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Technical Analysis, Fundamental Analysis, and Ichimoku Dynamics: A Bibliometric Analysis

Author

Listed:
  • Luís Almeida

    (GOVCOPP Unit Research, ISCA—Aveiro University, 3810-193 Aveiro, Portugal)

  • Elisabete Vieira

    (GOVCOPP Unit Research, ISCA—Aveiro University, 3810-193 Aveiro, Portugal)

Abstract

This article aims to contribute to the academic knowledge in the field of scientific production regarding decision support tools for investments in the capital market, specifically focusing on fundamental analysis, technical analysis, and Ichimoku dynamics. Bibliometric analysis, following the three main laws (Bradford’s Law, Lotka’s Law, and Zipf’s Law), was employed to evaluate scientific production, identify publication patterns, and uncover gaps and collaboration networks over the last thirty years. To achieve these objectives, 1710 relevant academic publications on the topic were analyzed and retrieved from the Web of Science (WOS) database, pertaining to the last 30 years, between 1990 and 22 May 2023. The significance of this article lies in the contributions of the findings, which advance scientific knowledge by identifying gaps in the knowledge and research, particularly in the limited literature on Ichimoku; our review reveals a growing trend of research in this area. Another notable conclusion is the emergence of new research topics and areas of interest, as well as the identification of collaboration networks among authors, institutions, and countries. Moreover, the article provides valuable insights for financial professionals and investors who are interested in applying these methodologies as methods for price forecasting. The highlighted results support investment decision making, trading strategies, and portfolio management.

Suggested Citation

  • Luís Almeida & Elisabete Vieira, 2023. "Technical Analysis, Fundamental Analysis, and Ichimoku Dynamics: A Bibliometric Analysis," Risks, MDPI, vol. 11(8), pages 1-24, August.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:8:p:142-:d:1210437
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    References listed on IDEAS

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    1. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    2. Stephen Brown & William Goetzmann & Alok Kumar, 1998. "The Dow Theory: William Peter Hamilton's Track Record Re-Considered," Yale School of Management Working Papers ysm85, Yale School of Management, revised 01 Apr 2008.
    3. 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.
    4. Gerritsen, Dirk F. & Bouri, Elie & Ramezanifar, Ehsan & Roubaud, David, 2020. "The profitability of technical trading rules in the Bitcoin market," Finance Research Letters, Elsevier, vol. 34(C).
    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. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    7. Junwen Zhu & Weishu Liu, 2020. "A tale of two databases: the use of Web of Science and Scopus in academic papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 321-335, April.
    8. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Riaz Ud Din & Salman Ahmed & Saddam Hussain Khan, 2024. "A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecasting," Papers 2401.11621, arXiv.org.
    2. Olcay Ozupek & Reyat Yilmaz & Bita Ghasemkhani & Derya Birant & Recep Alp Kut, 2024. "A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning," Mathematics, MDPI, vol. 12(17), pages 1-36, September.
    3. Purity Maina & Balázs Gyenge & Mária Fekete-Farkas & Anett Parádi-Dolgos, 2024. "Analyzing Trends in Green Financial Instrument Issuance for Climate Finance in Capital Markets," JRFM, MDPI, vol. 17(4), pages 1-24, April.
    4. Yensen Ni, 2024. "Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives," Energies, MDPI, vol. 17(12), pages 1-22, June.

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