Sabrient Systems Sabrient Systems LLC is an independent equity research firm that specializes in unbiased, fundamentals-based quantitative equity research. We use a computer-driven, quantitative methodology to analyze nearly 6,000 U.S. traded stocks and identify those that appear poised to out-perform or under-perform the
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...More market. The Sabrient methodology was developed by an experienced research team led by David Brown, a former NASA scientist and retired CEO of Telescan and designer of its premier stock search program, ProSearch. The research team uses a scientific approach to the creation and testing of multi-factor filters. Each filter targets a key area of traditional stock analysis, including value, growth, momentum, fundamentals, earnings, balance sheet, and group strength. Using an adaptive process, filters are continually tested and monitored to ensure that only the best performing filters are at work. Sabrient’s ratings begin with an extraction process in which each of the few best-performing filters scans the universe of equities to identify stocks having certain compelling attributes sought, but not sufficiently rewarded, by the current market. Then, a composite scoring system employs a broader set of “weighting” filters to rank the extracted stocks. To support individual investors, Sabrient uses the same sophisticated methodology to provide individual stock reports on a universe of approximately 5,600 stocks. Sabrient regularly tests the performance of its stock selections, using its primary list of published rankings and statistical techniques it believes appropriate. These recommendations have consistently outperformed relevant benchmarks across a broad range of investing styles, market caps, time frames and market conditions, demonstrating the robustness of Sabrient’s proprietary methodology. While past performance is no guarantee of future results, Sabrient believes it can maintain its strong performance through its rigorous, scientific approach to filter construction and ongoing backtesting within a dynamic and adaptive composite scoring system.
