IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v19y2012i1p1-18.html
   My bibliography  Save this article

A Radial Basis Function Approach To Earnings Forecast

Author

Listed:
  • Robert G. Biscontri

Abstract

The fundamental management problem of decision making in a climate where future values of important variables are unknown and can at best be estimated using traditional statistical techniques is addressed. The incorporation of forecast models into management decision‐support systems is critical for the overall success of organizational accounting information systems, where managers require confidence in the information that they use. The neural network paradigm has been described as a promising nonparametric approach, negating the required, and sometimes restrictive, statistical assumptions. The application of the neural network paradigm to the area of earnings forecasting is presented. A radial basis function (RBF) approach is developed and tested empirically using data from the Hong Kong Hang Seng 100 Index and macroeconomic data, mimicking an actual business valuation/forecast exercise. Results show that the RBF approach is superior to regression and financial analysts in earnings forecast. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:1:p:1-18
    DOI: 10.1002/isaf.333
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.333
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.333?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
    ---><---

    References listed on IDEAS

    as
    1. Chan, Louis K C & Hamao, Yasushi & Lakonishok, Josef, 1991. "Fundamentals and Stock Returns in Japan," Journal of Finance, American Finance Association, vol. 46(5), pages 1739-1764, December.
    2. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    3. Kang, Sh & Obrien, J & Sivaramakrishnan, K, 1994. "Analysts Interim Earnings Forecasts - Evidence On The Forecasting Process," Journal of Accounting Research, Wiley Blackwell, vol. 32(1), pages 103-112.
    4. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    5. Butler, Kc & Lang, Lhp, 1991. "The Forecast Accuracy Of Individual Analysts - Evidence Of Systematic Optimism And Pessimism," Journal of Accounting Research, Wiley Blackwell, vol. 29(1), pages 150-156.
    6. De Bondt, Werner F M & Thaler, Richard H, 1990. "Do Security Analysts Overreact?," American Economic Review, American Economic Association, vol. 80(2), pages 52-57, May.
    7. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    8. Lys, Thomas & Sohn, Sungkyu, 1990. "The association between revisions of financial analysts' earnings forecasts and security-price changes," Journal of Accounting and Economics, Elsevier, vol. 13(4), pages 341-363, December.
    9. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, September.
    10. Darrat, Ali F & Zhong, Maosen, 2000. "On Testing the Random-Walk Hypothesis: A Model-Comparison Approach," The Financial Review, Eastern Finance Association, vol. 35(3), pages 105-124, August.
    11. Abarbanell, Jeffrey S & Bernard, Victor L, 1992. "Tests of Analysts' Overreaction/Underreaction to Earnings Information as an Explanation for Anomalous Stock Price Behavior," Journal of Finance, American Finance Association, vol. 47(3), pages 1181-1207, July.
    12. Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    13. 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.
    14. Fred Collopy & Monica Adya & J. Scott Armstrong, 1994. "Research Report—Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts," Information Systems Research, INFORMS, vol. 5(2), pages 170-179, June.
    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. Brous, Peter Alan, 1992. "Common Stock Offerings and Earnings Expectations: A Test of the Release of Unfavorable Information," Journal of Finance, American Finance Association, vol. 47(4), pages 1517-1536, September.
    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. Teixeira, Joao C. & Rodrigues, Antonio J., 1997. "An applied study on recursive estimation methods, neural networks and forecasting," European Journal of Operational Research, Elsevier, vol. 101(2), pages 406-417, September.
    19. Brous, Peter A. & Kini, Omesh, 1993. "A reexamination of analysts' earnings forecasts for takeover targets," Journal of Financial Economics, Elsevier, vol. 33(2), pages 201-225, April.
    20. 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.
