Well, you said you got an email from his personal assistant, but I presume that he must have discovered you somehow. Like how did you come onto his radar? Yeah, sorry, m maybe uh I wasn't entirely clear there. So uh I believe what had happened was uh I think Blair was uh searching for somebody with some uh academic training to to write this paper. So the email wasn't I don't think was

uh only directed at me, but I think also at my uh PhD classmates. And I'm not sure if he also sent similar emails to to other schools in the area, in the Chicago area, or not. Um, so I think he sort of just was spreading a wide net trying to find a co author to write this paper. Okay. Now, one of the things I wanted to ask you around this is how come he reached out to someone like yourself? Like

You know, Blair has a team, you know, he's got catch'em trading nowadays and um there's a few other companies he has as well, like the the ETF, um, etcetera. Um Surely he has like researches on his on his team already who he could work with. Um how come he sort of reached out to someone who he'd never worked with before to work on this project?

So my understanding and I don't wanna speak too much for for a player here, so my understanding of the situation is uh Um, I think Blair has attempted to to write uh a white paper a couple of times in the past, um, before we worked together. Um and uh these white papers were were interesting but I I I don't think they were exactly at the level um to be um to be published in in in a in a top journal.

Um, so I think uh Blair maybe wanted just a little bit of academic touch on on the paper itself, um, to sort of get over the hurdle so we can um to have some legitimacy be published in in a good journal. Um so So that's that's sort of what we did in the end. So Blair and I um wrote this paper called the um a practitioner's defense return predictability.

Um forecasting a six month ahead equity risk premium. And uh eventually we got it published in the journal Portfolio Management, which is uh a widely read uh practitioner publication.

Okay. Now I I wanna dig into this a little more because it's it's a massive opportunity um on your part to be able to work with Blair Hull and I'm sure it was a an enjoyable experience. When you first started working with Blair, you know W we know Blair uh coming from a blackjack background, like he was big in blackjack before he ever came into trading. When you were speaking with Blair about, you know, his trading and thoughts on markets and investing, et cetera.

Was it obvious to you that there was still a blackjack influence which carried over into how he thought about financial markets? I think the short answer is yes, absolutely. I actually as much as I enjoy playing blackjack myself and uh all the blackjack uh books and blackjack education I I put upon myself. Um I I didn't realize I guess Polair was very low key about this. I didn't realize how big of a deal Black um Blair was in the blackjack community.

Um uh in fact, uh Ken Houston, um, who made uh this idea of team play famous. So team play is the idea that you have a team of blackjack players and uh you have a whole bunch of um You have a whole bunch of players scatter at different tables and if one table gets hot um he or she at the table signals for the big player to come in uh and bet really big. So this idea was later popularized by the MIT blackjack team. Um so and then it was later made into the movie twenty one.

And uh uh I realized that uh I think Blair actually had a lot to do um with Ken Houston's team. So so that was a very cool connection. Um so given that Blair was so heavily involved in in Blackjack.

Certainly, I think some aspects of blackjack come out. So there there's a few uh maximums if you all hear. So one is don't play if you don't have an edge. So In blackjack, the idea of keeping track of the cards that have been uh dealt is so you have a good idea of what the time varying edge you have um embedding. And that ranges from positive to negative. And when you when you don't have an edge, when the edge is to the house, you want to bet as small as you can, maybe the table minimum.

Um, in fact, at one point, uh maybe in the sixties or seventies, you're allowed to just stand behind the table and watch and not bet at all until the table gets hot. These days I think that behavior gets caught very quickly and the casinos don't like it very much. So so the first thing is don't play if you don't have an edge. Now if you do have an edge. Apparently sizing your bet is still very important. And you have to remove emotions.

from investing w once you've had your edge. And I I say investing here because I think Blair thought of uh blackjack as investing rather than rather than gambling. He thought of as a uh serious job. Um so that's uh you know it's it's a job that he knew he had a a system that worked and he followed it ri religiously. And I think you see that in in Blair's uh um catch'em trading as well as the uh Hall Tactical, the ETF company.

Yeah. That's funny you bring that up actually because when I interviewed Blair on the podcast, I think uh if you know he's interested in that, I think it was episode eighty five. Um, he spoke about all of those things.

