Portfolio Evaluation with the Vector Distance Based on Portfolio Composition

Heonbae Jeon, Soonbong Lee, Hongseon Kim, Seung Bum Soh, Seongmoon Kim

Research output: Contribution to journalArticlepeer-review


We propose a novel portfolio evaluation method, a distance-based approach, which directly evaluates the portfolio composition rather than portfolio returns. In this approach, we consider a portfolio as an estimator for an in-sample tangency portfolio, which we define as the optimal reference portfolio. We then evaluate the portfolio by computing its vector distance to the optimal reference portfolio. In search of the proper distance-based performance measure, we choose four representative vector distances and compare their suitability as a new portfolio performance measure. Through extensive statistical analysis, we find that the Euclidean distance is the most proper distance-based performance measure of the four representative vector distances. We further verify that a portfolio with a large Euclidean distance is not desirable because not only does it provide a low utility implied by the first four moments of portfolio returns, but also it is not likely to maintain its long-term performance. Hence, the Euclidean distance can complement the return-based performance measures by confirming the reliability of a portfolio in its investment performance.

Original languageEnglish
Article number221
Issue number1
Publication statusPublished - 2023 Jan

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea under grant NRF-2020S1A5A2A01045624; Yonsei University under grants 2021-22-0219 and 2022-22-0028; Yonsei Business Research Institute.

Publisher Copyright:
© 2023 by the authors.

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Mathematics(all)
  • Engineering (miscellaneous)


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