Jin's Note: I had the pleasure of chatting with Craig Basinger who manages a brand new ETF called the Redwood Behavioural Opportunities Fund. The following is a lightly edited transcript of that conversation.
Jin: Could you talk a little bit about yourself first? You started your investment career in 1995— could you give us a brief summary and highlights?
Craig: Yes. A long time ago! Yeah, I started in the industry on the private client side as an advisor and then shortly moved on to the buy side. Then, shortly thereafter, I moved into portfolio management - first as an analyst, and then, as time went on, I became the strategist at CIBC Wood Gundy, and also at Merrill Lynch Canada before CIBC Wood Gundy bought their advisors. And, I did that for quite some time, and then, more recently, came on board at Macquarie to be their Chief Investment Officer here in Canada, and, as you might know, Richardson GMP purchased Macquarie Canadian Wealth Division. So, I came over directly to Richardson GMP in that transaction. As you know working in wealth management in Canada, there is often some M&A activity going on..
Jin: Now, you’ve always had a bent towards Behavioural Finance, is that fair to say? Because you have a background in Psychology as well as Economics.
Craig: Yeah. It worked out to be quite fortuitous—the fact that now Economics and Psychology are getting more and more ingrained with each other.
Jin: Yes. The last few Nobel Laureates in Economics seem to be all Behavioural—.
Craig: Yeah—a few of them anyway. I think that side of it is quite fascinating. And, it’s evident. We see it in ourselves—a lot of the biases and shortcuts that we use and how we have evolved. And, if we can better understand how our brains operate, we can better understand how to manage money and, hopefully, in our case, we can better understand how to profit from other people’s mistakes.
Jin: So, speaking of behaviours and profiting from them: you recently launched an ETF called the Redwood Behavioural Opportunities Fund with a ticker BHAV. Could you tell us a little bit about that?
Craig: Yes, I’ll go back even a little bit before that. About three to three and a half years ago, we started dedicating quite a bit of our resources and analytical capabilities towards the behavioural finance side. Originally, we began this journey to help make us better portfolio managers, trying to make sure that we’re not suffering from behavioural biases in our process. Then we sort of had an aha moment, a little over a year ago. “If we could find instances, and examples, and situations where more and more people would be more likely to be suffering from a behavioural bias, could we not then develop strategies to try and profit from those mistakes?” And, that’s when we moved from research to better understand our own biases, to developing strategies to try and target instances in the market where there’s a high probability that behavioural bias is driving investor behaviour to one side.
Jin: I see. So, did you want to create an ETF in the beginning by using these strategies, or how did that evolve?
Craig: Oh, I’ll be honest. ETFs and funds are actually pretty much the same thing now—despite common perceptions. It's actually the same pool of assets. It’s just that we have a fund type that if people want to buy a mutual fund, they can buy it in a mutual fund format, and we have an ETF pipe into the fund as well. So, it’s really just depending on how people want to buy it. It’s the same pool of assets. They’re managed the same. It’s the same fees. There’s no arb one way or the other. It’s just another way to buy it.
Jin: I see. Could you tell us a bit more about the strategies that are involved in the fund?
Craig: Yes. we’ve developed about seven, and, obviously, we are constantly involved in strategy development testing and refinement, but we’ve been live for a couple of weeks now with actual money even though we’ve been developing this for almost a year and a quarter. We’ve been live for a couple of weeks, so I might as well talk about the strategies that have been active out of the gate, and, obviously, that would be the Earnings-over-Reaction strategy, given we’re in the heart of earnings season, and that option seems to be a pretty active strategy during this time in the quarter, and then also our Unloved-to-Less-Loved strategy.
Jin: Could you explain how the Earnings-over-Reaction strategy works?
