IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2017-06-9.html
   My bibliography  Save this article

The Application of Genetic Programming on the Stock Movement Forecasting System

Author

Listed:
  • Yi-Chi Tsai

    (Computer Science and Information Engineering, Chaoyang University of Technology, Taiwan,)

  • Cheng-Yih Hong

    (Faculty of Finance, Chaoyang University of Technology, Taiwan)

Abstract

The financial tsunami is a crisis that happened in 2007. It broke out in the United States, and then spread to the whole world. Taiwanese economy exhibited a negative growth of 7.53%, and the fluctuation is manifest in Taiwan stock index. It has been even dramatically losing 60%. Now, TAIEX has exceeded the level before the financial crisis. TAIEX closed at 10,383.94 on September 30, 2017. The establishment of the Stock Movement Forecasting System has become an important issue. This paper intends to demonstrate the application of an artificial intelligence system named GPLAB on the prediction of stock price movement in TWSE. GPLAB was built on biological evolutionary concept to realize fittest surviving rules in the natural selection process. This concept has been applied on the field of finance to build up forecasting models predicting future price movement within one day, one month and one season. The empirical results of this inter-discipline study has revealed this bio-financial hybrid system successfully predicted the stock price movement in a one-month forecasting range by 23% and 22% lower than the appointed benchmark during a random chosen period and a bear market period respectively. This empirical evidence suggests the market efficiency in TWSE is a semi-strong form market that stock price movement could be predicted with the analysis of historical data. This paper also further indicates the credibility of different technical and fundamental factors regarding to the prediction of future price movement in four different market situations including non-specific, static, bull and bear market period. At the end of this paper also revealed the strength and weakness of GPLAB as a financial forecasting tool. A short discussion concerning the system improvements regarding to the application of GPLAB is also included.

Suggested Citation

  • Yi-Chi Tsai & Cheng-Yih Hong, 2017. "The Application of Genetic Programming on the Stock Movement Forecasting System," International Journal of Economics and Financial Issues, Econjournals, vol. 7(6), pages 68-73.
    • Handle: RePEc:eco:journ1:2017-06-9
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/5542/pdf
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijefi/article/view/5542/pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Basu, Sanjoy, 1983. "The relationship between earnings' yield, market value and return for NYSE common stocks : Further evidence," Journal of Financial Economics, Elsevier, vol. 12(1), pages 129-156, June.
    2. repec:bla:jfinan:v:43:y:1988:i:2:p:507-28 is not listed on IDEAS
    3. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    4. Chan, Louis K. C. & Karceski, Jason & Lakonishok, Josef, 1998. "The Risk and Return from Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(2), pages 159-188, June.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    6. Simon Gervais & Ron Kaniel & Dan H. Mingelgrin, 2001. "The High‐Volume Return Premium," Journal of Finance, American Finance Association, vol. 56(3), pages 877-919, June.
    7. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    8. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    9. 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.
    10. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    11. Jensen, Michael C & Bennington, George A, 1970. "Random Walks and Technical Theories: Some Additional Evidence," Journal of Finance, American Finance Association, vol. 25(2), pages 469-482, May.
    12. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    13. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    14. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    15. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," The Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    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. 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.
    2. Fernando Rubio, 2005. "Estrategias Cuantitativas De Valor Y Retornos Por Accion De Largo," Finance 0503029, University Library of Munich, Germany.
    3. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, July.
    4. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    5. Yamani, Ehab, 2023. "Return–volume nexus in financial markets: A survey of research," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    7. David R Gallagher & Peter A Gardner & Camille H Schmidt, 2015. "Style factor timing: An application to the portfolio holdings of US fund managers," Australian Journal of Management, Australian School of Business, vol. 40(2), pages 318-350, May.
    8. Murtazashvili, Irina & Vozlyublennaia, Nadia, 2012. "The role of data limitations, seasonality and frequency in asset pricing models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 555-574.
    9. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    10. John Y. Campbell, 2000. "Asset Pricing at the Millennium," Journal of Finance, American Finance Association, vol. 55(4), pages 1515-1567, August.
    11. Massimo Guidolin & Manuela Pedio, 2019. "How Smart is the Real Estate Smart Beta? Evidence from Optimal Style Factor Strategies for REITs," BAFFI CAREFIN Working Papers 19117, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    12. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    13. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    14. Keith Lam & Frank Li, 2008. "The risk premiums of the four-factor asset pricing model in the Hong Kong stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 18(20), pages 1667-1680.
    15. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    16. Schwert, G. William, 2003. "Anomalies and market efficiency," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974, Elsevier.
    17. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    18. Miralles-Quirós, José Luis & Miralles-Quirós, María Mar, 2024. "Factor models and investment strategies in the renewable energy sector," Energy Economics, Elsevier, vol. 132(C).
    19. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
    20. Stuart Hyde & Mohamed Sherif, 2010. "Tests of the conditional asset pricing model: further evidence from the cross-section of stock returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(2), pages 198-211.

    More about this item

    Keywords

    Stock Movement Forecasting; GP ; Genetic Programming;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    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:eco:journ1:2017-06-9. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    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.