To kick off this issueâ€™s new department, we are pleased to introduce money manager George Necakov, CFA. George joined Royce & Associates in 1994 and has been involved in portfolio management since 1998. He is Director of Quantitative Strategies and manages the firmâ€™s quantitative product offerings, including the open-end mutual fund Royce Discovery Fund.
The Fund began operation on October 7, 2003, and has returned 19.4 percent through January 31, 2004. During this period, the benchmark Russell 2000 gained 13.74 percent. Royce has been a darling on Wall Street with a family of A-rated funds. Recently purchased by Legg Mason, Royce & Associates continues to cultivate successful managers.
George, what is quantitative investing?
A quantitative model can provide a disciplined, systematic structure within which to carry out investment and trading strategies. A central theme of quantitative investing is that historical data reveals patterns that can be identified and used for the purpose of making investment decisions. The essence of the process is crunching numbers or predetermined criteria, via a computer model or program, to determine whether a proposed investment or portfolio configuration is worthwhile or appropriate. The model relies heavily on back testing - the process of optimizing a trading strategy using historical data and then seeing whether it has predictive validity on current data.
Are you trying to minimize the human touch?
Some would argue that our quantitative investment process is less â€œpersonalityâ€ prone - that it relies less on subjective or intuitive judgments to make investment decisions. But human thought is needed as the ultimate guide. I would strongly argue that investment artistry is involved; itâ€™s just that it is worked into the equation through the creation of the model.
Is this the same as â€œblack boxâ€ investing?
Not at all. With black box investing the computer does all the analysis, makes all the assumptions and assertions, and all the decision making about what are appropriate investments for a portfolio. There is little or no human involvement and usually not much underlying rationale as to why certain data or variables are being looked at. The danger lies in finding spurious patterns that will cease to persist in the future. This is not how we manage Royce Discovery Fund.
What is the rationale behind the management of Royce Discovery?
The key tenets of Royceâ€™s value investment philosophy provide the basic rationale that underpins our quantitative model. These include: We want to understand a companyâ€™s enterprise value; we are seeking valuation discrepancies (not just statistically inexpensive stocks); and we want to identify companies with strong balance sheets, high internal rates of return, and that have the ability to generate cash flow.
Give us some insight into this model. How was it constructed and how does it work?
I began working on quantitative research for Royce more than seven years ago when I started examining - or back testing - the quantitative factors used to screen for candidates for Royce Low-Priced Stock and Royce Total Return Funds. Through this process, I was able to create a customized model that could be used to analyze the approximately 5,700 publicly traded micro-cap companies. Why micro-caps? Based on our extensive research, we discovered that this sector lends itself well to the model, perhaps because it is more thinly traded and less efficient. In building Royce Discovery Fundâ€™s portfolio, our process begins with running the universe of 5,700 micro-cap companies through the model. The modelâ€™s criteria all have to be translatable to a number. Thatâ€™s why visits with company management would be difficult to consider as a component of my portfolio building process - because you would have to develop a scoring system and populate the information historically.
The model does screen for information that is used by our other managers â€” returns on invested capital, financial leverage, P/E, price-to-book, etc. â€” and combines these with other quantitative measures. Because some stocks may appear statistically cheap, the model includes a method for trying to identify those companies that we believe reveal value as businesses and are thus not likely to remain at depressed prices indefinitely. However, we will seldom be in the position of knowing why a particular holding performed well over a certain time period while another did not. The model is set up to capture pricing inefficiencies using traditional value metrics, but is not designed to account for why the price of some positions rose while others remained low. The portfolio is highly diversified in an effort to minimize downside risk from any particular investment and to capture the small return inefficiency of a large group of stocks.
Also, the goal behind the model is to benefit from fundamental stock selection as opposed to market timing and sector timing. We do not attempt to make market timing bets by shifting between cash and stocks, nor will we try to make sector timing bets by over- or under-weighting sectors. The sectors should approximate the weightings in the micro-cap universe as a whole.
What is the rationale behind the fundâ€™s name, Royce â€œDiscoveryâ€ Fund?
The term â€œdiscoveryâ€ relates to a specific chess tactic that describes an attack on an enemy piece by moving one of your pieces to unmask a previously hidden attack. We felt that this concept parallels in some ways the process of unmasking previously obscured patterns that we can detect through back testing. At the same time, we like that it also connotes the process identifying undiscovered gems.
What was your path to becoming Royce & Associates Director of Quantitative Strategies?
I was a computer science major at New York University, and Royce initially hired me in 1994 to work in our Systems Department. As a programmer, I helped revamp the firmâ€™s portfolio management, order management, reporting, and performance systems. From the start of my tenure, I was keen on working on the investment side so I enrolled in the three-year CFA (Chartered Financial Analyst) program shortly after I joined. In 1997, after completing my tech development projects, I moved over to the investment side and began the quantitative back testing on Royce Low-Priced Stock and Royce Total Return Funds.
You are an avid chess fan and player. How has this impacted your thinking?
When I was a teenager the chess software programs available at that time could perform just slightly above than the average club level player, but certainly not at the master championship level. The way the computer plays the game of chess is completely different from the way humans play. The computer has an evaluation function, and it has a tree, and attempts to go down the tree and consider a large number of possible moves â€” an approach that a master would never take, but certainly closer to how an amateur might play. But over the past 20 years, they have so successfully tweaked the algorithms that now we have, on the size of a PDA, chess playing programs that play at the top percentile of all players. Thatâ€™s truly amazing. But they still fundamentally operate differently than would a strong tactical human player. Still the master chess player appreciates the power of these programs, and will grant them the respect they deserve based on the exhaustive computational power they employ to develop their tactics. While the masters will still develop their own overall strategy, they use these programs to check for tactical errors. I view the quantitative process that weâ€™ve developed in much the same way; we respect the model, but I am still the guiding force behind the process.
Royce Discovery Fund:
Investment Goal and Principal Strategies
The Fund seeks long-term growth of capital by investing in a broadly diversified portfolio of equity securities issued primarily by micro-cap companies (those with market capitalizations less than $400 million). George Necakov manages Royce Discovery Fund using a disciplined investment process that employs a proprietary quantitative model to construct and periodically rebalance the Fundâ€™s portfolio. This model implements a value approach by focusing on factors such as balance sheet quality, cash flow levels, and various other measures of a companyâ€™s profitability.