Craig: Yes, sure. So, our entire process, actually on all of our strategies, it’s sort of quant heavy at the beginning. We’re using a fair bit of quant screening and that type of thing to find different situations that we think there’s evidence of behavioural bias, and then, we start to refine our investments strategy around that—as far as adding other parameters that we find are indicative or important for driving our potential success. So, Earnings-over-Reaction is relatively straightforward. The fact is if you look at any company, they should be worth their discounted cash flow of the next twenty years or so, discounted back to today. However, during earnings reporting, often a company will miss by a penny or two, or beat by a penny of two, and the share price reacts and drops by 5–10% or jumps by 5–10 %. You’ve just to sit back and say, “Hold on a sec. Is this really accurate, does one quarter’s nickel matter all that much?” The fact is, when something drops by 8% or 10%, we think often this is an over-reaction to the downside. And, it can happen on the upside as well. During our model development we found other factors that help the efficacy of this strategy, such as what was the original price trend, the quality of company, its historical volatility. Higher quality companies tend to recover after misses more often while lower quality companies often give back gains following a positive surprise. So, this strategy is a long-short, so, it depends on the situation. Within the fund/ETF, we can short up to 20%. So, that gives us a little bit more flexibility on both sides of the Earnings-over-Reaction strategy.
Jin: I see. So, how do you determine whether a company is high quality or not? Do you determine by using the size of the company, or the consistency it’s shown in the past?
Craig: No. Not size. We’re using a pretty simple method. We’re usually looking at beta or a 270 day volatility relative to other companies within the overall market.
Jin: Do you pick this statistic based on some quantitative research?
Craig: Yes, we use a fair bit of machine learning as far as looking for additional parameters that we want to consider within each of our strategies. They’re not hard rules. It’s not like, “If this is counter trend, we have to do this.” So, it’s not a 100% rules-based, but we’ve just used a fair bit of quantitative analytical power to find types of characteristics that lend themselves to making the trades more successful, and that drives the human aspect of our review on individual trades.
Jin: So, it’s not like a simple rule that says, “When a stock drops after earnings, buy it”, or vice versa? There is a lot more involved in that?
Craig: Yes. So, the drop itself obviously flags the instance that this could be a potential trade, but then the humans take over, looking at a basket of other indicators that either give us more conviction or reduce conviction on whether we want to do that trade or not. So, it’s akin to playing chess with a computer at your side, as opposed to just being a computer, or just being a human.
Jin: Okay. So how much of a say does a human have on executing the strategies?
Craig: Oh, we have the final say. So, it’s actually up to us.
Jin: So, how do you guard against your own behavioural biases? Because I think that’s the argument for–I know some ETF providers who prefer just to automate things.
Craig: Well, automated trading is easier because then you don’t have to work as hard either!
Jin: That’s true, but the argument they gave me is that they take all the behavioural bias out of it.
Craig: Yeah. I’m not sure—because I think the behavioural bias can also get into the code and in as far as the algorithms you’ve developed in the first place. So, I wouldn’t buy that. It’s a legitimate argument that having hard, firm rules can help reduce some behavioural biases, but keep in mind that when a person actually develops those hard rules that, in itself, was probably eliciting some behavioural biases. I don’t think you can ever get rid of any of these things completely, but what I think is that with knowledge and tools, you can help mitigate a number of them. So, the fact that with a lot of our strategies–we don’t get married to any of these companies—we don’t ever view something as a core position. Everything has some rules to it. We have hard stops (Jin’s Note: A “hard stop” is a mechanism that automatically sells a stock once it hits a pre-determined price) that we use, and we have reset levels on the top side. So, we’ve taken a lot of the behavioural bias risks out of the equation. I don’t think you can ever get rid of them all though.
And, in the very sage words—earlier in my career I learned anybody can build the quantitative model. The biggest value add is actually understanding when these models work and when they might not work, and when you should actually stop using models. That is the most value add in the quantitative space right now. And, I think that most people are missing out on that now as everyone hands things over to the machine.
Jin: Yes. As a quant myself, I would actually agree with that statement. I’ve done some machine learning before and it would actually–it often gave me these results that are obviously wrong, just because the input, for one reason or another, is just wrong.
