Q:  What is the history of Stadion Money Management? A : Stadion Money Management was founded in 1991 by Timothy Chapman and Don Beasley. It is a privately owned money management firm based near Athens, Georgia, offering proprietary mutual funds, separate account management services, and active money management for retirement accounts in the form of a qualified default investment alternative. Stadion began managing assets using its proprietary investment strategy in January 1996. To reach a broader investor universe the Stadion Managed Portfolio mutual fund—employing the same investment strategy—was launched on September 15, 2006. Q:  What are the underlying principles of your investment philosophy? A : Our philosophy centers on managing downside risk. We attempt to participate when the financial markets are rising but when markets trend negatively and market risk increases, our concentration shifts toward defensive allocations. It is not unusual for us to move assets 100% to cash in our fully managed accounts. We believe avoiding big losses during market downturns is crucial. Q:  Would you highlight the focus of your organization? A : Our focus is not only to offer investors an opportunity to invest in equities, but also to mitigate the risk associated with that by using a model that tactically allocates assets among various classes, including cash, which we view as its own critical asset class. We are here to manage investors’ serious money, the money that absolutely has to be there when they need it. Q:  What is your investment strategy? A : We are tactical managers implementing an active strategy using Exchange Traded Funds (“ETFs”), including ETFs that focus on certain sectors or countries, as long as there is at least a six month trading history and sufficient trading volume. We consider ourselves generalists rather than specialists. We do not believe it’s necessary to understand specific companies; instead, we have built our model to identify sectors and security types that are likely to do well or not do well in different market conditions. We use ETFs because—in addition to low costs and efficient trading— they allow us to quickly construct a diversified portfolio. If an investment trend persists our momentum strategy is designed to add alpha or excess returns to the portfolio. Basically, the goal of the fund is to capture gains prudently during cyclical bull moves and hang onto those gains when the market rolls over into corrections or cyclical bear moves. Our objective is to launch into a new rally from a higher starting point. We strive to create long-term accumulation by capturing most of the upside and avoiding most of the downside. Q:  How do you identify momentum? A : To identify momentum, we use a number of measures. The first is a simple rate-of-change trend measure. We want to buy securities that are already trending up in value, so we look at the most recent market direction and five day rate of change of that trend measure. We also want securities that are better-performing on a relative basis, so we have a couple of relative performance measures to identify those. We also look at it from a risk-adjusted standpoint, considering beta and how it mixes with the overall portfolio. Upward momentum takes on added importance because we are not always invested. So when we are invested, we want to be invested in those securities that are more likely to be going up while we own them. We are not necessarily trying to capture that first part of a market rally, but rather the middle 60% to 70% of the up move. Q:  How do you decide when to be invested? A : We begin with a higher appreciation and understanding of investment risks and how they relate to different market environments. We believe our understanding of these two has helped us create and maintain an effective investment model. Our goal is to add securities with up-trending momentum where the risk reward ratio favors us. When risk moves higher in the model, we act accordingly to lower beta and reduce portfolio volatility. To do so, we use a basket of technical measures to assess market trends and risk. We have four primary indicators that are designed to identify trends. We look at short-term trends, (roughly one-to-three weeks), intermediate-term trends (more like four-to-six weeks) and longer-term trends (roughly six-to-eight weeks). Supporting these four primary indicators are a number of risk measures that view market breadth. And we monitor investor sentiment using our market relative strength measures. As an example, when large caps are dominant, we believe that investors are generally assuming a defensive posture, and when small caps are trading higher, we believe that investors are speculating, which, in our view, is generally a positive investor sentiment sign because the markets need such speculation to drive prices higher. We normalize the basket of indicators to a point total of 0 to 100. When our model drops to zero our model allocation is going to be zero. We follow our model and stick to it. As indicators improve, rising from zero to about 35 or 40, we will add risk assets in the portfolio. As our model points increase, we will further increase our risk allocation. As more indicators turn on, our model is indicating a more favorable risk environment, and we will eventually be fully invested. For simpler explanation, we divide the market into four risk categories. When the markets are high risk, as determined by our model, we are prepared to go 100% cash. When short term, intermediate, and long term trends are negative, and market breadth and investor sentiment measures are weak, the markets are already working against us. That is when we consider cash a very viable asset class. At the opposite end of the spectrum, our model is designed to identify good short-term and intermediate