I mean, that was one of the big things I took away from that uh that interview with Blair and I've repeated this a few times through various different mediums. If you don't have an edge, there's no reason to play. Like that's that's just like ingrained in me now after speaking with Blair.

It sounds so simple, but it's if you think about it, it's actually quite deep and and I think maybe a little too many people try to play when don't have an edge. Absolutely. Yeah. I mean like I've said, if if I think if traders actually

you know, especially newer traders actually understood that line and what it actually means to have an edge, they'll probably save themselves a lot of money and a lot of um heartache. I completely agree. Yeah. So why was it that you guys wanted to study or or not study, but research market timing. So so I think uh um for Blair of course who of course has uh has had a much longer career um be before he's uh ever met me, I think he's thought about market timing for for quite a while. Um and for me

I came about market time because I I was thinking about um perhaps uh new predictive analytics. Um I guess some people call these things machine learning. Uh how to apply some of these tools in into finance and predictability seems like a very natural place to do this. Um so there is uh Why tackle this question? Well, there's been uh debate about uh the feasibility of market timing, and uh there's been a stigma, if you will, associated with market timing in the past.

So the debate um has uh proponents on both sides. So Robert Merton, the nineteen ninety seven Nobel laureates, um as early as nineteen eighty said that it wasn't really possible to time the market at all, um or even to estimate the equity premium very well. Whereas a more recent paper um by two academics, Goyal and Welch, um showed that many of the return predictors that um have worked well in the past don't work very well out of sample.

So these are the guys that say you can't time the market, you can't even really predict returns very well. Whereas on the other side, early work by Falman French and Campbell and Schiller in the late eighties both showed that using the price dividend ratio, we can actually capture some expected return variation associated with future returns.

And more recently John Cochran wrote a paper in two thousand eight um that offers a strong argument in favor of predictability. So is really um in this background of Not having a completely resolved question that uh Blair and I found found this challenge interesting. Um, because much of the academic research is about uh statistical power, whether you can actually find statistically significant uh predictability.

And the fact that several of these papers, actually many of these papers now, show that you can establish some um statistical power in in um forecasting returns. I guess Blair, my take on on on this question is well, if you're able to forecast returns What does this mean economically? If you're able to um construct a market timing strategy with this knowledge, can you actually form a successful trading strategy?

And that's essentially our research question in in the paper. And I think uh that's also I will say more broadly um our research agenda in in thinking about predictability and turning it into a um actual trading strategy. Right.

What exactly is market timing referring to? Like is that just identifying um good times to be invested in the market and then times to be out of the market as well, or is there a little more to it? Yeah, so um In what I just said, I I I mixed up I I was using two terms um more or less interchangeably. I said market timing and return predictability. So um return predictability refers to whether you can forecast future market returns at all.

So given all we can see is historical data standing standing at time today, can we say something about what future returns may look like? So that's a statistical exercise trying to forecast the market. Now, if you are able to forecast the market, then potentially you can use that knowledge of where the market may go and adjust your position, you sh adjust your market exposure. And that is to your point what you said about uh um do you wanna overweight or underweight your your position? Um

uh to to sort of tactically take advantage of this knowledge. So I will call I will call it that market timing. Okay. And when you say forecast returns How specific are you trying to be? Like are you trying to say that five days from now the S P five hundred will be up five percent? Or are you trying to say, you know, there's a

You know, there's a eighty percent probability that it's gonna be up more than f five percent over the next five days and then twenty percent it might be, you know, less than that. Or like how specific are you trying to be when you talk about forecasting returns? So of course we wanna be as specific as we can, uh but unfortunately given that The information set you you ever have is only up to the day you're making this uh prediction.

Um you don't know what will happen in the future. And uh which means which means your forecast error, um there's only so much you can do to control how large forecast error may be. So I think it's very difficult to make the statement. Um Like the first one that you mentioned something about uh maybe a week from now return will be five percent. I think that's extremely difficult to to make. And all you can say is in all likelihood

uh in the next five days the return will be centered around some number. Um but the variance of that number may be maybe very large. Um so there is uh a lot of uncertainty associated with return predictability. But I think uh perhaps the important takeaway here is that even though you can't make precise statements about exactly where returns are gonna go.

