IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v7y2010i1p47-60.html
   My bibliography  Save this article

A maximal predictability portfolio using absolute deviation reformulation

Author

Listed:
  • Hiroshi Konno
  • Yuuhei Morita
  • Rei Yamamoto

Abstract

No abstract is available for this item.

Suggested Citation

  • Hiroshi Konno & Yuuhei Morita & Rei Yamamoto, 2010. "A maximal predictability portfolio using absolute deviation reformulation," Computational Management Science, Springer, vol. 7(1), pages 47-60, January.
    • Handle: RePEc:spr:comgts:v:7:y:2010:i:1:p:47-60
    DOI: 10.1007/s10287-008-0075-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-008-0075-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10287-008-0075-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew W. Lo & Jiang Wang, 2006. "Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model," Journal of Finance, American Finance Association, vol. 61(6), pages 2805-2840, December.
    2. Lo, Andrew W. & Mackinlay, A. Craig, 1997. "Maximizing Predictability In The Stock And Bond Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 1(1), pages 102-134, January.
    3. H. Konno & K. Tsuchiya & R. Yamamoto, 2007. "Minimization of the Ratio of Functions Defined as Sums of the Absolute Values," Journal of Optimization Theory and Applications, Springer, vol. 135(3), pages 399-410, December.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. Rei Yamamoto & Daisuke Ishii & Hiroshi Konno, 2007. "A Maximal Predictability Portfolio Model: Algorithm And Performance Evaluation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(06), pages 1095-1109.
    6. R. Yamamoto & H. Konno, 2007. "An Efficient Algorithm for Solving Convex–Convex Quadratic Fractional Programs," Journal of Optimization Theory and Applications, Springer, vol. 133(2), pages 241-255, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    2. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    3. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.

    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. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    2. Zhang, Wei & Wang, Guanying & Wang, Xingchun & Xiong, Xiong & Lei, Xuan, 2018. "Profitability of reversal strategies: A modified version of the Carhart model in China," Economic Modelling, Elsevier, vol. 69(C), pages 26-37.
    3. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    4. Leonid Kogan & Raman Uppal, "undated". "Risk Aversion and Optimal Portfolio Policies in Partial and General Equilibrium Economies," Rodney L. White Center for Financial Research Working Papers 13-00, Wharton School Rodney L. White Center for Financial Research.
    5. Johnson, Lorne D. & Sakoulis, Georgios, 2008. "Maximizing equity market sector predictability in a Bayesian time-varying parameter model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3083-3106, February.
    6. Khandani, Amir E. & Lo, Andrew W., 2011. "What happened to the quants in August 2007? Evidence from factors and transactions data," Journal of Financial Markets, Elsevier, vol. 14(1), pages 1-46, February.
    7. Malamud, Semyon & Vilkov, Grigory, 2018. "Non-myopic betas," Journal of Financial Economics, Elsevier, vol. 129(2), pages 357-381.
    8. Snigaroff, Robert & Wroblewski, David, 2021. "Earnings and liquidity factors," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 508-523.
    9. Cotter, John & Salvador, Enrique, 2022. "The non-linear trade-off between return and risk and its determinants," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 100-132.
    10. H. Konno & K. Tsuchiya & R. Yamamoto, 2007. "Minimization of the Ratio of Functions Defined as Sums of the Absolute Values," Journal of Optimization Theory and Applications, Springer, vol. 135(3), pages 399-410, December.
    11. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
    12. Michael Cooper & Huseyin Gulen, 2006. "Is Time-Series-Based Predictability Evident in Real Time?," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1263-1292, May.
    13. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    14. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
    15. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
    16. Vandana Goyal & Namrata Rani & Deepak Gupta, 2022. "FGP approach to quadratically constrained multi-objective quadratic fractional programming with parametric functions," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 594-602, June.
    17. Hauser, Shmuel & Kedar-Levy, Haim, 2018. "Liquidity might come at cost: The role of heterogeneous preferences," Journal of Financial Markets, Elsevier, vol. 39(C), pages 1-23.
    18. Doron Avramov, "undated". "Stock-Return Predictability and Model Uncertainty," Rodney L. White Center for Financial Research Working Papers 12-00, Wharton School Rodney L. White Center for Financial Research.
    19. Alain Guéniche & Philippe Dupuy & Wan Ni Lai, 2023. "Price contingent and price-volume contingent portfolio strategies," Journal of Asset Management, Palgrave Macmillan, vol. 24(3), pages 173-183, May.
    20. Korkie, Bob & Sivakumar, Ranjini & Turtle, Harry, 2002. "The dual contributions of information instruments in return models: magnitude and direction predictability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 511-523, December.

    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:spr:comgts:v:7:y:2010:i:1:p:47-60. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.