“I don’t want to trade much more money – I want to show that a mostly automated system can make money over the years in a pretty regularly recurring fashion.” – Luc Van Hof (Tweet)
In the second part of our interview with hedge fund founder Luc Van Hof, we dive into the philosophy and creation behind his trading models. We also discuss why he is a risk averse person, what hobbies help him stay focused at work, and what investors and fund managers can do to grow their business and trade smarter.
Welcome to Part 2 of our conversation with Luc Van Hof.
In This Episode, You’ll Learn:
- How to avoid model decay and how to avoid the risk when the model may stop working in the future.
- How to diversify your types of models – dynamic filtering that takes place. Automatic de-leveraging when a certain market goes down.
“So you focus on effective diversification which means – you have to diversify across markets, across trading approaches, and across time.” – Luc Van Hof (Tweet)
- How Luc chooses his models and why he does:
- Short term trend following
- Short term mean reversion
- What concepts for his models repeat themselves over and over again, pattern recognition.
- About volatility risk premium strategies.
“The risk premium strategy is the most important source of our return drivers.” – Luc Van Hof (Tweet)
- How he tests his models that have so many moving parts in short timeframes.
“We spend a lot of time making sure the data we use for our research is of good quality.” – Luc Van Hof (Tweet)
- His views on position sizing.
“Position sizing has become by far the most important component.” – Luc Van Hof (Tweet)
- What investors should look at in terms of risk management
- Maximum Exposure for a trade – determines the maximum risk that a trade can generate for the total of the portfolio.
- How Luc deals with drawdowns.
- Why he is a risk averse person.
- Why he is still researching other trading ideas when he thinks he’s found a way that mitigates risk effectively.
- How he gets his ideas from math puzzles, reading about geometry and logic.
- Why investors should look at the predictability of returns and how to convince investors what and how you are going to trade is something that is going to work.
- Why discipline is the main characteristic that people need to be a successful fund manager.
“Build a program and offer that program, so that every single one of your clients has the exact same thing.” – Luc Van Hof (Tweet)
- The books that he recommends for managers starting out wanting to be successful.
- How his hobbies such as nature, reading, and music help to keep him balanced in a busy financial world.
Resources & Links Mentioned in this Episode:
- Books that Luc mentioned in this episode:
- Definitions of terms mentioned in this episode:
This episode was sponsored by Swiss Financial Services:
Connect with Capital Hedge:
Visit the Website: www.CapHedge.com
E-Mail Capital Hedge: firstname.lastname@example.org
Follow Luc Van Hof on Linkedin
“If we can make something that is not going to suffer too dramatically in a down market, and is going to underperform in an up market, the client is going to like it.” – Luc Van Hof (Tweet)
Niels: You're listening to Top Traders Unplugged, episode number 034, where I continue my conversation with Luc Van Hof, Founder and CEO of Capital Hedge. This episode is sponsored by Swiss Financial Services.
Welcome back to Top Traders Unplugged. Where the best traders in the world come to share their experiences, their successes, and their failures. Let's rejoin the conversation with your host, veteran hedge fund manager Niels Kaastrup-Larsen.
Luc: ...It's not going to be extremely high. It's not going to be extremely low. We know it's going to be probably in that confidence interval, and then you have a better probability of reaching your objective in terms of what you were hoping for in terms of risk and return.
Niels: Let me try and give you a real life example, and I want to hear your feedback, because I think there are a lot of traders and investors out there who probably have similar experiences. We've done a lot of research in the strategies that I've been involved in and some of it has been in the relatively short term space and all I can say is that the research was very robust and not optimization, lots of different markets, and certainly tested across different markets as you suggested, and all the numbers going back 15, 20 years looked really robust. Then comes along 2010, 2011, 2012, I don't remember exactly when it is, but there is a time in that period, and 2013 in particular, as far as I recall, where the performance really changes. I just wonder what it is that you do differently, because it seems to me that you somehow can, and I don't know whether it's filtering in order to, because some of these models will go for a long time working fine, but there tends to come a time in a model's life, and especially in the short term trading space where a lot of firms would argue that there is decay on models life down to even years and then they have to come up with something new, but maybe that's a different topic, but there tends to come a time where most models will suffer. How do you, and I don't know whether you have been completely, but how do you minimize that and how do you avoid it still despite all the things that you said about testing it across different markets even those that's not where you're going to use it? Do you use some kind of filtering to avoid the periods where the market structure is just not right for that kind of model?
Luc: Well that's a most intriguing question. I think the honest answer is most people, I think all people; they don't know how to filter it and how to know when actually a model is degrading so strongly, in terms of performance that they should put it on the back burner and leave it aside. Well, the solution we think found to cope with that situation is to let the market tell us. What does that mean? Well, it brings us to the subject why do we trade short term? The reason is if I trade short term and in a week's time I get 10 bits of information telling me this is going normal, I'm having 5 winning trades and 5 losing trades and overall I'm making a bit of money. I'm happy. It's in line with what I see normally. But if I trade only 5 or 10 times a year and I have to wait a year to receive that same amount of information, well a year has gone by and maybe I've been suffering from a drawdown since April of this year and here we are in December, 8 months later let's assume, and I'm still collecting this information. If I'm trading short term this feedback which is given by the market and by, of course the result of the trading we have been doing, will allow us to extract some very valuable information in terms of well, this model in the current market environment apparently doesn't work very well, or this one is doing OK. So what can we do about it, and how can we use it?
The answer is pretty simple. In our case, and I'll just speak from my own experience, if we have in this library of more than 50 different models - trading models, we could say, we don't know which is the best model and what time to use it in which market, so which model are we going to employ for what market? The honest answer is no one knows. It could be this one, and we could be lucky, or we could have some bad luck. The short answer is we don't know. So we could say let's play this in a very naive fashion and employ them all across all markets. That would not be very efficient in terms of capital employment. So what do we do? We say well, we know how these models perform in a certain market environment. We don't know what the market is going to bring and how the market is going to evolve, however, if we have this model which is delivering and generating a lot of trades as a consequence delivering a lot of information in terms of the performance is good, or bad, mediocre, average, extremely good or bad. We will very quickly know from the prices, from the trades that we get back from the real market if this model is fitted to the current market environment or not.
We can actually then filter, and the filtering is done automatically, and say well, it's comparable to a fund the fund manager and well I'm having 50 plus managers to pick from, I assigned them on all of them, but I'm not going to invest all of my money with all 50, I'll just take these 10, 15, 20 managers, which are doing great in the current environment and they all trade a lot so we'll very quickly see that these managers are slipping away, losing performance, or doing great. Provided there is some kind of short-term feedback from what they are doing, I will be able to tell, well this manager is now suffering, this obviously a very difficult environment for this manager, or for this trading system to go back to my other example and I will know to allocate less capital to that strategy. So if we see clearly that a mean-reverting strategy or a contrarian investment approach would suffer if the market is trending, well very quickly we will be getting as a feedback from the market and the trading results very poor results so we should dis-allocate, so actually take away capital from that approach, and the trend following counterpart which flourish, would have some great returns and of course it will be given more capital to trade with. If you have this library of say different approaches, which all of them would never work well at the same time, but a significant portion would be doing a reasonable job, some of them would do a very good job, and others would really suffer, you after a while (getting enough information in terms of recent trades) get a very good feeling and indication what is working, but more importantly what is not working, and then you don't have to filter, actually employ the markets own information in terms of composing the portfolio.
