High-conviction equity portfolio optimization

We investigate the construction of well-diversified high-conviction equity portfolios using the portfolio diversification index (Pal). This paper is the first to investigate the out-of-sample properties of the PDI. Our research applies a novel portfolio selection algorithm to maximize the PDI of a portfolio of stocks in the Standard & Poor's S&P 500 index over the period 2000-2009. We construct equally weighted, well-diversified portfolios consisting of 5 to 30 stocks, and compare these with randomly selected portfolios of the same number of stocks. Our results indicate that investors using our algorithm to maximize the PDI can improve the diversification of high-conviction equity portfolios. For example, a portfolio of 20 stocks constructed using the algorithm with the PDI behaves out-of-sample as if it contains 10 independent stocks, ie, a PDI score of 10. This is a significant improvement over the PDI score of 7 that occurs with a randomly selected portfolio. Our research is robust with respect to the number of stocks in the investment portfolio and the time period under consideration.