Our proprietary methodology employs multi-factor modeling, filters, and fuzzy-logic scoring to identify stocks that appear poised to outperform or under-perform the market. The methodology was developed by an experienced research team led by David Brown, former NASA scientist.
Using a scientific approach, the team selects from more than 400 factors to create a library of over 100 multi-factor filters. Each filter targets a key area of traditional stock analysis, including value, growth, momentum, fundamentals, earnings, balance sheet, and group strength.
Sabrient uses an adaptive process to test filters on a continual basis to ensure that only the best performing filters are at work. These top filters are used to extract stocks that exhibit the desirable attributes, but have not yet been sufficiently rewarded. A composite scoring system employs a range of high-performing filters to refine the rankings of the extracted stocks, or to simply rank top-to-bottom any given universe of stocks.
Overview of the Sabrient Process