Niels: So to distill it down we're talking about diversification in terms of types of models and a dynamic filtering that takes place based on reason, profitability of each market model, a combination, and then it's an automatic deleveraging of a certain market model when profitability goes down, but I assume it's also an automatic re-leveraging or up leveraging of allocation once it starts making money again.
Luc: Absolutely. It's actually a three-step approach as we describe it. The first step is the most critical one. It's the one where people describe as how to avoid the black swan. So how can we make sure that we never have this big drawdown we're so afraid of, this 25% drawdown. We would like to keep it in the single digits, because we know how hard it is without a trading approach to get out of it. So we have to make sure all positions are at all times hedged so that we avoid this black swan situation. In other words, we focus on loss prevention and not so much on avoiding volatility. We can live with volatility provided it's good volatility, say it's volatility to the upside. We have to avoid digging a very deep hole.
Niels: So loss prevention happens by hard stops in the markets?
Luc: Well, actually the ultimate stops, as we call it, are options, because we can have hard stops in the market, even in the FOREX market where there is ample liquidity. Where we see trillions of dollars being traded, and we could say well, if we have a stop there we are never going to get slippage, or we wouldn't get slipped a lot. However, we don't know. If there is suddenly some bad news coming out. There could be some geopolitical event, whatever, the Euro could drop 100 points. Last week on Thursday when Draghi stopped talking, the Euro tumbled 200 pips in like 15 minutes, so you could have stops the market will just gap through it, so it doesn't work. If you have a hard stop and the market is closed, next morning it opens way beyond the stop it's not effective either, so what you need to do is either only trade when the market is open and have a protective off-setting hedge against it, which is by setting up positions using options where you know that if the market is going to tumble say in the extreme case to zero, let's assume the S&P drops from 2000 to zero, if you have a 1975 put option and you're now long in the market, you're not going to lose more than maximum 25 points if the S&P is at 2000, so you know that for sure. That's like the ultimate 24/7 stop without slippage. Of course, you pay a price for that, and that's the hedge. That's the cost of hedging, but to the extent you can recuperate it, you can finance that by doing some other strategies which of course will bring in some other types of risk you will be able to finance that partially if not completely and you will save and avoid the black swan.
Only when you have done that step, what I call the Capital Preservation, you move to the next one which is the Portfolio Construct and there you have to use diversification where you're not focusing on correlation, you're not focusing on co-variants because everyone knows these things change over time, so it's now very lowly correlated and it's highly correlated when there is a crash and the correlation goes to 1, so it's of no use, and so on. So you focus on effective diversification, which means you have to diversify across markets that's fine, most people do that. You have to diversify across trading approaches, not all people do that, but most people do that, but thirdly you have to diversify across time, and that's I think the best. That's like a free lunch when you say well, I'm trading the Euro, but not only on an intra-week basis, also on an intra-day basis, even on an intra-hour basis if needed, and so then you see different patterns, different trends which are not available under longer term timeframe, but maybe available on the very short term timeframe. So you can play some games within the same instrument, within the same market using different timeframes at the same time. That's true diversification. Then you have the allocation third element which you pointed to, which is, you wisely monitor the real-time performance and dis-allocate, or de-leverage or re-leverage when the opportunity is there. So you have this position sizing which is, of course, bring us to the Kairos.
Niels: Yes, I want to go there, but I want to understand this a little bit better. So essentially what you are saying is that you might be long in futures in the S&P for one of your models. You might even be long in the S&P for three different models with three different timeframes, but if you're long three different positions essentially in the S&P you would have to buy, as a minimum, puts for those models in the same market, and then you say on top of that you might finance that through another type of structure of the options. So I'm just thinking here and I'm probably wrong in my assumption, but I'd love to hear your comment, that's a pretty complicated position to manage, especially in the light of the fact that it's short term, so you have three or four different legs in one market, or should I say entry point in a market and then you've hedged that with a number of different types of options, is that correctly understood?
Luc: That is very correctly understood. I have to qualify it a little bit, and that has to do with the fact that we are not trading the same timeframe for the hedges and the directional position.
Niels: So you don't have to take them on and off at the same time?
Luc: Definitely not. So you take advantage of this huge advantage in the options market which is there for everyone and available at all times which is theta, so time erosion, which basically implies if you buy a long term option which is expiring say, in two months, it's not going to be twice as expensive as the one month option. It's much cheaper. It's going to be cheaper than two times the one-month option, so the longer you buy the hedge, relatively speaking, the cheaper it will become on a per day basis. So if you know you are trading a lot to the long side, in a specific market environment, with a specific model that is taking long trades, and you have this model being active and it will be generating trades on a regular basis. If the market is showing this directional trend to the upside, and you have this hedge in place, which is going to be there for several weeks if not longer, the hedge is not going to be extremely costly, not extremely expensive, moreover you don't need to do that with simply buying your put option. You can do something slightly more sophisticated with a put spread or a butterfly where you will basically be taking in some premium and hedging the first 5% of drop, and you have to make some assumptions, which is of course an assumption that the market is not going to fall more than 5% in a day, which it hardly does. Never the less, you can then hedge at a more efficient cost and at the same time put on some short term financing strategies which could be in the shape of call spreads that you are selling against it, say 3%, 4% above the market and then you have a pretty complex structure at first site but it boils down to a pretty simple synthetic derivative as you definitely know if you buy simply the stock and have the same time a put option against it, you could replace that by simply buying a call option.
It's exactly synthetically the same thing, and vice versa, if you play with this relationship between calls and puts and the stock or the underlying you could say well, I'm not going to go long the underlying itself, I'm going to do this via a long call and shorting a put option. So you can create this synthetically equivalent position in a much more efficient way. So you don't have to take the hedges on and off and that would create transaction costs because the bid ask spread is much wider on these options than on the futures or on the spot market, so it would be a very costly thing to do. While if you make sure that the hedges are in place and will be there, then you don't need to trade in and out of them at all times, what you can do is put some short term financing structures on top of it, but that's going to much less intense. So the position itself doesn't become too dramatically complex. It's going to be a long position with some caps on it and it basically can be described as OK we are long in the market, but we're not going to profit until infinity. We have a cap of 3%, 5%, 6%, 1.5%, who knows, which is going to be capping the performance and we do that capping on purpose because we know that realistically speaking the market is not going to advance 3% in a day, so let's try to exploit that and use that to finance some of the hedging that we need in case the market doesn't do what we hope - in this case go up, but rather goes down.
