IDEAS home Printed from https://ideas.repec.org/a/krk/eberjl/v2y2014i4p85-100.html
   My bibliography  Save this article

Real Time Investments with Adequate Portfolio Theory

Author

Listed:
  • Alina Kvietkauskienė

    (Vilnius Gediminas Technical University)

Abstract

The objective of this paper is to identify investment decision making schemes using the adequate portfolio model. This approach can be employed to project investment in stocks, using the opportunities offered by the markets and investor intelligence. It was decided to use adequate portfolio theory for investment decision making, simulation of financial markets, and optimisation of utility function. In order to achieve better performance of sustainable returns in equity portfolio, different markets, and existing companies’ equities and portfolios were selected, investigating their returns based on adequate portfolio theory. The main conclusion of article suggests investigating return on individual portfolio level. Real investment is a way to make sure of the soundness of applicable strategies. The portfolios were formed from stocks of USA, German and French markets and quoted, using adequate investment portfolio system, in DNB Trade demo version, what allows monitoring of the long-term investment experiment.

Suggested Citation

  • Alina Kvietkauskienė, 2014. "Real Time Investments with Adequate Portfolio Theory," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 2(4), pages 85-100.
    • Handle: RePEc:krk:eberjl:v:2:y:2014:i:4:p:85-100
    as

    Download full text from publisher

    File URL: http://eber.uek.krakow.pl/index.php/eber/article/download/63/pdf_6
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    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. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2016. "Efficient skewness/semivariance portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 331-346, September.
    2. Kassimatis, Konstantinos, 2021. "Mean-variance versus utility maximization revisited: The case of constant relative risk aversion," International Review of Financial Analysis, Elsevier, vol. 78(C).
    3. Ahmar, Ansari Saleh & Arifin, Andi Nurani Mangkawani, 2017. "Optimalisasi Risiko Saham Menggunakan Optimalisasi Portofolio Markowitz (Studi Kasus Saham Di Indonesia)," INA-Rxiv 5v27k, Center for Open Science.
    4. Simaan, Majeed & Simaan, Yusif & Tang, Yi, 2018. "Estimation error in mean returns and the mean-variance efficient frontier," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 109-124.
    5. John Griffin, 2015. "Risk Premia and Knightian Uncertainty in an Experimental Market Featuring a Long-Lived Asset," Fordham Economics Discussion Paper Series dp2015-01, Fordham University, Department of Economics.
    6. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    7. Alessandra Carleo & Francesco Cesarone & Andrea Gheno & Jacopo Maria Ricci, 2017. "Approximating exact expected utility via portfolio efficient frontiers," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 115-143, November.
    8. Tang, Qihe & Tong, Zhiwei & Xun, Li, 2022. "Insurance risk analysis of financial networks vulnerable to a shock," European Journal of Operational Research, Elsevier, vol. 301(2), pages 756-771.
    9. Hemant Jalota & Manoj Thakur, 2018. "Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 294-305, February.
    10. Ma, Guiyuan & Siu, Chi Chung & Zhu, Song-Ping, 2019. "Dynamic portfolio choice with return predictability and transaction costs," European Journal of Operational Research, Elsevier, vol. 278(3), pages 976-988.
    11. Trindade, Marco A.S. & Floquet, Sergio & Filho, Lourival M. Silva, 2020. "Portfolio theory, information theory and Tsallis statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    12. Salo, Ahti & Doumpos, Michalis & Liesiö, Juuso & Zopounidis, Constantin, 2024. "Fifty years of portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(1), pages 1-18.
    13. Levy, Moshe & Kaplanski, Guy, 2015. "Portfolio selection in a two-regime world," European Journal of Operational Research, Elsevier, vol. 242(2), pages 514-524.
    14. Meade, N. & Beasley, J.E. & Adcock, C.J., 2021. "Quantitative portfolio selection: Using density forecasting to find consistent portfolios," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1053-1067.
    15. Rooderkerk, Robert P. & van Heerde, Harald J., 2016. "Robust optimization of the 0–1 knapsack problem: Balancing risk and return in assortment optimization," European Journal of Operational Research, Elsevier, vol. 250(3), pages 842-854.
    16. Bi, Junna & Jin, Hanqing & Meng, Qingbin, 2018. "Behavioral mean-variance portfolio selection," European Journal of Operational Research, Elsevier, vol. 271(2), pages 644-663.
    17. Gatzert, Nadine & Martin, Alexander & Schmidt, Martin & Seith, Benjamin & Vogl, Nikolai, 2021. "Portfolio optimization with irreversible long-term investments in renewable energy under policy risk: A mixed-integer multistage stochastic model and a moving-horizon approach," European Journal of Operational Research, Elsevier, vol. 290(2), pages 734-748.
    18. Lassance, Nathan, 2022. "Reconciling mean-variance portfolio theory with non-Gaussian returns," European Journal of Operational Research, Elsevier, vol. 297(2), pages 729-740.
    19. Ray, Pritee & Jenamani, Mamata, 2016. "Mean-variance analysis of sourcing decision under disruption risk," European Journal of Operational Research, Elsevier, vol. 250(2), pages 679-689.
    20. Vanduffel, Steven & Yao, Jing, 2017. "A stein type lemma for the multivariate generalized hyperbolic distribution," European Journal of Operational Research, Elsevier, vol. 261(2), pages 606-612.

    More about this item

    Keywords

    investments; adequate portfolio; Markowitz theory; utility function; uncertainty;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:krk:eberjl:v:2:y:2014:i:4:p:85-100. 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: Piotr Stanek, PhD. (email available below). General contact details of provider: https://edirc.repec.org/data/aekrapl.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.