If you have an idea where it's gonna go, then you can do something about it. And that's what market timing is all about. Okay. So ultimately, what was like the outcome of this paper and the research project? So what we found is that uh Um there are all these academic papers talking about different return predictors, different variables that we can use to forecast equity premium at different frequencies actually. And we thought about combining these return predictors.

So there's some academic work um by Alan Timmerman um at uh UCSD as well as uh same some St. Louis academics. um on model combination or forecast combinations already. Um, but they don't quite necessarily um Go after the economic importance. So they they don't try to construct a uh a trading strategy. So Blair and I took a similar approach in combining return predictors.

And we show that if we combine 20 return predictors, that actually leads to strong enough forecasts and results to build a trading strategy. So our forecasting target is gonna be the six month ahead market excess returns. And what we find is that from two thousand one through two thousand fifteen, a back test of our market timing strategy

um is able to achieve twice the average returns of the S P 500 during the same period, with only half the volatility, thereby quadrupling the sharp ratio of buy and hold. So the strategy is only taking positions in spy the S P five hundred ETF.

um based on our statistical model. So it's uh in the end it's it's very simple strategy that overweights, underweights uh the market. But the outcome um I think is uh sort of remarkable in that All you're trying to do is forecast where the market is going and take positions related to your forecast, and you have you can actually do much better compared to the market.

Right. And I'm not sure if this would be detailed in the paper which is available, but Can you give us an example or a couple of examples of just a few of these return predictors that you mentioned? Sure. Um I mean all of this is public information. The the paper, uh as I mentioned earlier, is published in in in the journal Portfolio Management. It's also available um for free download on uh social sciences um research network or SSRN.

um if you like uh more details. But uh um I can give a a few examples of uh the return predictors. So more traditional return predictors such as price ratios, so variables such as dividend price ratio, price earnings ratio. Um Robert Schiller likes to use this CAPE cyclically adjusted price earnings ratio. So we include all these price variables which have been shown to be able to forecast um returns, especially a longer horizon.

Um but also some newer variables that have shown up in the literature more recently, such as the variance first premium. which we found to be a very strong return return predictor at around the three month horizon. So the variance risk premium is the difference between the implied volatility um of um of the um options on um

on the S P five hundred futures or or the VIX index uh minus the realized volatility of the S P five hundred. So it's the difference between applied and realized volatilities. And uh um it's persistently positive over time. And But there is significant time variation, and s this time variation does appear to be associated with with future market returns.

Cool. Well what I'll do is I'll find a link um to the paper and I'll put that in the show notes. So if anyone wants to read into this a little more and get some more context around it, uh it's all detailed in the white paper. Great. And I I want to mention that uh um this white paper is forecasting returns six months ahead. Um but recently Blair and I and uh one of uh Um Blair's colleagues, Petra, um we put out a a a new white paper uh forecasting one month ahead market returns.

So here the the goal is similar. We're trying to combine predictors, um, now with a slightly different methodology compared to the first paper, but now the forecasting horizon is different. So the contribution from each return predictor, and we use a slightly different set as well, um, will look different compared to the first white paper as well. So if you're interested, um this paper is also available on SSRN.

Okay, cool. Well I'll get a direct link from you um when we wrap this up. Yeah, awesome.

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Today you are in a research analyst role. Can you just tell us a little bit about what a typical day looks like for you? As a as a research analyst, uh I try to Well I guess the the central the central tasks for me um is to try to

try to think about our existing strategies, make sure they're robust, and try to come up with a additional s investment strategies. So to that end, I probably spend um probably a quarter to a third of my time reading Research papers, often academic research papers, um, and or industry reports to try to try to brainstorm ideas, think about ideas.

And uh probably another I wanna say a fifth to to a quarter of the time discussing ideas with my colleagues and probably half the time um actually prototyping models and playing with data and and try to um Either I guess either address client questions with uh uh with research or if there are no urgent questions, try to think about uh additional uh try to develop additional strategies. Right. So it involves a lot of rating, yeah. Yeah, I think so. Do you ever run out of things to read?

You know, it's incredible how much research i i is turning out all the time. So uh no I I think I'm constantly falling behind. Right. So how do you keep track like if you're doing a lot of reading like that, how do you sort of keep track of all the information you're taking in? Like does it ever get overwhelming at points? Like how do you manage that?