Niels: Do you have a time stop on all of your positions? I don't mean the hedges hear, I mean the actual positions?
Luc: The time stop... the currencies are actually intra-week, so if it's Friday evening, like it is now, we have no more positions in terms of the currencies. Why? Because the currencies if you were to start buying these options it would become too expensive, so the trades are taken off the books and re-initiated on the Monday, or the next business day provided the environment is still similar. So there is a time stop, but it's a very long one, say intra-week. So the longest trade we could have in a directional trade in the currency markets in DPI would be entering on the Monday morning and exiting on the Friday evening. But that's like truly exceptional because the tight stops will usually take us out way beforehand.
Niels: Sure, sure. Tell me a little bit about the structure of the models, so to speak. What kind of models do you... because that's obviously an important point we talked about it before, the diversification part of it, and so obviously short term trend following seems to be an obvious one, short term mean reversion seems to be an obvious one, what else have you come up with in terms of concepts that you feel replicate themselves, or repeat themselves over and over again? Luc: I think two other categories, or two other classes of models can be mentioned. One has to do with what we call pattern recognition. There's something which is known by most people. Let me give an example to try to clarify this. Everyone is familiar with this concept they call the round number magnets. So in the currency market, just to give one example, if the Euro/dollar is trading around $1.30, that's a big number. If it's trading through that number that's psychologically speaking quite a shock. When on that Thursday when Draghi was talking the market fell from $1.30 to $1.50 to below $1.30 on the news. I happened to be in the room that day, I saw it that the Euro started at $1.30 and said WOW, my telling this was very good, but I understood it was below $1.30. So something shocked the people and said this is worth mentioning this $1.30 is a round number. It's like a magnet and so the same thing happens on an intraday basis when the market is trading, so like now between $1.2950 and $1.30 so the market is kept within these ranges, and when it is close to that round number it's like a magnet, it acts like a magnet, it attracts attention, people will be clustering orders around that number. It's like if I'm long now, and the market is now trading $1.2955 I see, and I'm long in the Euro against the dollar, well I know a lot of people are going to sell it when it's going to be at $1.30 or even just in front of it. So what will happen, just before we have reached that $1.30 that's a round number magnet, a lot of sell orders will start kick in. It will be very tough relatively speaking for the market to go beyond that $1.30 level. It's like a serious hurdle. What you can do is, when it reaches that level or comes close to it be prepared to go short, of course not blindly shorting it, because of the market, because of some event, say Draghi talking again, could blow it through the level without any problem, but if no news event is coming out and the market is hesitant and hesitating at that level and then rose over and starts to come back down you would be ready to go short. Then it revisits probably the $1.2950 or even the $1.29 the figure. So it's trading between these levels.
These kinds of magnets, they are there in the market. People are aware of it, but you can easily exploit it and you can exploit it by using some common sense, where you say well, if I'm going to take profits and my system says normally speaking without any adjustment you should take profits at $1.3002, well that's probably going to be tough to reach. It's probably going to be much easier to reach $1.2993 or $1.2995 so we'll simply reduce the potential profit target to $1.2993 and the model does that automatically, which will actually increase the number of winning trades and give up 7 pips or 10 pips, but of course will increase the probability of reaching smaller targets. Same with the stops, so you don't put the stop just above the round number say at $1.2905 you would put it at $1.2893 for example so the market has to drop below the $1.29 serious support, or people suppose would be serious substantial significant support, and it will actually protect your position slightly longer. It will cost more money if it goes through it, but at least it's going to be less likely to get hit, and these kind of things can be incorporated into an algorithm and when you develop such an algorithm then you can actually test it and see is there some value in doing that? Is it worth the price of giving up say 10 pips in profit target and making my stop 10 pips wider, does it work? You can test it out. If it does, you can build it into a strategy.
So these kinds of patterns are what we call pattern recognition, and I gave the example of the round number because it's a well-known feature. That's something which we would categorize as one type of trading approach. The fourth one, apart from mean reverting and trend following, and this pattern recognition is what we call volatility risk premier strategies. It's all of the option world where we say, well, in the options we have a very difficult time trying to follow the prices because there are so many options on the same instrument across time, across strikes, and so on. It's a very highly leveraged instrument. It's non-linear. It moves like crazy, so we have to be able to slow it down somehow, and the way that we slow it down is actually by no longer looking at the price of the option, but by looking at its implied volatility. So we look at volatility, and we trade only volatility. We can't really predict price, and I think very few people can do that, but we can measure and once we have measure we can interpret and then predict volatility, so that we don't need to know what way the market is going, but rather how far the market may go. In what direction we don't know but actually we don't care because we're indifferent in terms of going up or going down in terms of the market's range.
It's more the range that we're interested in rather than the direction. So then we can play volatility strategies which will actually be based on the notion, OK we think that we can measure volatility as it is now. We can interpret it. We can say is this a relatively low volatility compared to what we think it should be? So the implied volatility as measured by the market is very obvious to measure, then we can compare that to what we think is a reasonable, logical volatility level. Where there is a big discrepancy, a sufficiently big discrepancy between the two then we can put on a trade, and that's something which has been working... I think if there is one strategy which hasn't suffered from this decay, which I think all strategies suffer from, well this may be a small exception to the rule, and that is this continuous high implied volatility over the true realistic and realized volatility. You have this premium which is actually logical if you think about it. People are scared for the market moving down, so they buy up these put prices, and at the same time, they need to finance them, and they do that by selling calls. So they drive the prices of the calls down. Hence, the implied volatility comes off, and they drive the prices of the puts up. Hence, the implied volatility goes higher, and it creates this famous skew.
So you can play that game by saying well I can now buy cheap volatility and sell slightly higher volatility and structure positions which are initially market neutral or delta neutral, but then start to move because the market is not going to sit still, although we would prefer it, but the sooner it starts to move you can basically then start to adjust the hedges and by doing that volatility risk premium strategy I think we covered the most important source of our profit center return drivers, because it's something which is relatively stable. It's not too difficult to predict, and it's relatively persistent. It's very unlikely, and it's not a very long duration but you have these phenomena that realized what true volatility is higher than the implied. It may spike during a couple of days, even weeks, like in 2008, 2011, it happened a couple of times, but it's mean reverting. So we have this very well-known characteristics of volatility. It is clustering. It is persistent, but of course it can be spiky occasionally, but not for a long time because it's mean reverting, and knowing that you can exploit it and build a program around that and that's what we try to do.
Niels: It's fascinating, Luc. I have one question though, how do you test a model that at the same time might trigger a position in the futures market, but at the same time you also have to account for the fact that you may then, at the exact same time, if you want to do it precisely have to put on a hedge in the options markets? Clearly this can be done, but I'm just curious how do you actually make something accurate when you have so many moving parts in something that could be very small timeframes?
