IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1905.08042.html
   My bibliography  Save this paper

Testing Sharpe ratio: luck or skill?

Author

Listed:
  • Eric Benhamou
  • David Saltiel
  • Beatrice Guez
  • Nicolas Paris

Abstract

Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be estimated statistically. This means that the Sharpe ratio used by funds is likely to be error prone because of statistical estimation errors. In this paper, we provide various tests to measure the quality of the Sharpe ratios. By quality, we are aiming at measuring whether a manager was indeed lucky of skillful. The test assesses this through the statistical significance of the Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute a modified Sharpe insensitive to used Capital. We provide various statistical tests that can be used to precisely quantify the fact that the Sharpe is statistically significant. We illustrate in particular the number of trades for a given Sharpe level that provides statistical significance as well as the impact of auto-correlation by providing reference tables that provides the minimum required Sharpe ratio for a given time period and correlation. We also provide for a Sharpe ratio of 0.5, 1.0, 1.5 and 2.0 the skill percentage given the auto-correlation level.

Suggested Citation

  • Eric Benhamou & David Saltiel & Beatrice Guez & Nicolas Paris, 2019. "Testing Sharpe ratio: luck or skill?," Papers 1905.08042, arXiv.org, revised May 2019.
    • Handle: RePEc:arx:papers:1905.08042
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1905.08042
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.
    3. Eric Benhamou & Beatrice Guez, 2021. "Computation of the marginal contribution of Sharpe ratio and other performance ratios," Working Papers hal-03189299, HAL.
    4. Parley Ruogu Yang & Ryan Lucas, 2021. "DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation," Papers 2110.11156, arXiv.org, revised Jul 2022.
    5. Eric Benhamou, 2021. "Distribution and statistics of the Sharpe Ratio," Working Papers hal-03207169, HAL.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1905.08042. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.