Machine learning portfolio simulating and optimizing apparatuses, methods and systems

Abstract

The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems ('MLPO') transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. Neural networks are used as encoder to generate a set of latent variables. Latent variables are simulated with neural networks as decoder such that the decoded simulated market scenarios follow dynamic dependencies and volatilities of historical market risk factors. A transfer layer is used between the encoder and the decoder to allow latent space variables to take on any distributions and any dependency joint distribution structures.