Evaluating Portfolio Performance by Highlighting Network Property and the Sharpe Ratio in the Stock Market

Authors: Zahra Hatami, Hesham Ali, David Volkman

Abstract:

Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios was compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks.

Keywords: Portfolio management performance, network analysis, centrality measurements, Sharpe ratio.

References:

[1] Z. Hatami, P. Chetti, H. Ali, and D. Volkman, “A novel population analysis approach for analyzing financial markets under crises–2008 economic crash and covid-19 pandemic,” in Proceedings of the 55th Hawaii International Conference on System Sciences, 2022.
[2] G. Curtis, “Modern portfolio theory and behavioral finance,” The Journal of Wealth Management, vol. 7, no. 2, pp. 16–22, 2004.
[3] M. Inuiguchi and T. Tanino, “Portfolio selection under independent possibilistic information,” Fuzzy sets and systems, vol. 115, no. 1, pp. 83–92, 2000.
[4] G. Curtis, “Modern portfolio theory and behavioral finance,” The Journal of Wealth Management, vol. 7, no. 2, pp. 16–22, 2004, publisher: Institutional Investor Journals Umbrella.
[5] F. J. Fabozzi, F. Gupta, and H. M. Markowitz, “The Legacy of Modern Portfolio Theory,” Institutional Investor, p. 16, 2002.
[6] V. Ricciardi and H. K. Simon, “What is behavioral finance?” Business, Education & Technology Journal, vol. 2, no. 2, pp. 1–9, 2000.
[7] H. Markowitz, “de Finetti scoops Markowitz,” Journal of Investment Management, vol. 4, no. 3, 2006.
[8] F. A. Sortino and L. N. Price, “Performance measurement in a downside risk framework,” the Journal of Investing, vol. 3, no. 3, pp. 59–64, 1994, publisher: Institutional Investor Journals Umbrella.
[9] F. A. Sortino, R. Van Der Meer, and A. Plantinga, “The dutch triangle,” The Journal of Portfolio Management, vol. 26, no. 1, pp. 50–57, 1999, publisher: Institutional Investor Journals Umbrella.
[10] M. E. Mangram, “A simplified perspective of the Markowitz portfolio theory,” Global journal of business research, vol. 7, no. 1, pp. 59–70, 2013.
[11] A. Beja, “On systematic and unsystematic components of financial risk,” The journal of finance, vol. 27, no. 1, pp. 37–45, 1972, publisher: JSTOR.
[12] P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, “Generalized louvain method for community detection in large networks.” IEEE, 2011, pp. 88–93.
[13] K. Okamoto, W. Chen, and X.-Y. Li, “Ranking of closeness centrality for large-scale social networks.” Springer, 2008, pp. 186–195.
[14] P. Bonacich, “Some unique properties of eigenvector centrality,” Social networks, vol. 29, no. 4, pp. 555–564, 2007, publisher: Elsevier.
[15] M. Barthelemy, “Betweenness centrality in large complex networks,” The European physical journal B, vol. 38, no. 2, pp. 163–168, 2004, publisher: Springer.
[16] V. Zakamouline and S. Koekebakker, “Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance,” Journal of Banking & Finance, vol. 33, no. 7, pp. 1242–1254, 2009, publisher: Elsevier.
[17] Z. Hatami, M. Hall, and N. Thorne, “Identifying early opinion leaders on covid-19 on twitter,” in International Conference on Human-Computer Interaction. Springer, 2021, pp. 280–297.
[18] Z. Hatami, H. Ali, D. Volkman, and P. Chetti, “A new approach for analyzing financial markets using correlation networks and population analysis,” in In Proceedings of the 24th International Conference on Enterprise Information Systems, vol. 1, 2022, pp. 569–577.