Chaotistan vs Normalstan, Momentum vs Trend, Sharpe Ratio
In this episode, we talk with Bastian Bolesta, CEO of Deep Field Capital, a systematic long volatility manager based in Switzerland.
We start off by talking about the difference between systematic and discretionary trading styles and why systematic may make more sense if you live in what Bastian calls Chaotistan instead of Normalstan.
We then dive into the difference between Momentum and Trend styles of investing and the types of markets that each thrive in, how using a Sharpe ratio to measure risk can actually increase the risk of a portfolio.
We also talk about the benefits of trading markets intraday instead of holding positions overnight and when it makes sense. Finally, we talk about the relationships between the US, Europe, and Asian markets and how investors can take advantage of it.
I hope you enjoy this conversation as much as I did.
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Have comments about the show, or ideas for things you’d like Taylor and Jason to discuss in future episodes? We’d love to hear from you at info@mutinyfund.com.
Transcript for Episode 10:
Taylor Pearson:
Hello and welcome. I’m Taylor Pearson, and this is The Mutiny Podcast. This podcast is an open-ended exploration of topics relating to growing and preserving your wealth, including investing, markets, decision-making under opacity, risk, volatility, and complexity. This podcast is provided for informational purposes only and should not be relied upon as legal, business, investment, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, Mutiny Fund, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed to not make specific trade recommendations nor reference past or potential profits.
Taylor Pearson:
And listeners are reminded that managed futures, commodity trading, ForEx trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors, and you should not rely on any of the information as a substitute for the exercise of your own skill and judgment in making such a decision on the appropriateness of such investments. Visit www.rcmam.com/disclaimer for more information.
Taylor Pearson:
In this episode, we talk with Bastian Bolesta, CEO of Deep Field Capital, a systematic long volatility manager based in Switzerland. We start off by talking about the difference between systematic and discretionary trading styles and why systematic may make more sense if you live in what Bastian calls Chaotistan instead of Normalstan. We then dive into the differences between momentum and trend styles of investing, what those mean, and the types of markets each thrive in, and look at how using a Sharpe ratio to measure risk can actually increase the risk of a portfolio. Finally, we dive into the benefits of trading markets intraday versus holding positions overnight and when that might make sense, as well the relationship between US, European, and Asian markets and how investors can take advantage of it. I hope you enjoy this conversation as much as I did.
Taylor Pearson:
Also, I want to apologize. The audio quality on my side is not as good as it should be. I had some technical difficulties that I will fix going forward, but I hope the interview will still be easy enough to listen to.
Taylor Pearson:
I’d love to just start off and have you talk a little bit about how you describe your trading philosophy broadly. How did you get into what you’re doing now, and how would you characterize what you’re doing now as a trader?
Bastian Bolesta:
Sure. You may know about our background path. We started as proprietary traders on the discretionary side. We had this in a previous discussion. The discretionary side was predominantly focused on arbitrage trading. So in terms of philosophy, what kind of risk you would like to take when you put your money at risk. Arbitrage trading, of course, is quite different to many other discretionary trading styles. On the discretionary trading side, we had certain trading ideas which were repetitive, so they occurred from time to time and then they weren’t around, and then they occurred again. It makes sense to look into how can I systemize something like that in order to always make sure that I can trade it in the same fashion as I’ve done it in the past?
Bastian Bolesta:
Our very early beginnings on the systematic trading side were really small, simple rules to make life easier so that you don’t have to be 100% at your best every single day as a human being. From these initial very simple programs, we started to get in additional support by traders joining our team who had similar background in chart analysis, looking at markets through the lens of analyzing market patterns, defining rules how to trade these patterns when they reoccur, and then basically put them at play. And we saw some benefit to that in order to diversify our discretionary trading approach with more and more systematic approaches, because we could do more at the same time. And we didn’t have to be ready every single day for this single event to occur, because if you’re not ready because you’re late or you’re having a headache or you had some issues back home, you may not be 100% as you were last time when you had this situation.
Bastian Bolesta:
So it was very, very human and natural tendency to start systemizing things, defining simple trading rules, put them in code, and have them traded. And that’s where the evolution basically took place from initially being a discretionary trader with small more rules being defined and automated to becoming nowadays basically a purely automated and quantitative trader. But our discretionary trading side still exists. It’s in our family office, and Deep Field Capital is basically the spin-out of the systematic trading unit of this family office. We now fully focus on systematic trading. It’s all quant-based. We don’t have any human interference in the sense of that we second guess what we do. Everything we do here nowadays is coded and calm, when we have peace, maybe. There’s a glass of wine sometimes when you have some late thoughts in the evening.
Bastian Bolesta:
So it is not under the influence of stressful events in the markets, and that’s quite a difference between what the family office is doing, where we still have the discretionary side of trading, still a strong focus on arbitrage, so definitely not as much heat as other people may face on the discretionary side. But the systematic side here is just different. We come up with ideas and test them in a secured environment, in our research environment, and we don’t have to run through these emotions and quick decision-makings when trading as we have to do as a discretionary trader.
Taylor Pearson:
How do you think about, what are the trade-offs with the discretionary versus systematic? You’ve chosen to keep parts of the family office discretionary. How do you think about when discretionary versus systematic makes sense? I guess maybe a part of that, too, is at some level you’re still sort of discretionary, not on a day-to-day basis or an hour-to-hour basis, but over the course of multiple years. What you’re doing at Deep Field is sort of evolving those strategies. I don’t know if you’d call that discretionary, but you’re changing the inputs to the program or the algorithms. How do you think about that trade-off?
