Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE

The major target of this article is to build a machine learning model furnishing an efficient and quick analysis for a large portfolio of stocks. Towards constructing such an efficient portfolio, we employ the Value‐at‐Risk (VaR) and Conditional Value‐at‐Risk (CVaR) as tools of well‐consolidated use for controlling the anomalies' presence of potential danger to the financial stability of the portfolio. It is shown how the well‐resulted k ‐means++ clustering technique is employed to cluster financial returns for the stocks of a system and then the risk measures of VaR and CVaR are obtained for the clusters to find the most and least riskiest groups of stocks. The proposed procedure is fast for clustering a financial large set of data by providing many features for each cluster.