Craig: Yes. And it’s hard. There’s times things work, there’s times things don’t. There’s a lot of factors that go into it. And, one of the things that actually scares me is there’s so many investment strategies have been built with data that goes back to the mid 80’s, because that’s when data became extremely rich and more readily available. The problem with that is that so many of these models have been built on an extended decline in both inflation and interest rates. You always wonder, “are these great models? Or are these great models as long as yields keep gradually declining?” But that is another story.
Jin: Yes. That’s a fascinating question, especially because some people are thinking the era of lower and lower interest rates are over.
Craig: And how will these models hold up?
Jin: Yeah, I agree—and that’s a whole other conversation.
Craig: Now we’re off topic! Sorry!
Jin: Yes. So, going back to your strategy then: your Earnings-over-Reaction strategy. Could you share a bit of the statistics behind your strategy? I don’t know if there’s like a p-value for how often a strategy works or not. Are you able to share something about that?
Craig: We obviously have batting averages for all the different strategies, especially the ones where we can quantify that side of it, and we certainly also look at trade optimization. I’m not going to get too deeply into the numbers because again some years they work really well, some periods they don’t. We understand that, and that’s why we have a number of different strategies. Typically, I’d say what we are looking for is something that has a reasonable batting average. Obviously, nothing is ever 100%, but if you can get a reasonable batting average, and then optimize, but I would say put in enough trade parameters around it to improve it a little bit, and then also have trading rules around it to help improve your dynamics a little bit more, then you can put all these things together and you can have a decent quality trading strategy. Like I said, we use stops to limit the downside risk. Again, none of these are core positions. So, hey, if it doesn’t work out, take our few basis points and move on. And that also helps to address one of the bigger behavioural biases out there, which is the loss aversion, which is where people hold on to their losers too long and keep on riding them down. On the flipside, we don’t have hard profit taking levels, what we have is reset levels, so on the topside, if trades are working well and they hit one of these reset levels, essentially the stop just gets moved higher. So, what this does is, it limits us from holding onto our losers too long and causing too much damage to the portfolio. On the flipside, it actually enables our winners to run for much longer, and that’s one of the other aspects of loss aversion—where people sell their winners too early.
Jin: I can testify that I’ve succumbed to both of those in the past.
Craig: We all have! I’ll be honest. There’s a few behavioural funds in the US similar to us, but not exactly. Most of the ones in the US tend to be fairly deep value and small cap which is, I guess you could argue, a behavioural bias, but that’s a broader market behavioural bias, but, obviously, we’re targeting individual instances of a behavioural bias—so pretty different. But, it’s also the most interesting aspect of this. We have an educational component to what we do as well. Teaching people about these behavioural biases can help improve their long-term returns. If you understand a lot of these biases, even knowing that they are there, you can actually start to better control.
Jin: So, we’ve been talking about the Earnings-over-Reaction strategy, and when I was reading through the list of strategies that you employ, it seems to me that the Emotional Cascade strategy, which is mentioned right next to Earnings-over-Reaction strategy—that strategy seems to be fairly similar in principle. Could you tell us a bit about differences between those two strategies?
Craig: Yes. So, the tricky part about the Emotional Cascade one is that it’s probably our least quantifiable strategy, and it does differ from Earnings-over-Reaction. So, Earnings-over-Reaction does trigger—it’s either an over to the positive side, or it’s to the negative side following earnings. The Emotional Cascade is something different as it is longer term. Over a period of weeks or months, the news just becomes almost myopically one sided. So, it’s either all negative, which is mostly the case, or, sometimes, all positive. But, most the cases we’ve come across have been all negative, and the share price continues to just get crushed, and it’s disconnected from anything in reality anymore because everybody’s just focusing on the negative. It actually builds up over time. The current trade we have in the portfolio right now on that side is PG&E, which is the electricity utility in California that primarily services Northern California. Obviously, some of their clients’ houses have burnt down with all the fires. So, losing customers isn’t good. There’s also a statute in California on the liability side that utilities can be held—if they are partially responsible for a natural disaster, utilities can be held somewhat liable and this can be $5 or $6 billion, so that’s huge. And, then they cut their dividend, and, you know, through all these—everything just going terribly wrong.
