Q:  What’s the investment philosophy of the fund? A: We believe that, in highly efficient markets, structuring intuition to eliminate emotion, and thus focus on what really makes the difference, is key to achieving superior performance results. A consistent approach, combining the speed and discipline of computer processing with the skills and adaptability of human judgment, provides optimal conditions to achieve this objective. The fund’s investment strategy is based on an active, bottom-up approach, combining the output of a proprietary quantitative model with a disciplined judgmental overlay by US market specialists. The model brings its ability to crunch multiple numbers quickly and reliably, to preselect a number of investment recommendations. Investment professionals, on the other hand, add value through a deep research to understand the fundamentals of each company. This allows them to detect any “special situation” that may have been misinterpreted by the computer, and make the final decision among the model’s recommendations. This strategy, designed for investors seeking a pure exposure to the US large cap equity market, results in a fully invested, concentrated portfolio. It aims to achieve a 200-300 bp annual excess return over the S&P 500 index with a trackingerror close to 7% (both measured over the medium term). Q:  What are you looking for in the long term? A: We’re trying to assess the real situation behind the market’s emotional reactions to the information we are receiving both at the micro and the macro level. Let’s assume for example that same store sales for a given month have just been reported at, say, 3%. Many investors just check how this figure compares to expectations and, if their forecast was 3.1%, they may react very negatively and immediately sell the stock. As far as we are concerned, the question we aim to ask ourselves is “What does this 3% tell us about the company’s current business?” This may lead us to interprete positively a datapoint that has disappointed the market, just because fundamentals eventually prevail when emotions have settled down. Q:  But your process is based on looking for growth companies, is that correct? A: Yes, we have a growth bias. It’s not a huge growth bias; the original objective is to deliver good performance over any market cycle and we aim to beat the S&P 500 at any stage of the cycle, but our research has shown that a growth bias would be helpful in achieving this objective. Q:  How that philosophy translates into an investment strategy and process? A: Securities are selected from an investment universe comprised of 500-600 stocks offering the broadest analyst coverage as well as the highest liquidity in the US market. Market capitalizations and sector representations in the S&P 500 Index are used as additional criteria in the quarterly reconstruction of that universe. The model first ranks stocks in accordance with a number of indicators, which include mainly growth-oriented parameters (static and dynamic measures of trends in profitability, using both reported and forecast data) and, to a lesser extent, valuation factors (mainly relative to earnings and their expected growth). Stocks held in the portfolio and falling to the bottom 30% of the resulting ranking constitute the model's Sell list, whereas the Buy list comprises all securities in the top 5% that are not yet held. The model's recommendations are then reviewed in detail by dedicated market specialists. Sell recommendations are checked with a special focus on identifying any particular biases which may have misled the model, as only very exceptional circumstances can lead us to override such recommendations. A careful study is carried out on all Buy candidates to fully understand their operating environment, activity, strategy, financial situation, and subsequently their outlook. The team's market specialists naturally discuss investment views on a daily basis, but investment decisions are normally made only once a month in a portfolio recomposition committee. In a market where plentiful, sometimes contradictory information may prove a source of premature decisions, this discipline guarantees that the process is kept free from any influence of emotion leading to erratic strategies. The model’s guidelines provide that a fixed number of stocks should be held. Sell candidates are therefore reviewed and validated first. Any sale triggers the selection of another stock for replacement. Such selection can be made freely by the market specialists, as long as it is made within the model's Buy list. The monthly committee enables the team to finalize its position in light of the latest indications received from the model. Consensus is sought, and usually reached, but a single professional (the fund manager) has the final say. It should be noted that every single parameter in the strategy, from stock selection criteria to the number of securities held and their respective weights, is the result of extensive in-house research on the market’s characteristics. The investment process is driven by, and therefore evolves with, the features of our model. These are upgraded from time to time as a result of the ongoing research process undertaken to regularly enhance the strategy. Q:  Could you give us an example of decisions influenced by the model? A: All of our decisions are influenced by the model, which sets limits to what our judgement can lead us to do: we almost systematically follow the model’s Sell recommendations, and we never consider buying a stock which is not in its Buy list. We have been using and refining that model for so long that, even when our strategy appears to be wrong and underperform for a while, we continue to follow the model because we know its periods of weakness are usually followed by strong rebounds. To take a concrete example, we sold Intel a couple of quarters ago. At that time, we didn’t have a very strong conviction that we were doing