IDEAS home Printed from https://ideas.repec.org/a/rfe/zbefri/v27y2009i1p171-196.html
   My bibliography  Save this article

Application of MACD and RVI indicators as functions of investment strategy optimization on the financial market

Author

Listed:
  • Dejan Eric

    (Institute of Economic Sciences, Belgrade, Serbia)

  • Goran Andjelic

    (University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia)

  • Srdjan Redzepagic

    (Institute of Economic Sciences, Belgrade, Serbia)

Abstract

The determination of trends and prediction of stock prices is one of the main tasks of the MACD (Moving Average Convergence Divergence) and the RVI (Relative Volatility Index) indicators of the technical analysis. The research covers the sample representing stocks which are continually traded on the financial market of the Republic of Serbia. Subject of this research is to determine the possibility of MACD and RVI indicators application in investment decision making processes on the financial market of the Republic of Serbia. The main goal of the research is to identify the most profitable parameters of the MACD and RVI indicators as functions of investment strategy optimization on the financial market. The main hypothesis of the research is that the application of the MACD and RVI indicators of technical analysis significantly contributes to investment strategy optimization on the financial market. The applied methodology during the research includes analyses, synthesis and statistical/ mathematical methods with special focus on the method of moving averages. Research results indicate significant possibilities in application of MACD and RVI indicators of technical analysis as functions of making optimum decisions on investment. According to the obtained results it is concluded that the application of the optimized MACD and RVI indicators of technical analysis in decision making process on investing on the financial market significantly contributes maximization of profitability on investments.

Suggested Citation

  • Dejan Eric & Goran Andjelic & Srdjan Redzepagic, 2009. "Application of MACD and RVI indicators as functions of investment strategy optimization on the financial market," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 27(1), pages 171-196.
    • Handle: RePEc:rfe:zbefri:v:27:y:2009:i:1:p:171-196
    as

    Download full text from publisher

    File URL: https://www.efri.uniri.hr/sites/efri.hr/files/cr-collections/2/andjelic-2009-1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, August.
    2. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    3. Dechow, Patricia M. & Hutton, Amy P. & Meulbroek, Lisa & Sloan, Richard G., 2001. "Short-sellers, fundamental analysis, and stock returns," Journal of Financial Economics, Elsevier, vol. 61(1), pages 77-106, July.
    Full references (including those not matched with items on IDEAS)

    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. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    2. Sid Ghoshal & Stephen Roberts, 2016. "Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes," Papers 1603.06202, arXiv.org, revised Jul 2018.
    3. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    4. Bohm, Volker & Wenzelburger, Jan, 2005. "On the performance of efficient portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 721-740, April.
    5. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857, April.
    6. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    7. Sid Ghoshal & Stephen J. Roberts, 2018. "Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting," Papers 1807.03192, arXiv.org, revised Jul 2018.
    8. Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2017. "Time series momentum and moving average trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 405-421, March.
    9. James Angel & Douglas McCabe, 2013. "Fairness in Financial Markets: The Case of High Frequency Trading," Journal of Business Ethics, Springer, vol. 112(4), pages 585-595, February.
    10. Michael McAleer & John Suen & Wing Keung Wong, 2016. "Profiteering from the Dot-Com Bubble, Subprime Crisis and Asian Financial Crisis," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 257-279, September.
    11. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    12. K. J. Hong & S. Satchell, 2015. "Time series momentum trading strategy and autocorrelation amplification," Quantitative Finance, Taylor & Francis Journals, vol. 15(9), pages 1471-1487, September.
    13. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    14. Sukanto Bhattacharya & Kuldeep Kumar, 2006. "A Computational Exploration of the Efficacy of Fibonacci Sequences in Technical Analysis and Trading," Annals of Economics and Finance, Society for AEF, vol. 7(1), pages 185-196, May.
    15. Nikolai Dokuchaev, 2015. "Modelling Possibility of Short-Term Forecasting of Market Parameters for Portfolio Selection," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 143-161, May.
    16. Chong Terence Tai-Leung & Poon Ka-Ho, 2017. "A new recognition algorithm for “head-and-shoulders” price patterns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    17. Sang Hyuk Kim & Hee Soo Lee & Han Jun Ko & Seung Hwan Jeong & Hyun Woo Byun & Kyong Joo Oh, 2018. "Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
    18. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    19. Zhu, Min & Atri, Said & Yegen, Eyub, 2016. "Are candlestick trading strategies effective in certain stocks with distinct features?," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 116-127.
    20. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.

    More about this item

    Keywords

    technical analysis; MACD indicator; RVI indicator; investment strategy; financial market;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:rfe:zbefri:v:27:y:2009:i:1:p:171-196. 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: Danijela Ujcic (email available below). General contact details of provider: https://edirc.repec.org/data/efrijhr.html .

    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.