IDEAS home Printed from https://ideas.repec.org/a/eme/arjpps/arj-01-2018-0002.html
   My bibliography  Save this article

Making investment decisions using XBRL filing data

Author

Listed:
  • Rimona Palas
  • Amos Baranes

Abstract

Purpose - The Securities Exchange Commission mandated eXtensible Business Reporting Language (XBRL) filing data provide immediate availability and easy accessibility for both academics and practitioners. To be useful, this data should provide information for decisions, specifically, investment decisions. The purpose of this study is to examine whether the XBRL database can be used with models, developed in previous studies, predicting the directional movement of earnings. The study does not attempt to examine the validity of these models, but only the ability to use the data in the analysis of financial statements based on these models. Design/methodology/approach - The study analyzes New York Stock Exchange companies’ XBRL data using a two-step logistic regression model. The model is then used to arrive at the directional movement of earnings between current and subsequent quarters. Additional models are created by dividing the sample into industry membership. Findings - The results classified companies as realizing an increase or a decrease in earnings. The final model indicated a significant ability to predict earnings changes, on average about 65 per cent of the time, for the entire model, and 71 per cent, for the industry-based models (higher than those of previous studies based on COMPUSTAT). The investment strategy created average quarterly return between 2.8 and 10.7 per cent. Originality/value - The originality of this study is in the way it examines the quality of XBRL data, by examining whether findings from prior research which relied on traditional databases (such as COMPUSTAT) still hold using XBRL data. The use of XBRL allows not only easier and less-costly access to the data but also the ability to adjust the models almost immediately as current information is posted, thus providing a much more relevant tool for investors, especially small investors.