Yeah, that's that's a good question. Um I think uh I think people do it differently and I can speak to uh how how I try to organize the information is uh every time I I read a paper, especially uh um

academic research paper. I try to think about what the big question is behind the paper. And uh typically good academic papers all have a big question behind them. And what what I mean by big question is uh when you read maybe um something like the the classic Fama French three factor model, mechanically you're reading about how

Um these two additional factors uh alongside the markets is able to explain average returns. But the big question behind that is how can we characterize average returns in the cross section? And for some papers is is is maybe easier than others, but but keeping this big picture in mind um helps me sort of put these papers into different categories and and that's very helpful for me in organizing these.

Okay. And do you ever read material outside of academia as well, like maybe some blogs or uh I don't know, other sources as well, or are you just focused on what comes out of um the academic world? So the so the academic world is is is very good for for a narrow focus and and uh um pieces that that uh go quite deep into narrow areas.

Um but at the same time you wanna get uh broader exposure as well. So um certainly sometimes I I take a look at um blogs. One of my favorites is uh actually by a former mentor of mine, uh Frank Debold at the University of Pennsylvania. Um he blogs about uh uh econometrics and statistics and um and sometimes um I guess more more random stuff.

Um but also uh newspapers, you know, um The Economist, uh the Wall Street Journal, these are all good places to try to find ideas because then um you get an idea if you if you read the um less uh research oriented publications, you get an idea what what is topical. What are people interested in?

Um and maybe maybe you come up with an idea uh to try to may maybe there's a puzzle in in the world that people don't quite understand and uh you spend some time thinking about it and that that turns into uh some interesting research.

Okay. And that blog you mentioned just a minute ago, um, what's the link to that? Do you know it off the top of your head? Uh I don't, but I can send you a link later. Okay, cool. That that um anyone listening, that can be found in the show notes. Um, now do you specialize in any particular asset class? My my time in Chicago, uh, I was trained to have a equities background.

Um but I've I've found that the statistical tools that uh you you build up as part of your uh toolkits often translate to other asset classes as well. So my current job I often look at uh commodities. um data as well. And I found actually much of the um skills I've accumulated at Chicago actually translates pretty well to to commodity futures compared to um equities.

Um but you do need to learn new institutional details about any new asset classes that you work in. Um in terms of uh specific market behavior, um I look at both cross sessional and accurate asset level time series behavior. So um I guess at a high level Um how do how do the amount of risk or how risk is priced move uh over time? And also um how do how how is risk different in the cross session across securities or or across assets and how do they vary?

Would you mind just explaining those things in a little more I guess more simple terms? Um, like when you talk about things such as how risk is priced, like Can you just go into that a little further and explain what that means? Sure, absolutely. Um so so I think in in modern asset pricing theory Um, the core of it is really to try to think about the interaction of two quantities. One is called risk quantity and one is called risk price.

And risk quantity is essentially how much risk there exists. So we can use uh maybe the the agri stock market is a good example here. Um so how much risk is really associated with with holding the stock market, holding the agri stock market, let's say the S P five hundred. Um and we think that that risk actually maybe changes over time. For example, if you bought equities in in uh Q four of two thousand eight, maybe the risk is higher compared to if you bought it um In March two thousand nine.

The other quantity is uh so called risk price, and that is for each qu for each unit of risk. And it's a little bit as abstract here, but uh Um I'll try to I'll try to explain. So for each unit of risk exposure that you hold, how much compensation are you getting? And I think maybe uh a good way to think about this is if we think about the capital asset pricing model. One unit of risk maybe um is market beta equals one.

So what do you get when you hold um an asset that has a market beta of one? Well, according to the Cap M, you should get the equity premium. If you hold an asset with a market better of one point five, then you have one and a half times as much risk. And uh you should get one and a half times the equity premium. So the beta here is risk quantity and the um the equity risk premium here is the risk price, is how much you get awarded for holding on a unit of risk.

So I wanna think about how do risk quantity and risk price interact and how do they change over time. So how are you measuring risk? Like what is how do you define what a unit of risk is? Generally from uh modern asset pricing theory, maybe starting with uh Markowitz, um, we're thinking about risk in terms of standard deviation.

