Jonathan Sage: In general, fundamental and quantitative strategies behave differently in different market cycles. They have complementary strengths and weaknesses, and each is more effective in different market environments. Fundamental research assesses company, industry, growth and valuation characteristics in a qualitative, subjective and more forward-looking manner. Fundamental analysts can react more rapidly to changes in conditions and investor sentiment, and their evaluations can better reflect market sentiment during asset bubbles and at major inflection points.
Quantitative models, on the other hand, are based on long-term historical data and provide a systematic and objective appraisal of a company's fundamentals and valuation. This allows us to exploit the persistence of various investment factors, such as valuation, quality, momentum, and others, over time. We have a long-term investment horizon in our quant models which pairs nicely with the long-term view of our fundamental platform.
So, we start with these two independent research signals, which have both added value through market cycles at MFS. We utilize the ratings of our fundamental research analysts and our quantitative models to provide an investment signal for every stock in our universe. Then the portfolio management team combines the two separate views into a single, holistic blended research score that employs a subjective constituent, drawn from our fundamental analysis, and a systematic and disciplined investment rigor, derived from the quantitative research. By utilizing both signals in a systematic way, we believe we can provide a consistent more stable outcome.
We work on a broad set of custom separate account strategies that reflects our clients' risk, return or strategy objectives, and each has its own defined benchmark. At the same time, we offer commingled vehicles with traditional growth strategies that are relative to traditional benchmarks and a traditional risk-return profile. Our goal is always to outperform those benchmarks through a full cycle with a controlled tracking error.