Q:  How has the long/short industry and Invesco Long/Short Equity Fund evolved? A : The Invesco Long/Short Equity Fund manages around $35 million and the fund was launched in December 2013. What has been most interesting in the long/short space over the last couple of years is the democratization of long/short equity funds in the form of new 40 Act mutual funds that offer investors greater access to the strategy. Previously, long/short equity was generally restricted to private partnerships like hedge funds, with their high minimum investment requirements, and other prohibitive costs involved with the hedge fund structure. Additionally, the partnership structure of hedge funds required a K1 form to be released to partners, which is often not available until later in the year, delaying the individual investor’s tax filing. Moreover, fees now in long/short equity mutual funds are generally attractive and offer investors transparency of fund holdings. Q:  What core principles guide your investment philosophy? A : One of the core principles is to identify companies that have improving earnings expectations. We believe there tends to be a trend that as expectations improve it leads to even higher expectations that ultimately leads to an increase in the stock price. There are two observations behind that - both kinds of behavioral reasons. First, sell-side analysts tend to herd together to avoid a career risk of making a forecast that stands too far out from the crowd. Second, we’ve seen that company managers try to lead analysts close to where the true earnings of the company will be, so at announcement time they can actually report results slightly better than the analysts’ forecast. That can lead to a pop in the stock’s price. It’s that kind of momentum that we are trying to capture with this particular principle. We think this strategy has three potential ways to add value over time-through stock selection and getting the spread right between longs and shorts, by getting industry forecasts right to be net long or short in these industries, and finally by getting the market exposure right. Q:  How can investors potentially benefit from the long/short equity investing strategy? A : Most investors are underfunded for their investment or retirement goals. Typically, investors need the kind of returns that can be available in the equity marketplace to achieve those goals. But over the last 15 years we’ve experienced two once-in-a-lifetime drawdowns- first with the tech bubble and then the global financial crisis in 2008. So, investors need to find a strategy that provides the potential for an equity-like return with less volatility in an effort to minimize their downside risk and help to compound their wealth over time. To me, that potential is what makes long/short strategies so compelling. Q:  What are the advantages of shorting? A : What’s so compelling about shorting stocks is that 99% of the equity investing world is focused on finding the outperformers- what is going to be the next great stock, the next great growth story. So, relatively speaking, much less attention is placed on those stocks that are going to underperform. But, mathematically speaking, there are a group of stocks that will be above average and a group of stocks below average. There is an opportunity for greater returns over time if you have the ability to identify those in both camps. One of the mistakes that people make is looking for sustainable outsized growth, which just doesn’t materialize, and so this is why investors generally tend to overpay for companies with recent high growth, despite the fact that most companies do not record 30% growth year after year. One of the things that drives the underperformance of these high growth potential stocks is the fact that investors overbid for them hoping they’ll be the next big winner. Q:  What strategy drives your shorting? A : At a high level we are a quantitative investment management team, so we take a very systematic and disciplined approach to stock selection. We are ultimately trying to identify four basic investment concepts for stocks. First, we are looking for companies with strong and improving earnings expectations. Second, we are looking for stocks where the market sentiment supports the earnings story. Third, we are looking for companies run by managers that are looking out for shareholders rather than trying to enrich themselves and for which the managers operate their businesses more efficiently on the resources they have. Finally, we are trying to find those kinds of companies that are fundamentally strong but available at an attractive discount relative to industry peers. This is a key element of how we invest and how we forecast returns based on what we call an industry neutral forecast. This industry neutral forecast does two things. Number one it makes fair comparisons. Let’s take a hypothetical case where profitability is considered a good way to differentiate among stocks and we compare software companies and supermarket companies. By direct comparison, it is probably true the worst-run software company is always going to be more profitable than the best-run supermarket. That’s just the nature of the businesses, so to make that direct comparison we think is unfair. Instead, we compare stocks within the same industry. This is a fair comparison and also a very symmetric comparison. Next, we forecast returns through our industry neutral process. For every industry there will be stocks that we like and think are going to be above average performers. Conversely, there will be stocks that we dislike and think are below average performers. We identify stocks to short based on those industry neutral forecasts. There is also a component of the investment process where we are performing a forecast at the industry level. We are using the same investment concepts of earnings expectations and market sentiment, etc. and applying them to industries. The nuance is that with some of this measurement we are comparing industries relative to themselves, because otherwise there are some industries that might always appear cheaper or more expensive than other industries. Finally, we arrive