So, the market knocked a third of its value off. The market knocked off $13 – $14 billion of market cap. It’s an under-levered company. Even if the lawsuit does go forward, lawsuits take years to get through, its pre-tax dollars, and you end up paying it years down the road. So, it could happen, but chances are the utility service for Northern California is still going to be the same utility a year from now. So, at some point, is, “Has all the news been extremely one-sided and just pushed things way too far down?” And, that was our view on that one, and that’s why we own our position. Now, you could get more shoes to drop. That is why this one is way less quantifiable. I mean you could get more shoes to drop. There could be something else out there, and this is why we have stop losses and something else in place to protect our downside.
Jin: Yes. As they say, there’s never just one cockroach—if that saying applies to this instance.
Craig: Well, once you’ve seen five cockroaches running around, really, does the sixth one matter?
Jin: Yeah. So, what’s fascinating about this strategy though is that it seems to run counter to another well know investment phenomenon, which is momentum. So, the principle behind momentum is that people tend to under react to some news because, like you said, people have loss aversion, and so people tend to hold onto losing stocks for way too long, and that results in a slow move down for prices, whereas the price should be going down way more when certain news comes out. But, your strategy would run counter to that. At some point, people are just becoming irrational by driving prices down. So, what do you think about momentum? How do those two strategies inter-play against one other?
Craig: So. Yes. I’m not sure if I agree too much on the momentum side, and this is why. One hundred percent, there is some evidence out there and there’s been some research—mind you there’s a white paper for everything now, so it doesn’t really matter—but there’s some research on earnings that when there’s a turning point, people are too slow to react. So, you know, when Amazon, all of a sudden, starts missing earnings–and this is obviously in the future–investors will be too slow to react. So, even though it’ll go down a little bit, the trade is actually everybody should start reducing–mind you for every seller, there’s a buyer. So, it’s potentially under-reaching on that side, and that’s potentially true if it actually is a turning point, or it actually has turned. But, if Amazon is going up and they miss a quarter, the question is then: is that the turning point?— in which case you are right: the momentum trade would be under-reacting and you should be selling it. Or, is it one quarter that had certain aspects, and it isn’t a turning point, it’s just a one quarter variance, because there’s a lot of variance out there too. We certainly do tend to be more mean reversion in our strategies–in some of them, not all of them, I’d say, but in some of them. We’re certainly tilted more on that side. I’d also say, on the momentum side, I think it gets tricky because momentum’s “growth”, kind of, now, but, momentum’s also driven by ETFs as well, just because of the structure of ETFs and the way they trade, because they’re all basket trading, so, there’s no price discrimination when flows come in or out of ETFs, which typically have been going in for the last number of years, obviously. So, that actually pushes up, actually feeds the momentum trade immensely. So, I don’t know if that’s a behavioural bias that actually could be a structural change in the market as we get this re-jigging of people moving from active managers who do price discovery more so over to passive basket trading for lower fees. That in itself could be driving the momentum trade, and I think it is over the last number of years, along with low interest rates, and it’s also been suppressing volatility.
Jin: Yes, that would make sense. Momentum started killing it over the past few years. But the value side in me always prefers reversion strategies. I could never really get into momentum.
