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    An Interview With Bill Ricks, Portfolio Manager, AXA Rosenberg International Sma

    by Maria Crawford Scott

    FUND FACTS
    AXA ROSENBERG INTERNATIONAL SMALL CAPITALIZATION FUND (RISIX)

    CATEGORY: Foreign

    PERFORMANCE (as of 6/30/03):

    COMPOUND ANNUAL RETURN (%)
      YTD 1Yr 5Yrs
    Fund 19.3 9.8 3.3
    Index* 21.9 8.6 -0.2

    *Nomura Global Small Cap Index.

    TOTAL ASSETS: (as of 7/30/03) $60 million

    CONTACT: Axa Rosenberg Mutual Funds 800/555-5737 www.axarosenbergfunds.com

    Since the beginning of the year, domestic stock funds have been getting the attention. However, things are looking up not just domestically, but worldwide as well, particularly in the developed markets.

    One fund that has fared well in the rough international markets over the last five years is the AXA Rosenberg International Small Capitalization Fund. The fund—with a five-star Morningstar rating—focuses on the small-cap sector of the developed markets overseas, using a highly quantitative approach. Currently, it has about $60 million in total assets.

    In early August, portfolio manager Bill Ricks discussed the management of the fund with Maria Crawford Scott.

    What is the investment objective and general philosophy of the fund?

      Our goal is to outperform the Nomura Securities Global Small Cap Index, which covers the developed markets worldwide, excluding the U.S., focusing on roughly the smallest 15% of each market. It is very broad-based, with roughly 7,500 names, although each stock has to be listed and traded on one of the major exchanges around the world.

      What we do is to choose, among all of the stocks in the index, the ones we think are most attractive and most likely to outperform the benchmark.

    The fund has over 600 different holdings. How do you go about picking the individual stocks?
      For each stock, we come up with a predicted return, which is what we think that stock is going to earn over the next 12 months relative to the market.

      To get that number, we have two stock selection models: one is a valuation model that estimates the fair value of each stock; the second one is an earnings-forecast model that tries to predict what that company is going to earn over the next 12 months in reported earnings.

      From those two numbers, we develop the predicted return, so that we can identify stocks that are priced well today and should, over the next 12 months, improve more than the market.

    What goes into the valuation formula?
      What we’re trying to do with valuation is to take the same approach that an investment banker does when coming up with a so-called ‘break up’ value. We want all the details that we can get on the company: What industries is the company in? Does it have any unique features, such as extra cash on the balance sheet or a pension fund that’s overfunded or underfunded? We get all of the information we can about each of the divisions that the company operates in, and then we compare that company to its competition in terms of profitability and efficiency. Of course, we already know the stock prices, and once we’ve got prices on the one hand and all of these features on the other hand, we run a regression analysis so that we’re putting the price up against the fundamentals. What we’re trying to do is take out of those prices what the market seems to be paying for each attribute. And from that, we’re able to estimate what the market is paying for any particular aspect of a company.
    That’s all quantitatively driven—you don’t have analysts looking at each individual company?
      We don’t have analysts visiting companies and that’s really one of the reasons why we have so many names in the portfolio. If you’re a qualitative manager out there visiting companies you may start off with 10,000 global companies, but ultimately you apply some screen to get it down to a more manageable number and then you go look at all those companies. At the end of the day your portfolio may be 50 or 100 names.

      In our case, we don’t have that kind of understanding of any individual name, but we have a great deal of confidence that a large group of stocks bought with this approach will have very attractive future performance in the market.

      It is a very quantitative, systematic approach that’s looking at very detailed information about companies and using that information to estimate fair value on the one hand and predict next year’s earnings on the other. With those two pieces of information—in both cases, comparing them to today’s price—we’re able to identify companies whose price is low relative to what that it’s going to be worth down the road.

    How many different variables are you looking at? Is it possible to divide a company up into all of those different variables without looking in detail at every single company?
      There are some 600 variables in the models. We have 170 different industries that we’ve identified. And within each of those industries we have three basic pieces of information: their operating earnings, their sales and their assets. We use those three pieces of information at the industry level. And then we use all of the other company-specific information that’s not available at industry levels—such as the cash on the balance sheet, the company’s debt level, and how much they spend on research and development.

      As for your second question, we do look in very great detail at each and every company. We just do it without visiting the companies. Every company in the world that is listed on an exchange has to report financial data in a certain format. They have to show what industries they’re in, and they have to provide details on the balance sheet and the income statement. It’s that detail that we’re using in this analysis.

      We regularly get new financial data and stock prices are always changing, so we’re trying to relate those stock prices with the fundamentals to see how the market is pricing each separate fundamental. And then we add up those pieces of value to come up with an overall estimate of fair value. It’s really a very good estimate of where the stock price is going to be heading over the next year.

    What goes into the earnings forecast?
      The earnings forecast has bout 25 variables, but two main components. One is based on fundamentals, and the other comes from the Street—individual analyst earnings estimates of companies, changes in those earnings estimates—anything that the Street provides in the way of earnings insight.

      Of course, we don’t take all earnings estimates at face value. Our variables look at what the predictions are relative to the likely profitability in that industry. And we’re looking at the characteristics of those earnings together with the fundamentals of a company.

      If everything from the fundamentals is pointing up and the analysts are forecasting an increase in earnings, the combination of those two signals is likely to come up with a very positive earnings forecast estimate in our model.

      On the other hand, if the analysts are very excited about the company and all of the fundamental characteristics—the profitability measures, the operating characteristics and so on—are pointing the other way, then that model is likely to come up with a quite negative view on the company despite the fact that analysts are quite positive on it.

