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High Frequency Performance Monitoring

High Frequency Performance Monitoring. Elroy Dimson & Andrew Jackson London Business School. Outline. Motivation Investment agreement…Features of agreement… Consultants control chart…Monitoring process Illustrations

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High Frequency Performance Monitoring

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  1. High Frequency Performance Monitoring Elroy Dimson & Andrew Jackson London Business School

  2. Outline • Motivation Investment agreement…Features of agreement…Consultants control chart…Monitoring process • Illustrations An extreme event… Frequent monitoring…Manager termination… Probability of a -2s event. • Analysis of overlapping returns • Extensions Impact on client profitability… Non-normal returns • Conclusion

  3. Investment Agreement • Extract from Statement of Investment Principles: “Managers are chosen on the basis of their capability to add value through superior long-term performance. Managers are expected to follow a consistent investment process with risk exposure that is stable over time” • Typical Investment Management Agreement: “The objective is to produce a return from inception of 1% per annum above the benchmark, subject to a minimum time period of three years. The return will not fall more than 3% below the benchmark in any 12-month interval”.

  4. Features of Agreement • Skilled managers (Goetzmann & Ibbotson ‘94) • No gaming (Khorana ‘96) • Gains from good performance (Sirri & Tufano ‘98) • Penalty from underperformance (Brown & Statman ‘97) • Conflict between target (+1% pa) and floor (-3%)

  5. Consultants Control Chart

  6. Monitoring Process • Monitor tracking error (Roll ‘92) • Specify minimum acceptable return (Sortino ‘99) • Check performance frequently (Brown, Harlow & Starks ‘96) • Compare return relative to tracking error(Grinold & Kahn ‘95) • Interpret performance correctly (Marsh ‘91)

  7. An Extreme Event • Assume: Tracking error = 4%, MAR = -10%

  8. Intra-Year Monitoring • Same example: Tracking error = 4%, MAR = -10%

  9. Manager Termination • Termination Rule: Terminate if 2s below target over one year

  10. Probability of a -2s event

  11. Probability of a -zs event

  12. Analysing Overlapping Returns • Analytic solution is difficult due to overlapping periods. • Method 1 : Simulation from multivariate normal distribution. • Method 2 : Analytic approximation formula.

  13. Effect on Client Profitability • Investment manager’s incentive • Assume the termination rule we saw earlier - ie fire if rolling one-year return is <-2 standard deviations below target. (net fee = 40bp, $500m FUM)

  14. Extension: Non-Normal Returns • If excess returns are non-normally distributed, then effect is magnified. • Assume t-distribution (5df) to get the following results.

  15. Conclusion • Failure to adjust for high frequency performance monitoring may lead to costly actions such as strategy revisions or manager termination. • This paper suggests some methods to make appropriate adjustments. • We show there are big incentives for managers to educate investors about the implications of high-frequency monitoring. • Full paper is in JPM Winter 2001, and may be downloaded from ww.ssrn.com

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