IDEAS home Printed from https://ideas.repec.org/a/taf/apfiec/v16y2006i18p1375-1388.html
   My bibliography  Save this article

The predictive power of quarterly earnings per share based on time series and artificial intelligence model

Author

Listed:
  • Syouching Lai
  • Hungchih Li

Abstract

The purpose of this study is to compare the forecasting ability among an Autoregressive Integrated Moving Average (ARIMA) model, Transfer Function (TF) model, Artificial Neural Network (ANN) model and Genetic Algorithm (GA) model. To evaluate forecasting accuracy, two dimensions are taken into consideration: (a) deviation between an actual quarterly Earning Per Share (EPS) value and forecasted quarterly EPS value, and (b) direction changes from quarter to quarter between an actual quarterly EPS value and forecasted quarterly EPS value. Both the quarterly basic EPS (BEPS) and diluted EPS (DEPS) data were applied in order to forecast the future quarterly basic EPS. Empirical results have shown that the TF model outperforms the ARIMA model. Therefore, the time lags setting of the TF model is adopted in the other two models: GA and ANN. The empirical results reveal that the GA model has the best forecasting accuracy under both BEPS and DEPS, while the ANN model has been shown to have the worst forecasting accuracy under both BEPS and DEPS. In addition, there is not enough evidence to support that the using of diluted EPS data would yield higher accuracy than that of using basic EPS data in the aspect of deviation.

