IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v31y2015i2p582-584.html
   My bibliography  Save this article

A note on the integration of the alpha alignment factor and earnings forecasting models in producing more efficient Markowitz Frontiers

Author

Listed:
  • Beheshti, Bijan

Abstract

There is a rich body of literature describing the association of earnings forecasting models with stock returns. We use an earnings forecasting model that employs the forecasted earnings yield, earnings per share forecast revisions, and breadth of earnings per share forecasts to serve as a stock selection model. The earnings forecasting model is an input to a portfolio optimization analysis in which fundamental and statistical-based risk models are used. Moreover, an alpha alignment factor is employed to aid in portfolio construction.

Suggested Citation

  • Beheshti, Bijan, 2015. "A note on the integration of the alpha alignment factor and earnings forecasting models in producing more efficient Markowitz Frontiers," International Journal of Forecasting, Elsevier, vol. 31(2), pages 582-584.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:582-584
    DOI: 10.1016/j.ijforecast.2014.12.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016920701400185X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2014.12.005?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. Ramnath, Sundaresh & Rock, Steve & Shane, Philip, 2008. "The financial analyst forecasting literature: A taxonomy with suggestions for further research," International Journal of Forecasting, Elsevier, vol. 24(1), pages 34-75.
    2. Guerard, John B. & Markowitz, Harry & Xu, GanLin, 2015. "Earnings forecasting in a global stock selection model and efficient portfolio construction and management," International Journal of Forecasting, Elsevier, vol. 31(2), pages 550-560.
    3. Edwin J. Elton & Martin J. Gruber & Mustafa Gultekin, 1981. "Expectations and Share Prices," Management Science, INFORMS, vol. 27(9), pages 975-987, September.
    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. Xia, Hui & Min, Xinyu & Deng, Shijie, 2015. "Effectiveness of earnings forecasts in efficient global portfolio construction," International Journal of Forecasting, Elsevier, vol. 31(2), pages 568-574.
    2. Guerard, John B. & Markowitz, Harry & Xu, GanLin, 2015. "Earnings forecasting in a global stock selection model and efficient portfolio construction and management," International Journal of Forecasting, Elsevier, vol. 31(2), pages 550-560.

    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. John B. Guerard & Harry Markowitz & Ganlin Xu & Ziwei Wang, 2018. "Global portfolio construction with emphasis on conflicting corporate strategies to maximize stockholder wealth," Annals of Operations Research, Springer, vol. 267(1), pages 203-219, August.
    2. Guerard, John B. & Markowitz, Harry & Xu, GanLin, 2015. "Earnings forecasting in a global stock selection model and efficient portfolio construction and management," International Journal of Forecasting, Elsevier, vol. 31(2), pages 550-560.
    3. Gillam, Robert A. & Guerard, John B. & Cahan, Rochester, 2015. "News volume information: Beyond earnings forecasting in a global stock selection model," International Journal of Forecasting, Elsevier, vol. 31(2), pages 575-581.
    4. John B. Guerard & Ganlin Xu & Harry Markowitz, 2021. "A further analysis of robust regression modeling and data mining corrections testing in global stocks," Annals of Operations Research, Springer, vol. 303(1), pages 175-195, August.
    5. Shao, Barret Pengyuan & Rachev, Svetlozar T. & Mu, Yu, 2015. "Applied mean-ETL optimization in using earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 561-567.
    6. Qu, Li, 2021. "A new approach to estimating earnings forecasting models: Robust regression MM-estimation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1011-1030.
    7. John B. Guerard, Jr. & Robert A. Gillam & Harry Markowitz & Ganlin Xu & Shijie Deng & Ziwei (Elaine) Wang, 2018. "Data Mining Corrections Testing in Chinese Stocks," Interfaces, INFORMS, vol. 48(2), pages 108-120, April.
    8. Stolowy, Hervé & Jeanjean, Thomas & Erkens, Michael, 2011. "The economic consequences of increasing the international visibility of financial reports," HEC Research Papers Series 957, HEC Paris.
    9. Cowan, Arnold R. & Salotti, Valentina, 2020. "Anti-selective disclosure regulation and analyst forecast accuracy and usefulness," Journal of Corporate Finance, Elsevier, vol. 64(C).
    10. Sébastien Galanti & Anne-Gaël Vaubourg, 2020. "Unbundling financial services: The case of brokerage and investment research," Economics Bulletin, AccessEcon, vol. 40(1), pages 473-484.
    11. Ryan D. Leece & Todd P. White, 2017. "The effects of firms’ information environment on analysts’ herding behavior," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 503-525, February.
    12. Gülcan Erkilet & Gerrit Janke & Rainer Kasperzak, 2022. "How valuation approach choice affects financial analysts’ target price accuracy," Journal of Business Economics, Springer, vol. 92(5), pages 741-779, July.
    13. Bert De Bruijn & Philip Hans Franses, 2018. "How Informative Are Earnings Forecasts? †," JRFM, MDPI, vol. 11(3), pages 1-20, July.
    14. Sinha, Rajesh Kumar, 2021. "Macro disagreement and analyst forecast properties," Journal of Contemporary Accounting and Economics, Elsevier, vol. 17(1).
    15. Bert de Bruijn & Philip Hans Franses, 2015. "How Informative are the Unpredictable Components of Earnings Forecasts?," Tinbergen Institute Discussion Papers 15-032/III, Tinbergen Institute.
    16. Gaétan Breton & Alain Schatt, 2000. "Rôle et caractérisation de l’analyse financière," Revue d'Économie Financière, Programme National Persée, vol. 59(4), pages 147-161.
    17. Chen Su, 2023. "The price impact of analyst revisions and the state of the economy: Evidence around the world," The Financial Review, Eastern Finance Association, vol. 58(4), pages 887-930, November.
    18. Jiao, Yawen, 2024. "Managing decision fatigue: Evidence from analysts’ earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 77(1).
    19. Chahine, Salim & Daher, Mai & Saade, Samer, 2021. "Doing good in periods of high uncertainty: Economic policy uncertainty, corporate social responsibility, and analyst forecast error," Journal of Financial Stability, Elsevier, vol. 56(C).
    20. Higgins, Huong, 2013. "Can securities analysts forecast intangible firms’ earnings?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 155-174.

    More about this item

    Keywords

    Background; Methodology; Results;
    All these keywords.

    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:eee:intfor:v:31:y:2015:i:2:p:582-584. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.