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Following the Smart Money to Alpha

Tue, 8 Jan 2013

TFS' Eric Newman, whose team earned Alternative Managers of the Year honors, says watching for insider signals mainly in small caps allowed their fund to pull in the best absolute return in its category in 2012.

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Video Transcript

Nadia Papagiannis: Hello, my name is Nadia Papagiannis. I am the director of alternative fund research for Morningstar, and today I have with me Eric Newman, one of the portfolio managers for TFS Market Neutral Fund, ticker symbol TFSMX.

Eric, thank you being with us today.

Eric Newman: Thanks, Nadia.

Papagiannis: Your fund was nominated and won the first ever Alternative Fund Manager of the Year award. Congratulations.

Newman: Thanks.

Papagiannis: Eric, so your fund is a market-neutral fund. Could you briefly describe its strategy?

Newman: Sure. TFS Market Neutral fund is a quantitative fund. We take long positions and short positions. We maintain a constant ratio of those longs and shorts in the equity piece. So for every dollar in the fund, we have a $1 long and $0.67 short. So what that means is that we're not really trying to make money by timing the market and we're not trying to wake up one day and say, "Oh, we think the market is going to rise the next month, so let's change our ratio." We're really trying to make money on the spreads between those longs and shorts.

Papagiannis: And your fund concentrates on smaller-cap stocks?

Newman: Yes, we concentrate on smaller caps. That's not because we're experts in small caps, but that's because that's where we find the alpha, that's where we find the inefficiencies in the market. We run separate quantitative factors, and so, we have nine factors that are running in the fund right now. Many of those do have a long side and a short side which hedge each other. Some can be long-only or short-only.

Papagiannis: And what types of factors are you looking for?

Newman: We have three main categories, or buckets of factors. One of them is financial statement analysis. This is using data that's provided by the company, typically on a quarterly filing. These are things that fundamental managers would look at. So we could be looking at earnings, or quality of earnings, or assets and those types of things.

The second category is what we call, following the smart money or the smart people. This could be signals from management. So if you see all of the insiders are buying the stock of the company and they weren't doing that for the last five years, well, that means something. It could be an increase in dividend. It could also be informed traders and other asset classes. So, for example, you can look at the CDS market or the bond market. If there are changes there, an increase or decrease in price, does that signal that the equity is going to move?

The third category are imbalances in the market. So do you think there is going to be too much demand or not enough demand over a period of time? An example of that, people talked a lot just recently at the end of the year about tax-loss harvesting. So people who are selling positions not because they have any fundamental reason to do so, but because they want to lock in taxable gains or losses to offset gains. Those are inefficiencies we think we can find, we can back test them over a long period of time, and see if they are actually durable, if they actually have worked well for many, many years. And if so, we can put those in the fund.

We try to find these sort of separate types of models, separate nine models in the fund that have low correlation to each other. And then of course, we also want to have a low correlation and a low beta to the overall markets. The market is getting much more correlated. It's much harder to find to sort of stumble into low correlation. It used to be you could buy a small-cap fund, a large-cap fund, and an international fund, and they would have low correlation to each other. That's not really true anymore. And so, we think it's important to have strategies that are built from the ground up to have low correlation and that's what we try offer with our alternative funds.

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Papagiannis: So in 2012 your fund returned 7.8%, and that was the best absolute return in the category. And it was one of the best risk-adjusted returns in the category. So can you describe what worked and what didn't work last year?

Newman: Yes, so I mentioned those three buckets. The financial statement analyses, which are more of these sort of classical factors. You have following the smart money or the smart people, and then the market imbalances. Those later two categories are the ones that really drove the performance of the fund in 2012. The fundamental category, the first category, actually lost money for the fund. Not a lot of money but a little bit. Of course, it was more than offset by the other categories, and this is a trend that we've seen--not losing money, but not adding as much value. These classical factors, fundamental factors haven't worked well for the last several years.

We've actually dedicated just about 100% of our research, besides trying to make the current models better, 100% of our research on these other two categories, the nonfundamental factors. We think really we're not in an environment where they've added money. I think maybe the trades are getting too crowded or something else, so what really drove the performance were these sort of nonstandard factors, these ideas that we've developed really during the last several years that we've continued to develop. We think that investors are paying us not just to keep running the same model that we've run for eight years, but to find new ideas and new strategies that are working in the marketplace today.

Papagiannis: So the fundamental factor models that you say haven't been working well recently, that's pretty much what contributed to the quant meltdown of 2007 and 2008, correct?

Newman: Yeah, that's right. These are the strategies that people were using for 20-plus years in the quant space. In August 2007, when they had this quant meltdown, there was heavy overlap. People had many of the same positions. So there was a sort of domino effect that one fund for whatever reason had to sell out of their positions in a panic, and their longs went down or shorts went up. And that caused somebody else to have a margin collar, to have to sell in the panic and you have this domino effect.

