IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v48y2016i4d10.1007_s10614-015-9552-1.html
   My bibliography  Save this article

The Portfolio Heuristic Optimisation System (PHOS)

Author

Listed:
  • N. Loukeris

    (University of Macedonia)

  • I. Eleftheriadis

    (University of Macedonia)

  • E. Livanis

    (University of Macedonia)

Abstract

The efficient representation of the accurate corporate value on the stock price is vital to investors and fund managers that desire to optimise the net worth of the overall stock portfolio. Although the efficient market hypothesis sets limits, the practice of markets is an ideal place of manipulation, and corruption on prices. The accounting statements, evaluated by support vector machines and the SVM hybrids under genetic algorithms provide superiority in portfolio selection, on condition. A specific genetic hybrid SVM outperformed all examined SVM models being a powerful tool in financial analysis. We also offer the integrated model of portfolio selection, PHOS.

Suggested Citation

  • N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.
    • Handle: RePEc:kap:compec:v:48:y:2016:i:4:d:10.1007_s10614-015-9552-1
    DOI: 10.1007/s10614-015-9552-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-015-9552-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-015-9552-1?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.

    References listed on IDEAS

    as
    1. Shuisheng Zhou & Jiangtao Cui & Feng Ye & Hongwei Liu & Qiang Zhu, 2013. "New smoothing SVM algorithm with tight error bound and efficient reduced techniques," Computational Optimization and Applications, Springer, vol. 56(3), pages 599-617, December.
    2. Lamont, Owen & Polk, Christopher & Saa-Requejo, Jesus, 2001. "Financial Constraints and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 529-554.
    3. Smith, David B. & Nichols, Donald R., 1982. "A market test of investor reaction to disagreements," Journal of Accounting and Economics, Elsevier, vol. 4(2), pages 109-120, October.
    4. Tristan Fletcher & John Shawe-Taylor, 2013. "Multiple Kernel Learning with Fisher Kernels for High Frequency Currency Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 217-240, August.
    5. Ball, R & Brown, P, 1968. "Empirical Evaluation Of Accounting Income Numbers," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 159-178.
    6. Periklis Gogas & Theophilos Papadimitriou & Maria Matthaiou & Efthymia Chrysanthidou, 2015. "Yield Curve and Recession Forecasting in a Machine Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 635-645, April.
    7. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    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. Stelios Bekiros & Nikolaos Loukeris & Nikolaos Matsatsinis & Frank Bezzina, 2019. "Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 647-667, August.

    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. Xuekui Zhang & Yuying Huang & Ke Xu & Li Xing, 2023. "Novel modelling strategies for high-frequency stock trading data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    2. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    3. Hou, Kewei & Xue, Chen & Zhang, Lu, 2017. "Replicating Anomalies," Working Paper Series 2017-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    4. Yao Zheng & Peihwang Wei & Eric Osmer, 2022. "The relation between earnings and price momentum: Does it vary across regimes?," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1145-1213, April.
    5. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    6. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    7. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    8. Jeroen Suijs, 2008. "On the Value Relevance of Asymmetric Financial Reporting Policies," Journal of Accounting Research, Wiley Blackwell, vol. 46(5), pages 1297-1321, December.
    9. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    10. Katsiaryna Bardos & Steven E. Kozlowski & Michael R. Puleo, 2021. "Entrenchment or efficiency? CEO‐to‐employee pay ratio and the cost of debt," The Financial Review, Eastern Finance Association, vol. 56(3), pages 511-533, August.
    11. Alderson, Michael J. & Betker, Brian L. & Halford, Joseph T., 2021. "Fictitious dividend cuts in the CRSP data," Journal of Corporate Finance, Elsevier, vol. 71(C).
    12. Petr Korab & Jitka Pomenkova, 2017. "Credit Rationing in Greece During and After the Financial Crisis," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(2), pages 119-139, April.
    13. Mei Luo & Shuai Shao & Frank Zhang, 2018. "Does financial reporting above or below operating income matter to firms and investors? The case of investment income in China," Review of Accounting Studies, Springer, vol. 23(4), pages 1754-1790, December.
    14. Falavigna, Greta & Ippoliti, Roberto, 2023. "SMEs’ behavior under financial constraints: An empirical investigation on the legal environment and the substitution effect with tax arrears," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    15. Veith, Stefan & Werner, Jörg R. & Zimmermann, Jochen, 2009. "Capital market response to emission rights returns: Evidence from the European power sector," Energy Economics, Elsevier, vol. 31(4), pages 605-613, July.
    16. Monica Martinez-Blasco & Vanessa Serrano & Francesc Prior & Jordi Cuadros, 2023. "Analysis of an event study using the Fama–French five-factor model: teaching approaches including spreadsheets and the R programming language," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    17. Joliet, Robert & Muller, Aline, 2016. "Are foreign earnings disclosures value-relevant?," Research in International Business and Finance, Elsevier, vol. 37(C), pages 170-183.
    18. DuCharme, Larry L. & Malatesta, Paul H. & Sefcik, Stephan E., 2004. "Earnings management, stock issues, and shareholder lawsuits," Journal of Financial Economics, Elsevier, vol. 71(1), pages 27-49, January.
    19. Al-Amri, Khalid & Davydov, Yevgeniy, 2016. "Testing the effectiveness of ERM: Evidence from operational losses," Journal of Economics and Business, Elsevier, vol. 87(C), pages 70-82.
    20. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.

    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:kap:compec:v:48:y:2016:i:4:d:10.1007_s10614-015-9552-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.