Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Constructing investment strategy portfolios by combination genetic algorithms

The classical portfolio problem is a problem of distributing capital to a set of securities. By generalizing the set of securities to a set of investment strategies (or security-rule pairs), this study proposes an investment strategy portfolio problem, which becomes a problem of distributing capital to a set of investment strategies. Since the investment strategy portfolio problem can be formul...

Constructing New and Better Evaluation Measures for Machine Learning

Evaluation measures play an important role in machine learning because they are used not only to compare different learning algorithms, but also often as goals to optimize in constructing learning models. Both formal and empirical work has been published in comparing evaluation measures. In this paper, we propose a general approach to construct new measures based on the existing ones, and we pr...

How can machine learning help stock investment?

The million-dollar question for stock investors is if the price of a stock will rise or not. The fluctuation of stock market is violent and there are many complicated financial indicators. Only people with extensive experience and knowledge can understand the meaning of the indicators, use them to make good prediction to get fortune. Most of other people can only rely on lucky to earn money fro...

Sparse Greedy Matrix Approximation for Machine Learning

Asymmetric Correlations of Equity Portfolios∗

Correlations between U.S. stocks and the aggregate U.S. market are much greater for downside moves, especially for extreme downside moves, than for upside moves. We develop a new statistic for measuring, comparing, and testing asymmetries in conditional correlations. Conditional on the downside, correlations in the data differ from the conditional correlations implied by a normal distribution b...