So how much uh how how much can what what asset you hold, how much can that how much can its return vary over time and uh how much can that return vary in the cross section? Um, another measure that I mentioned is is market beta. So if your beta is one, then you're holding on to as much risk as if you are holding on to the S P five hundred.

But if you held market better point five, you only hold on to half as much risk as the S P five hundred and so on and so forth. And just going back to an earlier point you made, you said that You know, a lot of your education was centred around equities, but, you know, nowadays a lot of your uh research is centred around commodities. Is there any reason for that or is it just a matter of the uh the fund you're at? is a very commodities driven fund.

Yeah, I think that I think that's largely largely it. Um and I I actually had some apprehension um in accepting this job or trying to think about how my equities training would translate. to to commodities and uh and I was convinced um by my current colleagues that uh the tools I possessed actually did translate um from equities commodities and and fortunately they they are largely right Um, but I did have to really spend a lot of time um

learning the institutional details of commodity futures um which are which are quite different. The micro the market microstructure of commodity futures are quite different compared to equities. Um for example, Futures have a term structure, so when one futures contract expires, you need to figure out how to quote unquote roll into the next nearest futures contract. And that doesn't exist if you're holding on to um let's say IBM stock because that's that's just a a claim on all

Um future cash flows. So once you buy it, you can hold on to it until you sell. You don't ha ever have to um manage it in that you don't have to sell it and and buy it back as you would for futures. Right. Yeah. I mean that's understandable.

I'd love to hear a little bit about your actual like research process. One of the first questions around that I I might start with is You know, how do you actually come up with new ideas for research projects? Like where do these come from? I I I I'd a guess, I mean, a lot of it probably comes from the amount of reading you do, but um yeah, can you share a little bit around that? Yeah, sure. And I think we touched a little bit uh upon this already and uh

My my I think my process is really just trying to trying to read broadly, both academic papers um as well as newspapers or magazines, um, and just try to try to think about what are some interesting ideas? What What what are some things that when you read, uh you're puzzled by? And if you if you're puzzled by it, chances are many people many other people are. And um if they aren't able to answer it maybe it's an interesting question and you should think harder about it.

Um I like to keep a research notebook whenever I read so that uh I can write down any ideas that come to mind. Be because it's very hard to know which ideas will work in the end. So all you can do is really try a lot of ideas, throw out the ones that don't quite work and really focus on the ones that do work. And this process has worked pretty well for me. Yeah. Okay. So is there any like preliminary work you do when you

Let's say you you have a new idea, right? You you put an idea in your notebook Is there any kind of preliminary work you do? Like looking into that idea before you actually decide to invest too much time on an idea that doesn't hold weight. Like is there anything you can do to kind of filter the good from the bad before throwing away too much time? Yeah, I think this is uh extremely important um in in trying to manage your your time for research.

Um so as I mentioned, I like to keep a research notebook uh and write down basically all my thoughts. And it turns out uh many of these unfortunately are not very good. So what I want to do to filter out the ones that are not very good from the ones that may have some potential um is to try to come up with uh sharp tests. So often these are empirical ideas, so I can test them using data.

Uh so I want to come up with sharp tests, so I can quickly uh do a preliminary test to see if the idea has any potential to work. Um, so I would try to think about the sharpest prediction this this idea would have and try to think about how to test that um in what's i in in in data.

And so for for each of these ideas I I don't wanna spend more than maybe a day or two in really um trying to see if they ha have any any value pursuing and just through this process alone, um This probably throws out I wanna say seventy, eighty percent um of the ideas already. Uh and then the remaining ones I try to spend more time on and dig a little deeper. Okay. And just to be clear, when you say sharp, that's got nothing to do with the sharp ratio, does it?

