IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/46218.html
   My bibliography  Save this paper

Rozmyte zbiory probabilistyczne jako narzędzie finansów behawioralnych
[Fuzzy Probabilistic Sets as a Tool for Behavioural Finance]

Author

Listed:
  • Piasecki, Krzysztof

Abstract

The book is divided into five parts. The essence of behavioural finance is presented in the first parts. Fuzzy generalizations of some mathematical concepts are presented in the second part. The impact of selected behavioural premises for imprecise estimation of expected return is described in the third part. In the fourth part are considered financial instruments under uncertainty and imprecision risk. In the fifth part fuzzy probabilistic sets are applied for actuarial mathematics.

Suggested Citation

  • Piasecki, Krzysztof, 2011. "Rozmyte zbiory probabilistyczne jako narzędzie finansów behawioralnych [Fuzzy Probabilistic Sets as a Tool for Behavioural Finance]," MPRA Paper 46218, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:46218
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/46218/1/MPRA_paper_46218.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/46526/1/MPRA_paper_46218.pdf
    File Function: revised version
    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. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. 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.
    5. Yong Fang & Kin Keung Lai & Shouyang Wang, 2008. "Fuzzy Portfolio Optimization," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-77926-1, July.
    6. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    7. Kent D. Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 2001. "Overconfidence, Arbitrage, and Equilibrium Asset Pricing," Journal of Finance, American Finance Association, vol. 56(3), pages 921-965, June.
    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. Krzysztof Piasecki, 2012. "The basis of financial arithmetic from the viewpoint of utility theory," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 22(3), pages 37-53.
    2. repec:wut:journl:v:3:y:2012:id:1044 is not listed on IDEAS
    3. Elwira Gross-Gołacka & Marta Kusterka-Jefmańska & Bartłomiej Jefmański, 2020. "Can Elements of Intellectual Capital Improve Business Sustainability?—The Perspective of Managers of SMEs in Poland," Sustainability, MDPI, vol. 12(4), pages 1-23, February.

    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. Weber, Martin & Welfens, Frank, 2007. "How do markets react to fundamental shocks? : An experimental analysis on underreaction and momentum," Papers 07-42, Sonderforschungsbreich 504.
    2. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021, January-A.
    3. Achim BACKHAUS & Aliya ZHAKANOVA ISIKSAL, 2016. "The Impact of Momentum Factors on Multi Asset Portfolio," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 146-169, December.
    4. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    5. Wen-Jun Xue & Li-Wen Zhang, 2016. "Stock Return Autocorrelations and Predictability in the Chinese Stock Market: Evidence from Threshold Quantile Autoregressive Models," Working Papers 1605, Florida International University, Department of Economics.
    6. Glaser, Markus & Nöth, Markus & Weber, Martin, 2003. "Behavioral finance," Papers 03-14, Sonderforschungsbreich 504.
    7. Ebert, Sebastian & Hilpert, Christian, 2019. "Skewness preference and the popularity of technical analysis," Journal of Banking & Finance, Elsevier, vol. 109(C).
    8. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 397-407.
    9. David Hirshleife, 2015. "Behavioral Finance," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 133-159, December.
    10. Xue, Wen-Jun & Zhang, Li-Wen, 2017. "Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models," Economic Modelling, Elsevier, vol. 60(C), pages 391-401.
    11. Xu, Shaojun, 2023. "Behavioral asset pricing under expected feedback mode," International Review of Financial Analysis, Elsevier, vol. 86(C).
    12. John Y. Campbell, 2000. "Asset Pricing at the Millennium," Journal of Finance, American Finance Association, vol. 55(4), pages 1515-1567, August.
    13. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2005. "Evidence on the speed of convergence to market efficiency," Journal of Financial Economics, Elsevier, vol. 76(2), pages 271-292, May.
    14. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    15. Loann David Denis Desboulets, 2017. "Co-movements in Market Prices and Fundamentals: A Semiparametric Multivariate GARCH Approach," Working Papers halshs-02059302, HAL.
    16. Ramiah, Vikash & Xu, Xiaoming & Moosa, Imad A., 2015. "Neoclassical finance, behavioral finance and noise traders: A review and assessment of the literature," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 89-100.
    17. Taoufik Elkemali, 2023. "Uncertainty and Financial Analysts’ Optimism: A Comparison between High-Tech and Low-Tech European Firms," Sustainability, MDPI, vol. 15(3), pages 1-22, January.
    18. Semen Son Turan, 2014. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies," Information Management and Business Review, AMH International, vol. 6(6), pages 317-328.
    19. Sandrine Jacob Leal, 2015. "Fundamentalists, chartists and asset pricing anomalies," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1837-1850, November.
    20. Chaonan Lin & Nien‐Tzu Yang & Robin K. Chou & Kuan‐Cheng Ko, 2022. "A timing momentum strategy," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1339-1379, April.

    More about this item

    Keywords

    behavioural finance; fuzzy set; uncertainty; indistinctness; ambiguity; fuzzy probabilistic set; return rate; risk;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:pra:mprapa:46218. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.