Eigenportfolios of US equities for the exponential correlation model

Ali N. Akansu, Anqi Xiong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, the eigendecomposition of a Toeplitz matrix populated by an exponential function in order to model empirical correlations of US equity returns is investigated. The closed-form expressions for eigenvalues and eigenvectors of such a matrix are available. These eigenvectors are used to design the eigenportfolios of the model, and we derive their performance for the two metrics. The Sharpe ratios and profit-and-loss curves (P&Ls) of eigenportfolios for twenty-eight of the thirty stocks in the Dow Jones Industrial Average index are calculated for the end-of-day returns from July 1, 1999 to November 1, 2018, several different subintervals and three other baskets in order to validate the model. The proposed method provides eigenportfolios that mimic those based on an empirical correlation matrix generated from market data. The model brings new insights into the design and evaluation of eigenportfolios for US equities and other asset classes. These eigenportfolios are used in the design of trading algorithms, including statistical arbitrage, and investment portfolios. Here, P&Ls and Sharpe ratios of minimum variance, market and eigenportfolios are compared along with the index and three sector exchange-traded funds (XLF, XLI and XLV) for the same time intervals. They show that the first eigenportfolio outperforms the others considered in the paper.

Original languageEnglish (US)
Pages (from-to)55-77
Number of pages23
JournalJournal of Investment Strategies
Issue number1
StatePublished - Mar 2020

All Science Journal Classification (ASJC) codes

  • General Economics, Econometrics and Finance
  • Strategy and Management


  • Eigendecomposition
  • Eigenportfolios
  • Exchange-traded fund (ETF)
  • Exponential correlation model
  • Karhunen–Loeve transform (KLT)
  • Market exposure
  • Market portfolio
  • Minimum variance portfolio
  • Principal component analysis
  • Profit and loss (P&L) curve
  • Sharpe ratio
  • Toeplitz matrix


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