Q:  What is the investment philosophy behind the fund? A : In four words, great returns, less risk. This is a multi-cap fund by mandate, so there are no capitalization restrictions. The objective is to provide superior risk-adjusted performance relative to our benchmark, the Russell 3000 Growth Index, and also to our Lipper and Morningstar category, which is mid-cap growth. We also relate our performance to the S&P 500, because for most of our clients it’s an overall measure of market performance. We have done pretty well in our investment results both on the return side, and on the risk side. The philosophy driving the fund is that it is possible to beat the market through quantitative modeling processes. The fund’s focus is on small and mid-cap stocks, or SMIDs. Over 90% of the stocks in this fund would generally be classified as SMIDs. Q:  Why do you believe that quantitative modeling is the most effective way to manage money? A : I believe that the process we’ve chosen is the right one for three reasons. First, it is efficient. Given the high density of information flow in today's digitized world, one needs to quickly capture data, and separate the true information from the noise. Quantitative modeling does that job efficiently. Second, it is insightful. Empirical research has uncovered inefficiencies in terms of biases and time delays. Investors simply do not incorporate information as quickly or accurately as the efficient markets theory would dictate. This is a major source of excess returns, or alpha. As this phenomenon is most pronounced in small and mid-cap stocks, that is where our focus lies. Third, it is scientifically rigorous and disciplined. By using a process that has been successful for decades, one is not likely to switch tracks due to temporary market disruptions. Most people follow their judgment, thinking that their profitable decisions were based on skill, whereas they may have been based upon luck. A quantitative process doesn't tend to confuse skill and luck. It is also a much more decisive process that can take into account anything of relevance in terms of risks, returns, transaction costs, taxes, etc. It is also realistic to consider the concept of a bet. Whenever one is deviating from a benchmark, one is making a bet for or against a security, an industry, or a sector. How big a bet should one make? A Quant approach would tie that bet, or make it proportional, to one's level of conviction. That's my philosophy, and some of the beliefs that I have adhered to over time. My views of quantitative models go back almost to the time when I started managing money in 1984, and have intensified over that time. Quantitative modeling is more efficient than traditional fundamental analysis - comparing companies against peers, talking to management, etc. Moreover, much of what is uncovered via fundamental analysis is already built into security prices. Q:  What is the significance of the name The New Economy Fund? A :We launched the fund simultaneously with four other funds, and we wanted to offer something unique in each to our customers. Originally, the fund had a somewhat greater technology focus, and was even in Lipper's technology sector category for 20 months (where it was frequently the number one fund for performance). But it was never a technology fund from our perspective, and we never had nearly as much technology as a typical tech sector fund. We have always had the Russell 3000 Growth Index as our benchmark. We look for innovative companies, but innovation may be found in any industry. According to our definition, innovation may be in a process, a product, or a management style. A company that is high in the rankings based on our quantitative process, and is outperforming its peers in terms of earnings characteristics must be doing something innovative. We don't have a specific screening process that says our universe is comprised only of certain innovative companies. We do, however, focus on ensuring that we stay a growth fund. Q:  What is your strategy in trying to achieve superior returns versus Russell 3000 Growth Index? A : In our methodology, the sector weighting process is step one. We have an investment policy committee comprised of our senior portfolio managers who review the economy, interest rates, fed policy, etc., on a monthly basis. We develop our equity and fixed income strategies based upon that review. Part of the outcome is deciding which sectors we want to emphasize with a positive bet, de-emphasize with a negative bet, or remain neutral. That is a guideline to all of our portfolio managers for our funds and for client money invested in separate accounts. We apply these adjusted weights or bets to the sector weights of the benchmark. I still have a leeway of three to five percentage points in each sector. I review the results of our multi-factor modeling process at the sector level, and the industry level as I have found that the top 20% is particularly valuable for over-weighting, as is the lowest 20% for under-weighting. I work with a universe of about 3,700 stocks. There are four major quantitative categories: price-momentum, which also utilizes some volumerelated components; earnings momentum variables, including earnings changes, revisions, surprises, growth rates; valuation measures; and risk factors. They are reviewed individually, but also in multi-factor sector modeling to derive estimates for the expected alpha contribution