IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v383y2007i1p125-133.html
   My bibliography  Save this article

Adaptive use of technical indicators for the prediction of intra-day stock prices

Author

Listed:
  • Tanaka-Yamawaki, Mieko
  • Tokuoka, Seiji

Abstract

We examine the effectiveness of frequently used technical indicators for intra-day forecast by applying them on the tick data of various stock prices. We show that the optimal combination of a few indicators chosen for each stock by using evolutional computation provides us a good forecast on the level of the future price at several ticks ahead.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:1:p:125-133
    DOI: 10.1016/j.physa.2007.04.126
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437107005067
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2007.04.126?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    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. Popov, Maxim & Madlener, Reinhard, 2014. "Backtesting and Evaluation of Different Trading Schemes for the Portfolio Management of Natural Gas," FCN Working Papers 5/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Bao, Te & Corgnet, Brice & Hanaki, Nobuyuki & Riyanto, Yohanes E. & Zhu, Jiahua, 2023. "Predicting the unpredictable: New experimental evidence on forecasting random walks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    4. Didenko Alexander & Demicheva Svetlana, 2013. "Application of Ensemble Learning for views generation in Meucci portfolio optimization framework," Review of Business and Economics Studies, CyberLeninka;Федеральное государственное образовательное бюджетное учреждение высшего профессионального образования «Финансовый университет при Правительстве Российской Федерации» (Финансовый университет), issue 1, pages 100-110.
    5. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    6. Tai-Liang Chen & Ching-Hsue Cheng & Jing-Wei Liu, 2019. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1967-1987, November.
    7. 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.
    8. Ilia Zaznov & Julian Kunkel & Alfonso Dufour & Atta Badii, 2022. "Predicting Stock Price Changes Based on the Limit Order Book: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-33, April.
    9. Ben Moews & Gbenga Ibikunle, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Papers 2002.10385, arXiv.org.
    10. Tai-Liang Chen, 2012. "Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-series," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 38-50, February.
    11. Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
    12. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    13. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    14. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.

    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:eee:phsmap:v:383:y:2007:i:1:p:125-133. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.