term trends, when long term trends are favorable, and investor sentiment is positive. With our breadth and other technical data supporting continued upward price movements, we believe we are in an environment where we want to be fully invested to take advantage of rising momentum. Of course, external, non-market forces sometimes influence markets one way or the other. For instance, it is impossible to effectively model news-driven events that can impact market action. But in these circumstances we have a rule set that helps us react to that market action fairly quickly. In all cases, it is the model that drives when to increase or decrease risk. At Stadion, the choices are model driven and disciplined. No emotion, predicting, or guesswork. Q:  Are your models designed to capture the short-term pattern or a full cycle? A : Good question. In fact, we consider ourselves to be a full market cycle manager, and that is precisely what we are trying to accomplish. We want to grab the majority of the cyclical bull price movements and avoid the majority of the cyclical bear moves. Q:  How do you improve the efficiency of your model? A : We focus on two things: Researching the efficiency of all of our measures and setting an effective sell discipline. As an example we research periods in time when we might widen stops, narrow them, or maybe slow the model down. At the same time, we have to be very careful because these may involve very short-term periods that might last six, seven or eight months, and we certainly do not want to significantly alter a model that has performed well over the last sixteen years. But our research process is always ongoing. Both the model itself and the primary drivers dictating how we grab risk or remove risk from the portfolio are always under review. Q:  How has your model evolved in the last sixteen years? A : Very slowly and precisely. We have not made many changes in our process because our experience hasn’t suggested a needfor them. We have always used trend, investor sentiment and market breadth data to identify how we want to be invested in the market. Along the way, we have added a few components we deemed beneficial. For instance, instead of relying on a single trend measure, we now use several. The goal has been to establish a confirmation progression that gives us a greater comfort level in the final decision process. We also incorporated “weight of the evidence” into our model. Today, with the overall weight of the evidence built in, we retain the original concept, but within what we believe to be a more conservative, mathematical framework. Q:  What is your portfolio construction process? A : Typically, the portfolio when fully invested will range somewhere between eight and fourteen different ETFs, with weightings that can vary depending on what we see in the market, or what type of momentum we have identified. We generally try to get broad market exposure with the bottom 30% to 50% of the portfolio. For the top portion of the portfolio, we focus on the momentum players and we do not equally weight them. So, instead of buying just the S&P 500 Index when, for example, technology and healthcare are driving the performance in the index, we will overweight specifically in those sectors. Or, if emerging markets start screaming through the roof, they may be added to the portfolio because emerging market ETFs will rise in our rankings as they reflect that strong upward momentum. As a rule, we will not have more than 20% in any one sector-specific ETF or country-specific ETF. We generally keep it under that but that is our stated maximum. The only ETFs that we avoid are leveraged ETFs and inverse ETFs. We are long only or a cash manager, and we approach cash as an asset class. For instance, if we experience a market like we did in 2008, our models will move the portfolio to a non-correlated asset class like cash or short term fixed income to wait for a better opportunity to make money. In reality, we have always considered cash as a viable asset class. As far as benchmarking is concerned, we do not use any index as a yardstick to our performance. Our benchmark is basically absolute return performance over time. Q:  Why do you avoid anything that is leveraged? A : We do not want to leverage the portfolio because that will ultimately add volatility, which we seek to avoid. Also, bearing in mind the way leveraged ETFs are structured, we would have to make those leveraged moves on a daily basis as opposed to over time because of the way they rebalance. The same thing is true with inverse ETFs. We want to maintain the kind of balance between volatility and returns that advisors can use to enhance portfolio efficiency. Q:  What is your sell discipline? A : Our sell discipline is driven by our risk limit that we assign to each of our holdings. The fund’s sell discipline is basically a trailing stop on each individual holding in the portfolio. An ETF is added to the portfolio based on strong upward momentum, but if that momentum shifts into a negative direction, we will sell. Q:  How do you define risk and what do you do to mitigate it? A : We define risk as the impairment of capital. We attempt to mitigate risk through the use of our model and a rule overlay which includes our sell discipline. We start out by using downside risk measures to identify risk. Then we try to minimize drawdowns within the portfolio over time. This means, as we’ve previously discussed, that we consider cash a vital asset class because it helps protect the portfolio against these downward price movements. And we do not fear volume moves. As a tactical manager, we will sometimes make very large tactical shifts as necessary to add beta into the portfolio, or to remove beta risk from the portfolio depending on our model’s assessment of the risk environment at the time.