Carlos is a passionate individual for investments and technology with 10- year experience in long/short equity portfolio management using a “quantamental” approach. He earned his Msc in Finance at AFI Business School, and his degree in Business & Economics at the University of Alicante. He is also a CFA and graduated in Game Theory and Competitive Strategy at the London School of Economics. Carlos has augmented his skills at NYC Data Science Academy to increase the amount of alternative information used in his investment models and develop machine-learning tools that enhance his investment process. Some recent research pieces include “Quant Screening Backtesting: Turnaround Stocks”, “Using Weekly Box Office Numbers to Predict Movie Stocks Sentiment” and “NLP applied to Profit Warning Prediction”.
NYC Data Science Academy’s mission is to provide accelerated data science training programs that prepare people for employment as data science professionals and to offer continuing education courses for professional development.
