Online learning is machine learning, in real time from successive data samples. Meta online learning consists in combining several online learning algorithms from a given set (termed portfolio) of algorithms. The goal can be (i) mitigating the effect of a bad choice of online learning algorithms (ii) parallelization (iii) combining the strengths of different algorithms. Basically, meta online l...

This article examines the efficiency of five risk measures in the framework of portfolio optimization for the stocks of four China’s stock markets and investigates which risk measure has the best performance in making asset allocation decisions. The data used are the historical monthly stock returns from 1998 to 2002. Although the downside risk measures are thought to be consistent with investo...

In this tutorial paper we introduce different approaches to Markowitz portfolio optimization, and we show how to solve such problems in MATLAB, R and Python using the MOSEK optimization toolbox for MATLAB, the Rmosek package, and the MOSEK Python API, respectively. We first consider conic formulations of the basic portfolio selection problem, and we then discuss more advanced models for transac...

One of the most important concerns of investors in financial markets is choosing a share or stock portfolio that is optimal in terms of profitability. To this end, there are many ways in which the stock portfolio has been chosen. The optimal portfolio selection is a portfolio management goal. In this dissertation, the DEA technique has been used as a new and reliable way to select the stock opt...

In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing explicit solutions of dynamic portfolio choice problems. I also present three applications that are no...

This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according their financial ratios in order improve the performance of investment portfolios. First, we computed belonging S&P 500. Subsequently, assessed stocks’ moats an evaluation defined between 0 and 5 each ratio. The sum all provided score 100 c...

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series are associated to the coupling strengths between maps. The simulati...