Binary switch portfolio

Tengfei Li, Kani Chen, Yang Feng, Zhiliang Ying

Research output: Contribution to journalArticlepeer-review


We propose herein a new portfolio selection method that switches between two distinct asset allocation strategies. An important component is a carefully designed adaptive switching rule, which is based on a machine learning algorithm. It is shown that using this adaptive switching strategy, the combined wealth of the new approach is a weighted average of that of the successive constant rebalanced portfolio and that of the 1/N portfolio. In particular, it is asymptotically superior to the 1/N portfolio under mild conditions in the long run. Applications to real data show that both the returns and the Sharpe ratios of the proposed binary switch portfolio are the best among several popular competing methods over varying time horizons and stock pools.

Original languageEnglish (US)
Pages (from-to)763-780
Number of pages18
JournalQuantitative Finance
Issue number5
StatePublished - May 4 2017


  • Aggregating algorithm
  • Asset return
  • Bayesian analysis
  • Portfolio selection
  • Supervised learning
  • Universal portfolio

ASJC Scopus subject areas

  • General Economics, Econometrics and Finance
  • Finance


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