Bastian Bolesta:
It’s a fair point. Starting from a virtuous perspective, it’s quite easy to differentiate between discretionary and systematic by actually taking out the immediate human activity on the systematic side, so the need to ad hoc react to certain things should not happen on the systematic side. But of course, as you said, in the bigger picture, as soon as you have an idea what you would like to research, as soon when you shape your research process and you define as a team what you would like to look at, you have discretionary decisions where to put your priorities. And as a result, your systematic programs will evolve, because this new research will influence your programs.
Bastian Bolesta:
But the key differentiating aspect is, while one is trading, this doesn’t happen on the human basis on the systematic side. Our rules, the way we receive data, price, volume, order book data, how it’s analyzed and what kind of signals are generated based on the data we receive, this is all fully automated and is systemized based on rules who do not need human intervention at the time when they basically are applied.
Bastian Bolesta:
Whereas on the discretionary side, you have different shades of discretionary. You have discretionary traders who use technical analysis. You have discretionary traders who also have models which suggest that a position should be taken, but ultimately on the discretionary side, the trader decides him or herself if they want it executed, sometimes even in terms of sizing and things like that. So that’s a key difference.
Bastian Bolesta:
But going back to the first part of the question, the trade-offs, the team from background has predominantly worked on the systematic side. But we have people who have studied things like economics, international relations, and it can be quite painful as a human being when you have an opinion that something is happening in China, and that’s why something should happen in the US, and ultimately you would like to bring in all your knowledge, what you have read and all the papers and everything. And ultimately, it doesn’t really matter, because the engine doesn’t care, doesn’t know. It is using price and volume and other things and then basically takes a position or it doesn’t. It doesn’t care what President Xi did this morning or if President Trump is now Twittering something.
Bastian Bolesta:
Whatever’s happening in our real world will trickle down into this financial world, in terms of markets reacting to the news. And markets reacting to the news means we have price movements, we have increasing volumes, more participants basically joining whatever is happening to the up or the downside. And this is basically analyzed and recognized and captured by quantitative trading models, and then they react based on predefined approaches and patterns.
Bastian Bolesta:
What we like personally on the systematic side is that it really takes away the stressful moment of decision-making. All of us, all human beings basically, have the issue that we would like to apply certain things which we have learned in life to the financial markets as well, but probably the financial markets are one of these areas, playing fields where whatever is happening in real life couldn’t be worse for you when applying it to the financial markets. There are certain arguments that it’s basically 180 degrees. Especially when you’re a successful businessman, you have developed certain technologies, things like that, so you have proven yourself being successful in life, but this does not mean that you are good on the financial markets, because certain gut feelings, how to react in specific situations, may actually put you exactly in the worst spot on the financial market side.
Bastian Bolesta:
And this is amplified when you’re doing it in a discretionary way, because then you have to decide ad hoc in that specific moment. If you code your stuff, if you come up with your ideas in a calm environment, you can still test it, and then you can basically apply it to the markets. And you, to a certain degree, by applying systematic and quantitative trading approaches, you address this human bias, our weaknesses that we would like to act immediately when something is happening. The urgency is to react, the urgency to protect things, to earn them back. A lot of human behaviors all of us basically possess are not very beneficial in making financial decisions, specifically ad hoc when trading.
Taylor Pearson:
Yeah, I guess the way I’m almost thinking about it now is, the idea behind discretionary that makes sense to me is, there’s some amount or some type of information that you can’t get into a computer effectively. I know people try to do symptom analysis or something. But at least in theory, human brains are probably still significantly more powerful than computers at certain types of pattern-matching and that kind of stuff, but at the same time, to your point, are vulnerable to emotions, biases, overreaction. People tend to buy high and sell low, and so what’s the balance? I guess that makes sense to me, the way you’re talking about. You’re able to use, at a discretionary level, think about on a month-to-month or year-to-year basis, how can we encode these principles, these patterns I’m noticing as a human into some form of an algorithm and then take the emotion in the moment out of the trading?
Jason:
To piggyback on Taylor’s point a little way, especially when you’re trading in the vol space, you always have this undulating vol surface. And you have forced buyers and sellers of options, whether it’s pension funds or sovereign wealth funds. So it’s almost like you have to think discretionarily about you guys’ experience in the space over multiple business cycles, and then that discretionary experience maybe colors how you implement your algos on a daily trading basis, and that way it takes the emotion out. But you need that 30,000-foot view of multiple business cycles and thinking, well, there might be a forced seller here where vol looks cheap. That’s because somebody’s implementing, they’re unwinding a position. Or how do you think about it that way?
Bastian Bolesta:
Certainly, you have to zoom out from time to time and review your research process and your approaches from different and new angles. We always call that Normalistan and Chaotistan. There may be new things happening in Chaotistan in a certain way as it has never occurred, may occur now, and your models may encounter this. You can best address that by using as much data as available and not fine tuning your models to very specific and small periods or specific small patterns, because then they may not be generally applicable to whatever’s happening going forward.
