No abstract

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by first employing principal component analysis sector wise to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second order stochastic dominance criteria to derive the final optimal investment. We consider a total of 11 well known financial ratios corresponding to each sector representing four categories of ratios, namely Liquidity, Solvency, Profitability, and Valuation. PCA is then applied sector wise over a period of 10 years from April 2004 to March 2014 to extract dominant ratios from each sector in two ways, one from the component solution and other from each category on the basis of their communalities. The two step Sectoral Portfolio Optimization (SPO) model integrating the SSD criteria in constraints is then utilized to build an optimal portfolio. The strategy formed using the former extracted ratios is termed as PCA-SPO(A) and the latter one as PCA-SPO(B).The results obtained from the proposed strategies are compared with the SPO model and one step nominal SSD model, with and without financial ratios for computational study. Empirical performance of proposed strategies is assessed over the period of 6 years from April 2014 to March 2020 using a rolling window scheme with varying outof-sample time line of 3, 6, 9, 12 and 24 months for S&P BSE 500 market. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino ratio, Rachev ratio, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in portfolio selection process.