IDEAS home Printed from https://ideas.repec.org/p/ecl/ohidic/2018-07.html
   My bibliography  Save this paper

Capital Market Anomalies and Quantitative Research

Author

Listed:
  • Birru, Justin

    (Ohio State University)

  • Gokkaya, Sinan

    (Ohio University)

  • Liu, Xi

    (Miami University of Ohio)

Abstract

Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quant-backed analysts exhibit more efficient forecasting behavior on anomaly predictors--stock recommendations and target prices issued on anomaly-longs (anomaly-shorts) are more (less) favorable. Investment value of such analysts' research is higher and their research reports are more likely to discuss implications of quantitative modeling and market anomalies. Quant research facilitates "smart money" trades of institutional clients on anomaly stocks--Quant research is associated with an increased (decreased) likelihood of purchasing underpriced (overpriced) stocks. Market participants recognize Quants--thematic reports authored by Quants generate abnormal reactions for corresponding stocks. Finally, we provide evidence consistent with quantitative research increasing market efficiency by attenuating cross-sectional predictability of anomaly based long-short strategies.

Suggested Citation

  • Birru, Justin & Gokkaya, Sinan & Liu, Xi, 2018. "Capital Market Anomalies and Quantitative Research," Working Paper Series 2018-07, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  • Handle: RePEc:ecl:ohidic:2018-07
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3163677_code1542588.pdf?abstractid=3152641&mirid=1
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Pungaliya, Raunaq S. & Wang, Yanbo, 2023. "Machine invasion: Automation in information acquisition and the cross-section of stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
    2. Guo, Li & Li, Frank Weikai & John Wei, K.C., 2020. "Security analysts and capital market anomalies," Journal of Financial Economics, Elsevier, vol. 137(1), pages 204-230.
    3. Ryan Flugum, 2021. "The trend is an analyst's friend: Analyst recommendations and market technicals," The Financial Review, Eastern Finance Association, vol. 56(2), pages 301-330, May.

    More about this item

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

    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:ecl:ohidic:2018-07. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cdohsus.html .

    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.