at a forecast of return for all the stocks in our investable universe. For the Invesco Long/Short Equity Fund that investable universe is the S&P 500 and other stocks that also fit in that market cap range – about 700 stocks altogether. Ultimately, 70% of our forecast is based on the individual stock from that industry neutral forecast and about 30% of its forecast return is based on the return to its industry. The majority of what is going to drive our short position is the individual stock’s return, but then there is also an influence from the industry itself so that will also help us determine what stocks to short. In summary, we have a bottom-up view and then overlay it with the industry view and that’s been our focus on selecting stocks too short for the portfolio. Q:  What factors lead to your estimate creation? A : I think it is important to start with the philosophical belief behind it because, while we are quantitative investment managers, we are investors first. On this team the people run the machines, the machines don’t run the people. Any idea that makes its way into the investment process has to have a projectable rationale and for us that means there must be a fundamental or behavioral rationale behind why an inefficiency exists in the marketplace. We also have to have a belief as an investor that this is something that is sustainable - not just now but in the future. So, unlike some other quantitative investment managers, we are not simply looking for a relationship among data that existed in the past. We are actually trying to consider what investors desire or shy away from or the characteristics of equities that will drive returns now and in the future. Q:  How does your quantitative process seek to capture momentum? A : The investment process used by our team in all the funds it manages has the potential to work best in trend-like environments – whether that trend is up or down or sideways. Over the long term we are very focused on quality and fundamentals, so when the market is rewarding companies with strong and improving earnings, good market sentiment, shareholder friendly management and attractive valuations, we have historically done well. Of course, past performance can’t predict future results. Particularly for investment concepts like earnings expectations or market sentiment - which includes price momentum - inflection points can be challenging. However, the return forecasting model we have put together has been explicitly designed to provide a balance among the investment concepts so what we would expect - and what we have observed over time - is that there is a low and oftentimes negative correlation between how those momentum-type measures, like this earnings momentum, behave and also how our valuation or management quality concepts behave. So we are explicitly looking for diversification in the signals behind this return forecasting. Therefore, we expect that even after a very strong run-up in price for a stock and the momentum signal looks really positive and very compelling, the valuation may look very unattractive relative to its industry peers because of where its price is relative to its earnings, profits, cash flow, and other metrics. Q:  Why it is important to capture that kind of momentum? A : It is important to capture that kind of momentum because price tends to follow earnings and if you can get ahead of an increase in an earnings announcement - whether it is coming from the analyst community or even more importantly from the company itself - then those analysts and investors tend to reassess where they think the stock price should be based on the new earnings information. If you can get ahead of earnings that are going to rise then you should be able to benefit from that move; or, vice-versa, if you know that earnings are going to decline, the stock price is not far to follow. The earnings announcements from the company themselves are quarterly, but the change in analyst expectations can be at any point, so they are monitored by the team daily. If an analyst comes out with a new forecast, then our forecast of that stock changes. Q:  What is your research process? How does a stock idea become a holding? A : Basically at the beginning of every year each research member is asked to submit their 10 best ideas for what they think is going to add value going forward. The entire team’s combined set of ideas are culled down to a more manageable set by the head of research. After getting a manageable list of ideas, the research member who submitted the idea is asked to create a small team with other members and put together the initial projectable rationale to be presented later to the entire team. As good quantitative managers, we start doing a lot of statistical work and assess how the inefficiency has performed over time and in different market environments like the stable accommodative environment from 2002 to 2006, or periods like the financial crisis, the tech bubble, etc. We seek to determine how this factor might relate to other factors already in the model, what is its correlation, drawdown and turnover. The next step is to present the idea to the regional research team. There will be a lot of iteration and if the idea makes it past the regional team it is presented to the global team where the iteration starts again. Ultimately, if the idea is going to be available for inclusion in the process it has to pass that global team. During this process, any member of that research team can say there is still some work to be done in our efforts to thoroughly analyze the investment thesis or find hidden flaws or problems. If the idea can survive that gauntlet, it is then available for the investment process. There is nothing in the process that is specific to the long side or the short side. There are some pieces of the forecasting model that have more impact on one side or the other, however. One of the nuances that is important here is that this is not a one size fits all model. We realize that not all stocks behave the same way, so we have tailored this investment approach to different contexts. For instance, we treat mega-cap stocks differently than volatile growth