This document summarizes research on using disparate data sources and fuzzy logic systems to predict stock prices. It discusses using feature selection techniques like recursive feature elimination to select relevant inputs from multiple data sources for modeling stock movement and price. Experimental results show the fuzzy logic models can accurately predict stock movement and price changes based on selected features from technical analysis, fundamental data, and other sources. The document proposes combining the movement and price prediction models to help with stock selection decisions. Future work is suggested to expand the data sources, rules, and use error-driven methods to improve the models.