Q:  What is the investment philosophy behind managing the Old Mutual Long Short Analytic Fund? A: Our philosophy is based on the belief that success in the investment markets requires a style that is disciplined and systematic, but at the same time, dynamic and capable to adapt to changing market environments. We also believe that to be successful, you need to pay a lot of attention to cost and risk controls. In the current environment, where information is relatively inexpensive, the main challenge is what to do with it. In other words, the most important element of our process is how we weight each of the different data factors within the framework of the respective business cycle and economic environment. Since we have started running this process in 1996, we constantly test ways to come up with a better weighting scheme. Everyone looks at performance, but going forward, we believe that our edge and core competence is the ability to continuously innovate and come up with ways to incorporate all the new data into a pricing model, and then incorporate that into the ranking process and, ultimately, into performance. Q:  Why have you chosen a long/short strategy? A: Based on our research and experience, we believe that structures that allow you to go short provide the client with more consistent value-added. In this fund, for every $100 invested by the client, we buy $120 worth of stock and short $20. As a result, there is more stock selection in the fund than in a traditional long-only fund. With a long-only structure, if you don’t like a stock, all you can do is not hold or underweight it, which wouldn’t add any value for the client, especially for the stocks with tiny weights in the S&P 500. We estimated that by shorting the stocks that we don’t like, we achieve better results. We started using that strategy in our institutional accounts and in our separate accounts about five ears ago with very good results. In January 2006 we also transformed this fund into long/short fund to create more alpha, while keeping the fees unchanged. Q:  How do you translate that philosophy into an investment strategy and process? A: The portfolio is invested 100% in U.S. stocks, and our goal is to outperform the large-cap U.S. indexes, regardless whether the S&P 500 or the Russell 1000 as both of them are cap-weighted benchmarks. Our universe consists of the largest 1,000 companies in the U.S, or the 1,000 stocks in the Russell 1000 Index. Since we are a quantitative firm, the most important element of our process is generating the rankings. The process starts with collecting a variety of fundamental and technical data on each of the companies in our universe, weighting the data in terms of importance, and then, on a daily basis, ranking the stocks in our universe from 1 to 1,000. We collect data on 70 indicators, including valuation numbers, technical information, risk variables, volatility, and profitability indicators. We also collect information on the insider buying or selling and analyst revisions. Then we evaluate the importance and the weight of each of these characteristics. For example, prior to 2000, investors focused mostly on price momentum and growth characteristics. But when the tech bubble burst, everyone became focused on valuation. In 2002, when we lost confidence in analysts, the focus shifted towards dividend yields. And last year, because of the leveraged buyouts, investors began to focus on sales-to-price as a valuation characteristic. So our process involves analyzing the data for the last month, the last year, and the last three years, to examine the relationships between each of the different characteristics and returns. Then we use the strength of that correlation to weight the variables. In the last three years the strongest relationship between variable and return is the sales-to-price ratio. As a result, the companies with higher than average sales-to-price ratio receive a big positive score. Our biggest underweight now is the dividend yield. Investors are actually shying away from companies with dividend yield, which is typical behavior for the late stages of recovery when growth opportunities are scarce. The companies that pay high dividends usually lack growth opportunities within the company itself, so that characteristic gets a negative weight in our process. Q:  Could you explain in more detail the process of classifying the 70 indicators that you collect data on? A: To find which indicators are important in the current environment, we examine each of the 70 indicators and the relationship between the indicators and the recent stock returns. If an indicator is important, there will be a big positive correlation or a big negative correlation. If an indicator has no correlation, then it is not that important. To adapt to market changes, we change the weightings based on the 36-month moving average of the correlations. If something has been in favor for 36 months, it will get a heavy weight in our ranking process. Although we update the weightings every month, we have found that the 36-month window is the best way to forecast what will work next month. The system is exponentially weighted, which means that the last month would get a higher weighting than the months before that. Q:  How quickly does the strategy respond to market changes? For example, how does your system react to the already three months of a different environment in the subprime area? A: In the last three months we have decreased our exposure to highly levered companies. Now we have a greater emphasis on operating margin than three months ago. From a risk standpoint, we’re in a more volatile environment, so we have increased the names and the diversification in the portfolio. But we never change the portfolio instantly, just on the basis of the data from the last three months, because in many cases a two-month period doesn’t signify a new environment. Sometimes it is an adjustment or a shock, and sometimes it is an actual regime change. So the challenge is to react quickly enough to capture the regime change and slowly enough to avoid getting whipsawed. If your strategy is very responsive, you’d be continuously chasing your tail. That is why it is crucial to have a disciplined