The Effects of Sample Sizes on the Accuracy of EV and SSD Efficiency Criteria

Johnson, Keith H.; Burgess, Richard C.

doi:10.2307/2330273

Cited by 29 publications

(19 citation statements)

References 6 publications

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“…The E-V and SD rules are equally sensitive to sampling problems [3], and no inference can be made as to which rule yields the "correct" It is hoped that these results, and those yet to come, will ease the burden of many of the arbitrary assumptions that we are presently forced to make in empirical investigations of efficient portfolio performance.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Sampling Fluctuations on the Required Inputs of Security Analysis

Burgess¹,

Johnson²

1976

The Journal of Financial and Quantitative Analysis

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Section: Introductionmentioning

confidence: 99%

The Effects of Sampling Fluctuations on the Required Inputs of Security Analysis

Burgess¹,

Johnson²

1976

The Journal of Financial and Quantitative Analysis

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“…Johnson and Burgess [9] partially confirmed this in a study of estimation risk for normal populations and small samples. They simulated samples from two normal populations and used the data to find the dominant alternative.…”

Section: Related Studiesmentioning

confidence: 73%

The Effect of Estimation Risk in Performing Stochastic Dominance Efficiency Tests

Ritchken

Gupta

1984

Decision Sciences

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In many applications involving the construction of efficient sets, the parameters of the alternatives are estimated using small examples. As a result, inefficient alternatives may be included in the sample efficient set and efficient alternatives left out. This paper investigates the effects of estimation risk when there are more than two alternatives and limited information. The study meals that estimation risk is a severe handicap to the practical implementation of stochastic dominance and mean-variance efficiency analysis.Subject Arm: portforiio AnalysLr and Risk and Uncertahty.

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“…To investigate the effects of sample variability, Johnson and Burgess (1975) and Kroll and Levy (1980) present simulation results assuming a pair of c.d.f.s, F and G. These studies construct simulated sample c.d.f.s F m ( G n ) based on m ( n ) independent samples from c.d.f. F ( G ).…”

Section: Error Probabilitiesmentioning

confidence: 99%

Sampling Error in First Order Stochastic Dominance

Stein

Pfaffenberger

French

1987

J of Financial Research

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This study analyzes the standard method of testing for first order stochastic dominance from a statistical viewpoint and applies a boundary crossing algorithm to approximate the resulting error probabilities. Error probabilities can be estimated even when the two distributions are not equal. This approach, which is useful when large sample simulations are not feasible, helps clarify some of the unusual results obtained by Kroll and Levy in an earlier study.

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The Effects of Sample Sizes on the Accuracy of EV and SSD Efficiency Criteria

Cited by 29 publications

References 6 publications

The Effects of Sampling Fluctuations on the Required Inputs of Security Analysis

The Effects of Sampling Fluctuations on the Required Inputs of Security Analysis

The Effect of Estimation Risk in Performing Stochastic Dominance Efficiency Tests

Sampling Error in First Order Stochastic Dominance

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