Bastian Bolesta:
Of course it’s a trade-off to have something which is very, very specialized and doing well in certain settings, but at the same time to be broadly enough defined to do also well if these settings don’t occur and if something new is happening and occurring. Ultimately, probably zooming really high out, it leads us back to the behavioral finance aspect of things. Even when markets change, market structure changes, new market participants join, new products are developed, a lot of investments take place now on the passive side of investing, with ETFs, massive inflows and passive outflows from actively management. And this changes the market structure. It changes how markets react to news in comparison to what has happened in the past.
Bastian Bolesta:
Consider all these people who now can immediately sell the entire market, because they have an ETF. This took quite some time and effort prior to that, when you want to get rid of all these positions. Now you just sell your ETF. And this has a different impact on markets. So it’s important to be broadly in order to absorb these changes in environment, but ultimately it’s human beings making these decisions. Even arguably on the systematic side, we are coding our programs, so it’s human beings thinking about markets. It’s human beings thinking about Extremistan and Normalistan and defining in these codes.
Bastian Bolesta:
And if you go back to these human biases, on the downside, the fear, the panic, which leads to overreactions. Or the relief when things are going better, or maybe even great on the upside move, which then maybe leads to the next down move. This is all rooted in us as human beings being active in these markets, despite the fact that we are employing a lot of technology. We still define stop losses, which may be triggered and then start a cascade of stop losses, and things get worse and worse because all these people basically react on their emotions.
Bastian Bolesta:
And ultimately, we would argue that being a quant is one way of addressing or overcoming human bias. Not all of them, but it’s certainly a approach where you can be more disciplined, where you can follow your rules, because you have set the rules. It’s not about goals and objectives, it’s more about really following these rules. You may know one author, he is called Daniel Crosby. He has written a book called The Behavioral Investor, and he is a psychology doctor by profession. He does a lot of work about these human behaviors and what kind of biases we have to face on a day-to-day basis and why it makes sense to systemize approaches to make us aware of what kind of weaknesses we have, based on our natural background. So how we react to fear and panic and that we have this need and urgency to do action.
Bastian Bolesta:
And if you think about a sell-off in markets, what happens is you have this cascade of people have these stop losses. They want to protect themself, and then they’re all the way down. They want to get rid of it, because they fear of losing more. And then afterwards, the market is recovering again, and then they missed out on this opportunity. And the recovery may take place on the same day or on the next day, but do you have the stamina or the calmness as a human being when you just lost a substantial part of your fortune to say, “Well, I’m still in the game, because I set the rules in that, and maybe it’s different this time”?
Bastian Bolesta:
There are a lot of good discretionary traders out there, and I would guide investors to specialized investors, who have a certain understanding of the agricultural market, who have specific knowledge there, may have access to specific datasets which they analyze, but then they still come up with their long-term experience what happens to natural gas in this and this cycle or what happens to soy if Brazil has more rain. But you see, even here in these examples, a lot of data is coming into play, so it’s not really a guy reading a newspaper and coming up with an opinion anymore. It’s all data-based, but ultimately if the human being is making the decision in the end or you have a systematic automated way of executing your signals, that’s probably the key difference then between systematic and discretionary. And both have their places in the world, and a good mixture of things is certainly desirable.
Taylor Pearson:
Really interesting. Bastian, I know you’ve done a lot of work on momentum. I guess I sort of think about trading trades at a high level, value, momentum, and trend are the three buckets I group things in. I think most people get the idea of value. A lot of people, I think, come into investing through Warren Buffett and then Graham and price-to-book and [inaudible 00:19:26]. I think on the surface, momentum and trend, it’s not obvious how those things work. I wonder if you could maybe unpack that for us, and then just talk about your thoughts on momentum and some of the work you’ve done there.
Bastian Bolesta:
Sure. It’s a tricky package to be unpacked, because-
Taylor Pearson:
That’s why I’m glad you’re doing it.
Bastian Bolesta:
… there are a lot of definitions of momentum out there. We are focused on a very specific one, where it becomes very difficult to really differentiate between trend and momentum, as we trade it on the intraday basis. Ultimately, we are looking after price movements, and so do trend approaches. We look for price momentum, and trend basically, zoomed out, also looks for continuation of certain price movements, but generally speaking, on the longer term. Why, for example, our intraday program on equities is an intraday momentum program, the point here is to make the key differentiation between mean reverting programs, who basically follow the principle of whatever’s happening at the moment will revert back to the mean. You’re executing a signal in the opposing direction of the currently evolving movement.
Bastian Bolesta:
Whereas what most of our programs basically do, and what the focus of momentum basically means, is that we are defining and executing signals which express a continuation of this price movement. I wouldn’t do the entire momentum space justice to really dive very deeply in there, because very often, momentum players basically use a lot of fundamental data as well. They don’t only use price. They’ll use other ratios and value criteria to define if a stock, for example, is valuable. And if your criteria is showing a certain momentum, that would also be considered as momentum trade.
Bastian Bolesta:
But in our case, being intraday, being fully systematic, on the quant side, momentum is a differentiating factor in regards to trend, which tends to be longer in terms of holding periods, tends to be longer in terms of what price movements are monitored and identified. So in our case, a couple of minutes in one direction is definitely a momentum move already. In the trend-following space, that means several hours if not days, or we’re talking weeks, of price movement in one direction. It’s basically then generating a signal. So the key differentiating factor between the intraday momentum or price momentum and trend is certainly the time horizons and what you try to capture, in terms of what kind of data you absorb and monitor, and at the same time, how long you hold your positions.
