Improving portfolio investment performance with distance-based portfolio-combining algorithms

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We propose distance-based portfolio-combining algorithms to improve out-of-sample performance in the presence of estimation errors. Our algorithms use approaches similar to the shrinkage method but with a different weighting scheme: the Euclidean distance. The Euclidean distance of a portfolio is its 2-norm distance to the in-sample tangency portfolio. These algorithms aim to construct a portfolio with a small Euclidean distance by making a convex combination of any number of portfolios. We propose three distance-based portfolio-combining algorithms: a distance-based portfolio combination, a distance-based asset combination, and a distance-based asset combination with systematic errors (DAc-S). Each algorithm consists of two steps. First, we predict the Euclidean distance of each portfolio using time-series forecasting methods. Second, we increase (decrease) the combination level of a portfolio whose predicted Euclidean distance is small (large). We use 11 empirical data sets, and the numerical results show that our proposed algorithms significantly improve the Sharpe ratio with reasonably low portfolio turnovers. This outperformance stems from successfully decreasing the Euclidean distance. Most important, the DAc-S achieves a significantly high Sharpe ratio and the smallest Euclidean distance.

Original languageEnglish
Pages (from-to)941-959
Number of pages19
JournalJournal of Financial Research
Issue number4
Publication statusPublished - 2022 Dec 1

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 2020‐22‐0390 and 2021‐22‐0094; Yonsei Business Research Institute.

Publisher Copyright:
© 2022 The Southern Finance Association and the Southwestern Finance Association.

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance


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