    21. Fried, Dov & Givoly, Dan, 1982. "Financial analysts' forecasts of earnings : A better surrogate for market expectations," Journal of Accounting and Economics, Elsevier, vol. 4(2), pages 85-107, October.
    22. John C. Easterwood & Stacey R. Nutt, 1999. "Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism?," Journal of Finance, American Finance Association, vol. 54(5), pages 1777-1797, October.
    23. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    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. Amitabh Dugar & Siva Nathan, 1995. "The Effect of Investment Banking Relationships on Financial Analysts' Earnings Forecasts and Investment Recommendations," Contemporary Accounting Research, John Wiley & Sons, vol. 12(1), pages 131-160, September.
    2. Charles G. Ham & Zachary R. Kaplan & Zawadi R. Lemayian, 2022. "Rationalizing forecast inefficiency," Review of Accounting Studies, Springer, vol. 27(1), pages 313-343, March.
    3. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    4. Sunil Mohanty & Edward Aw, 2006. "Rationality of analysts' earnings forecasts: evidence from dow 30 companies," Applied Financial Economics, Taylor & Francis Journals, vol. 16(12), pages 915-929.
    5. Lorenzo Casavecchia & Gerhard Hambusch & Justin Hitchen, 2022. "The impact of analyst forecast errors on fundamental indexation: the Australian evidence," Journal of Asset Management, Palgrave Macmillan, vol. 23(5), pages 400-418, September.
    6. Philip L. Baird, 2020. "Do investors recognize biases in securities analysts’ forecasts?," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 623-634, October.
    7. Reza Espahbodi & Amitabh Dugar & Hassan Tehranian, 2001. "Further evidence on optimism and underreaction in analysts' forecasts," Review of Financial Economics, John Wiley & Sons, vol. 10(1), pages 1-21, March.
    8. Brown, Philip & Clarke, Alex & How, Janice C. Y. & Lim, Kadir J. P., 2002. "Analysts' dividend forecasts," Pacific-Basin Finance Journal, Elsevier, vol. 10(4), pages 371-391, September.
    9. 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.
    10. Espahbodi, Reza & Dugar, Amitabh & Tehranian, Hassan, 2001. "Further evidence on optimism and underreaction in analysts' forecasts," Review of Financial Economics, Elsevier, vol. 10(1), pages 1-21.
    11. Wang, Ming-Hui & Ke, Mei-Chu & Liang Liao, Tung & Chiang, Yi-Chein & Hsu, Chuan-Hao, 2020. "Alternative estimation method of earnings growth rate for PEGR strategy," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    12. Marco Aiolfi & Marius Rodriguez & Allan Timmermann, 2010. "Understanding Analysts' Earnings Expectations: Biases, Nonlinearities, and Predictability," Journal of Financial Econometrics, Oxford University Press, vol. 8(3), pages 305-334, Summer.
    13. Basu, Sudipta & Markov, Stanimir, 2004. "Loss function assumptions in rational expectations tests on financial analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 171-203, December.
    14. Pathiranage, Nandana P.W. & Jubb, Christine A., 2018. "Does IFRS make analysts more efficient in using fundamental information included in financial statements?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(3), pages 373-385.
    15. Anna M. Cianci & Satoris S. Culbertson, 2010. "The Impact of Motivational and Cognitive Factors on Optimistic Earnings Forecasts," Chapters, in: Brian Bruce (ed.), Handbook of Behavioral Finance, chapter 11, Edward Elgar Publishing.
    16. K. C. Kenneth Chu & W. H. Sophia Zhai, 2021. "Distress risk puzzle and analyst forecast optimism," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 429-460, August.
    17. Douglas Stevens & Arlington Williams, 2004. "Inefficiency in Earnings Forecasts: Experimental Evidence of Reactions to Positive vs. Negative Information," Experimental Economics, Springer;Economic Science Association, vol. 7(1), pages 75-92, February.
    18. X. Frank Zhang, 2006. "Information Uncertainty and Analyst Forecast Behavior," Contemporary Accounting Research, John Wiley & Sons, vol. 23(2), pages 565-590, June.
    19. Stanimir Markov & Ane Tamayo, 2006. "Predictability in Financial Analyst Forecast Errors: Learning or Irrationality?," Journal of Accounting Research, Wiley Blackwell, vol. 44(4), pages 725-761, September.
    20. Wu, Yanran & Zhang, Chao, 2022. "Hard to arbitrage, hard for analysts to forecast," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

    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:wly:isacfm:v:19:y:2012:i:1:p:1-18. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.