Luc: That's a true challenge. It starts actually with the biggest component which I think few people pay enough attention to and that's the quality of the data. We spent a lot of time making sure that the data we are using for the research is of a good quality. So people say well; we spent way too much time cleaning this data, checking them out, making sure the data are valid, well saying they come from the exchange they should be fine. Well, you still would see some out prices, some very bad ticks and they will totally create problems if you program it in and just blindly apply it. So you have to start with the data component and if the data is good and everyone knows that garbage in, garbage out statement, well if the data is bad you can't expect to have something like a reliable strategy in the first place. So once you have clean data, and you have a pretty stable set of historical tick by tick data, and we bought data initially in 1998, 1999 and then started building this huge gigantic tick by tick database, which we need. Why? Because we need to be able to test on an intra-day basis, on a tick by tick basis how the market evolved during the day. It's not enough to know the market opened at 100 and closed at 102. Fine we are up. Well, if the stop was at 98 the market dipped to 95 unfortunately we were kicked out and we had a loss, not a profit. So we need to tick by tick patterns because it's a very past dependent story.
If we test these various models, we look at them as individual singular components. So we don't say well, at this point in time what's happening across the portfolio, we will look at each individual component and say well, in this system, which is active on the S&P, we have to go long, or there's a stop, or there's a target. That's one thing, but the hedges are there because we know that if volatility spikes it will do this or it will do that, so the hedges will not so much be triggered by the opening of a position, which will be there. So it's not a one to one match. It's not what people would call a perfect hedge. It's not, OK we buy one S&P future, one mini contract, so we should put on an options spread which is having the exact same delta as that. So it's not the perfect hedge, which it can’t be I think, because the delta, by definition is changing and the delta is very much dependent on your input. If you have a different view than I have in terms of what is current in volatility, your calculation of the delta will be different, hence your hedge is going to be different. So it's impossible to say well, if two people look at the same screen and look at the same position, they will determine the exact same hedge, that's not the case, because the deltas will be different.
Niels: Tell me about position sizing. I know that's a very integral part of Kairos, but it's probably something that is important to you in general, why is position sizing so critical and why is it often an underestimated component of a trading system?
Luc: I think for us it has become by far the most important component. The reason is that everyone, and there is really no exception that I can imagine, there is not a single investor who shouldn't care about it. The trader usually knows about it or realizes after the fact, when he has a good trade and says, well, look my size was really too small. I should have put on a bigger trade. If it's bad, he'll say well, my trade was way too big, if only I had half the position I would have been able to stomach the loss much easier. So it is an eternal thing to do. The good thing about it is it is the only element within the investment process that you can truly control, because all of the others, how big the move in the market is going to be, how much profit you're going to make, how volatile the market is going to be, we can estimate it and hope for the best, but at the end of the day we don't know and no one knows. While this one is one really critical element which we can control so if you have this position sizing, which allows you to say, well, if everything goes to zero, across the board, what am I down. It depends, obviously, on my investments in the markets.
If I'm with a $1,000,000 portfolio, I'm only invested $100,000 even if the market goes to zero I can be down no more than 100,000, so it's a 10% maximum risk. If however I would be leveraged, I would have positions for $2,000,000, well I could be down not $1,000,000, but $2,000,000, so not only would I have lost my $1,000,000 in investment, I would owe the prime broker an additional $1,000,000. So this leverage, or deleveraging can be critical in determining the risk and return of the portfolio. Now what is even better is that it's neatly linked to option theory, where you say I can actually simulate a position in the market by using a fraction of the stock, a fraction of the underlying, and it's, of course, the delta, as we mentioned earlier. We say, well, if this market is going to go up my option is going to go by the delta. We can do the equivalent, or turn it the other way around to say instead of buying that position, we will be buying a fraction of the position that we hoped to acquire, and if the market goes well, according to our expectations, we will increase the size. If, however, the market is going to come down, we are going to do the exact opposite and decrease the position. With the options it's done automatically, because the delta is going to go down when it's going away or in the opposite direction and on the other hand if the market is increasing the delta will follow its way.
So with Kairos we say let's lead this one step further and say let's use the portfolio of very different assets. So we could use fixed income elements; we could use real estate, ETFs; we can use the equity markets in the emerging markets in Europe and in the Far East, in the US; small cap; large cap; we could dream up whatever; we could use even the volatility indices; we could use oil and energy; we could use gold; or whatever. We combine it into a wide cross-asset solution. So we say, well, let's take 8, 10, 12 different assets or groups of ETFs which combined make a very nice, and cover the global investment spectrum. How much should we invest in each of them? We all know that the equity returns are going to be superior in the long run compared to the fixed income, so we should invest more, but it depends on the risk. If we can have half the return, but only with 1/4 of the risk, we should actually favor the one with the lower return. So we can normalize it across risk.
We can build this portfolio, and we can say, well, what happens if the market goes up? What should we do if the market goes down? By simulating that we can come up with an algorithm that will automatically tell us what it's like now, at this point in time, not in the future, not yesterday, but now the best position to be in given a couple of constraints. The constraints can be, I want a maximum drawdown of this; I want a recovery as fast as possible. This is a program which is not so much in trying to maximize the probability of having a high return, or trying to maximize the avoidance of huge risks. It's more trying to maximize the probability of having a positive return, a positive recurring, steady, robust performance. You don't try to maximize profits, you try to say, let's try to find a way to replicate something which is like the ideal for an institutional investor, let's say a 6% to an 8% compounded return year, after year, with hardly any shocks. If we can make something, which is not going to suffer too dramatically in a down market; which is going to underperform by definition in an up market, well, the client is probably going to like it. We can control the risk because we can control the position sizing and hence, this is the cornerstone of the strategy.
Niels: How often do you have to adjust the position sizing to do that?
Luc: Well, the short answer is, it depends, and it depends with the market. If you talk about volatile instrument like for example the volatility index and the inverse of it, like the XF as it is called, we might have to intervene maybe once or twice a week given this relatively quiet market such as the fixed income market, then actually we could have to intervene maybe once every two week or three weeks. So it is very dependent on the volatility in the market. On average, it is true that we have to trade about once a week, on average, per market. It is reasonable. It is definitely not high frequency.
Niels: So we talked about Kairos in terms of the position sizing, but what actually determines which way it's investing, where it's allocating in the risk, in the first place? What kind of models... so if we go back to what we talked about before: you can have trend following models, you can have mean reversion models, and so on, and so forth, how would you describe the models themselves inside Kairos?
