In finance, regression models or time series moving averages can be used to determine the value of an asset based on its underlying traits. In prior work we built a regression model to predict the value of the S&P 500 based on macroeconomic indicators such as gross domestic product, money supply, produce price and consumer price indices. In this present work this model is updated both with more data and an adjustment in the input variables to improve the coefficient of determination. A scheme is also laid out to alternately define volatility rather than using common tools such as the S&P’s trailing volatility index (VIX). As it is well known during times of increased volatility models like the Black-Scholes will be less reliable, hence, this work can be used to identify such times in a forward moving timeframe rather than using trailing economic indicators.