TY - JOUR
T1 - Portfolio optimization by price-To-earnings ratio network analysis
AU - Yan, Xiangzhen
AU - Yang, Hanchao
AU - Hou, Chunxiao
AU - Zhang, Shuguang
AU - Zhu, Peican
N1 - Publisher Copyright:
© 2022 World Scientific Publishing Company.
PY - 2022/7/30
Y1 - 2022/7/30
N2 - This paper introduces Price-To-Earnings Ratio Network (PEN) analysis as an alternative to mean-variance analysis for portfolio optimization. The equivalence among Price-To-Earnings (P/E) ratios, node degree distribution and capital allocation distribution is established with a Havel-Hakimi network structure. Such equivalences allow network entropy to be a measure of portfolio diversification and robustness. Our empirical analysis finds a linear correlation between in-sample network entropy and out-of-sample portfolio returns. Then, a return-entropy efficient frontier is introduced to interpret the return-diversification trade-offs. Further, we compare the out-of-sample performance of portfolios optimized with PEN analysis against those optimized with mean-variance analysis, showing that PEN-optimized portfolios outperform mean-variance efficient portfolios in returns, given a low-To-medium P/E ratio level. This outcome accords with the P/E effect that stocks with low P/E ratios are undervalued and will provide increased returns in the future. In addition, regarding global financial risk events such as the financial crisis in 2008, the Euro debt crisis in 2013 and Brexit in 2016, this study finds that the PEN-optimized portfolio size increased significantly (even to more than 300 stocks) to mitigate systemic risk, while mean-variance efficient portfolios were not sufficiently diversified.
AB - This paper introduces Price-To-Earnings Ratio Network (PEN) analysis as an alternative to mean-variance analysis for portfolio optimization. The equivalence among Price-To-Earnings (P/E) ratios, node degree distribution and capital allocation distribution is established with a Havel-Hakimi network structure. Such equivalences allow network entropy to be a measure of portfolio diversification and robustness. Our empirical analysis finds a linear correlation between in-sample network entropy and out-of-sample portfolio returns. Then, a return-entropy efficient frontier is introduced to interpret the return-diversification trade-offs. Further, we compare the out-of-sample performance of portfolios optimized with PEN analysis against those optimized with mean-variance analysis, showing that PEN-optimized portfolios outperform mean-variance efficient portfolios in returns, given a low-To-medium P/E ratio level. This outcome accords with the P/E effect that stocks with low P/E ratios are undervalued and will provide increased returns in the future. In addition, regarding global financial risk events such as the financial crisis in 2008, the Euro debt crisis in 2013 and Brexit in 2016, this study finds that the PEN-optimized portfolio size increased significantly (even to more than 300 stocks) to mitigate systemic risk, while mean-variance efficient portfolios were not sufficiently diversified.
KW - diversification
KW - entropy
KW - Havel-Hakimi network
KW - portfolio optimization
KW - Price-earnings ratio
UR - http://www.scopus.com/inward/record.url?scp=85131864747&partnerID=8YFLogxK
U2 - 10.1142/S0217979222501077
DO - 10.1142/S0217979222501077
M3 - 文章
AN - SCOPUS:85131864747
SN - 0217-9792
VL - 36
JO - International Journal of Modern Physics B
JF - International Journal of Modern Physics B
IS - 19
M1 - 501077
ER -