No no uh no they doesn't. I I just mean um a prediction that that can be um clearly rejected or or uh or not. So let's say someone's listening to this right now and they're keen to get better at researching their own ideas. Is there anything from your research process that you would suggest less experienced researchers can emulate? I think the most important thing I will suggest is to try the ideas. Don't just read. When you read, um, you feel pretty smart often because you're reading

Peep stuff that that that has been published that uh you're reading stuff written by other very smart people. Um So it's easy to sort of fall into the trap of of reading too much and not actually trying out ideas. Um if you don't try the ideas you never know if they're any good and you can't really you can't really produce um any research. So I think you actually learn much more when you're trying out these ideas because you can understand the nuances and the details. Um

which you y there's no way to get from just reading alone. Um so my main suggestion is to to to just try the ideas. Don't don't be afraid the ideas won't work out. Most of mine don't. Um

But don't just read. Um and a related point is that ideas change as you try them. But you won't know that and you won't know how they will change un unless you do it. So often um what happens is you start out thinking in one direction and then as As you try the idea and as you interact with the data, you realize that maybe your initial Um hunch is

Only fifty percent right. And you have to adjust your direction a little bit. And now your research has taken um a slightly new direction. And often you have to do this many times. You have to really iterate this process many times before you get um any interesting results at all that are that are worth presenting or or worth discussing. Um so I think you you have to keep an open mind and just try the ideas. Yeah.

And I I must say I like how you point out that a lot of the things you try actually don't work. Like they don't amount to anything, you know, they uh discarded. Yeah, I I think that's not uh that's not unique to me um from my both my classmates and and my mentors, uh my current colleagues. Um all the empirical research I talked to I I think uh share a similar Um not quite frustration in the sense that this is part of research.

But a similar experience, um, in that many of the ideas are just uh you know, you you you read something, you write something down, you think it's the greatest idea since sliced bread, you come back to it in a couple of days and you realize, oh

This is actually not very good. Uh that actually happens quite often. It's okay. That's that's part of the process. No, I'm sure a lot of us listening, or a lot of the uh everyone who's listening to this, I'm sure can relate to that on some level. Now I want to ask you, are there like any Yeah, obviously you went through this finance PhD programme and I'm sure you learned a lot during those those five years.

Is there any skills which you use on a day to day basis though? Because you know, some things are are nice to know, but you don't use them too frequently. What are some of the tools you possess or the skills or different forms of analysis which you use on pretty much a daily basis? So I prefer to start my analysis uh very simple. So I still conduct expor ex exploratory data analysis that my uh maybe second statistics class

uh in college um taught me to do. So look at summary statistics, look at histograms, scatter plots, time series plot. Just try to get an idea what the data looks like. And um that actually gives you pretty good sense whether your hypothesis has uh any chance of working or not. Um and it turns out uh just simple linear regression um actually goes a long way in

in discovering patterns in the data. If the idea is strong and the um the empirical findings robust, uh just a simple regression um should show you how it works. So I think Oh despite all all the all the fancy techniques that uh I have learned uh in grad school uh and elsewhere, um I sort of still stick to some of the most basic tools uh to try to to try to um construct my thoughts.

And you can go to more sophisticated tools as necessary, but uh you don't want to start with something overly complex because otherwise you sort of get lost in the details and um you know you can't you can't really interpret a complex model very well. So by doing linear regression, what sort of things is that likely to reveal and and show to you? So it's it's uh obviously has some has some drawbacks in that it is it is a linear approximation to to things that are not linear. Um

But often um in in finance and asset pricing um many many things can be linearized. So regression actually is is a very good uh um at least initial tool to to get an idea of how your hypothesis may work.

So and and that I think that ties ties in closely with uh just looking at scatter plots, just just look at uh time series plots, just just look at what the data looks like. Um actually goes a long way as well. Okay. Yeah. Now what stands between you As a research analyst, coming up with a new idea, something which you think holds weight, has potential. What stands between that point and actually that idea or that new research being implemented into, you know, live trading within the fund.

I'm gonna answer a little bit more broadly here. I I I think it depends on your holding period, um if uh for for your investment strategy. If you're going after uh lower frequency risk premiums, um something on the order of months or or longer then I think uh execution is less important because you're trying to pick up um true risk premiums. So you don't have to you don't need really too heavy a machinery for trading if your holding period is a little bit is is that long.

But if you go into higher frequencies, maybe intraday or or days, or of course high frequency training itself. then execution becomes very important. And so you may need to set up serious trading platforms because uh execution may be where you make a significant fraction of your profit. Okay, but do you have to get like this checked off by anyone who might be a level above you or anything like that? Like, are there any processes around there?