the right thing, but we did not find any serious reason to conclude that the model was wrong, and therefore we applied our discipline. AMD was, once again, trying to gain market share and often in the past, Intel has been able to fight back such attempts quickly and effectively just by cutting prices. But since then we’ve seen the development of their battle, which confirmed that selling was the right move. Q:  What does the research process on the quant side include? A: The best way of predicting the market’s future behaviors is to look at the way it has reacted in similar situations in the past. But for this to work, you must observe such long periods that you can reasonably assume you’ve met all the instances that might happen in the future. In other words, our model relies on back tests, but these back tests are run over extremely long periods of time. Many of our competitors use 3 or 5 years of historical data to back-test models, while we use 3 or 5 decades. That’s the only way we can be sure that we’ve met both bull and bear markets, both strong and weak economic growth periods, volatile and stable environments, a few market crashes, and a few bubbles. Running back tests over such a long period of time can be a challenge because for the 1970s or the 1960s, when nothing was computerized, it is more difficult to find reliable data. So we spend a lot of time rebuilding proprietary databases containing macroeconomic, company and market information, to make sure that they are clean, perfectly accurate, and reliable. The research cycle itself starts with back testing of the way each individual indicator is working. If we want to know if the strategy based on finding low P/E shares is valid in the US, we would check how the indicator performed in the very long term. If it performed well, we would divide the period into sub-periods to check how this indicator works under all types of environments. We would measure its effectiveness, compare it, and would get into more and more detail about its reliability and consistency. Gradually, we eliminate indicators that do not seem satisfactory. Then we work on different ways of combining the good ones properly. Once we’ve found the right combination, we work on how it can be used to actually build a portfolio; from which level the stock should be considered a sell if it goes down in the ranking, from which level it can be considered a buy if it goes up in the ranking. We also determine how many stocks we should have in the portfolio, how we should weight them, how often we should rebalance the portfolio, etc. Q:  What are the general rules that you follow in terms of portfolio construction? What’s the turnover of the fund? A: The portfolio construction rules are simple. We hold 50 stocks that are equally weighted. They have a target weight of 2% and we let them deviate by 0.35% before we reweight them. Of course, we have tested much more sophisticated solutions but, with the indicators we had selected, none of them appeared to work better than equal-weighting. As for turnover, it has varied over time depending on market environment but the number of stocks replaced in any given year has been between 26% and 58%. It is relatively low but I should point our long-term focus. We still have a few stocks I never bought myself, although I’ve been managing this strategy for more than 10 years. Sometimes we buy a stock and sell it a month later if something has changed, but the typical time of a stock in our portfolio is in excess of 2 years. Q:  How do you monitor the risk and what risk control measures do you have? A: This is probably one of the most differentiating points in our approach. Risk management is undertaken primarily by built-in control mechanisms introduced as early as the development stage of the quantitative model. The backtests that are carried out to build a well-performing, stable strategy involve an indepth evaluation of the model’s structural risk characteristics under all kinds of environments. Although our model does not attempt to quantify each portfolio's ex-ante tracking- error explicitly, it has been tuned to generate a relatively stable risk over time, thanks to the complementary contributions of its stock selection principles. The most commonly used systems estimate and manage risk on the basis of an assumption that each asset's individual risk profile can be assessed through an analysis of its past evolutions. We tend to doubt the reliability of ex-ante tracking error calculations produced by such systems because, at each company's level, too many exceptional events can occur that would make this assumption completely wrong. In the approach we have adopted, the only hypothesis regarding the future is that the behavioral patterns of market participants, which we identify through backtests, will not suddenly undergo significant changes. This assumption, in our view, is much more acceptable, because any structural, durable change in the behavior of the millions of investors who form the market would only take place slowly, and thus be detected by our continuous research effort. Other more explicit factors, such as the maximum sector deviations from the index and the number and weight of portfolio holdings are only marginal additions to the risk control process. The judgmental overlay also plays a role in limiting risk, as its detailed assessment of the company's outlook primarily aims to identify any bias or weakness that may impede a proper evaluation of all the potential downside by the model. Moreover, the fund management team uses several other tools to monitor the various aspects of the risk involved in their portfolio. In particular, an internal """"""""dashboard"""""""" providing numerous comparisons to evaluate the differences between the portfolio’s profile and that of the index is produced every month. Finally, the team runs the Barra Aegis and UBS PAS tools, to help analyze risk from a different standpoint.