Suggested Citation

  • Rimona Palas & Amos Baranes, 2019. "Making investment decisions using XBRL filing data," Accounting Research Journal, Emerald Group Publishing Limited, vol. 32(4), pages 587-609, November.
  • Handle: RePEc:eme:arjpps:arj-01-2018-0002
    DOI: 10.1108/ARJ-01-2018-0002
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/ARJ-01-2018-0002/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1108/ARJ-01-2018-0002/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1108/ARJ-01-2018-0002?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. Holthausen, Robert W. & Larcker, David F., 1992. "The prediction of stock returns using financial statement information," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 373-411, August.
    2. R Bird & R Gerlach & AD Hall, 2001. "The prediction of earnings movements using accounting data: An update and extension of Ou and Penman," Journal of Asset Management, Palgrave Macmillan, vol. 2(2), pages 180-195, September.
    3. Bernard, Victor L. & Thomas, Jacob K., 1990. "Evidence that stock prices do not fully reflect the implications of current earnings for future earnings," Journal of Accounting and Economics, Elsevier, vol. 13(4), pages 305-340, December.
    4. Beaver, Wh, 1968. "Information Content Of Annual Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 6, pages 67-92.
    5. Bambang, Norman Setiono Strong, 1998. "Predicting Stock Returns Using Financial Statement Information," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5‐6), pages 631-657, June.
    6. Lee, Cwj, 1985. "Stochastic Properties Of Cross-Sectional Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 213-227.
    7. Rosenberg, Barr & Houglet, Michel, 1974. "Error Rates in CRSP and Compustat Data Bases and their Implications," Journal of Finance, American Finance Association, vol. 29(4), pages 1303-1310, September.
    8. Debreceny, Roger & Farewell, Stephanie & Piechocki, Maciej & Felden, Carsten & Gräning, André, 2010. "Does it add up? Early evidence on the data quality of XBRL filings to the SEC," Journal of Accounting and Public Policy, Elsevier, vol. 29(3), pages 296-306, June.
    9. Ou, Ja, 1990. "The Information-Content Of Nonearnings Accounting Numbers As Earnings Predictors," Journal of Accounting Research, Wiley Blackwell, vol. 28(1), pages 144-163.
    10. Pervin K. Shroff, 1999. "The Variability of Earnings and Non-Earnings Information and Earnings Prediction," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 26(7&8), pages 863-882.
    11. Bambang Setiono, 1998. "Predicting Stock Returns Using Financial Statement Information," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5&6), pages 631-657.
    12. Pervaiz Alam & Charles A. Brown, 2006. "Disaggregated earnings and the prediction of ROE and stock prices: a case of the banking industry," Review of Accounting and Finance, Emerald Group Publishing, vol. 5(4), pages 443-463, November.
    13. Rimona Palas & Amos Baranes, 2017. "The Prediction of Earnings Movement Using Mandated XBRL data ? Industry Analysis," Proceedings of Economics and Finance Conferences 4507381, International Institute of Social and Economic Sciences.
    14. Robert J. Bloomfield & Robert Libby & Mark W. Nelson, 2003. "Do Investors Overrely on Old Elements of the Earnings Time Series?," Contemporary Accounting Research, John Wiley & Sons, vol. 20(1), pages 1-31, March.
    15. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    16. Stober, Thomas L., 1992. "Summary financial statement measures and analysts' forecasts of earnings," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 347-372, August.
    17. Ball, R & Brown, P, 1968. "Empirical Evaluation Of Accounting Income Numbers," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 159-178.
    18. Jacqueline L. Birt & Kala Muthusamy & Poonam Bir, 2017. "XBRL and the qualitative characteristics of useful financial information," Accounting Research Journal, Emerald Group Publishing Limited, vol. 30(01), pages 107-126, May.
    19. Victor Bernard & Jacob Thomas & James Wahlen, 1997. "Accounting†Based Stock Price Anomalies: Separating Market Inefficiencies from Risk," Contemporary Accounting Research, John Wiley & Sons, vol. 14(2), pages 89-136, June.
    20. Finger, Ca, 1994. "The Ability Of Earnings To Predict Future Earnings And Cash Flow," Journal of Accounting Research, Wiley Blackwell, vol. 32(2), pages 210-223.
    21. Ray Ball & Lakshmanan Shivakumar, 2008. "How Much New Information Is There in Earnings?," Journal of Accounting Research, Wiley Blackwell, vol. 46(5), pages 975-1016, December.
    22. Pervin K. Shroff, 1999. "The Variability of Earnings and Non‐Earnings Information and Earnings Prediction," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 26(7‐8), pages 863-882, September.
    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. Rimona Palas & Amos Baranes, 2017. "The Prediction of Earnings Movement Using Mandated XBRL data ? Industry Analysis," Proceedings of Economics and Finance Conferences 4507381, International Institute of Social and Economic Sciences.
    2. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    3. Photis Panayides & Neophytos Lambertides, 2011. "Fundamental Analysis and Relative Efficiency of Maritime Firms: Dry Bulk vs Tanker Firms," Chapters, in: Kevin Cullinane (ed.), International Handbook of Maritime Economics, chapter 5, Edward Elgar Publishing.
    4. Josef Fink, 2020. "A Review of the Post-Earnings-Announcement Drift," Working Paper Series, Social and Economic Sciences 2020-04, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    5. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    6. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    7. Victor Bernard & Jacob Thomas & James Wahlen, 1997. "Accounting†Based Stock Price Anomalies: Separating Market Inefficiencies from Risk," Contemporary Accounting Research, John Wiley & Sons, vol. 14(2), pages 89-136, June.
    8. Stina Skogsvik, 2008. "Financial Statement Information, the Prediction of Book Return on Owners' Equity and Market Efficiency: The Swedish Case," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(7-8), pages 795-817.
    9. Michael Ettredge & Richard Toolson & Steve Hall & Chongkil Na, 1996. "Behavior of earnings, stock returns, accruals, and analysts' forecasts following negative annual earnings," Review of Financial Economics, John Wiley & Sons, vol. 5(2), pages 147-162.
    10. Ettredge, Michael & Toolson, Richard & Hall, Steve & Na, Chongkil, 1996. "Behavior of earnings, stock returns, accruals, and analysts' forecasts following negative annual earnings," Review of Financial Economics, Elsevier, vol. 5(2), pages 147-162.
    11. Fink, Josef, 2021. "A review of the Post-Earnings-Announcement Drift," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    12. Avkiran, Necmi K. & Morita, Hiroshi, 2010. "Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool," Pacific-Basin Finance Journal, Elsevier, vol. 18(3), pages 254-271, June.
    13. Liu, Chao-Shin & Ziebart, David A., 1999. "Anomalous security price behavior following management earnings forecasts," Journal of Empirical Finance, Elsevier, vol. 6(4), pages 405-429, October.
    14. Mary E. Barth & Greg Clinch & Doron Israeli, 2016. "What do accruals tell us about future cash flows?," Review of Accounting Studies, Springer, vol. 21(3), pages 768-807, September.
    15. Ball, Ray & Brown, Philip, 2019. "Ball and Brown (1968) after fifty years," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 410-431.
    16. Martin Wallmeier, 2009. "Kapitalmarktwirkungen der Berichterstattung zur Unternehmensleistung," Schmalenbach Journal of Business Research, Springer, vol. 61(2), pages 212-224, March.
    17. Duarte Trigueiros, 2019. "Improving the effectiveness of predictors in accounting-based models," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 20(2), pages 207-226, June.
    18. Jean-Francois Gajewski & Bertrand Quere, 2001. "The information content of earnings and turnover announcements in France," European Accounting Review, Taylor & Francis Journals, vol. 10(4), pages 679-704.
    19. Schnaubelt, Matthias & Seifert, Oleg, 2020. "Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements?," FAU Discussion Papers in Economics 04/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    20. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.

    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:eme:arjpps:arj-01-2018-0002. 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: Emerald Support (email available below). General contact details of provider: .

    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.