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 (PCA) sector wise to filter out dominant financial
ratios from each sector and thereafter, use the portfolio optimization model
incorporating second order stochastic dominance (SSD) 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 two nominal SSD models, 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 out-of-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.Comment: 26 pages, 12 table