Bastian Bolesta:
Our two programs are intraday-only. At the end of the day, they’re out, so they’re only after things which happen intraday. And if you zoom out, there are certain things which only happen intraday. You don’t identify them as soon as you are zoomed out on a virtual perspective. If you look over data of a couple of weeks, you miss out these little opportunities which are still there, the overreactions which happen on an intraday basis, but they are gone at the end of the day. And that’s basically what we specialize in, that we would like to protect the portfolios of our investors and benefit from these very short-term opportunities which can arise because of two things when things are going well, but predominantly also when things are going very badly. They may be in a recovery at the end of the day, so the opportunity is gone. But if you trade it intraday, you can lock in these profits.
Bastian Bolesta:
So it’s a different return profile. It comes with benefits of being certainly less correlated than other strategies. So the intraday and short-term space is more heterogeneous in general, in comparison to longer strategies such as trend. But again, in this case, as back to our discussion on discretionary and systematic, they both have value to it. Trend has been around for many, many years. It is also driven and rooted in behavioral finance. And you can benefit from it by applying it to your portfolio. But it’s just capturing something different and may not be as reactive and equipped to very short-term shocks. Trend very often has the issue that it is identifying something, and then if there’s a reversal, you give back certain returns or you miss out on opportunity until the trend is more established and you can react to it.
Bastian Bolesta:
This is something we have also seen most recently. Trend had a very good year trading on the bond side, but we had the reversal in the not-too-distant past, a couple of weeks ago, and this basically resulted in them giving back certain amounts of open trade equity, so to say. But it’s still very valuable. It has added value to portfolios, but it’s different to what we’re doing. We’re just in a very different space with other opportunity sets, which are more related to times of crisis.
Taylor Pearson:
Speaking to the times of crisis point, I know you’ve also done a fair amount of research on fat tails and the existence of fat tails in markets and how those show up. I’d love to just hear you talk a little bit about first, what fat tails are and how you think about them, and then how that influences your trading and investing strategies.
Bastian Bolesta:
Sure. The expression of tails or the fat tails is coming from a view through the lenses of a normal distribution, so it’s our normal world again. We had this earlier. We said, well, what happens out there in the real world may not necessarily help you on the financial markets. A normal distribution has a certain shape. This is bell curve, everybody has seen somewhere in the past. If you have these tail events or fat tails, that basically means that what happens, happens to a more extreme and to a larger extent more frequently as well. So if you have, for example, a distribution of daily returns of the S&P, the majority, the larger part of this bell is basically little up and little downs, and everybody knows that. That’s the normal up and down.
Bastian Bolesta:
But if you look at the edges, normal distribution would suggest a certain number of events with certain up and down days. But if you have fat tails, basically that means that these down days, they’re substantially larger and happen more frequent than a normal distribution would suggest. And this can also happen on the positive side. So for example, if we look at the S&P since the 1930s, the 5% percentile on the bottom basically explains roughly 40% of the losses of the S&P over that period. If you go to fat tails on the right hand side, it’s a 5% percentile on the positive side explains roughly, I think, 35% of the gains.
Bastian Bolesta:
So that basically means, 40 and 35, that’s 75%, that a larger part of these returns in the S&P are actually driven and explained by more extreme moves than what is happening on the daily up and down. And as a result, it can be beneficial to take a closer look what’s happening there. Generally speaking, most people don’t fully understand that these rare events are actually not as rare, that they may happen more frequent in terms of occurrence and could be more extreme in terms of what a normal distributed perception of the world would suggest.
Bastian Bolesta:
As a result, the normal distributed world does not necessarily apply to capital markets, and it makes sense to take a closer look how you can address that and how you can, in our case, come up with a systematic way of identifying tail events and benefiting from it as well. So in our case, it’s not just about protecting yourself. It is also about benefiting from these extreme events, which happen from time to time and can have a tremendous impact on your portfolio.
Taylor Pearson:
I notice you wrote a piece that I read, talking about the February 5, 2018. In the VIX complex, there was a move that I think you said was according to the normal distribution. I guess when I think of normal distribution, I usually think of, like, height, as the iconic example. The average height globally is like 5’10” for a male, or something. If you got everyone in your high school and you lined them up on the soccer field or whatever, most people would be around 5’10”, and then you’d have a few people that were really tall. I’m trying to convert this to centimeters. I don’t know what 5’10” is in centimeters, but you know, a few are really tall.
Taylor Pearson:
But in markets, you have the certain volatilities. You have a lot of eight-foot-tall people or ten-feet-tall people and two-feet-tall people, more than the normal distribution would predict. And you actually say with the VIX in February 2018, you had one of these 100-foot-tall, 200-foot-tall person type of events, a 1-in-48-billion-year move. I was just curious if you could maybe walk us through just what happened there and the dynamics. And then if you feel like contributing more general lessons about fat tails and how that showed up in markets and how it could show up in other markets.
Bastian Bolesta:
Sure. Well, if we travel back to February 2018 and actually take a look prior to that Volmageddon, how we internally call it from time to time, because the VIX volatility had this tremendous movement, the markets prior to that were rather calm. 2017 had a very low-vol environment, also going into 2018. This was a very low-vol environment with only gradual small up and down moves in equity markets. The equity markets were going up in general, but very slowly, not with substantial large moves. And as a result, volatility was really low.