Yeah, um and I I don't want to speak too much uh about the exact process um at my uh um at my current job. Um but I think uh uh broadly You know, in in investment management firms, that there there is certainly a process to go from research to implementation. Um, and I think How big of a step that is, I I think depends on what I mentioned earlier about holding period. So if If it is a lower frequency um strategy and uh uh f execution is not terribly important, then maybe the language that that

you did the research and maybe in R or or Python or MATLAB is good enough. It's fast enough because you're not trying to go for speed um to to sort of just take your research, take your portfolio and uh implement it. But if you're going for higher frequency then um something like R is probably not gonna be fast enough. It's not gonna be industrial strength. So you have to um Either link it to something like C or give your code to a financial engineer to to um to code it up in something like C to

So that so the code actually runs fast enough um to to trade in real time. Okay. And then let's say something is trading in real time. How do you measure the accuracy of your research after that point? So I think perhaps the the best thing, um the best metric here is uh Just a out of sample test, a good old fashioned out of sample test of of your hypothesis. Anytime you you formulate a research strategy, there's gonna be some sort of back test if it's a quantitative approach.

Um and uh by its nature the back test is likely gonna be overfitted um because you played around with the data for so long, it's inevitable that uh in some places you you sort of tilt the the cars in your favor, if you will.

So the outer sample is completely new data that you haven't seen and um your hypothesis has hasn't um hasn't mind this this new data set so that I think I think the best way to judge um how good your model is is to run it in out of sample period for six months, for three months, six months, um whatever time you have, and to see to compare its performance, compare it to your back test.

And typically there's gonna be some sort of decay compared to a back test because the back test is overfitted. Um but how much that decay indicates how good your strategy is. For example, if you have a back test, your strategy had a sharp ratio of Two and a half. And in the live period, um, once you started running the strategy for six months, your sharp ratio was one.

That means your sharp ratio decayed sixty percent. So in that example, if your back test sharp ratio is two and a half and uh um and you start maybe running a paper portfolio or rating or running the strategy live for six months and you get a sharp ratio of one, um, that's a big drop compared to two and a half, which means which probably means that your back test is not as believable as you might you might have thought. Whereas in in another example, if uh

Um in your back test your sharp ratio is one and a half and in your live period your sharp ratio is one point two. That difference is quite a bit smaller. So that this shows at least in the period that you tested, in the outer sample period that you tested. Um your strategy looks fairly robust compared to your back test. Okay. Okay. So is is sharp ratio one of the measures you use to determine how much decay you're you're happy with?

Certainly it's w it's one of the metrics. It it's by no means the only metric and um I think different um people may focus on uh may put different weights on on on the following metrics, but they all look at returns, uh cumulative returns, volatility, sharp ratio, cumulative drawdown, maximum drawdown, um, and probably some sort of tail correlation. Okay. And what what do you mean by tail correlation?

So in extreme events, what happens to your strategy? So perhaps in the global financial crisis in late two thousand eight. Um when the equity markets um really drop quite a bit, what happened to your strategy? Did your strategy drop with equity markets? If so, that means uh your strategy um

That means when equity markets perform really poorly, so does yours, even if normally your strategy isn't may not be closely correlated with equity markets. So um so these tail events um are you know, really bad outcomes at the extreme ends.

So, Chao, one of the last things I'd like to ask you about is um Academic, uh, practitioner, um, have you found any challenges or a disconnect maybe between the academic research world and actually real world implementation as a practitioner?

You know, I I think disconnect is somewhat strong, but but I do think that academic research and real world practitioners have different research focus. Um so naturally naturally they will think about the problem um maybe what looks like a similar problem in very different ways. For example, academia cares about formulating testing theories, which means abstracting from certain real world issues.

Whereas industry cares about implementation. Um so issues academia ignores becomes very important. So for example, one thing I found um after I started working and after I left academia is that Trading cost and factory investing and turnover um is supremely important. And this is something that doesn't come out of uh work such as the Fama French three factor model. Um So when you actually try to uh try to trade maybe a value or momentum factor, um

the slippage, so so the trading costs you incur um and the transactions costs you incur are are actually quite important. Um and maybe as important as the risk premium you found. Uh another aspect of this is uh Um is Perhaps uh intermediaries, so how important they are. And I think recent academic finance has started thinking about uh um how important maybe broker dealers or or um banks, uh how they matter in the financial system, how how they can really uh make or break

um the overall financial system. Um but apparently practitioners have been aware of this for decades, uh if not longer. Um so so I think it's just due to the nature of their focuses, they think about problems differently and sometimes they have trouble talking to one another. How come trading costs are often neglected when it comes to academic research? Like it's It's something there there's no avoiding it. You know what I mean?

