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Predicting Stock Returns Using Financial Statement Information

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  • Bambang, Norman Setiono Strong

Abstract

We examine the profitability of the Ou and Penman (1989a) Pr trading strategy and the Holthausen and Larcker (1992) Prob trading strategy over the period 1980–1992 in the UK. This is a test of whether an investor can earn abnormal returns by exploiting fundamental accounting data. We employ alternative abnormal return metrics and research designs to control for risk. Using a UK dataset offers an independent test because the UK differs from the US in its formal and informal financial reporting environment, its structure of share ownership, and the behaviour of its economy over the study period. We find consistent evidence that an investor could have used a summary measure of financial statement information to predict future abnormal returns by indirectly predicting one‐year‐ahead earnings changes, but only weak and inconsistent evidence that an investor could have used a summary measure of financial statement information to predict one‐year‐ahead stock returns directly. We offer some thoughts on the reasons for these different results.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jbfnac:v:25:y:1998:i:5-6:p:631-657
    DOI: 10.1111/1468-5957.t01-1-00205
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    Cited by:

    1. 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.
    2. Benito Arruñada & Luis Vázquez & Giorgio Zanarone, 2009. "Institutional constraints on organizations: the case of Spanish car dealerships," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 15-26.
    3. Roumani, Yaman & Nwankpa, Joseph K. & Roumani, Yazan F., 2016. "Examining the relationship between firm’s financial records and security vulnerabilities," International Journal of Information Management, Elsevier, vol. 36(6), pages 987-994.
    4. 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.
    5. Demmer, Matthias, 2015. "Improving profitability forecasts with information on earnings quality," Discussion Papers 2015/16, Free University Berlin, School of Business & Economics.
    6. Nikola Petrovic & Stuart Manson & Jerry Coakley, 2009. "Does Volatility Improve UK Earnings Forecasts?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 36(9‐10), pages 1148-1179, November.
    7. Cemile ÖZGÜR, 2019. "Hisse Senedi Getirileri İle Finansal Oranlar Arasındaki İlişkinin Araştırılmasında Bir Panel ARDL Uygulaması," Istanbul Management Journal, Istanbul University Business School, vol. 0(86), pages 97-112, June.
    8. 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.
    9. 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.
    10. 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, September.
    11. Chau Duong & Gioia Pescetto & Daniel Santamaria, 2014. "How value-glamour investors use financial information: UK evidence of investors' confirmation bias," The European Journal of Finance, Taylor & Francis Journals, vol. 20(6), pages 524-549, June.

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