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Monitoring Active Portfolios Using Statistical Process Control

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Book cover Computational Approaches to Economic Problems

Part of the book series: Advances in Computational Economics ((AICE,volume 6))

Abstract

We consider the problem of estimating the performance of a portfolio via an on-line algorithm; i.e. the return of the portfolio is measured at regular (typically monthly) intervals, and every time a new return for the portfolio is received, the estimate of the portfolio’s current performance is updated. An alarm is raised when sufficient statistical evidence accrues to determine that the portfolio is not meeting some prespecified criterion of satisfactory performance.

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References

  • Bagshaw, M. and R.A. Johnson, 1975, ‘The effect of serial correlation on the performance of CUSUM TESTS II’, Technometrics 17, 73–80.

    Article  Google Scholar 

  • Banzal, R.K. and P. Papantoni-Kazakos, 1986, ‘An algorithm for detecting a change in a stochastic process’, IEEE Trans. Information Theory IT-32(2), 227–235.

    Google Scholar 

  • Blake, C.R., E.J. Elton, and M.J. Gruber, 1993, ‘The performance of bond mutual funds’, Journal of Business 66 (3), 371–404.

    Article  Google Scholar 

  • Grinblatt, M. and S. Titman, 1989, ‘Portfolio performance evaluation: Old issues and new insights’, Review of Financial Studies 2, 393–421.

    Article  Google Scholar 

  • Johnson, R.A. and M. Bagshaw, 1974, ‘The effect of serial correlation on the performance of CUSUM tests’, Technometrics 16, 103–122.

    Article  Google Scholar 

  • Jensen, M., 1969, ‘Risk, the pricing of capital assets, and the evaluation of investment portfolios’, Journal of Business 42, 167–247.

    Article  Google Scholar 

  • Kemp, K., 1961, ‘The Average Run Length of the cumulative sum chart when a V-mask is used’, Journal of the Royal Statistical Society, B 23, 149–153.

    Google Scholar 

  • Lorden, G., 1971, ‘Procedures for reacting to a change in distribution’, Ann. Math. Stat. 42, 1897–1908.

    Article  Google Scholar 

  • Lucas, J.M. and R.B. Crosier, 1982, ‘Fast initial response for Cusum quality control schemes: Give your Cusum a head start’, Technometrics 24, 199–205.

    Article  Google Scholar 

  • Moustakides, G.V., 1986, ‘Optimal stopping times for detecting changes in distributions’, Ann. Stat. 14 (2), 1379–1387.

    Article  Google Scholar 

  • Page, E., 1954, ‘Continuous inspection schemes’, Biometrika 41, 100–115.

    Google Scholar 

  • Sharpe, W.F., 1992, Asset allocation, management style, and performance measurement’, Journal of Portfolio Management (Winter), 7–19.

    Google Scholar 

  • Sharpe, W.F., 1994, ‘The Sharpe ratio’, Journal of Portfolio Management (Fall), 49–58.

    Google Scholar 

  • Stoyan D., 1983, Comparison Methods for Queues and Other Stochastic Models, New York: John Wiley and Sons.

    Google Scholar 

  • Neumann, J., R.H. Kent, H.R. Bellinson, and B.I. Hart, 1941, ‘The mean square successive difference’, Ann. Math. Stat. 12, 153–162.

    Article  Google Scholar 

  • Yashchin, E., 1993a, ‘Performance of Cusum control schemes for serially correlated observations’, Technometrics 35, 37–52.

    Article  Google Scholar 

  • Yashchin, E., 1993b, ‘Statistical control schemes: Methods, applications and generalizations’, International Statistical Review 61, 41–66.

    Article  Google Scholar 

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© 1997 Springer Science+Business Media Dordrecht

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Yashchin, E., Philips, T.K., Stein, D.M. (1997). Monitoring Active Portfolios Using Statistical Process Control. In: Amman, H., Rustem, B., Whinston, A. (eds) Computational Approaches to Economic Problems. Advances in Computational Economics, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2644-2_13

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  • DOI: https://doi.org/10.1007/978-1-4757-2644-2_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4770-3

  • Online ISBN: 978-1-4757-2644-2

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