IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2105.10306.html
   My bibliography  Save this paper

Turnover-Adjusted Information Ratio

Author

Listed:
  • Feng Zhang
  • Xi Wang
  • Honggao Cao

Abstract

In this paper, we study the behavior of information ratio (IR) as determined by the fundamental law of active investment management. We extend the classic relationship between IR and its two determinants (i.e., information coefficient and investment "breadth") by explicitly and simultaneously taking into account the volatility of IC and the cost from portfolio turnover. Through mathematical derivations and simulations, we show that - for both mean-variance and quintile portfolios - a turnover-adjusted IR is always lower than an IR that ignores the cost from turnover; more importantly, we find that, contrary to the implication from the fundamental low but consistent with available empirical evidence, investment managers may improve their investment performance or IR by limiting/optimizing trade or portfolio turnover.

Suggested Citation

  • Feng Zhang & Xi Wang & Honggao Cao, 2021. "Turnover-Adjusted Information Ratio," Papers 2105.10306, arXiv.org.
  • Handle: RePEc:arx:papers:2105.10306
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2105.10306
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Felipe Aparicio & Javier Estrada, 2001. "Empirical distributions of stock returns: European securities markets, 1990-95," The European Journal of Finance, Taylor & Francis Journals, vol. 7(1), pages 1-21.
    2. Herskovic, Bernard & Kelly, Bryan & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2016. "The common factor in idiosyncratic volatility: Quantitative asset pricing implications," Journal of Financial Economics, Elsevier, vol. 119(2), pages 249-283.
    3. Ding, Zhuanxin & Martin, R. Douglas, 2017. "The fundamental law of active management: Redux," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 91-114.
    4. Vermaat, M.B. & van der Meulen, F.H. & Does, R.J.M.M., 2008. "Asymptotic behavior of the variance of the EWMA statistic for autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1673-1682, September.
    5. Zhuanxin Ding & R. Douglas Martin & Chaojun Yang, 2020. "Portfolio turnover when IC is time-varying," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 609-622, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    2. Zhong, Angel, 2018. "Idiosyncratic volatility in the Australian equity market," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 105-125.
    3. Nektarios Aslanidis & Charlotte Christiansen & Neophytos Lambertides & Christos S. Savva, 2019. "Idiosyncratic volatility puzzle: influence of macro-finance factors," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 381-401, February.
    4. Dai, Yingtong & Harris, Richard D.F., 2023. "Average tail risk and aggregate stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    5. Gao, Lin & Hitzemann, Steffen & Shaliastovich, Ivan & Xu, Lai, 2022. "Oil volatility risk," Journal of Financial Economics, Elsevier, vol. 144(2), pages 456-491.
    6. Barney Hartman‐Glaser & Hanno Lustig & Mindy Z. Xiaolan, 2019. "Capital Share Dynamics When Firms Insure Workers," Journal of Finance, American Finance Association, vol. 74(4), pages 1707-1751, August.
    7. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    8. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    9. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    10. Alessandria, George & Choi, Horag & Kaboski, Joseph P. & Midrigan, Virgiliu, 2015. "Microeconomic uncertainty, international trade, and aggregate fluctuations," Journal of Monetary Economics, Elsevier, vol. 69(C), pages 20-38.
    11. Bartram, Söhnke M. & Brown, Gregory W. & Stulz, René M., 2016. "Why does idiosyncratic risk increase with market risk?," CFS Working Paper Series 533, Center for Financial Studies (CFS).
    12. Ernesto Pasten & Raphael S. Schoenle & Michael Weber & Michael Weber, 2017. "Price Rigidities and the Granular Origins of Aggregate Fluctuations," CESifo Working Paper Series 6619, CESifo.
    13. Dew-Becker, Ian & Giglio, Stefano & Kelly, Bryan, 2021. "Hedging macroeconomic and financial uncertainty and volatility," Journal of Financial Economics, Elsevier, vol. 142(1), pages 23-45.
    14. John Y. Campbell & Martin Lettau & Burton Malkiel & Yexiao Xu, 2023. "Idiosyncratic Equity Risk Two Decades Later," Critical Finance Review, now publishers, vol. 12(1-4), pages 203-223, August.
    15. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
    16. Borochin, Paul & Wu, Zekun & Zhao, Yanhui, 2021. "The effect of option-implied skewness on delta- and vega-hedged option returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    17. Su, Zhi & Shu, Tengjia & Yin, Libo, 2018. "The pricing effect of the common pattern in firm-level idiosyncratic volatility: Evidence from A-Share stocks of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 218-235.
    18. Panzica, Roberto Calogero, 2018. "Idiosyncratic volatility puzzle: The role of assets' interconnections," SAFE Working Paper Series 228, Leibniz Institute for Financial Research SAFE.
    19. Simon Oh & Jessica A. Wachter, 2018. "Cross-sectional Skewness," NBER Working Papers 25113, National Bureau of Economic Research, Inc.
    20. Raman Uppal & Harjoat Bhamra, 2016. "Do Individual Behavioral Biases Affect Financial Markets and the Macroeconomy?," 2016 Meeting Papers 1358, Society for Economic Dynamics.

    More about this item

    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:2105.10306. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.