Yeah, and uh, you know, maybe maybe to you, Aaron, this is this is uh you know, dead obvious. You know, how can you possibly ignore trading costs? It's one of the most important things. Um but in academic research, um, trading costs has been relegated to to one strand of research uh called market microstructure. So that's that's when people study um

how how market participants trade and how they impact prices, how they impact liquidity, and so on. But that's only one strand of asset pricing literature and and it's probably not even probably not even the biggest. Right. So cross sectional asset pricing on the other hand is another strand of research that that that's much bigger. So thinking about uh you know size, value, momentum, maybe profitability and other other factors that determine um average returns in the cross section um

in the absence of transactions costs. And I think that that's just because um Academia needs to break the problem down to very specific pieces and try to dig very deep understanding each piece. So when you try to understand average returns in the cross section, um academics traditionally have ignored or mostly have ignored um transactions costs.

under this setting. That doesn't mean they they don't acknowledge the existence of transactions costs. Just in this setting, studying this particular question, they choose to ignore it. Um of course You know, perhaps uh to your point, you can't totally ignore transactions costs when you do this analysis. Um, and I think that's precisely the points that practitioners make.

And were there any preconceptions that you had about trading and investing when you made the crossover from being an academic to uh being a practitioner, like you once you actually started working in the field, were there any preconceptions you had about trading and investing prior to then?

Yeah, I think this is very much related to um to the disconnect between or or or to some disconnect between academic research and and real-world implementation. Um so As part of my training, I I viewed risk premiums as perhaps the most important aspect of a strategy. But I've since learned that it's only it's only one aspect. And uh trading costs or or or uh market microstructure effects are at least as important as the risk premium itself. Uh market impact is

much more important than I previously thought. Liquidity is more important. Um even when I sp even in school when I spent a lot of time thinking about liquidity, I I still underestimated how important it would be for for a portfolio strategy.

Um also when to rebalance the portfolio turns out to be very important because uh um liquidity in the in the marketplace is uh constantly changing over time. And if you rebalance the portfolio during a large rebalance, let's say uh the Russell index is rebalanced um in June and you also choose to rebalance in that time, liquidity in the marketplace will be quite bit quite a bit deeper compared if you rebalance at a time that nobody else is trading. The frequency rebalance is also important.

Um and of course these are just a few few points that I realized uh are at least as important, if not more, compared to just risk risk premiums themselves that I think I I didn't quite get the perspective while I was in school.

Yeah, no, I think those are all really interesting points that you make there. If someone is keen to find out a little bit more about yourself, I believe you have a website Um, do you want to share the link? Sure. Um it's uh So I can I can send you the link once again. Uh that's probably the easiest way. Okay. Um not everyone who listens to this will see the show notes though, like'cause some people just listen to it on iTunes, Spotify, etc. Um is the link a bit hard to

Uh that's okay. So if you uh if you just Google my name, uh Xiao Chao, X-I-A-O, Q I A O And you put maybe Chicago after my name, I think it should be the um the first link you get in Google. Ah, perfect. Okay, cool. Yeah, so unfortunately my name overlaps with uh with like a historical character in a video game. So if you just put Chao Chow, you'll get like this anime girl. And that's certainly not me.

Right, good to know. Good to know. Uh do you happen to be on Twitter or any other social network? Um, I'm not on Twitter, Ashley. Um I have Facebook and have LinkedIn. Okay, cool. Well I'm sure someone can find you on LinkedIn as well if they want to.

I must say I really enjoyed this conversation, Chao. It was nice to speak to you again after meeting and um again speaking with you at Quantcon. So yeah, thanks very much. I'm glad we got the chance to actually um record this and do it as a podcast. Hey, thanks very much, Erin. Likewise I enjoyed our chat very much. Thanks a lot for having me. Okay. We'll speak soon. Take care, Aaron. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes.

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