process, which keeps human emotion out of it. We may test shorter than 36-month windows because we live a world where people react faster, but we would do that test every five or six months to see whether we should change the model. Most of the time, we don’t see a big gain from that change. The day-to-day process of managing the portfolio is heavily systematized and accounts for about 20% to 30% of our effort. The remaining 60% or 70% are devoted to looking at new factors to test and incorporate in our model, if proven to have an effect. From a research standpoint, that’s where we spend the bulk of our effort as opposed to going out, visiting companies, and doing the fundamental, data digging type of research. In our universe, especially given Regulation FD, there is no superior access to information. But we can aim to process that information better than everybody else. Q:  What factors have you found changing in the past two or three years? A: During the past two-three years, coming up with a quantitative way to measure leverage really helped us. Most people think of leverage as the debt-to-equity ratio, but this ratio actually hides a lot of leverage, such as the leverage from leases. We measure leverage as the sensitivity of a company’s stock price to changes in junk bond returns. It is a company’s beta with respect to junk bonds, which means that companies with high beta to junk bonds are highly leveraged, and vice versa. In the current environment, the companies with high beta sensitivity to junk bonds did really well. That was a significant long position in our portfolio in the recent environment of easy access to credit and low interest rates. But you have to find a way to measure, quantify, and incorporate the measure into your process. Five years ago, we added insider trading data to our model because there is very systematic data from the SEC on insiders buying and selling. Through testing, we found that insider selling wasn’t that informative, but insider buying was very informative. Most insiders own a lot of company stock and tend not to buy more stock unless something attractive is going to happen. The lack of insider activity tends to be a very negative signal, because most insiders are either buying or selling stocks. If you’re an insider, and you know something bad is going to happen, you’re not going to buy the stock, but you can’t sell it because that’s illegal. Q:  What are the main considerations when constructing the portfolio? A: A major consideration is whether the positions are expected to outperform their respective indexes after we account for the expected trading cost. The portfolio is similar to the S&P 500 in terms of average market capitalization and sector exposure, but differs in terms of holding as an actively managed long/short portfolio. Right now we have about 120 stocks but the number varies depending on the volatility of the market and because we’re targeting the specific risk level. For example, during the tech bubble, we had about 200 stocks in the portfolio, while a year ago, when volatility was low, we could afford a relatively concentrated portfolio of 85 stocks. So we don’t target a specific the number of names. Rather, the number of names is a function of the volatility. Q:  Why have you chosen to target a specific volatility level and not a risk-adjusted return? A: From our perspective, when somebody buys this fund, he or she buys exposure to the large cap U.S. market and the alpha that we generate on top of that. We target similar volatility to the S&P 500 to provide the large-cap exposure or behavior that investors are looking for. If we allow twice the volatility, from an asset allocation standpoint, investors will end up with more volatility in their portfolio than they desire. Q:  What’s your buy and sell discipline and the catalysts that you look for? A: The main catalyst is the ranking process. On a daily basis, we collect all the data on all the companies in the universe, and we score each stock from 1 to 1,000 based on onemonth expected return. Then we look at the portfolio to see whether it has a higher score than the index and we have a range and a target for that. If it is lower than the target, we rebalance the portfolio taking transactions costs into account. When we do rebalancing, we don’t just buy one name and sell one name. We go through an optimization process to make sure that the portfolio keeps its volatility and sector exposure similar to the index, and then we buy and sell a host of names to raise the average ranking of the portfolio. So portfolio construction is not a name-by-name process, but focuses on the entire portfolio because that’s what the client’s holding. Q:  Trading costs have come down substantially in the last five or ten years. Why is it important for you to focus on the cost element? A: In the large-cap universe, there are stocks that are very cheap to trade, and there are stocks that are very expensive to trade. When a stock is mispriced, or 10% below your estimate of fair value, it has 10% expected return. If it costs you 2%, including market impact, to get in, and 2% to get out, you’re only left with 6% expected return. That means that you may be better off with a liquid stock that has expected return of 7%, and costs of 0.5% to get in and 0.25% to get out. We’re always looking at that tradeoff and the ability to get in and out in a meaningful way. Overall, we look at the net cost of ownership of each stock. Q:  What is your view on risk control? A: We target beta of 1 and a tracking error within 3% to 4% of the S&P 500. We also keep the average market cap to be similar to that of the S&P 500 and the sector exposure within 1% of the S&P 500, so it is a diversified portfolio. We don’t engage in market timing and we maintain a volatility forecast for every stock on our universe that is based on the recent volatility and the option market. If a stock gets very risky, our position shrinks to reflect that. In that way, we attempt to guard against stocks that have a lot of risk associated with them, stocks that may be in a takeover position or stocks that may have some litigation surrounding them. We make sure that the individual events don’t cost us more than we can tolerate in the fund.