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Step Down and Step Up Statistical Procedures for Stock Selection with Sharp Ratio

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

Stock selection by Sharp ratio is considered in the framework of multiple statistical hypotheses testing theory. The main attention is paid to comparison of Holm step down and Hochberg step up procedures for different loss functions. Comparison is made on the basis of conditional risk as a function of selection threshold. This approach allows to discover that properties of procedures depend not only on relationship between test statistics, but also depend on dispersion of Sharp ratios. Difference in error rate between two procedures is increasing when the concentration of Sharp ratios is increasing. When Sharp ratios do not have a concentration points there is no significant difference in quality of both procedures.

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Acknowledgement

The authors are partly supported by National Research University Higher School of Economics, Russian Federation Government grant, N. 11.G34.31. 0057 and RFFI grant 14-01-00807.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Rodionova 136, Nizhny Novgorod, 603155, Russia

    A. P. Koldanov, V. A. Kalyagin & P. M. Pardalos

Authors
  1. A. P. Koldanov
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  2. V. A. Kalyagin
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  3. P. M. Pardalos
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Corresponding author

Correspondence to A. P. Koldanov .

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Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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Koldanov, A.P., Kalyagin, V.A., Pardalos, P.M. (2015). Step Down and Step Up Statistical Procedures for Stock Selection with Sharp Ratio. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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