Luc: OK, that's a pure volatility driven engine, so to speak, so the only thing which is of impact in terms of deciding shall we put a position on, if so, what direction is going to be determined by volatility? If the market is not showing a lot of volatility there won't be a lot of trades passing through, however, we start with a naive portfolio, where we say, well we need this balance of say 8, 10, 12 different ETFs covering a certain spectrum of the market and we start with a neutral position. A neutral position would be something like a delta 50, say 1/2 long, so we have an initial position which we are willing to play with. From that moment onwards is determined by the volatility in the market. We're going to play like the role of a market maker. When the market is coming down, we may be buying something more; when the market is then moving up a little bit, we may be scaling out a little bit, and so depending on that volatility there will be some trades triggered. Every single component will, first of all at the very start, have this optimal initial allocation and that will be a fraction of the total initial portfolio value. Let's say we have $1,000,000, we have 10 EDFs, we may start with 10% on average in each of the 10, but then on a daily basis there will be some targets, upside and downside target levels, and they will be used to rebalance. As we build the hedging in the options, we don't need to delta hedge every single tick, every single one point move in the S&P, but only when a significant change in the delta position is occurring. So we have this rebalancing levels which are known beforehand and we know we don't do anything unless, at the end of the day, at a certain price level, we go beyond a certain price. Then, of course, we know we have to intervene, we have to decrease or increase the deltas. It's very automated, it's extremely transparent, and we know beforehand what we are going to do. I could tell you now, OK if the market is going to go below that level we will be buying S&Ps or selling S&Ps. That's not going to change in the course of today. It will only, on a daily basis, be recalculated and then come up with new levels where we have to do something about the ideal position.
Niels: I wonder. This to me sounds like Kairos is an end of day type system where you need to adjust your position. DPI, does that need to be run intra-day or is that also, you run the system once a day and then you have your levels, you have your stops, or whatever type of orders you use, or is that not possible to do it that simply?
Luc: Well for Kairos you are absolutely right. It's a pure end of day system, pure end of day trading strategy, while with DPI we have some intra-day moves. They are dependent on the time series you are using, and the time series are what we call not in calendar time. In other words we don't look at hourly bars, we don't look at the bar every five minutes, but it will be bars which are based on volatility. In other words, the more a market moves, the bigger the bar will become, and we will collect more prices during a certain interval. If the market is extremely quiet, and in an hour - say in the last 60 minutes, it only trades 50 times; well we collect these 50 prices, but the market may not have moved a lot. While in the next hour it may also move 50 times, but make bigger jumps. Hence, we have to express that in a different fashion. So we use this so-called volatility buckets to compare one number of price changes, so it's basically a number of price changes that we compare from one period to the next. With that, we can very quickly determine either there's a trend change or not. Once we do that we have to intervene, which basically means that it can happen anytime during the day - during our trading day. The trading day is very well known. It's between 8 o'clock and 10 o'clock in the evening, so it's a 14 hour period during which something can happen. We don't trade outside these trading hours. We have stops, or we have options against the positions in case a position is taken overnight, which it is most of the time, except, of course, over the weekends. If not, only trades can take place between 8 and 22 hours, but we don't know when they are going to take place.
Niels: Why are you afraid of the weekends if you have your hedges?
Luc: The hedges in the FOREX are not perfect because their options are too expensive. If we have them in the options markets, or on the stock indices we are not afraid of them there. We take them overnight, and we hold positions for 3, 4, 5, weeks. So there it's a different ballgame, and there I'm referring to the volatility risk premium, so where we try to extract volatility premier my selling options that we a short volatility, which is then hedged by buying other options and these positions are carried overnight and over the weekend. In directional trading, in the currencies we don't do that.
Niels: We talked about risk management already, so I'm not going to go too much into detail on that, but I wanted to ask you, generally speaking, and you also mentioned that correlations are not necessarily a great way of looking at risk and so on, and so forth, but in terms of risk management, what would you say is the most important thing investors should look at? What would be a good measure, or however we put it, that really describes the risk of a strategy, and in your case your strategy?
Luc: Well what we use, which is an in-house measure, or risk-return statistic, is what we call the Capital At Risk Ratio. What it does is like a simple ratio, a very simple feature, we say what's the net annualized return of this program, and we compare that to the maximum drawdown. So it basically expresses how much return you can generate for every 1% of risk and the risk being maximum drawdown you take onboard. In other words, if I have a program that makes a 10% return, and it has a maximum drawdown of 20%, that capital at risk ratio is .5. If we have another one which has a 5% return, but it has a maximum drawdown of also 5%, it's a one to one capital at risk ratio, which is quite attractive. So that's one fundamental ratio which is used in designing and evaluating a trading strategy, but two others which few people I think are using is what we say, well, what is the probability that an account is still below the starting level after a certain period of time? So that depends on the investor, but the investor could say well, if I start today, how likely is it that I'm in profit in 12 months time, or in 36 months’ time? If you have a very high value for that, you basically say there is a low probability of seeing an underwater situation after these 12 months, or 36 months.
The third one that we are using, and these feel like the critical ones, is how quickly do we recover, because it is nothing as far as trading as making a new high, new investors come in, then the performance drops off and it remains there below that new high for like an eternity, and all these new investors that joined then, because of the huge, nice performance from the previous months, they are sitting underwater and remain there, and they are not 5%, maybe 6%, 7%, 8% away from the high and it takes an eternity, first of all to make a new high, but also you need to realize how much do we drop off from that maximum? We have a statistic that gives a good indication of the potential for recovery after a drawdown. How often are we in a situation that is less than X% of the high. In other words, what we use mostly is how many months are within 5% of the previous high. When we see that on this Kairos program, for example, in 90% of cases, if you look at the end of month result, we are within 5% of the previous high. It means that only in 95% of cases will we be further than 5% away, so the 10% is actually indicative of how likely it is that we make a serious drop.
Niels: Sure. Clearly these statistics are based on long-term simulation and long term real trading. How far back do you feel that you need to go in order to get confident that those numbers will stick. The reason that I ask is that trend followers, for example, which is a strategy that has been around for a long time, so there is some historic data that can be gleaned from. Clearly the drawdown profile of some of these strategies changed in the last couple of years. The drawdowns became longer; they became deeper, but you wouldn't have expected that if going into say 2010 everything looked normal. So how do you get confident that those kinds of statistics won't change?
Luc: Well it's really a question, in my opinion, of how frequently do you see this system generating trading signals? If it's a high-frequency trading system like we don't employ, which might fire signals a hundred times a day, you would only need a couple of weeks to have a vast amount of data, which you can look at and say well, this is the profile of the system. If you have on the other extreme of the curve a trend follower, a long term trend follower, he might need 10 years to have a reasonable idea in terms of what is valuable. For us, it's like in-between. If we have 3 to 5 years of backtested data on a new trading idea, and we have enough trades going through during that period; let's say that we have 100 trades at least a year, then we can with say 500 trades have a pretty good idea of what we have to confront and what we are likely to see in the near future, provided the market doesn't change, of course.
Niels: Yeah, yeah. On a day to day basis, is there any risk measure where you say that you use on a daily basis, saying yeah, this is a risk number that I look at, and that is important to me? Again, these numbers that you are referring to, they're not going to change on a daily basis, but in order to look at what is my risk right now, here and now, is there anything there you found useful?
