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Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning

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  • Nikola MILOSEVIC

    (School of Computer Science, University of Manchester, UK.)

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," Journal of Economics Library, KSP Journals, vol. 3(2), pages 288-294, June.
    • Handle: RePEc:ksp:journ5:v:3:y:2016:i:2:p:288-294
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    References listed on IDEAS

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    1. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    2. Anne Margaret Lee, 2005. "A study in time," Nature, Nature, vol. 436(7049), pages 438-438, July.
    3. 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.
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    Cited by:

    1. Iulian-Cornel LOLEA & Ioan-Radu PETRARIU & Adriana GIURGIU, 2021. "ARIMA vs. MACHINE LEARNING IN TERMS OF EQUITY MARKET FORECASTING," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 30(2), pages 299-308, December.
    2. Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem," Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
    3. Akhilesh Prasad & Priti Bakhshi & Arumugam Seetharaman, 2022. "The Impact of the U.S. Macroeconomic Variables on the CBOE VIX Index," JRFM, MDPI, vol. 15(3), pages 1-25, March.
    4. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    5. Dushmanta Kumar Padhi & Neelamadhab Padhy & Akash Kumar Bhoi & Jana Shafi & Muhammad Fazal Ijaz, 2021. "A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators," Mathematics, MDPI, vol. 9(21), pages 1-31, October.
    6. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.

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    More about this item

    Keywords

    Machine learning; Long term investment; Equity; Stock price prediction.;
    All these keywords.

    JEL classification:

    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)

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