Suggested Citation

  • Syouching Lai & Hungchih Li, 2006. "The predictive power of quarterly earnings per share based on time series and artificial intelligence model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1375-1388.
    • Handle: RePEc:taf:apfiec:v:16:y:2006:i:18:p:1375-1388
    DOI: 10.1080/09603100600592752
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/09603100600592752
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09603100600592752?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. Brown, Lawrence D., 1993. "Earnings forecasting research: its implications for capital markets research," International Journal of Forecasting, Elsevier, vol. 9(3), pages 295-320, November.
    2. Dechow, Patricia M. & Kothari, S. P. & L. Watts, Ross, 1998. "The relation between earnings and cash flows," Journal of Accounting and Economics, Elsevier, vol. 25(2), pages 133-168, May.
    3. Hopwood, Ws & Mckeown, Jc & Newbold, P, 1982. "The Additional Information-Content Of Quarterly Earnings Reports - Intertemporal Disaggregation," Journal of Accounting Research, Wiley Blackwell, vol. 20(2), pages 343-349.
    4. Brown, Philip, 1993. "Comments on 'Earnings forecasting research: its implications for capital markets research' by L. Brown," International Journal of Forecasting, Elsevier, vol. 9(3), pages 331-335, November.
    5. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
    6. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    7. Christopher J. Neely & Paul A. Weller, 2002. "Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics," Review, Federal Reserve Bank of St. Louis, vol. 84(May), pages 43-54.
    8. Lensberg, Terje & Eilifsen, Aasmund & McKee, Thomas E., 2006. "Bankruptcy theory development and classification via genetic programming," European Journal of Operational Research, Elsevier, vol. 169(2), pages 677-697, March.
    9. Dechow, Patricia M., 1994. "Accounting earnings and cash flows as measures of firm performance : The role of accounting accruals," Journal of Accounting and Economics, Elsevier, vol. 18(1), pages 3-42, July.
    10. W. S. Hopwood & P. Newbold, 1980. "Time Series Analysis In Accounting: A Survey And Analysis Of Recent Issues," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(2), pages 135-144, March.
    11. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    12. Collins, Wa & Hopwood, Ws, 1980. "A Multivariate-Analysis Of Annual Earnings Forecasts Generated From Quarterly Forecasts Of Financial Analysts And Univariate Time-Series Models," Journal of Accounting Research, Wiley Blackwell, vol. 18(2), pages 390-406.
    13. Griffin, Pa, 1977. "Time-Series Behavior Of Quarterly Earnings - Preliminary Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 15(1), pages 71-83.
    14. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    15. Lee, Chi-Wen Jevons & Chen, Chung, 1990. "Structural changes and the forecasting of quarterly accounting earnings in the utility industry," Journal of Accounting and Economics, Elsevier, vol. 13(2), pages 93-122, July.
    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. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    2. Richard M. Morton, 1998. "The Incremental Informativeness of Stock Prices for Future Accounting Earnings," Contemporary Accounting Research, John Wiley & Sons, vol. 15(1), pages 57-81, March.
    3. Rä‚Zvan Popa, 2020. "Improving Earnings Predictions With Neural Network Models," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 26, pages 77-96, December.
    4. Pagach, Donald P. & Warr, Richard S., 2020. "Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited," Advances in accounting, Elsevier, vol. 51(C).
    5. Lorek, Kenneth S., 2014. "A critical assessment of the time-series literature in accounting pertaining to quarterly accounting numbers," Advances in accounting, Elsevier, vol. 30(2), pages 315-321.
    6. van Lent, L.A.G.M., 1999. "Incomplete contracting theory in empirical accounting research," Other publications TiSEM 088f797d-9fa4-4081-98f4-1, Tilburg University, School of Economics and Management.
    7. S. P. Kothari & Charles Wasley, 2019. "Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years," Journal of Accounting Research, Wiley Blackwell, vol. 57(5), pages 1117-1159, December.
    8. Jumming Hsu & Xu-Ming Wang & Chunchi Wu, 1998. "The Role of Earnings Information in Corporate Dividend Decisions," Management Science, INFORMS, vol. 44(12-Part-2), pages 173-191, December.
    9. Rountree, Brian & Weston, James P. & Allayannis, George, 2008. "Do investors value smooth performance?," Journal of Financial Economics, Elsevier, vol. 90(3), pages 237-251, December.
    10. He, Shuoyuan & Narayanamoorthy, Ganapathi (Gans), 2020. "Earnings acceleration and stock returns," Journal of Accounting and Economics, Elsevier, vol. 69(1).
    11. Carnes, Thomas A. & Jones, Jefferson P. & Biggart, Timothy B. & Barker, Katherine J., 2003. "Just-in-time inventory systems innovation and the predictability of earnings," International Journal of Forecasting, Elsevier, vol. 19(4), pages 743-749.
    12. Sean Shun Cao & Ganapathi S. Narayanamoorthy, 2012. "Earnings Volatility, Post–Earnings Announcement Drift, and Trading Frictions," Journal of Accounting Research, Wiley Blackwell, vol. 50(1), pages 41-74, March.
    13. repec:ebl:ecbull:v:7:y:2008:i:11:p:1-20 is not listed on IDEAS
    14. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
    15. Higgins, Huong, 2013. "Can securities analysts forecast intangible firms’ earnings?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 155-174.
    16. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020. "Exploiting ergodicity in forecasts of corporate profitability," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    17. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    18. Dana Hollie & Philip B. Shane & Qiuhong Zhao & Steven Cahan, 2017. "The role of financial analysts in stock market efficiency with respect to annual earnings and its cash and accrual components," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(1), pages 199-237, March.
    19. Tsalavoutas, Ioannis & André, Paul & Evans, Lisa, 2012. "The transition to IFRS and the value relevance of financial statements in Greece," The British Accounting Review, Elsevier, vol. 44(4), pages 262-277.
    20. Karine Michalon & Sandrine Lardic & François Dossou, 2005. "Earnings forecast bias - a statistical analysis," Post-Print halshs-00142773, HAL.
    21. Silhan, Peter A., 2014. "Income smoothing from a Census X-12 perspective," Advances in accounting, Elsevier, vol. 30(1), pages 106-115.

    More about this item

    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:taf:apfiec:v:16:y:2006:i:18:p:1375-1388. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAFE20 .

    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.