Niels: What we have developed and integrated in all of the strategies is a concept which we call Maximum Exposure Per Trade, MEXT. Maximum Exposure Per Trade determines up front the maximum risk that a trade can generate for the total portfolio. Even if we come to the worst, worst, case the thing may lose .1% responsible and goes by this one single trade so that max for that specific trade, let's assume, is .1%, so we also have, apart from the MEXT, which is Maximum Exposure Per Trade, also a Maximum Exposure Per Day, where we say we don't want to see a day where we are down 8%, or down 5% , or down 3%, no we cap it at 1%. So even if all of the positions we have currently, go to their stop losses, and are being kicked out, and really turn out to be in the worst possible trading environment, you would be down 1%. So that's like a maximum exposure per day. We look at that statistic, and that's again fed into the model to determine the optimal trading size. If we know that we have only two systems. So let's assume that one is in the S&P and on in the Euro, and the stops on it are $1,000 each and we don't want to lose more than $10,000, well we know what the position can be as a maximum, and we know if we're getting stopped out and we won't take a second trade in the following trade in these two models, well that would actually be the drawdown we can expect as a maximum.
So we have it on a daily basis, on a weekly basis, on a monthly basis, and even on a yearly basis. Like many pension funds, and that has been my experience for at least 10 years, they say well, this IMA this management agreement will basically stipulate, if you see a drawdown of X% and the X could be, in our case, like 5% or 10%, if you exceed that, then we have to talk again. If you see a 5% drop from peak to trough, then we have to have a conversation. We won't stop the trading, but that is considered a significant red alert for us to take action. If, God forbid, at one point in time we'd be down 10%, let's assume, we would stop trading automatically. So we have this built in, like a circuit breaker, on the futures market where it will basically stop trading. Pension funds, especially say, well we have these potential liabilities, we don't really want to see a situation where the assets are going down by more than X percent. Knowing that upfront, and building that into the model, we can be confident that we're not going to go beyond that level, which of course would mean that we would trade smaller, and smaller size the closer we would be approaching that. Which is like, again, as people do in option trading, which is the delta equivalent.
Niels: Yeah. It is fascinating because I'm looking at some numbers. You have a nice comparison with other short-term managers in your material and obviously I'm not entirely sure though, I would say that your numbers are, over the same time span as the other ones, because I do seem to see that there are some drawdowns that are quite large with other managers compared to yours and I'm not entirely sure whether it's from lifetime of the program and today, and so on, and so forth, but that's kind of beside the point, because I have this section that I tend to ask my guests about which is the drawdowns, which you so eloquently described how a 5% drawdown, for you, is really uncomfortable than a 10% is God forbid that should happen. Clearly these things are not something that most, certainly in the trend following world would worry about. So I was going to ask you how you cope with the emotional roller coaster about drawdowns, but I'm not sure there are any emotions just from a 5% drawdown at all?
Luc: Well, I think that's very personal, to the extent that some people feel like shell shocked when they are down 10%, and other people, they wouldn't blink an eye when they are down 10%. So it's very subjective. In my case, I'm a rather risk averse person, I think in terms of trading and there I say, well, if I can avoid this single digit drawdown from becoming a double-digit drawdown, I feel at ease, and I feel comfortable. I'm not panicking when we are down 5%. Of course, I wouldn't like it. No one would like it when he's down 5%, I think, but it's nothing to worry about, if that is to be expected. If my program says you're not going to see a 2% drawdown, which would be kind of unrealistic I think, and then you would see a 5%, then something is wrong. By the same example, if I would be confronting a 20% drawdown, something is very wrong. While if I would be trading in a different environment with different trading systems where it's not totally unthinkable to be down 20% and still be up for the year, then there is nothing to worry about, but it's a very subjective thing, and in our case, on the one risk unit, so the standard version like a 5%, 8% drawdown, is like the norm. That's what would be considered normal; other people would have much higher expected return, and of course goes hand in hand with that, a much higher expected maximum drawdown. Niels: You mentioned a couple of time that you are a risk averse person. Where does that come from?
Luc: Well that I don't know, frankly. I think it's really... it has to do with probably my, call it, quantitative background. I know that as I hinted about it earlier in our talk, when the drawdowns become too difficult and too big, it becomes nearly impossible, or virtually impossible and mathematically extremely unlikely to even recover from that drawdown. So in other words, if you have a system where you are counting on making between 5% and 10% and suddenly you are now facing a drawdown of say 30% it's going to be taking a long time before you're recovering from the drawdown, given the distribution of returns, where you can have an up year or a down year. So we need too long a period for people to remain patient and be confident in the program that you will be recovering. So I try to avoid it, avoiding that problem by just avoiding a big drawdown in the first place. That's I think my logical, not so emotional reasoning behind it. Secondly I think, emotionally speaking I'm not the type of trader who is comfortable taking very big swings. I wouldn't want to trade a couple of instruments only and bet the whole portfolio on a couple of trades. That's not my trading style because I believe in diversification as I said, across markets, across instruments and also across timeframes. It goes without saying that I need a lot of different positions at the same time to be comfortable in a fully invested portfolio.
Niels: You clearly run a very advanced strategy, and you've certainly thought a lot about it and you keep a very close eye on the risk management, no doubt, that is clear from our conversation. You've done a lot to hedge the risks and so on, and so forth. What keeps you awake at night, and I don't mean weekends, because I think you probably sleep pretty well on weekends with no positions on, so what keeps you awake at night? What kind of risk is still there that you simply can't avoid? What kind of risk is there in your strategy that still keeps you pondering about how to do it?
Luc: Well I think I could maybe rephrase the question a little bit: why... you could by the same token maybe ask me, why are you still researching for other trading ideas?
Niels: So let me ask you that, why are you still researching for other ideas?
I think it has to do with the fact that you're always trying to improve. Someone who is trading for 10 years, 15, years, 20 years, whatever is definitely driven by this, is passionate about it, is keen on continually improving, because you realize that the systems have a certain relatively limited life. So we need to keep up with the research, markets are changing, systems are performing less than before, so you need to replace them, so that's an ongoing process, so the fact that it is such a challenging environment to work in and to come up with new trading ideas, test something out, see if this works under this and under that environment, is so fascinating that that would be the thing that would keep me awake, so that is why I would be reading books and articles about sometimes vaguely related matters, but still having indirectly an influence on trying to come up with a new trading system with the ultimate idea of trying to build a better risk adjusted return, so it comes down to that. Niels: Where do you get your ideas from?
Luc: Well, the short answer is reading and reading a lot, I think, and listening to a lot of sources and that can be something related to say, on the one hand, binary crosswords, and then puzzles, where you get new combinations, so ideas from and it could be reading about something in geometry or something about logic, it could be anything, so it's really a question of trying to be very open minded, or trying to be very open minded in absorbing new information of which you may not realize, at least I don't realize, up front this could be useful or this could be worthwhile studying or looking into further and maybe without subconsciously, maybe you realize, or you don't realize that it could be integrated into the building of a new trading idea.
Niels: You mention in your information that I had a chance to look at that, I guess we all look for the Holy Grail and it would appear to me that you found something after 20 years of research, where you feel that you are getting closer at least, is that a fair statement? Do you think you found something that combined is close to the optimal, and if so, is there a chance that we can actually end up doing too much research, because you already found what works?