Bastian Bolesta:
Then out of the blue, to a certain degree, you have this massive movement on February the 5th, where the S&P lost substantially, in comparison to the previous moves, and at the same time, the VIX basically had a immense explosion to the upside, because of the inverse relationship between the VIX and the S&P. Of course, this was such an extreme move, in terms of standard deviations, because prior to that, volatility was really low. If the market basically loses 3.5, 4%, but prior to that the movements were just a couple of basis points up and down, or maybe 50, 40 basis points on a given day, now if it’s 3.5, this is a substantial move, and as a result, an extreme event in comparison to our tails.
Bastian Bolesta:
Generally speaking, because of certain aspects, what we described before, different market participants, different products, these all played a role what happened in February as well. This was also a structural issue, a structural incident, where a lot of people being active in the volatility space basically traded highly leveraged instruments, which then reinforced these movements once the VIX passed through a certain level. This is, to a certain degree, an expression that markets have changed and evolved over the past couple of years and that new instruments and new participants may create new extreme scenarios.
Bastian Bolesta:
It’s to the fact what we described earlier, that if you are able to identify these scenarios, you’re in a good position. And at the same time, it’s rather likely that they will happen more frequently going forward, because of suppressed volatility levels, because of central banks playing a role in financial markets they haven’t played in the past. This creates pressures in markets, which is released from time to time, and February last year was one example of that.
Taylor Pearson:
I’d love to hear, how do you think about that role, the causes of the suppressed volatility and the role central banks have played, and how that’s changing? How do you think about that dynamic on the markets?
Bastian Bolesta:
In very simple terms, a lot of cheap liquidity basically allows asset prices to have levels which are not necessarily supported and sustained by fundamental factors. And you have a lot of buying-the-dip scenarios, where a normal shakeout of good and not-as-good companies would basically result in a different playing field after such a shakeout, hasn’t really occurred. And as a result, once things are happening, they become more extreme. We had this also in Q4 in the tech sector, where tech was basically suddenly in the focus, and you had tremendous volatility in tech in October and in December last year.
Bastian Bolesta:
This is certainly to the nature that a lot of capital has been available, and a lot of participants basically have bought stories which do not necessarily play out on the fundamental side, in terms of investing in a certain company. We currently, while speaking, we have this case with WeWork, where a lot of money basically has been put into a company which ultimately is renting out office space in a very modern way. Nice offices, I like the vibe and everything. It certainly caters to a generation of people working all over the globe and feeling home wherever they are, and preferring the standardized and cool vibe of these offices. But ultimately, it’s not really a tech company. It has been priced and valued as a tech company.
Bastian Bolesta:
Billions have been flowing into WeWork, and we are currently trying to understand as market participants what is really happening there. Because news means that just this could be something of a larger thing, where because of cheap money and central banks flooding markets with liquidity, participants may not invest according to the rule book as they have in the past.
Bastian Bolesta:
A good thing, I’m a bit out of my turf here, in regarding that question, because I’m a quant. As a result, I don’t have to read the newspapers as frequently and not interpret if WeWork is worth what it’s claiming to be or if it’s a good investment or not. Ultimately, what happens there will play out in markets, in terms of price movements of the NASDAQ reacting to it, because other already-listed companies may have similar issues as WeWork. So that’s basically the bridge I wanted to build.
Bastian Bolesta:
If you have these new valuations of the situation, this basically plays out in movements, which systematic programs are positioned to capture, while not being forced to interpret what central banks are doing and if they will continue to do so. Same goes for the Fed. If the Fed is lowering rates or doesn’t, ultimately it will play out shortly after the information is released. These press conferences, they can be a bit volatile, because there, this is life happening, where questions are asked and people and market participants interpret it. What does he really mean by that? How does it compare to how he has said it the last three times? Ultimately, I would go berserk if I had to make discretionary decisions based on that, sweaty all over, what does it really mean, what shall I do with the portfolio, of my portfolio and of my clients?
Bastian Bolesta:
So I’m really happy that we have these rules, which incorporate a lot of Fed decisions in the past. So we have a lot of data, basically, how the Fed has made decisions and how interviews or the press conferences have played out afterwards. And ultimately, so we can lean back a little bit, observe, and hope and enjoy, but this doesn’t really influence our trading, and only from a very remote perspective influence our research process.
Jason:
And isn’t that one of the joys to being a quant? Just to use the most recent example, it was like your typical discretionary fundamental trader, if they’re looking at negative interest rates, they lose their minds, right? This is not in our economics 101 book. I’m going to lose my mind. This is insane. People shouldn’t be doing this. And as a quant, you’re just riding that position down. You’re like, “It may be insane, but my models are telling me this is the direction.”
Bastian Bolesta:
Correct. Fully agree. Fully agree. And you don’t really have to argue about this as this is not normal, we have to return to normal. Or this is the new normal. Ultimately, you should on the research side build your models on broad enough datasets to incorporate these different shades of normal, or how people would define it as normal or not normal. And this goes back to what we said earlier, that one of the tricky parts on the systematic side is that you may curve it into certain scenarios, that you are specialized on a very specific setting and are dependent of the reoccurrence of the setting in a certain pattern. And that’s why it becomes important that these models are robust, that your approaches can be applied to different periods in the business cycle, that you can apply them to different markets as well.
