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Monitoring Active Portfolios Using Statistical Process Control

  • Emmanuel Yashchin
  • Thomas K. Philips
  • David M. Stein
Part of the Advances in Computational Economics book series (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.

Keywords

False Alarm Tracking Error Excess Return Sequential Probability Ratio Test Unsatisfactory Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bagshaw, M. and R.A. Johnson, 1975, ‘The effect of serial correlation on the performance of CUSUM TESTS II’, Technometrics 17, 73–80.CrossRefGoogle Scholar
  2. 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
  3. Blake, C.R., E.J. Elton, and M.J. Gruber, 1993, ‘The performance of bond mutual funds’, Journal of Business 66 (3), 371–404.CrossRefGoogle Scholar
  4. Grinblatt, M. and S. Titman, 1989, ‘Portfolio performance evaluation: Old issues and new insights’, Review of Financial Studies 2, 393–421.CrossRefGoogle Scholar
  5. Johnson, R.A. and M. Bagshaw, 1974, ‘The effect of serial correlation on the performance of CUSUM tests’, Technometrics 16, 103–122.CrossRefGoogle Scholar
  6. Jensen, M., 1969, ‘Risk, the pricing of capital assets, and the evaluation of investment portfolios’, Journal of Business 42, 167–247.CrossRefGoogle Scholar
  7. 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
  8. Lorden, G., 1971, ‘Procedures for reacting to a change in distribution’, Ann. Math. Stat. 42, 1897–1908.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Moustakides, G.V., 1986, ‘Optimal stopping times for detecting changes in distributions’, Ann. Stat. 14 (2), 1379–1387.CrossRefGoogle Scholar
  11. Page, E., 1954, ‘Continuous inspection schemes’, Biometrika 41, 100–115.Google Scholar
  12. Sharpe, W.F., 1992, Asset allocation, management style, and performance measurement’, Journal of Portfolio Management (Winter), 7–19.Google Scholar
  13. Sharpe, W.F., 1994, ‘The Sharpe ratio’, Journal of Portfolio Management (Fall), 49–58.Google Scholar
  14. Stoyan D., 1983, Comparison Methods for Queues and Other Stochastic Models, New York: John Wiley and Sons.Google Scholar
  15. Neumann, J., R.H. Kent, H.R. Bellinson, and B.I. Hart, 1941, ‘The mean square successive difference’, Ann. Math. Stat. 12, 153–162.CrossRefGoogle Scholar
  16. Yashchin, E., 1993a, ‘Performance of Cusum control schemes for serially correlated observations’, Technometrics 35, 37–52.CrossRefGoogle Scholar
  17. Yashchin, E., 1993b, ‘Statistical control schemes: Methods, applications and generalizations’, International Statistical Review 61, 41–66.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Emmanuel Yashchin
  • Thomas K. Philips
  • David M. Stein

There are no affiliations available

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