Tesseract Signal uses unsupervised & supervised learning methods to design a portfolio with the highest probability of outperforming the S&P 500 index. Symbols are rated from 0 to 1 on their likelihood to out or under-perform the S&P500
The Tesseract Signal
The Tesseract signal is based on a novel combination of unsupervised and supervised learning methods. A multidimensional scaling procedure is used to measure the underlying factors driving returns for equities in the S&P 500.
Using a more precise measure of fundamental characteristics such as quality and momentum, a set of tree-based ensemble models examine how these factors relate to economic and market conditions (for example, volatility, sentiment, and fund flows) to design a portfolio with the highest probability of outperforming the S&P 500 index.