Luc: Yeah, what I think I've found is something which is achievable, which is realistic, because every trader who has been active for a certain period of time, must realize that a so-called holy grail probably doesn't exist, or not in the sense that people describe it. But I think something which can be realistic and which is achievable that can be found, and I think everyone can find it, but it's a question of trying to find the balance between what kind of risk am I willing to absorb and what kind of timeframe am I allowing for this trading system to work so that it can perform? So the trading idea which is actually at the heart of Kairos, is something that I started playing with and believe it or not it's like 20 years ago when I had this idea, OK if I can do this, and I combine A with B and C, this would be like the ultimate solution and there is no way you could not make money. So you're going to make money at all times. But there are two very annoying qualifiers to that, and the first one was you need a lot of money; actually you need an unlimited amount of money, so no one has it that's clear. The second one, which I think is even tougher is you need an unlimited amount of time, so if you have unlimited time and unlimited money then first of all you could ask the question then why do it? You have what you need. You have unlimited time and unlimited capital, so why even trade?
But let's assume that you want to make something, and you have at your disposal this vast treasury of capital and a huge amount of time, let's say, 5, years, 10 years to trade and to prove that something works. If you have that you can develop some mathematical algorithms which will churn out some profits before trading costs, and that's an important statement, I think. That will be guaranteed if there is enough volatility. So if there was no volatility the thing would fall apart. You need some volatility. You need the market to move in order to make profits on the way up or on the way down. But say, assuming volatility is there, and you don't need to trade it in one single market, you could build a portfolio of maybe 5 or even 10 different instruments which are, let's say, lowly correlated to the extent these correlations hold up for the time being, well now we have these portfolio of different instruments, they each move at a certain speed, during the weeks, during the months of the year, and some of them will be going up, some of them will be going down, some of them will not move that much, still you will be able to extract information and some value from that price move. If you don't have the unlimited capital, you need something to basically protect the capital that you have, which is in essence some stop losses. At a certain point in time you need to say, well, in this one component out of the five, or out of the 10, I need to take my losses. This is too much. I started off with a position of X, and now it has even increased in size, or it's still the same, doesn't matter, but I'm down so much it has too severe an impact on the total portfolio, so I need to do something about it. Actually, I'm going to take the position off the books. You need to build that into the algorithm that's the first step.
Secondly time, if there is enough time, and by time I mean several months, several years, then you can come up with something which will show enough volatility that there is a possibility to create and generate these returns. You combine it all, and now you have a portfolio of maybe say 5, 10 different instruments, and you say I'm going to trade them according to a certain algorithm. I will take losses if such, and such a situation occurs, and it will be painful, I may be down 10%, could be, but that's part of the game, but in exchange for that 10% maximum risk I'm going to take, we're talking about a capped risk. I can probably count on making X percent. If that X is high enough for the risk you are willing to absorb, if the worst case scenario appears, or comes around the corner, then you are in good shape, and then you could say this is something I could trade with a lot of confidence, with a lot of faith, and that's for me, I think close enough to a holy grail because then you know I have my risk capped. I have probably also my profits capped, but they are capped at such a level that I don't mind, that they are capped if they are at 15% compounding, that's fair enough. Given the risk of say 10% or whatever, that's a very nice balance. Then I think you have something which is workable and then maybe one day you could say, well I don't need to do that much research anymore.
Niels: Sure. I want to move on to the next section, but I'm not going to go into it too much because we've already had a delightful 2 hours and 20 minutes talking here. This is more about the business as such, and the question is very simple, what's the biggest challenge that you have right now?
Luc: Well I think the biggest challenge is to bring this new program Kairos, which is slightly different from DPI, to bring that to the market and convince people that this is probably, as we described two minutes ago, very close to a potential holy grailish type of investment. This has the features that a realistic investor could hope for, like capped risk - fine you can't lose more than X and that X can be leveraged, if someone says I'm willing to take a 20% hit in the absolute worst case scenario, but not 20.1% it can be provided. What is the return I can achieve given a normal volatility in these 10 different markets - fine that's it - bringing that message across and in a way explaining that and trying to teach that to people, that's like my biggest challenge I think at this point in time. What I would like to add is the fact that this is contrary to DPI, the more scalable strategy makes it even more intriguing because I think that after 20 years of research the first strategy where I don't see a lot of impact from the scaling, the trading in bigger size than it currently is. Which is of course opening the doors to a potential for trading this in significant size without hurting the performance as most strategies that we have developed are in a way still destructive ones if you trade them in too big a size, well this one I think the impact will be affordable.
Niels: Why do you want to manage more money, Luc? You have a team of two to three people. You have almost 200 million dollars under management, most people who can do the math, they would say that's a pretty solid business, and you have good performance, why do you want to manage more money?
Luc: Well I don't really want to manage much more money, I want to basically show that a system which can be traded in a nearly automated fashion can make money over the years in a pretty regular, recurrent fashion. This is something which could be valuable for pension funds, for endowments, for foundations, where people count on the relatively regular performance but with capped risk and provided they are willing to sacrifice some of the extraordinary returns which are not going to be available, so it's going to be like a 25% potential return, but because you finance that protection by giving up some of the upside as people know, or should realize, then you could be in a good product. It's not my pure ambition to raise the assets under management dramatically, but of course, as we have this program, and we developed it and I think it's a very good product, I think it's worth presenting it to the world so to speak.
Niels: Sure, sure. Great stuff. I have one more question left in this section, and then we're going to move on to the last one which is called general and fun, so that will certainly spark a few interesting things, but let me just ask you one question before we go there. You've been in the game for a very long time. You've answered; I'm sure, many questions from investors, you've filled out endless numbers of due diligence questionnaires, what is the question that investors fail to ask you?
Luc: WOW, that's a very good question. Well, I think we may have touched upon it earlier during the talk when you said well, do investors look at the track record like this is what I can have if I invest now, or should I rather look at the circle of predictability of returns? I think for me; the main thing that people should ask is what are you going to trade and how actually are you convinced that what you are offering is something which is going to work? That is something that few people ask. They say well, what do you expect to be a return, or what's going to be your risk, things like that, but I think where it's really critical is what makes this program unique, and why would I have confidence in this program, and why do you think is this going to solve my problem of finding a good investment?
Niels: Yeah, absolutely. Now, final section, final stretch, general and fun. Let's see, there's a few things to choose from, but let me just ask you one thing. What do you think it takes today for someone starting out wanting to be a great trader, fund manager, what do you think are some of the personal traits if I can put it like that that they need to possess in order to be successful?
