IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0209922.html
   My bibliography  Save this article

Forecasting leading industry stock prices based on a hybrid time-series forecast model

Author

Listed:
  • Ming-Chi Tsai
  • Ching-Hsue Cheng
  • Meei-Ing Tsai
  • Huei-Yuan Shiu

Abstract

Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.

Suggested Citation

  • Ming-Chi Tsai & Ching-Hsue Cheng & Meei-Ing Tsai & Huei-Yuan Shiu, 2018. "Forecasting leading industry stock prices based on a hybrid time-series forecast model," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-24, December.
    • Handle: RePEc:plo:pone00:0209922
    DOI: 10.1371/journal.pone.0209922
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0209922
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209922&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0209922?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. Valeriy Gavrishchaka & Supriya Banerjee, 2006. "Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting," Computational Management Science, Springer, vol. 3(2), pages 147-160, April.
    2. Chen, Sichong, 2012. "The predictability of aggregate Japanese stock returns: Implications of dividend yield," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 284-304.
    3. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2012. "Oil prices, exchange rates and emerging stock markets," Energy Economics, Elsevier, vol. 34(1), pages 227-240.
    4. Bagella, Michele & Becchetti, Leonardo & Adriani, Fabrizio, 2005. "Observed and "fundamental" price-earning ratios: A comparative analysis of high-tech stock evaluation in the US and in Europe," Journal of International Money and Finance, Elsevier, vol. 24(4), pages 549-581, June.
    5. Bessembinder, Hendrik, 2003. "Quote-based competition and trade execution costs in NYSE-listed stocks," Journal of Financial Economics, Elsevier, vol. 70(3), pages 385-422, December.
    6. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    7. Tanaka-Yamawaki, Mieko & Tokuoka, Seiji, 2007. "Adaptive use of technical indicators for the prediction of intra-day stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 125-133.
    8. Politi, Mauro & Millot, Nicolas & Chakraborti, Anirban, 2012. "The near-extreme density of intraday log-returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 147-155.
    9. Hassapis, Christis & Kalyvitis, Sarantis, 2002. "Investigating the links between growth and real stock price changes with empirical evidence from the G-7 economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(3), pages 543-575.
    10. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    11. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    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. Yiqi Deng & Siu Ming Yiu, 2022. "Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News," Papers 2206.14452, arXiv.org.

    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. Terence Tai-Leung Chong & Wing-Kam Ng & Venus Khim-Sen Liew, 2014. "Revisiting the Performance of MACD and RSI Oscillators," JRFM, MDPI, vol. 7(1), pages 1-12, February.
    2. Aspris, Angelo & Malloch, Hamish & Svec, Jiri, 2024. "Option implied dividends and the market risk premium," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    3. Yang, Lu & Cai, Xiao Jing & Hamori, Shigeyuki, 2018. "What determines the long-term correlation between oil prices and exchange rates?," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 140-152.
    4. Alessandro Barattieri & Matteo Cacciatore, 2023. "Self-Harming Trade Policy? Protectionism and Production Networks," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 97-128, April.
    5. Zhang, Zhikai & Wang, Yudong & Xiao, Jihong & Zhang, Yaojie, 2023. "Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions," Resources Policy, Elsevier, vol. 80(C).
    6. Daniya Tlegenova, 2015. "Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan," Papers 1508.07534, arXiv.org.
    7. Bedoui, Rihab & Braiek, Sana & Guesmi, Khaled & Chevallier, Julien, 2019. "On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model," Energy Economics, Elsevier, vol. 80(C), pages 876-889.
    8. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    9. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    10. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    11. Semenov, Andrei, 2021. "Measuring the stock's factor beta and identifying risk factors under market inefficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 635-649.
    12. Ozlem Goktas & Aycan Hepsag, 2011. "Do stock returns lead real economic activity? Evidence from seasonal cointegration analysis," Economics Bulletin, AccessEcon, vol. 31(3), pages 2117-2127.
    13. Charles K.D. Adjasi, 2009. "Macroeconomic uncertainty and conditional stock-price volatility in frontier African markets: Evidence from Ghana," Journal of Risk Finance, Emerald Group Publishing, vol. 10(4), pages 333-349, August.
    14. Trifan, Emanuela, 2004. "Entscheidungsregeln und ihr Einfluss auf den Aktienkurs," Darmstadt Discussion Papers in Economics 131, Darmstadt University of Technology, Department of Law and Economics.
    15. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    16. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    17. Wu, Shue-Jen, 2023. "The role of the past long-run oil price changes in stock market," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 274-291.
    18. de Oliveira Souza, Thiago, 2019. "A critique of momentum anomalies," Discussion Papers on Economics 5/2019, University of Southern Denmark, Department of Economics.
    19. Kyritsis, Evangelos & Serletis, Apostolos, 2018. "The zero lower bound and market spillovers: Evidence from the G7 and Norway," Research in International Business and Finance, Elsevier, vol. 44(C), pages 100-123.
    20. Afshan, Sahar & Yaqoob, Tanzeela & Zaied, Younes Ben & Mishra, Shekhar & Mishra, Sibanjan, 2024. "Oil shocks and currency behavior: A dual approach to digital and traditional currencies," Global Finance Journal, 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:plo:pone00:0209922. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.