Craig: So, and here’s the other tricky part, right. A pure value guy—. If you’re a value manager, you go out and you do your research on whatever company it is, and you say, “it’s trading below in terms of value, so I’m going to buy some.” So, all else being equal of course, for that intrinsic value to actually narrow, you need another active manager to come to the same conclusion as you, right. And the problem is–I don’t want to say this—with fewer active managers—. With fewer and fewer dollars focused on those kinds of strategies, if there’s fewer of them, it can potentially take a lot longer, because more of the money is just being thrown into baskets. Then, it’s going to lift a handful of companies a lot more than others, and that’s going to self-perpetuate until it doesn’t. And, that’s why I think the traditional approaches on value and growth–I just think active managers have to start—. I mean, it’s all trying to find a mispriced asset, and trying to profit from it—whether a long-short, whether it’s a growth guy, whether looking, whether under-representing Amazon’s growth projector. It’s all trying to do the same thing. I just think it’s time to start looking at people’s behavioural biases as a potential source of that mispricing.
Jin: It seems that in the past few years more and more smart beta and quasi active funds have been coming out. Do you think that will change the dynamics of the market? Say, momentum will be less of a strong phenomenon probably in the future?
Craig: Yes, potentially. I don’t think it’s just smart beta. All the active managers have this component of smart beta built into their portfolios, and then they layer on their other aspects to it too, but I think what probably could tilt this–what will tilt this–I think interest rates moving higher could bring volatility back into the market. And, that could rattle some things, and it also might disrupt the momentum trade in a pretty big way. And then, the other aspect is investing. This isn’t math, right. This is a social science, and the market itself changes because of what the participants are doing, and as the participants get more and more in one direction, the market continues to change more and more until, at some point, it snaps in the other direction. And I think that out there somewhere—we have no way to actually predict when—it’s getting longer in the tooth as far as momentum, and passive, and some other structural things. But, even on the passive side, we’ve developed some strategies in our indexing bias. It’s not a bias! it’s not even behavioural. It’s a market inefficiency, and I think you need to start looking for market inefficiencies everywhere you can find them, and that’s the role of active portfolio management.
Jin: Could you tell us a little more about the role of indexing strategy that you mentioned?
Craig: Yes. Well, on the surface most people would say when Company ABC gets added to the index: “That’s fantastic news—I want to own them.” On the surface, it’s a vote of confidence and there’s all this passive money and that sort of thing. But, what we found, actually, is quite interesting. Companies, when they get added to the index, tend to perform pretty poorly and vice versa, when the get taken out, they tend to do a bit better, and here’s the reason why. This is one of the other aspects of this. There should be a reason behind all your strategies as well that makes them make sense. But, what we found out is that if you’re the 600th largest companies in the US, you’re obviously not in the S&P 500, but, then you have a great year, and you shoot out the lights or whatever. You’ve done really well, and you rise up to being the 475th largest. So then, some smart guys, Portfolio Managers, notice this, and say, “You know what this thing’s probably going to get added to the S&P 500.” So, even before any news comes out, I’m going to start buying some, just in case it gets added. Then the committee sits down and they say Company ABC, we’re adding to the S&P500. So, then, the passive guys will start to buy. They’ll wait for the community to meet, then they’ll start to buy and they’ll push the price up even more. So, you’ve had these different layers, and they keep pushing the price, pushing the price. Then, all of a sudden, the day they actually get added to the index, all these buyers are now long, and it mean reverts because it was just pushed too far too fast by too many different participants motivated by different factors. And, you get this mean reversion, and you see the same thing on companies getting kicked out of the index where they get beaten down. The announcement comes out they’re going to get removed in a week’s time, two weeks’ time, and it gets pushed down even further with the passive guys bailing on it, and, then, once it gets kicked out, it snaps back over the next weeks and months.
Jin: Yes, it makes sense.
Craig: That strategy will probably fade, so this one of the other aspects of any of these kinds of strategies, you know: do your homework, because it’s a social science. If more people start doing these things—. If more people start doing our indexing strategies, or spin offs, or other types of strategies—will the potential alpha (Jin’s Note: alpha refers to higher than “normal” returns) decline over time, and that’s a risk. Some of our own strategies will suffer from that earlier, including the indexing bias, which is why we continue to test biases and things on an ongoing basis, and why we tend to develop new strategies, and are working on others as well going forward. But one of the things we’re pretty confident on is that the ones that have a pure emotional or behavioural bias at the core—we’re pretty sure that investors are going to keep making these mistakes for a long, long time, even if they learn about them. I mean, it might mitigate it on the margin, but we think more broadly speaking—we just think that’s going to be a long-term source of misplaced assets for us.