Bastian Bolesta:
For example, in case of the intraday momentum program, this was initially developed on the equity trading side. We just traded the S&P. And then the first big test was basically what happens if we apply this approach also to other equity markets in the US? And does it actually work in Asia? And that’s basically how the program has grown from being S&P-focused intraday momentum approach to a global approach now trading equities in Japan and in Hong Kong, because ultimately, what these algorithms try to identify and capture are very fundamental omnipresent developments, price movements, or patterns. Why? Because they’re ultimately rooted in behavioral finance again. It’s human beings reacting to the Fed, reacting to what WeWork means or doesn’t.
Bastian Bolesta:
And to a certain degree, you have this in a more extreme form in Asia, for example, if you compare the data. In Hong Kong, we have a larger number of retail investors in comparison to the US. As a result, the market is more volatile. So you have a larger number of trading opportunities there on the intraday trading side, for example, than in the US markets, because of less matured markets, because of more retail investors participating, coming back to the fact that human bias creates volatility. We all so desperately want to have certainty, and by trying to get certainty and manage whatever’s happening, we create uncertainty for us, because this is basically volatility playing out in markets and encountering our own biases, so to say.
Taylor Pearson:
Yeah, I want to come back and talk a little bit about what you said, in terms of differences between the Asian markets and American markets and how you convert that. But getting back to the quant side of things, I’ve heard you talk about and write about optimizing for a high Sharpe ratio, which is a measure of historical volatility. It can actually be a very dangerous thing to do. Normally you’d think, and the economics 101 textbook, finance 101 textbook, a high Sharpe ratio means this is a safe investment, it’s not very volatile. But you’ve actually done some research and written about that could actually create exposure to tail events or what we used to call negative convexities in Extremistan. I was wondering if you would just walk us through the logic there and how you think about it.
Bastian Bolesta:
Sure. Sharpe has been around as a ratio for quite some time. It’s an interesting ratio, because you basically look at returns over standard deviations of volatility, so to say. And as high as your return is in comparison to whatever volatility you have there, as high as the Sharpe ratio is. So it’s generally speaking a desired ratio, and you want it to be high. But the second question, which should immediately come afterwards after you’re so excited about a high Sharpe ratio, would be why is it high? Where does it come from? What kind of returns are fueling this ratio?
Bastian Bolesta:
For example, if you have certain size steady returns, this basically brings down your volatility, so your Sharpe ratio goes up. A perceived certainty, with volatility being low in your return stream, creates the perception that this is a good investment. But the certainty could be brought by being what we would call short vol. If you’re an option writer, for example, not that option writing is always loss-making, but you can take countermeasures to that, but if you generate premiums by selling options, so you cash in the premiums, you encounter the situation that you have a very steady cashflow on the positive side. But if a situation occurs where basically you as the option writer have to step in, because you have sold someone an option and this option basically now has changed in value dramatically, you have this extreme event.
Bastian Bolesta:
As a result, it’s a balance of the desired certainty as a human being, and a return which steadily goes up is low vol, until it doesn’t. So it’s always a very important aspect to really look at Sharpe and have an understanding where this is coming from. Often, high Sharpe ratios are basically driven by being short vol. It is very seldom that you have high Sharpe ratios with positive skew, with positive surprises, so to say, and having a long vol profile.
Bastian Bolesta:
This is most of the time in the systematic side of trading, because it is counterintuitive to human behavior how, one, you would react to a situation occurring ad hoc and impacting your portfolio. So what we did basically is that we normalized Sharpe and said, let’s have a look how Sharpe relates over skew, skew being an expression of the frequency and the magnitude of positive surprises or negative surprises. A positively-skewed strategy has positive surprises with substantial upside, where a negatively-skewed, or as you also said, a negative convexity on top of it, basically has its negative surprises, where you’re doing fine for so many days and you have this great Sharpe ratio, until you doesn’t.
Bastian Bolesta:
So if you normalize that, it becomes very interesting, because it creates you a picture of how you have achieved the Sharpe ratio. Have you achieved it by having exposure to certain risk you’re not necessarily aware of as an investor until it’s materializing, and it’s too late? Looking at this normalized Sharpe puts you in a better position to understand how a manager is basically generating its returns and what you could expect in more extreme scenarios. A lot of systematic managers, and specifically our field, is basically being focused on these long vol positively-skewed return streams, which are then also uncorrelated to the rest of the space, which is very desirable, but also boring to a certain degree.
Bastian Bolesta:
The intraday momentum program only trades if there’s a larger up or down move in the equity market. If that’s not happening, nothing is happening. And sometimes then you get the call, “Why haven’t you traded for the last six, seven, eight days?” Well, there wasn’t larger move in the markets. Imagine you step in the shoes of an option writer here. Clients will be really happy with you, because you have produced these regular return streams for months, and everything is fine. Same goes, mean reverting strategies, which have excelled in the last couple of years because of generally very low volatility levels.
Bastian Bolesta:
If you trade back to the mean, that’s beneficial if volatility is low, because they are ultimately short vol. But if something is happening, you can actually lose much more than you have made until that point, if you don’t take certain countermeasures. So there are certainly option writers out there who do a really great job, because they protect themself and they take additional measures. They sometimes even apply programs like ours on top of what they’re doing, so that a positively-skewed and long vol program can actually add value in times of crisis, compensating for the losses they may face because of the short vol positions.