      However, in the small-cap area, because you don’t have a lot of investors and analysts following these companies, the most important aspect is not the analyst estimates but the fundamentals of the company—things like profitability, trends in profitability, comparison of a company’s profitability with that of the industry. What that captures is something that’s come to be known in financial circles as ‘mean reversion’ in earnings. If a company’s more profitable than it’s ever been and more profitable than everyone else in the industry, and that’s all you know about the company, the likelihood is that next year it’s going to have lower earnings just because of competitive forces in the economy. So earnings and profitability trends are very important in the earnings forecasting model.

    Another group of important fundamental data is operating characteristics—for instance, is the inventory growing faster than the sales?
      One of the things that was very useful in the last few years is what we call ‘quality of earnings’ measures. This is the kind of measure that helped keep us out of the Enrons and the WorldComs of the world. The idea is that if the company’s generating lots of earnings, but the cash flow isn’t there, and they’re not paying any taxes on the earnings, then it’s somewhat suspicious.
    You have about 600 names in the portfolio. Do you have a limit on the percentage you’ll put into each name?
      We do have limits and they’re roughly driven by the size of the company in the index. In the small-cap arena, you have to think small percentages. Whereas in the S&P 500 you might have Microsoft or GE that’s 3% of the index, in the small-cap space there’s no stock above 1%. The biggest company may be half a percent. If we like a ‘big’ company that’s a half a percent of the index, then we might put 1½% of the portfolio in that name. Whereas if it’s a tiny little name in the index, maybe five basis points of the index, then even if we like it a lot, we’re going to put in no more than 20 or 30 basis points of the portfolio in that stock.
    Taking the combination of the two, the earnings and the valuations, what level does a company have to reach in order to make it onto the list?
      What we do with both of those numbers is reduce those to a predicted return relative to the market. If a company is trading at, say, $100 and we think it’s worth $120, that’s a 20% gap between its price and what it’s worth. The last question we ask is: Historically, how much in a given year has that 20% meant in terms of excess returns in the market? We’re looking at history to say, well, given the 20% misvaluation, what do I expect the return relative to the market to be this year? I do the same with the earnings forecast. If the company’s expected to grow its earnings this year at the rate of 10% of price, historically that would have meant outperformance in the market, but by how much? I look to history to translate the signal coming out of the model into a predicted return in the marketplace.

      Once I’ve done that then I just add those two predicted returns together, roughly speaking, to get a single predicted return for each company above the expected market return. It may range from –25% up to +25%. The first thing that we do is throw away all the negative predicted returns—those expected to have returns below the market—and just concentrate on the positive predicted return stocks.

      In general, stocks that are in the portfolio have anywhere between 6% and 20% positive predicted returns relative to the market—in other words, 6% to 20% above the market’s expected return.

    How diversified are you relative to the different countries in the index, and also in terms of industry diversification?
      If you look at our profile of both countries and industries, you’re going to see something fairly similar to the benchmark. We don’t take big country bets and we don’t take big industry bets. We’re trying to find the most attractive stocks in every industry. So the approach is really not to overweight industries or countries, but to really look for the most attractive stocks in each case.
    How are things like currencies and accounting differences dealt with by the model?
      We put every country into a single common currency and then run the analysis. What we’re getting, then, is a value for that stock at the current exchange rate.

      In terms of adjusting for accounting differences, our approach is to get as much detail as we can so that we’re comparing companies on a like-for-like basis. If you’re just working with earnings, then that’s a little hard because you’ve got to adjust everything to put all companies on the same basis. But if you get the details of the income statement and the balance sheet, you can see what accounting methods the company is using. Working at the more detailed level you’re able to, in effect, adjust the differences across companies.

    What about on the sell side? How do you decide when you’re going to sell a stock?
      Well the beauty of having a predicted return for every company is that there’s no emotion or second-guessing. You basically buy it when it’s attractive. And if you buy a stock when it has a 10% positive return (relative to the market), you’re obviously not going to sell it until it starts drifting down toward a zero predicted return. Very often when it’s a 3% or 4% predicted return, you’ve already gotten some of the value out of it but some other name becomes more attractive.

      Every moment of the day, the stocks that we own have to compete with all the other names out there. And if we bought a stock two weeks ago and now it’s realized most of its value or another name looks more attractive, the model will recommend moving out of one and into the other. We don’t second-guess it. We don’t say ‘well, I think this stock still has a ways to go.’ Its predicted return is now close to zero and something else is more attractive. It’s an automatic buy and sell discipline.

    Your approach has done particularly well in the small-cap international market. Do you think you’re capturing more inefficiencies and that the inefficiencies are greater in the small-cap market?
      In general, we see worldwide that the outperformance is greater in small cap than it is in large cap, and we do think it’s because there are greater inefficiencies in that end of the market. And we think that will persist. After all, you’re dealing with small companies that aren’t as well followed, so the opportunity to add value is there.
    For an investor who’s putting money into your fund, what’s the greatest risk they’re facing?
      The greatest risk is really that that end of the market is not rewarded. Whether it’s stocks generally or international stocks in particular, there’s always the risk that equities go down. That means that, even if we’re outperforming by 5% or 6%, if the equity market is down 20%, our fundholders are still down, even though they are only down 15% with us. We can’t control market levels, so that’s always a risk.

      Having said that, I think that the international end of the market these days, and particularly small caps, is attractively priced. You’re getting dividend yields in the 3% range and stocks are trading at around 10 times next year’s earnings—very attractive. But, of course, there’s no guarantee that they can remain attractively priced.

→ Maria Crawford Scott