Jin: You don’t think some artificial, super-intelligent robot will remove all the biases?
Craig: I still think human plus robot beats robot, so we’ll see.
Jin: Okay, so, if some of the strategies became unprofitable, would you take them out, and would you insert new ones in for the same fund?
Jin: So, the list of strategies could change at any time really?
Craig: Yes, for sure.
Jin: So, the last thing I want to get some information on is the Unloved-to-Less-Unloved strategy. You mentioned that as one of the big drivers in today’s market, or at least in the past few weeks. Could you talk some more about that?
Craig: Yes for sure—but keep in mind though that [although] we launched two weeks ago, two weeks and a day now, there hasn’t been an index change, and there hasn’t been any company spin offs and that’s why. [With] some of the other strategies, we actually need an instance in the market, something to happen, for us to have a potential trading opportunity. So, that’s one of the reasons that the earnings has been pretty active in the Unloved-to-Less-Unloved.
This one actually comes from herd behaviour, and has been on confirmation by us. So, if you’re given two companies to buy, and one has a whole bunch of buy ratings on it from [unintelligible word, n.t.] analysts, and the other has a couple of buys, a couple of holds, and, maybe, a sell, you would probably feel more comfortable buying the one that has all the buy ratings because all the analysts say “buy” and they’re the experts and nobody is disagreeing with that. And, if you were an advisor, you’d probably feel more comfortable recommending that as opposed to the one that has fewer buys, and that’s even if it doesn’t work out. You can always say, “Hey, but everyone said, ‘buy’!” And, if you think about herd mentality, where you feel more comfortable being in the big herd with everybody agreeing with one another, that’s it—that is the consensus for one company versus another: one has a whole lot of “buys”.
But, what we found is that, over time, the actual companies with all the buy ratings tended to underperform the other ones. So, [what] we did with this for the US market with the S&P 100 and the Toronto Stock Exchange 160—so focussed mostly on large cap, so we didn’t get a size bias in our research—and, basically, what we did is: we took the top quintile of each at the beginning of the year, so the top 20% of the names with the most buys and the top 20% with the least buys, and we would track their performance over the subsequent year. What we found was that over the last 20 years, its been relatively consistent—not every year, but relatively consistent—in both markets that the ones, The Unloved—the ones with fewer buys—actually outperformed the ones with all the buys. So why is that? A couple of reasons: 1. If a company has a whole bunch of buys on it already, well, that’s not new news. Everyone’s already said, “Buy.” It’s usually analysts’ changes that actually have a bigger impact on price performance. Then, also, if everyone says “Buy” on something, then, what’s the next rating change going to be? You can’t say “super buy”. Well, I guess you can, but downgraded. So, that was an instance of behavioural bias in the market place, and potential market impact, so, we started developing strategy around it. We looked at–you know, we could have just gone out, shorted all the guys with the buy ratings, and gone along with the Unloved, but, what we actually found more profitable was to find companies that were Unloved for at least three months or longer. So, it’s below a certain threshold of percentage of analysts’ recommendations that are buying; it’s obviously a low percentage. So, it’s below that for three months or longer. So, they’ve gone through this Unloved period, and then they start to see some upgrades. That’s where the herd behaviour kicks in again, because very often there will be one analyst or two analysts who are more forward thinking, upgrade it through our threshold and, then, as time goes on, more people jump on board, and analysts operate the same way. They say, “other people are upgrading this, the performance is doing better, I’m going to upgrade it too.” And, we just found that if it went Unloved for 3 months or longer; if the price performance was poor during that period both on an absolute and/or a relative basis; if then we layer on other factors or characteristics that make it ideal (like it’s ideal if there’s some insider buying—it’s not required, but it’s certainly a positive, and it’s where it becomes a little bit less hard-rules-base, and a little bit more human-takes-over); and then, once we start getting some upgrades, we get in there, and, what we’ve found is, those companies tend to perform quite strongly.