Taylor Pearson:
Yeah, I think it’s probably a simplified analogy. I was thinking of picking up nickels in front of bulldozers. That’s just such a clear visual. You can pick up 10,000 nickels in front of a bulldozer and it looks great, but you only need to fall and trip and get hit by the bulldozer once for things to end very badly.
Bastian Bolesta:
Definitely. As long as you look forward, it feels so good, because you’re picking up these pennies. The thing is, that the way how markets have evolved, sometimes there’s a really crazy driver or a malfunction of the engine of the bulldozer, and it may accelerate for whatever reason, not being part of your modeling. So no, the bulldozer has always had the five miles per hour. Why did it accelerate this day? And then you’re run over, basically. You were right all the time until that point, and then it’s too late.
Taylor Pearson:
Yeah. No, that’s a great addition. I’ve thought of it that way. You’ve mentioned a few times, you’ve been talking, you’re focused on trading intraday. I think when most people think about short-term trading, or I was, there’s transaction costs. You’re going in and out very quickly. This is very inefficient. I’d love to hear you just talk about why you do that, how you came to that, and why that makes sense to you and seems to work.
Bastian Bolesta:
Sure. Different angles to that. Certain things happen only intraday, in comparison to a perspective of holding a position longer. You can differentiate between how markets behave and move overnight in comparison to intraday. Overnight, a lot of the information in regards to evaluations of company earning reports and things like that are released after the market has closed. So they are basically absorbed, and whatever is basically expressed in a price movement up or down results in a gap up or down. So you can’t really trade that, if you don’t already have the position. And liquidity to actively engage overnight is not necessary there, and you can’t trade all instruments. Not all instruments are available overnight.
Bastian Bolesta:
So there’s a difference between how markets behave overnight and intraday, but intraday you’re certainly faced with the situation that you have transaction costs because you have to go in and out within one session. And markets tend to be mean-reverting intraday, because the news absorbed overnight because of an earning report is basically expressed in the first price in the morning. And then people start reinterpreting, but if there’s no follow-through, basically, from other angles, markets will most likely come back a little bit, either recover if it’s a down move, that’s basically called closing the gap or partially closing the gap, or basically coming down a little bit if it was an upwards move.
Bastian Bolesta:
The tendency of markets being mean-reverting intraday makes trading intraday momentum even more difficult. You may recall that we have an intraday momentum program, but we address that by being only after the large movements. We’re focused on the tail, so if something is happening intraday and has this magnitude, and the quality of the momentum is basically on such a high level, then it’s very likely it follows through, and markets will see a larger move to the up or more dominantly also to the downside. So it can be very beneficial, then, to engage intraday and generate this alpha, which you will not have if you zoom out and you take the entire session or you hold positions for several days.
Bastian Bolesta:
It’s a very attractive alpha source, which needs to be approached carefully, because you can be lured into taking positions which are then not following through. Ultimately, that’s where most of our research over the last eight years has taken place. It is about solving a lot of data, analyzing this data in real time, to come up with a quality criteria of the evolving market move. And only if this criteria is of a certain level, or the quality of the momentum move is of a certain level, we would engage. As a rule of thumb, this means a larger move, for example, in the S&P up or down a percent.
Bastian Bolesta:
It becomes more likely that we become active if the market is up or down, but still, ultimately it’s these, we call these proprietary qualifiers, they calculate the quality criteria absorbing these vast amounts of data, which weren’t available 10 years ago. And the computing power to do that wasn’t available 10 years ago. So it’s all technology-driven, and now having access to technology, much cheaper, much widely on a global scale, and has enabled this type of approaches in trading. This helps us to stay away from trouble, in terms of getting lured into positions which are mean-reverting or getting chopped, so in and out multiple times and paying the price on the fee side as well.
Bastian Bolesta:
To a certain degree, it would be more beneficial, specifically looking back for two or three years, if you would focus on the mean-reverting side, because we had this rather low-vol environment for such a long period. There were explosions of vol, as in February last year or then in October and December last year, in August this year, so we had three major sell-offs in August where the markets dived substantially. But ultimately in the end it recovered. The S&P was only marginally down, and we are now trading close to all-time highs.
Bastian Bolesta:
It takes some effort not to forget that we already had these occurrences where suddenly we had more extreme events happening in equity markets, but ultimately we are in a very long bull market, more than 10 years. This may lure people into taking positions and investing in certain things which have fared really well but may not be positioned to deal with changes in the environment, more elevated levels of volatility for longer periods of time. So it can be done official if you at least don’t suffer from that. If you even benefit from these changes, that’s great. And if you generally also are able to make some money in the current environment, then it’s very desirable to have that. It is not what you should do as your entire portfolio. You should have a mixture of things.
Bastian Bolesta:
So I’m not saying that mean-reverting approaches and option writing, as I said earlier, on the discretionary side, that they don’t have their value. A good mixture of things is certainly the best, looking after correlations. And intraday strategies, not just ours but generally, have very low correlations to most of the other asset classes and trading approaches, so they can be beneficial to your portfolio, independent from the actual return contribution. So it doesn’t even have to be really high, because as soon as you adding something which is uncorrelated, you’re diversifying your portfolio, and it becomes more robust and more stable. And if it’s then actually delivering when the rest of your portfolio may be under stress, that’s great.
Taylor Pearson:
I want to come back. You mentioned thinking about taking some of the things you’re doing and applying them to different markets, things you’re doing in the S&P, how they apply to other markets in the US or European markets or Asian markets. I’m just curious, how do you start thinking about that relay race, markets are closing in one part of the world and opening in another part of the world, and how volatility or risk can cascade through those markets? How do you think about that?
