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The prediction of earnings movements using accounting data: An update and extension of Ou and Penman

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Abstract

The greater interest apparent in the recent academic literature in the impact of corporate earnings information on the valuation of shares has prompted an updating of the seminal work of Ou and Penman (1989) on the role that accounting information can play in predicting future movements in earnings relative to expectations. The Ou and Penman analysis has also been extended by considering two measures of expectations; by covering a more recent time period; by encompassing the UK and Australian markets in addition to the US market; and by applying a new methodology for developing the forecasting models. The study found that a model based on accounting information retains merit as a means of forecasting movements in trend-adjusted earnings, but that these forecasts no longer provide the basis for a profitable investment strategy. The models used to forecast earnings surprise gave mixed results, but nonetheless, some of the resulting investment strategies were consistently profitable.

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  • Ron Bird & Richard Gerlach & Anthony D Hall, 2001. "The prediction of earnings movements using accounting data: An update and extension of Ou and Penman," Published Paper Series 2001-2, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    • Handle: RePEc:uts:ppaper:2001-2
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    File URL: https://link.springer.com/article/10.1057/palgrave.jam.2240043
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    Cited by:

    1. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Muñoz de Prat, Javier & Delen, Dursun, 2022. "Can customer sentiment impact firm value? An integrated text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Eric Melse, 2008. "Accounting in three dimensions: a case for momentum revisited," Journal of Risk Finance, Emerald Group Publishing, vol. 9(4), pages 334-350, August.
    3. 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.
    4. 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.
    5. Kheiri Chari , Mohammad & Aliheidari Bioki , Tahereh & Khademizare , Hasan, 2013. "The New Method for Ranking Grouped Credit Customer Based on DEA Method," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 8(4), pages 75-98, October.
    6. 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.
    7. Silvania Neris Nossa & Alexsandro Broedel Lopes & Aridelmo Teixeira, 2010. "Stock repurchases and fundamental analysis: an empirical study of the brazilian market in the period from 1994 to 2006," Brazilian Business Review, Fucape Business School, vol. 7(1), pages 1-22, January.
    8. Jasim Al-Ajmi, 2009. "Investors' use of corporate reports in Bahrain," Managerial Auditing Journal, Emerald Group Publishing, vol. 24(3), pages 266-289, March.
    9. Eric Serrano Ferreira & Valcemiro Nossa & Bruno Cesar Aurichio Ledo & Arilda Magna Campanharo Teixeira & Alexsandro Broedel Lopes, 2008. "Comparison Of The Residual Income Valuation, Abnormal Earnings Growth And Free Cash Flow Models: An Empirical Study Of The Brazilian Capital Market," Brazilian Business Review, Fucape Business School, vol. 5(2), pages 143-162, May.

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