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Tangent portfolio weights without explicitly specified expected returns

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  • Paskalis Glabadanidis

    (Business School, Accounting and Finance, University of Adelaide)

Abstract

In this article, I propose an extension of the Treynor–Black model to a case where the investor is not fully invested in the stock market at the outset and there is no need to explicitly specify securities’ expected returns. I derive explicit tangent portfolio weights based on a factor model of securities’ expected returns. The computational burden of the model is linear in the number of securities in the portfolio and does not involve any matrix inversion. I present an empirical application using the market model of Sharpe, the three-factor model of Fama and French and the four-factor model of Carhart with up to 30 industry portfolios between 1963 and 2012 and up to 1000 US stocks starting in 1992 until 2012. The portfolios perform well out-of-sample relative to the entire US value-weighted stock market portfolio with dividends reinvested from the Center for Research in Security Prices. The proposed framework can be extended in a straightforward way to time-varying factor models with multiple state variables affecting securities’ expected returns and factor loadings.

Suggested Citation

  • Paskalis Glabadanidis, 2014. "Tangent portfolio weights without explicitly specified expected returns," Journal of Asset Management, Palgrave Macmillan, vol. 15(3), pages 177-190, June.
    • Handle: RePEc:pal:assmgt:v:15:y:2014:i:3:d:10.1057_jam.2014.22
    DOI: 10.1057/jam.2014.22
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    References listed on IDEAS

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    1. Elton, Edwin J & Gruber, Martin J & Urich, Thomas J, 1978. "Are Betas Best?," Journal of Finance, American Finance Association, vol. 33(5), pages 1375-1384, December.
    2. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    3. Elton, Edwin J & Gruber, Martin J & Padberg, Manfred W, 1976. "Simple Criteria for Optimal Portfolio Selection," Journal of Finance, American Finance Association, vol. 31(5), pages 1341-1357, December.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    6. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    8. Treynor, Jack L & Black, Fischer, 1973. "How to Use Security Analysis to Improve Portfolio Selection," The Journal of Business, University of Chicago Press, vol. 46(1), pages 66-86, January.
    9. Vasicek, Oldrich A, 1973. "A Note on Using Cross-Sectional Information in Bayesian Estimation of Security Betas," Journal of Finance, American Finance Association, vol. 28(5), pages 1233-1239, December.
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    Cited by:

    1. Paskalis Glabadanidis, 2022. "Portfolio weights concentration: optimal strategies and equilibrium implications," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 19(3), pages 572-582, May.
    2. Paskalis Glabadanidis, 2020. "Portfolio Strategies to Track and Outperform a Benchmark," JRFM, MDPI, vol. 13(8), pages 1-26, August.
    3. Paskalis Glabadanidis, 2016. "Maximizing excess return per unit variance: A novel investment management objective," Journal of Asset Management, Palgrave Macmillan, vol. 17(7), pages 486-501, December.

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