We also had much more leverage in the space in 2007, I think, than you do now. So there were lots of people with lots of assets chasing these same factors, and it led to this very, very crowded trade that when everybody exited, they exited in a panic. And it sort of drove this very poor performance, this crash in August 2007.

Papagiannis: But your fund didn't crash. It lost less than 8% in 2008 because it's offset--it's long and short--and because it's not so leveraged.

Newman: Right. I think the big thing there really is the leverage, because we're in small caps, the returns were actually higher. We can take the same strategy. In the large caps you can make a couple percent alpha, but in the small-cap space, you make much, much more. So other funds that needed to build products that could handle $10 billion or $20 billion of assets need to be leveraged 4 or 5 to 1. So they were much more leveraged, and so when this crash came, it hit them much harder.

For us, that's one of the benefits of being this niche player in small caps, is that we don't have to use leverage in order to give investors what we think is pretty good performance in the fund.

Papagiannis: So do you think that these fundamentals factors aren't working anymore because everybody has a computer and everybody can run the same kinds of models?

Newman: Yes, it is amazing, and the power we have on our desktop now versus, you can say, 10 years ago to sort of mine through lots and lots of different factors is amazing. So I think that 20 years ago, if you could actually rank stocks by earnings quality, you really had something because maybe you're going to a newspaper or sort of an old-style terminal to sort of get data.

Now that everybody can do it, we think that these are less appealing; there are too many people doing them. And if there are opportunities out there, there are a lot of smart people who are going to go and find them and arbitrage these ideas away. That's why we really want to focus on some of these nonstandard, more interesting data sets that we think can still offer value

Papagiannis: Some examples of that might be?

Newman: Yes, some examples of things we've looked at are patent filings, for example. So we look at a company and look at the number of patent filings compared with others in its sector or compared with its research budget and see whether or not that that adds value and whether that predicts the future performance. That data is pretty hard to get. If you have a patent they don't just give you a ticker, and if there is a ticker change, they don't tell you. So we actually kind of like that challenge, this sort of a barrier to entry so to speak for people to go and sort of look at this data and back-test it. Another that I mentioned earlier is the CDS data. If a company--suddenly it cost more to buy insurance on their bonds, that may mean something. Does that predict equity movement? So those are sort of a couple of examples of ideas that we are looking at and pursuing, types of ideas that we would love to add to the fund, and not some of these traditional fundamental factors.

Papagiannis: I think one of the things that makes your fund and your firm so good is that you are constantly looking at these data sets and you're constantly looking to improve your existing models and to add better and more robust models, but also that you're cognizant of your capacity. So can you talk a little bit about how you recognize capacity, and why you closed your fund?

Newman: So as I mentioned, we trade mostly in the small-cap space. We do have some mid-cap and large-cap positions where we find opportunities, but for the most part we're in small caps. And we have internal models which we developed that says how much can we trade of a given stock in a given day in order to not move the market. The problem is you can run a back test that shows great performance, but if you impact the stock, then your back test isn't relevant anymore. So each strategy has its own capacity limit calculated for these individual trades. So what percentage could you trade in a given day, and that rolls up to sort of an overall capacity of the fund.

Papagiannis: But you do have funds that are open?

Newman: We do, so we have two other mutual funds. We have the TFS Small-Cap Fund, which generally takes the long-only portion of the market-neutral strategy, again many of which are long/short. And we have our newest product which is the TFS Hedged Futures Fund, which takes a similar approach where we take quantitative models and we apply those to the futures market, whether we're using data from the term structure of the futures or we're looking at the informed traders again within the futures market.

Papagiannis: So Eric, one of the things that we like about Fund Managers of the Year is that, not only are they good at getting returns for investors, but they're good stewards of capital. And so one of the things that we like about your firm is that you're cognizant of capacity and closed the fund, but there are other aspects of good stewardship at your firm that we like as well. Can you talk a little bit about how you align the interests of the managers with your shareholders?

Newman: Sure. We develop products that we want to put our own money into. That's sort of one of unofficial guiding principles. In fact, the owners and portfolio managers at TFS, we have a rule that they have to have at least half of their personal assets invested in TFS products. We think that is a pretty good indicator that we're eating our own cooking, so to speak. We also do lots of other things that I think aren't really noticed very often. So we don't take soft dollars for example. I think that's 90% of the fund industry does, so when they place to trade the fund pays a commission, but part of that commission actually ends up coming back to the manager and they pay for research or data or something else. We don't do that. We don't charge 12b-1 fees; we don't charge sales loads of any kind. So we tried to develop a product that we said if we're going to put our own money into it, we don't want to play these games. We're going to charge one management fee and not have other fees we collect, and we are going to put half of our money into our products.

Nadia Papagiannis: Well, thank you so much, Eric. Congratulations again on your work.

Eric Newman: Thanks, Nadia.

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