IDEAS home Printed from https://ideas.repec.org/a/ibf/ijbfre/v4y2010i1p149-158.html
   My bibliography  Save this article

Fundamental Analysis With Artificial Neural Network

Author

Listed:
  • Birol Yildiz
  • Ari Yezegel

Abstract

This study performs fundamental analysis and cross-sectional prediction of stock return with neural network technology. Eighteen financial ratios are used as the input vector and one-year ahead stock returns are used as the output vector. The fundamental analysis trading strategy generated by artificial neural networks yields an average annual abnormal return of 22.32% after controlling for market risk, book-to-market, size and momentum effects. Our results highlight neural network’s ability to predict future returns in NYSE/AMEX/Nasdaq securities for the period 1990-2005. Artificial neural network technology stands out as a valuable tool for fundamental analysis and forecasting equity returns in the U.S. markets.

Suggested Citation

  • Birol Yildiz & Ari Yezegel, 2010. "Fundamental Analysis With Artificial Neural Network," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(1), pages 149-158.
    • Handle: RePEc:ibf:ijbfre:v:4:y:2010:i:1:p:149-158
    as

    Download full text from publisher

    File URL: http://www.theibfr2.com/RePEc/ibf/ijbfre/ijbfr-v4n1-2010/IJBFR-V4N1-2010-10.pdf
    Download Restriction: no
    ---><---

    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. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    3. Lev, B & Thiagarajan, Sr, 1993. "Fundamental Information Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 31(2), pages 190-215.
    4. Greig, A.C., 1992. "Fundamental Analysis and Subsequent Stock Returns," Papers 51, Rochester, Business - Ph.D.,.
    5. Beaver, Wh & Clarke, R & Wright, Wf, 1979. "Association Between Unsystematic Security Returns And The Magnitude Of Earnings Forecast Errors," Journal of Accounting Research, Wiley Blackwell, vol. 17(2), pages 316-340.
    6. 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.
    7. Greig, Anthony C., 1992. "Fundamental analysis and subsequent stock returns," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 413-442, August.
    8. Freeman, Rn & Tse, S, 1989. "The Multiperiod Information-Content Of Accounting Earnings - Confirmations And Contradictions Of Previous Earnings Reports," Journal of Accounting Research, Wiley Blackwell, vol. 27, pages 49-79.
    9. George Albanis & Roy Batchelor, 2007. "Combining heterogeneous classifiers for stock selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 1-21, January.
    10. Abarbanell, JS & Bushee, BJ, 1997. "Fundamental analysis, future earnings, and stock prices," Journal of Accounting Research, Wiley Blackwell, vol. 35(1), pages 1-24.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.
    2. Doina PRODAN-PALADE, 2017. "Bankruptcy risk prediction models based on artificial neural networks," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 418-418.
    3. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.

    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. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    2. Jun, So Young & Kim, Dong Sung & Jung, Suk Yoon & Jun, Sang Gyung & Kim, Jong Woo, 2022. "Stock investment strategy combining earnings power index and machine learning," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    3. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    4. 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.
    5. Gregory D. Kane & Ryan D. Leece & Frederick M. Richardson & Uma Velury, 2015. "The Impact of Recession on the Value-relevance of Accounting Information," Australian Accounting Review, CPA Australia, vol. 25(2), pages 185-191, June.
    6. Andreas Charitou & George Panagiotides, 1999. "Financial analysis, future earnings and cash flows, and the prediction of stock returns: evidence for the UK," Accounting and Business Research, Taylor & Francis Journals, vol. 29(4), pages 281-298.
    7. 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.
    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. 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. 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.
    12. 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.
    13. Hirshleifer, David & Kewei Hou & Teoh, Siew Hong & Yinglei Zhang, 2004. "Do investors overvalue firms with bloated balance sheets?," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 297-331, December.
    14. Papanastasopoulos, Georgios & Thomakos, Dimitrios & Wang, Tao, 2011. "Information in balance sheets for future stock returns: Evidence from net operating assets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 269-282.
    15. Krauss, Christopher & Krüger, Tom & Beerstecher, Daniel, 2015. "The Piotroski F-Score: A fundamental value strategy revisited from an investor's perspective," FAU Discussion Papers in Economics 13/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    16. Iwasaki, Takuya & Kitagawa, Norio & Shuto, Akinobu, 2023. "Managerial discretion over initial earnings forecasts," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    17. Li Li Eng & Xi Tian & T. Robert Yu, 2018. "Financial Statement Analysis: Evidence from Chinese Firms," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-32, December.
    18. Meena Sharma & Preeti Sharma, 2009. "Prediction of Stock Returns for Growth Firms—A Fundamental Analysis," Vision, , vol. 13(3), pages 31-40, July.
    19. Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
    20. Dafydd Mali & Hyoung‐joo Lim, 2021. "Do Relatively More Efficient Firms Demand Additional Audit Effort (Hours)?," Australian Accounting Review, CPA Australia, vol. 31(2), pages 108-127, June.

    More about this item

    Keywords

    Fundamental Analysis; Stock Market; Neural Network;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

    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:ibf:ijbfre:v:4:y:2010:i:1:p:149-158. 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: Mercedes Jalbert (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.