IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i6p1911-d151260.html
   My bibliography  Save this article

Dynamically Controlled Length of Training Data for Sustainable Portfolio Selection

Author

Listed:
  • Sarunas Raudys

    (Faculty of Mathematics and Informatics, Institute of Informatics, Vilnius University, Naugarduko st. 24, LT-03225 Vilnius, Lithuania)

  • Aistis Raudys

    (Faculty of Mathematics and Informatics, Institute of Informatics, Vilnius University, Naugarduko st. 24, LT-03225 Vilnius, Lithuania)

  • Zidrina Pabarskaite

    (Faculty of Mathematics and Informatics, Institute of Informatics, Vilnius University, Naugarduko st. 24, LT-03225 Vilnius, Lithuania)

Abstract

In a constantly changing market environment, it is a challenge to construct a sustainable portfolio. One cannot use too long or too short training data to select the right portfolio of investments. When analyzing ten types of recent (up to April 2018) extremely high-dimensional time series from automated trading domains, it was discovered that there is no a priori ‘optimal’ length of training history that would fit all investment tasks. The optimal history length depends of the specificity of the data and varies with time. This statement was also confirmed by the analysis of dozens of multi-dimensional synthetic time series data generated by excitable medium models frequently considered in studies of chaos. An algorithm for determining the optimal length of training history to produce a sustainable portfolio is proposed. Monitoring the size of the learning data can be useful in data mining tasks used in the analysis of sustainability in other research disciplines.

Suggested Citation

  • Sarunas Raudys & Aistis Raudys & Zidrina Pabarskaite, 2018. "Dynamically Controlled Length of Training Data for Sustainable Portfolio Selection," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1911-:d:151260
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/6/1911/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/6/1911/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Balvers, Ronald J. & Mitchell, Douglas W., 2000. "Efficient gradualism in intertemporal portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 21-38, January.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. 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.
    4. Adam Zaremba & Jacob Shemer, 2017. "Country Asset Allocation," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-59191-3, December.
    5. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    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. Sarunas Raudys & Aistis Raudys & Zidrina Pabarskaite & Ausra Liubaviciute, 2022. "Immunology-Based Sustainable Portfolio Management," Sustainability, MDPI, vol. 14(5), pages 1-11, February.
    2. Seyoung Park & Eun Ryung Lee & Sungchul Lee & Geonwoo Kim, 2019. "Dantzig Type Optimization Method with Applications to Portfolio Selection," Sustainability, MDPI, vol. 11(11), pages 1-32, June.

    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. Valle, C.A. & Meade, N. & Beasley, J.E., 2014. "Absolute return portfolios," Omega, Elsevier, vol. 45(C), pages 20-41.
    2. Xidonas, Panos & Hassapis, Christis & Soulis, John & Samitas, Aristeidis, 2017. "Robust minimum variance portfolio optimization modelling under scenario uncertainty," Economic Modelling, Elsevier, vol. 64(C), pages 60-71.
    3. Daniel Felix Ahelegbey & Paolo Giudici & Fatemeh Mojtahedi, 2022. "Crypto Asset Portfolio Selection," FinTech, MDPI, vol. 1(1), pages 1-9, February.
    4. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.
    5. Juan F. Monge & Mercedes Landete & Jos'e L. Ruiz, 2016. "Sharpe portfolio using a cross-efficiency evaluation," Papers 1610.00937, arXiv.org, revised Oct 2016.
    6. Vijaya Krishna Kanaparthi, 2024. "Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis," FinTech, MDPI, vol. 3(1), pages 1-22, February.
    7. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2020. "Does sophistication of the weighting scheme enhance the performance of long-short commodity portfolios?," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 164-180.
    8. Iwanicz-Drozdowska Małgorzata & Rogowicz Karol & Smaga Paweł, 2023. "Market-moving events and their role in portfolio optimization of generations X, Y, and Z," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 59(4), pages 371-397, December.
    9. Cui, Tianxiang & Ding, Shusheng & Jin, Huan & Zhang, Yongmin, 2023. "Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach," Economic Modelling, Elsevier, vol. 119(C).
    10. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    11. Carroll, Rachael & Conlon, Thomas & Cotter, John & Salvador, Enrique, 2017. "Asset allocation with correlation: A composite trade-off," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1164-1180.
    12. Gatfaoui, Hayette, 2019. "Diversifying portfolios of U.S. stocks with crude oil and natural gas: A regime-dependent optimization with several risk measures," Energy Economics, Elsevier, vol. 80(C), pages 132-152.
    13. Leal, Marina & Ponce, Diego & Puerto, Justo, 2020. "Portfolio problems with two levels decision-makers: Optimal portfolio selection with pricing decisions on transaction costs," European Journal of Operational Research, Elsevier, vol. 284(2), pages 712-727.
    14. Harris, Richard D.F. & Stoja, Evarist & Tan, Linzhi, 2017. "The dynamic Black–Litterman approach to asset allocation," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1085-1096.
    15. Erindi Allaj, 2020. "The Black–Litterman model and views from a reverse optimization procedure: an out-of-sample performance evaluation," Computational Management Science, Springer, vol. 17(3), pages 465-492, October.
    16. Xidonas, Panos & Mavrotas, George & Hassapis, Christis & Zopounidis, Constantin, 2017. "Robust multiobjective portfolio optimization: A minimax regret approach," European Journal of Operational Research, Elsevier, vol. 262(1), pages 299-305.
    17. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Hsin, Yi-Ting & Sheu, Her-Jiun, 2022. "Omega portfolio models with floating return threshold," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 743-758.
    18. Nathan Lassance & Frédéric Vrins, 2021. "Minimum Rényi entropy portfolios," Annals of Operations Research, Springer, vol. 299(1), pages 23-46, April.
    19. Pierre O. De souza & Tiago P. Filomena & João F. Caldeira & Denis Borenstein & Marcelo B. Righi, 2017. "Risk parity in the brazilian market," Economics Bulletin, AccessEcon, vol. 37(3), pages 1555-1566.
    20. Cristiano Arbex Valle, 2024. "Portfolio optimisation: bridging the gap between theory and practice," Papers 2407.00887, arXiv.org, revised Sep 2024.

    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:gam:jsusta:v:10:y:2018:i:6:p:1911-:d:151260. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.