Bastian Bolesta:
Sure. It’s different, different, different perspectives on that. Generally speaking, it’s a global world which has become more and more interconnected. Information travels so fast that you don’t have, or you only have rare opportunities to trade something isolated from the rest of the world, and then later it is basically absorbed as information somewhere else. But still, you have situations where markets are closed. So for the presidential election in the US, for example, in 2016 or next year, again, we have the situation that the markets are closed when the numbers are coming through. And as a result, they can’t really react to it. The majority of your positions basically are locked in for the night, and it can be beneficial to whatever happens while these news are interpreted, that they play out in markets in Asia.
Bastian Bolesta:
And you have situations where news rolled around the world, where you have the opportunity to first trade in Asia, and then they’re triggering something in Europe, and then you trigger something in the US. It’s a perfect scenario for intraday trading program, because you have compounding impacts. You can trade your capital multiple times a day. But it does not happen very often, so that’s not the key reason to trade globally. I would rather say that the key benefit is that if you trade globally, you generally always have something being active, monitoring what’s happening, and can react to the market. So if something is happening in the overnight in the US, you may be able to capture it in Asia, or in a different form in Europe.
Bastian Bolesta:
Going back to what I said earlier, that news are absorbed overnight and then markets get up or get down, they will still react, certainly, intraday after a major event which happened overnight. But the immediate first price when the markets open, what happening in these first minutes or hour, basically will already strongly determine the interpretation of the news what has happened during the night. And then there will be reinterpretations and certainly creating more trading opportunities and volatility, but ultimately, if you trade something on a global basis, you may be in a position to basically already react to newly unfolding events ad hoc.
Bastian Bolesta:
And that’s why we have made the intraday momentum program, for example, a global approach. It doesn’t work all the time, because their markets are also disconnected. There may be local incidents which have an impact on the local market. For example, we have the unfortunate situation in Hong Kong, where there’s a lot of tumult about the future of Hong Kong and how people would like to live in interconnection with the role of China in Hong Kong, and this had an impact on Hong Kong itself. At the same time, the bigger picture, there are the US-Sino trade war or trade escalation questions, which also have an impact.
Bastian Bolesta:
So you may have insulated cases where Hong Kong is reacting totally different to what you would assume as a proper reaction to whatever has happened in regards to the trade war in Washington a couple of hours earlier, because they are reacting to what is happening on the ground. So you do not necessarily always have this clear picture of trading these trends around the world around the clock, but being generally capable of reacting to news in the most liquid form while the markets are open, that is very beneficial.
Taylor Pearson:
Well, Bastian, this is really insightful. Jason, do you have something? Do you want to jump in?
Jason:
Just real quickly, I was thinking about this morning, there’s very few managers in this far corner of long volatility trading or tail risk trading, and I was thinking about a lot of the managers we talk to, almost all of them are not in that traditional corridor of New York City-Greenwich, Connecticut. What do you think are the pros and cons of you guys being based in Switzerland, if any?
Bastian Bolesta:
Better skiing.
Taylor Pearson:
Diversification.
Bastian Bolesta:
Generally, I wouldn’t mind to be in New York as well. We are Europeans, because we were born here. Some of my team members have studied in the US. I’ve worked in the US as well. It’s a great place to live and to work. And predominantly US markets are dominating the world in terms of capital markets, for sure. That’s a good place to be. To be in Europe, it’s a good spot to basically cover Asia, Europe, and the US, because basically you have an early morning and then you still have a glimpse on what’s happening in Asia. Then you have the entire session, basically, in Europe. And then the US closes at 10:00 our time, so that’s perfectly fine as well. But it’s a very poor argument, being a quantitative systematic automated trader, simply because ultimately, I don’t really have to actively watch the markets. We have operators onsite, which basically make sure that the programs are running proper, but we don’t have to interpret it, what’s happening in Asia at the moment.
Bastian Bolesta:
It is a good spot to be in, time zone-wise. It helps us with investors, because we have investors in Hong Kong and in Asia. And we have some European and then the majority in the US as well. It’s an interesting spot to be in, and it comes with certain lifestyle choice benefits, skiing, as I said earlier. But New York and this area, it’s a very nice place to be as well, and it helps to mingle with peers and other people, so we are quite frequently in New York. I think that’s all you can say about that random choice of being in Switzerland. Better chocolate as well, probably.
Taylor Pearson:
The important things. Well actually, this was very fun and insightful. I appreciate your time. If people want to learn more about you or reach out, what’s truly the best way? Where do you point people to, to learn more about you and/or Deep Field?
Bastian Bolesta:
Probably if you Google Deep Field Capital, you will find a lot of things of what we have been doing and talking about in the past. And dropping us an email at info@deepfieldcapital.com certainly would be one way how to approach it.
Taylor Pearson:
Great. Thank you very much.
Bastian Bolesta:
Thank you. Thank you for having us.
Taylor Pearson:
Thanks for listening. If you’d like more information about Mutiny Fund, you can go to mutinyfund.com or better yet, drop us a message. I am taylor@mutinyfund.com, and Jason is jason@mutinyfund.com. And we’ll get back to you. You can find us on Twitter, @MutinyFund, and I am @TaylorPearsonMe.
