The 1st step in the investment decision-making process is to apply the proprietary Predictive Earnings Surprise (PES) algorithm to the S&P 500 stock universe. This predictive quantitative model is built off of solid set of fundamental inputs. The algorithm provides a comparative metric which directs attention into areas with-in the equity universe that are forecast to provide the highest likelihood of an earnings surprise. The primary input for the algorithm is fundamental research derived from various Wall Street Wirehouse sources. The algorithm then sorts the various stock universes into the 10 S&P 500 sectors and then ranks these 10 sectors according to likelihood of surprise at the Sector level. This is done by a process which provides a numerical deciles ranking on each sector formulated from the individual stock scores in each sector. The individual securities are then ranked in each of these sectors, again according to the probability of surprise at the individual security level. Various predictive fundamental and technical factors are then applied to the top ranked stocks within each sector. This is done in order to try and provide protection for the portfolio in the event that the individual stocks don’t experience an earnings surprise. The primary purpose of the entire design process is to provide protection for the portfolio in the event of a breakdown at the previous step. The result is an optimized buy list of individual securities located in Sectors w/the highest probability of an earnings surprise at the Sector level, and individual security level. This list is an optimal combination of stocks with the highest quality ratings in each sector, the highest short-term investment opinion, the highest, secure dividend, the lowest valuation (p/e) and the lowest volatility (beta) stocks confirmed by overlaying the list with two technical factors. These holdings are analyzed quarterly and the models then rotated according to the above process. Historically, the portfolio has performed quite evenly across a variety of business cycles due to the multi-disciplinary nature of the design process. While nothing “always” works, this process tends to work better during noisy markets (animal spirits), and tends to provide less opportunity for outperformance in markets when earnings don’t matter and/or when markets become more trending, momentum, consensus based frothy markets.