stocks. For mega-cap stocks we think there is a lot more information from valuation metrics, but if you look at volatile growth there is a lot more information from momentum metrics, so we do try to tailor this approach to the context that matters. Everything that we do is democratic and team oriented so everybody’s opinion matters and is encouraged. Ultimately for an idea to move forward it has to be a unanimous acceptance of the idea. I think this is really the strength of our process, of our research team and ultimately of our strategy: that we’ve got this global group. Everybody has different backgrounds and experiences. We have a PhD in Physics, we have a former Russian chess champion on the team, we’ve got a lot of different viewpoints and experiences and they are all contributing to this process whereby these ideas that are brought to the table and that research develops are reviewed and ultimately agreed to or not by this full team. Q:  Can you briefly describe your organizational structure? A : Our research is global, and the team is comprised of 22 individuals. The main offices are in Boston, New York and Frankfurt, Germany and we also have smaller offices in Tokyo and Melbourne. Again, our stock selection process is by consensus agreement from all 22 members. Ultimately, part of the role of the regional heads and the global head of research is to help guide the discussion, but the global head of research cannot and does not override the decisions of the research team members nor does he dictate the work that the members do. The themes that the team is working on are developed by the teams and are not handed down from the global head of research by dictating who works on what project. Portfolio management, on the other hand, is local. Since the Long/Short Equity Fund employs a US investable universe, the US portfolio management team is responsible for it. So, from a practical standpoint, the whole global research team supports the strategy, and all five portfolio managers in the US also support it from a construction perspective. Decisions are based on consensus and there is no overriding authority among portfolio managers. Our fund - and all the strategies the team manages - is not run by a single fund manager supported by a couple of analysts. We use a functional team approach and what that means for us is that we have a global research team of 22 members who basically spend each day working on better ways to forecast return, risk and transaction costs. And we have a portfolio management team who spends each day taking those forecasts of return and risk and transaction costs to build portfolios where we seek to maximize the return to fund shareholders and stay true to the parameters of the strategy. Q:  What other factors involve your research process? A : Because we have a systematic quantitative forecasting approach, we update this forecasting model every day to produce a forecast of return for all 700 stocks in the investable universe. We also have risk forecasts that are based on our own proprietary risk model, so when we rebalance the fund - which typically happens about once a month - we use what is called an optimization process. What that really means is we have a computer algorithm that simultaneously is trying to maximize expected return, to set risk based on our forecast of market risk, and to minimize transaction costs - all while keeping the strategy within its particular balance and parameters. The important takeaway here is that this all happens simultaneously. It is a risk/reward decision throughout, rather than a process that builds portfolios solely focused on maximizing return and then kind of looking at risk as an afterthought. A stock that makes its way into the portfolio is an outcome of this process. What we are going to buy are stocks with high and improving forecast of returns with an attractive risk profile, and what we are going to short are stocks with a low and deteriorating return forecast with a good risk profile. The shorts are there to seek return and, not just to diversify the longs. So both longs and shorts are there for return potential. Q:  How do you construct your portfolio? A : Right now we are close to max net exposure, with about 190% long and 100% short. We have approximately 160 names long and 80 names short. The shorts fluctuate between 70 to 100 names and the longs somewhere between 125 to 175 names. In the Invesco Long-Short Equity Fund, we tend to be 100% short or close to it. We can be anywhere between 100% and 200% long, which means on a net basis we will be somewhere between zero to 100% long. The difference between being zero and 100% net long is a result of our forecast of market risk. As we see market risk increasing we will decrease the fund’s exposure to the market and vice versa - when we see market risk decreasing we will increase the fund’s exposure to the market. We impose a maximum of 2% in any individual position to ensure diversification across the portfolio, and that is 2% either long or short. It should also be noted that even if we are net long in the fund at the fund level, an individual industry could actually be net short. Year-to-date we have generally been long energy and utilities and generally been short discretionary stocks. Our industry neutral forecasting approach seeks to produce a balanced portfolio with a constraint of 25% to any one industry. We reference the S&P 500 Index as the performance benchmark. Q:  How do you define and attempt to control risk? A : At the fund level we define risk as standard deviation of monthly returns. We try to control risk through our proprietary risk model. This model produces risk forecasts at both the individual security level, as well as the portfolio level. We also consider and control for risk when building our return forecasting model. We explicitly build balance and diversification among the investment concepts in the model, as well as in the quantitative stock selection factors that underlie them. Furthermore, for the model and factors, we analyze such data as drawdowns, correlations, and performance in particular historical periods, like the tech bubble and subsequent sell-off.