Mathematical Foundation for Ensemble Machine Learning and Ensemble Portfolio Analysis

48 Pages Posted: 16 Oct 2018

See all articles by Eugene Pinsky

Eugene Pinsky

Boston University Metropolitan College Department of Computer Science

Date Written: September 4, 2018

Abstract

In ensemble machine learning we combine decisions of experts to derive at a decision that is better than the individual ones. The process of combining these decisions can be as simple as majority voting or simple averaging or it can be more complicated and involve multiple steps. In this paper we consider the application of ensemble machine learning to the problem of constructing portfolios from individual decisions of multiple experts. We will compare the performance of portfolios constructed by simple averaging and by a novel multistage decision algorithm. This new algorithm constructs a portfolio from subsets of stocks in individual portfolios. Compared to these individual portfolios and a portfolio constructed by simple averaging, the portfolios constructed by proposed method could result in higher annualized return and a modest increase in volatility. We provide extensive numerical comparisons on the viability of the new approach.

Keywords: Machine Learning, Ensemble Learning, Portfolio Construction

JEL Classification: C02

Suggested Citation

pinsky, eugene, Mathematical Foundation for Ensemble Machine Learning and Ensemble Portfolio Analysis (September 4, 2018). Available at SSRN: https://ssrn.com/abstract=3243974 or http://dx.doi.org/10.2139/ssrn.3243974

Eugene Pinsky (Contact Author)

Boston University Metropolitan College Department of Computer Science ( email )

Boston, MA 02215
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
215
Abstract Views
848
Rank
242,286
PlumX Metrics