Jin: I have a somewhat more cynical theory as to why some companies with buy ratings underperform, and maybe you can tell me if I’m off track or not. I think that if a company is about to issue shares, then an analyst would then put a buy rating just so the firm can get more business. I’m aware that this not supposed to happen, but to me it seems like the incentive is there, so it might happen.
Craig: I don’t totally disagree. I wouldn’t be as cynical. I would say that, in a lot of cases, companies that certainly do issue a lot of shares will tend to lean to—. If they go to market quite often, yes, chances are they’ll attract more buy ratings than other companies. But, I also think the industry’s been gradually changing and the amount of companies deciding to go with one banker over another because of the analysts’ rating is just not what it used to be, and that’s because the markets’ participants have changed, and there’s so many different dynamics that are in there now. So, I agree it’s probably there on the margin, but I’m not sure it’s a huge driver anymore.
Jin: Yes. I do agree that, in general, ethical behaviour seems to be increasing, at least for the investment bankers that I’ve seen.
Craig: Yes, and their whole industry’s changing too. And, yes, I think, It’s all for the better. I just don’t think–I think that was probably a lot more prevalent ten years ago.
Jin: Right, okay. So, a lot of your strategies—and this is the last question–a lot of your strategies tend to be short-term oriented. You know, Earnings-over-Reaction. Emotional Cascade may be is more long-term, like you said, but the Unloved-to-Less-Unloved is also short-term. Would you then say that there’s a lot of turnover in your portfolio?
Craig: Yes. I would say we’re probably pretty active, and the different strategies—. Some of them we can own for quite some time, because if it keeps doing well and keeps hitting its reset level, we’ll just be gradually bringing the stock up behind it, and we’ll be comfortable owning it for years. But, on the flipside, if something we buy into based on what we think is a behavioural bias over-reaction in the market, then we’ll bail on it pretty quickly. I would say our trading is certainly more active than a traditional value or growth manager, because we’re looking for instances in the market to actually trade.
Jin: I see. So, do you hold a lot of cash in your portfolios then?
Craig: No. So, we balance it. We wrestled with that as well because, you know, if you’re a value manager you can go out and you can build a value portfolio and you can say, I’m going to overweight whatever financials, underweight energy, pick your names, put your portfolios together, and then go out, launch your fund, and then, as soon as you get some money, just go out and buy that portfolio, and you make changes as you go along. Ours is obviously quite different. We clearly need something to happen in the market. We need somebody to make what we think is a behavioural mistake for us to pounce on that potential opportunity, whether it’s around earnings, or analyst upgrades, or index changes–whatever. Whatever the underlying strategy. We need an actual instance to create a potential trade opportunity for us. So, we originally wrestled with, “so, what do we do? Do we just sit on cash and wait for these things to happen?”, because sometimes these things will be very busy–earnings season, or major index changes. And, there’s going to be other times when we’re not going to be nearly as busy. So, we wrestled with what to do. Do we just sit in cash? And, what we decided was: we’re a North American equity strategy so, basically, the component of our portfolio that’s not active in an individual trade, implementing an individual strategy, is sitting in either the TSX or the S&P.
Jin: I see. So, go passive, unless there’s a strategy?
Craig: Yes. Like today, we have a number of trades on, and we’re reducing our ETF position because we have trades in the market, so we just sell some of the index and go into those trades. And then, if we take those out and they hit their stop levels, or we close out those positions and we have excess cash, we’ll probably just throw it back into the ETF.
Jin’s Note: I would like to thank Craig for taking his time to answer my questions. If you would like to learn more about the Redwood Behavioural Opportunities Fund, please visits its official site here.