of each stock. The process uses standard screening approaches. I have, in effect, ten buckets to fill corresponding to the ten S&P economic sectors. Stocks in each sector are chosen on the basis of their expected alpha contribution according to the above combined characteristics. I also review price momentum as a separate model, as well as an earnings momentum model. This process creates a buy list, which may include existing holdings and additional stocks. I also consider the stocks that I want to sell that are in the lower deciles. The fund has been growing consistently over time in terms of new money so there are usually funds being added to most sectors. Stocks that are poised to under-perform are sold, and new positions are added. I may also add to existing holdings. At the end of the second quarter the fund had 234 stock positions, which is a typical number. Having more stocks works to minimize unique risks. The chance of having a highly negative impact in the fund from a single stock whose value implodes is essentially zero. Q:  Would you highlight the portfolio construction process? A : I believe in the equal weighting process conceptually, and adhere to it as an approximation. It provides an automatic bias for SMIDs relative to a cap-weighted benchmark. Historically, nearly all periods of five or more years display a significant outperformance of small versus large caps. For example, the average annual excess returns of SMID versus large cap since 1975 has been 2.6%. That’s a strong incentive favoring excess SMID weighting. There is also a more conceptual argument. Consider that there is some fair or true value of any stock that is unknown due to noise. It can be defined as the present value of the stock one-year into the future. There is going to be a variation around that true fair value, either positive or negative, but if you employ a cap-weighting process, you will tend to overweight overvalued stocks, and underweight undervalued stocks. That is why almost any other weighting scheme, particularly equal weighting, will outperform the cap weighting over any significant time period. Those are the essential drivers of the portfolio process. We also make sure that we don't have any unintended bets by reviewing risk management metrics. Q:  Do you build the positions in the portfolio starting with let’s say 0.5% and then slowly add to it? A : If I think a stock qualifies for the portfolio, it usually gets a position approximately equal to the average for its sector. For example, this means that if a sector is allocated $10 million, and there are 20 stocks to be held, each stock will receive approximately one-twentieth of the $10 million. If a stock performs better than average, its weight will automatically increase, and conversely, decrease with below average performance. Healthcare stocks are good examples right now. Since it is difficult to find many attractive healthcare stocks, I have selected fewer positions than normal, and have given them higher individual weights rather than selecting stocks with inferior expectations. I also let my winners run, as long as their positions don't become too large. At two percent, a position would likely be considered too large. Otherwise, as long as the stock is performing as expected or better, I will maintain that position. Q:  How strict is the rule for the 2-percent limit on individual positions? A : It is a rule of thumb. Right now my largest position is about 1.5 percent of the fund. If I see it at 2 percent, I will think about cutting it back, but it is not a strict rule. In fact, I don't recall having had anything above 2%, so it has never been an issue. In part, it doesn't happen because there has been substantial new money coming into the fund on a consistent basis. Q:  Do you find value in arranging management meetings or visiting conferences? A : I have visited some technology conferences but I don't really focus on that aspect, particularly because the time required to visit 200+ companies is prohibitive, not to mention the hundreds more that are potential buys. The quantitative process works very effectively and efficiently, and the proof is in the returns. Our trust shares are in the top 4% of Lipper's rankings from inception through June 30th, and in the top 18% of Morningstar’s category on a 3-year trailing basis as of July 29th. In terms of market risk, we are 20-25% below average. In terms of total risk as measured by standard deviation we are typically about 20% below the market. The fund currently enjoys a 4-star Morningstar rating, having both above average returns and below average risk. Q:  Any thoughts on the risk control measures other than diversification? A : In terms of reducing risk, the diversification built into the fund has worked out very well. With 234 positions, we have negligible unique risk. In terms of sector risk, we overweight sectors that are outperforming, and vice-versa to a measured degree because that's where the greatest excess returns are found. The deviation from my benchmark is due in part to our policy committee’s assessment, and in part due to the quantitative selection process. From time to time I compute the Information Ratio relative to the Russell 3000 Growth Index. As of the end of May, the number was 0.96, an exceptionally high number. It basically means that for the amount of risk in the fund relative to its benchmark, the fund’s shareholders have been well rewarded.