Luc: I think the main characteristic is what people describe as discipline. If you are disciplined... there are two elements to this puzzle, one has to do with discipline, so you need to be very organized and disciplined to bring this to a good end, but in order to be able to bring up that discipline, you need to have enough passion to be interested in, and really intrigued by the markets and not money wise, but just I think the intellectual challenge of like, how come this is so difficult? Why are so many thousands of people staring at these same screens and it's 80% losing money and it's 15% breaking even or barely, and only 5% make this huge winning trades, how come? What is the secret so that people think that there is a secret, and so you need to be intrigued by that and have some pure passion for discovering something like that. So you need to be able to live that market that's really important to be able to continue to work hard and long hours and don't consider it work. I don't consider it as work. That's I think a condition.
Niels: Now this is maybe an easy question for you, given the fact that you read so quickly that you do, but what book would you recommend from the probably many books that you have read to people who want to improve their own trading or for investors who want to also take the next step. Is there any particular book that springs to mind that has made an impression on you and why?
Luc: I think it is really a question of reading many books about the subject to give like an overview, because some things are pretty straightforward for one person, and a totally new and revealing to the second person, so it's very difficult to come up with one book that I think..
Niels: I'll allow you two then.
Luc: OK, well the Market Wizards (Market Wizards, Updated: Interviews With Top Traders by Jack D. Schwager) definitely a very good one to give you some insight in what people do to become a good trader; how they approach these markets and how they build their career and their systems and their investment firm. I think that's definitely a good one. The other one which I personally liked a lot and which gave me a lot of inspiration is the book about the Prediction Company (The Predictors: How a Band of Maverick Physicists Used Chaos Theory to Trade Their Way to a Fortune on Wall Street by Thomas A. Bass, copyright 1999), which has to do with predicting price and basically showing to me that it's a very tough thing to even try that if these people, which were really top notch scientists, had such a tough time in coming up with something that is really tradable, so and how they ended up as being part of O'Connor and later UBS, that was a fascinating book for me, so these two books actually, were for me eye openers in terms of that this is something I need to take into account if I want to build this investment company as I want it.
Niels: I'm just curious here, Luc, books like that that clearly are important for you, do you still read them as quickly as you do when you do speed reading?
Luc: Oh no, definitely not. Speed reading I hardly do at all. I love reading a lot, but I don't speed read unless I need to. So let's assume I need to go over a document, and I only have 15 minutes time, and then I would scan over it. I can guarantee I don't... I'm no longer at 1/2 the speed I used to be.
Niels: Now based on everything that you've learned over the last 25 years or so, if you were going to start today, is there anything that you would do different do you think?
Luc: Well, I think the main thing that I would do slightly different, but not dramatically different is having even a bigger focus on what you want to do. At the very beginning, when I started AIM in 1990, these 4 or 5 clients said well, can you also look at this market, and can you also look at that market and being like a tiny boutique firm, it became too difficult to spend attention and to pay attention to these different markets and trading strategies and one would like to do, say fixed income trading and the other one would say I'd like to do some OTC options on my long term US stock portfolio. So if you are not focusing as intensely as you should, especially at the beginning of the building of the business, it's going to be very tough. I think it was a good thing that one of our investors said you really should try to come up and build this into a program, and instead of just offering call it investment advice and say, well I'm an investment manager, if you want I can manage your portfolio, that's probably not a good approach, so build a program and offer that program so that every single one of your clients has the exact same thing that he's buying from you, be it at a different leverage, that's a different story, but it's the same concept, be it in a slightly different format, but the concept should be identical and should be highly concentrated. That's maybe an error that I made in the early years, when I was trading too many different things, or trying to trade too many different things, and focusing on too many different subjects. So I think focus is critical, so that's probably one thing I should have added to the trades next to the discipline and maybe focus.
Niels: That is a good point, and you know, of course, what focus stands for?
Luc: Tell me.
Niels: Follow One Course Until Success.
Luc: (laugh) That's a very good one.
Niels: (laugh) Anyway, two questions left and then you're off. We've already touched upon one thing which I think is quite interesting, but let me ask you this one anyway, is there a fun fact that you can share about yourself that people who even know you may not know about you?
Luc: Hmmm. That people who don't know me may not know?
Niels: Could be a hidden passion, something that you don't share with many people, now you're just sharing it with me, of course and a few thousand people.
Luc: I'm pretty open, I should say to that extent that people... my friends they know me pretty well, so they know my hobbies, they know my interests, so there is not too much that they don't know, which would surprise them when I told them I am an avid angler, I like fishing, this kind of things and they'd say well, that's something I would never have expected from you. So these kinds of things. My close friends, and not only my close friends but most people, they know that so that I am very interested in music, and very interested in reading, very interested in nature in general, so these kind of things are rather well known I should say.
Niels: It helps to keep you balanced in a busy financial world, for sure.
Luc: Oh absolutely.
Niels: Now my last question, I said to you earlier today that investors don't always ask the right questions. They may even fail to ask some very important questions, so I need to be critical of myself as well, so I'm going to ask you what did I miss, what were the questions that I should have asked, that I didn't or is there something that you think we haven't quite covered yet that you want to add?
Luc: I think we had a very in-depth interview, a very good talk, and covered I think what I would have liked to cover starting with the history, the concepts, the trading philosophy, the various programs, the risk management, I think that these are the key features that people, during a normal due diligence don't even touch upon. Some of them of course they relatively pay attention to, but others they might forget, but I think we covered most of it. I don't think you left a lot out. I would be surprised if you left an important question out.
Niels: Great stuff. Now before we finish the conversation, could you tell our listeners where they can best reach out to you and learn more about Capital Hedge?
Luc: Oh sure, so they should actually contact my colleague in Burn, in Switzerland, because the company is based just outside Burn in Switzerland, although I'm researching and doing all the research ideas from Belgium, I'm in Switzerland on a regular basis, but they could contact my colleague Bernhard Steiner, who is running the company in Switzerland and he can be reached at simply email@example.com.
Niels: Fantastic stuff. Let me also say to all the listeners that all of the details from our conversation will, of course, be in the show notes on the webpage which is TOPTRADERSUNPLUGGED.COM. Luc, this has been very, very interesting. It's been fascinating and I really do appreciate your time, your openness and I think we all learned quite a lot from your experiences, so thank you so much for sharing this and I hope, of course, that we can catch up at a later stage and hear how things develop and perhaps learn even more about your strategies and I'll also be encouraging people to look Bernhard up and see if they can't reach out to you directly as well. So thank you so much. It's been a pleasure.
Luc: OK, thank you Niels, it was really a pleasure having this interview and talking with you. It's truly inspiring; I really liked it.
Niels: Lovely, thank you so much and take care.
Luc: Thank you, my pleasure.
Ending: Thanks for listening to Top Traders Unplugged. If you feel you learned something of value from today's episode, the best way to stay updated is to go on over to iTunes and subscribe to the show so that you'll be sure to get all the new episodes as they're released. We have some amazing guests lined up for you, and to ensure our show continues to grow, please leave us an honest rating and review on iTunes. It only takes a minute, and it's the best way to show us you love the podcast. We'll see you next time on Top Traders Unplugged.
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Date posted: 25 Sep 2014no comments