IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1603.00751.html
   My bibliography  Save this paper

Equity forecast: Predicting long term stock price movement using machine learning

Author

Listed:
  • Nikola Milosevic

Abstract

Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity's future price over the long time. Our method is able to correctly predict whether some company's value will be 10% higher or not over the period of one year in 76.5% of cases.

Suggested Citation

  • Nikola Milosevic, 2016. "Equity forecast: Predicting long term stock price movement using machine learning," Papers 1603.00751, arXiv.org, revised Nov 2018.
    • Handle: RePEc:arx:papers:1603.00751
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1603.00751
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    2. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    3. Robert B. Barsky & J. Bradford De Long, 1993. "Why Does the Stock Market Fluctuate?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(2), pages 291-311.
    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. N'yoma Diamond & Grant Perkins, 2022. "Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning," Papers 2212.08734, arXiv.org, revised Dec 2022.

    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. Nikola MILOSEVIC, 2016. "Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning," Journal of Economics Library, KSP Journals, vol. 3(2), pages 288-294, June.
    2. Benos, Evangelos & Sagade, Satchit, 2012. "High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market," Bank of England working papers 469, Bank of England.
    3. Miguel Henry & George Judge, 2019. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series," Econometrics, MDPI, vol. 7(1), pages 1-16, March.
    4. Ben Slimane, Faten & Padilla Angulo, Laura, 2019. "Strategic change and corporate governance: Evidence from the stock exchange industry," Journal of Business Research, Elsevier, vol. 103(C), pages 206-218.
    5. Reitz, Stefan, 2006. "On the predictive content of technical analysis," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 121-137, August.
    6. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    7. Lars Lochstoer & Harjoat S. Bhamra, 2009. "Return Predictability and Labor Market Frictions in a Real Business Cycle Model," 2009 Meeting Papers 1257, Society for Economic Dynamics.
    8. Jesus Gonzalo & Tae-Hwy Lee & Weiping Yang, 2008. "Permanent and transitory components of GDP and stock prices: further analysis," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 1(1), pages 105-120.
    9. Bank, Matthias & Baumann, Ralf H., 2016. "Price formation, market quality and the effects of reduced latency in the very short run," Research in International Business and Finance, Elsevier, vol. 37(C), pages 629-645.
    10. Garvey, Ryan & Huang, Tao & Wu, Fei, 2016. "Why do traders choose dark markets?," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 12-28.
    11. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    12. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    13. Madsen, Jakob B., 2002. "The share market boom and the recent disinflation in the OECD countries: the tax-effects, the inflation-illusion and the risk-aversion hypotheses reconsidered1," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(1), pages 115-141.
    14. Nathan S. Balke & Mark E. Wohar, 2006. "What Drives Stock Prices? Identifying the Determinants of Stock Price Movements," Southern Economic Journal, John Wiley & Sons, vol. 73(1), pages 55-78, July.
    15. Robert J. Shiller, 2003. "From Efficient Markets Theory to Behavioral Finance," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 83-104, Winter.
    16. Stefanescu, Razvan & Dumitriu, Ramona, 2015. "Conţinutul analizei seriilor de timp financiare [The Essentials of the Analysis of Financial Time Series]," MPRA Paper 67175, University Library of Munich, Germany.
    17. Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    18. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    19. Bartlett, Robert P. & McCrary, Justin, 2019. "How rigged are stock markets? Evidence from microsecond timestamps," Journal of Financial Markets, Elsevier, vol. 45(C), pages 37-60.
    20. Bansal, Ravi & Khatchatrian, Varoujan & Yaron, Amir, 2005. "Interpretable asset markets?," European Economic Review, Elsevier, vol. 49(3), pages 531-560, April.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1603.00751. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.