IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p3033-d1189422.html
   My bibliography  Save this article

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

Author

Listed:
  • Simona Hašková

    (Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceské Budejovice, Czech Republic)

  • Petr Šuleř

    (Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceské Budejovice, Czech Republic)

  • Róbert Kuchár

    (Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceské Budejovice, Czech Republic)

Abstract

The article presents the predictive capabilities of a fuzzy multi-criteria evaluation system that operates on the basis of a non-fuzzy neural approach, but also one that is capable of implementing a learning paradigm and working with vague concepts. Within this context, the necessary elements of fuzzy logic are identified and the algebraic formulation of the fuzzy system is presented. It is with the help of the aforementioned that the task of predicting the short-term trend and price of the Tesla share is solved. The functioning of a fuzzy system and fuzzy neural network in the field of time series value prediction is discussed. The authors are inclined to the opinion that, despite the fact that a fuzzy neural network reacts in terms of applicability and effectiveness when solving prediction problems in relation to input data with a faster output than a fuzzy system, and is more “user friendly”, a sufficiently knowledgeable and experienced solver/expert could, by using a fuzzy system, achieve a higher speed of convergence in the learning process than a fuzzy neural network using the minimum range of input data carrying the necessary information. A fuzzy system could therefore be a possible alternative to a fuzzy neural network from the point of view of prediction.

Suggested Citation

  • Simona Hašková & Petr Šuleř & Róbert Kuchár, 2023. "A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study," Mathematics, MDPI, vol. 11(13), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:3033-:d:1189422
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/3033/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/13/3033/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdullah H. Alenezy & Mohd Tahir Ismail & S. Al Wadi & Muhammad Tahir & Nawaf N. Hamadneh & Jamil J. Jaber & Waqar A. Khan & Basil K. Papadopoulos, 2021. "Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions," Journal of Mathematics, Hindawi, vol. 2021, pages 1-10, August.
    2. Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
    3. Simona Hašková & Petr Fiala, 2019. "A fuzzy approach for the estimation of foreign investment risk based on values of rating indices," Risk Management, Palgrave Macmillan, vol. 21(3), pages 183-199, September.
    4. Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    5. Pradip Debnath & Hari Mohan Srivastava, 2021. "Optimal Returns in Indian Stock Market during Global Pandemic: A Comparative Study," JRFM, MDPI, vol. 14(12), pages 1-13, December.
    6. Simona Hašková & Petr Fiala, 2019. "Correction to: A fuzzy approach for the estimation of foreign investment risk based on values of rating indices," Risk Management, Palgrave Macmillan, vol. 21(3), pages 200-200, September.
    7. Vadim Moshkin & Dmitry Kurilo & Nadezhda Yarushkina, 2023. "Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series Anomalies," Mathematics, MDPI, vol. 11(5), pages 1-13, March.
    8. Simona Hašková & Petr Šuleř & Tomáš Krulický, 2021. "Advantages of fuzzy approach compared to probabilistic approach in project evaluation," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(2), pages 446-456, December.
    9. Castillo, Oscar & Melin, Patricia, 2020. "Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Rong Zhang & Baabak Ashuri & Yong Deng, 2017. "A novel method for forecasting time series based on fuzzy logic and visibility graph," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 759-783, December.
    11. Volodymyr Polishchuk & Miroslav Kelemen & Beáta Gavurová & Costas Varotsos & Rudolf Andoga & Martin Gera & John Christodoulakis & Radovan Soušek & Jaroslaw Kozuba & Peter Blišťan & Stanislav Szabo, 2019. "A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    12. Robert Stefko & Jaroslava Heckova & Beata Gavurova & Tomas Valentiny & Alexandra Chapcakova & Dagmara Ratnayake Kascakova, 2022. "An analysis of the impact of economic context of selected determinants of cross-border mergers and acquisitions in the EU," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 6385-6402, December.
    13. Kozinets, Robert V., 2019. "YouTube utopianism: Social media profanation and the clicktivism of capitalist critique," Journal of Business Research, Elsevier, vol. 98(C), pages 65-81.
    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. Simona Hašková & Marek Vochozka & Jiří Kučera, 2023. "A fuzzy evaluation model of manufacturing machinery in terms of sustainable business," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 10(4), pages 71-88, June.
    2. Silvia Biraghi & Rossella Chiara Gambetti & Angela Antonia Beccanulli, 2020. "Achieving cultural relevance in technomediated platforms: instant cultural branding and controversial clicktivism," Italian Journal of Marketing, Springer, vol. 2020(2), pages 163-187, September.
    3. Dong-Rui Chen & Chuang Liu & Yi-Cheng Zhang & Zi-Ke Zhang, 2019. "Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices," Complexity, Hindawi, vol. 2019, pages 1-17, October.
    4. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Yuriy Bilan & Serhiy Kozmenko & Alex Plastun, 2022. "Price Forecasting in Energy Market," Energies, MDPI, vol. 15(24), pages 1-6, December.
    6. Wen, Tao & Jiang, Wen, 2018. "An information dimension of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 388-399.
    7. Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2020. "Increasing the Sensitivity of the Method of Early Detection of Cyber-Attacks in Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 13(11), pages 1-18, June.
    8. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    9. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    10. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    11. Fernando Reche & María Morales & Antonio Salmerón, 2020. "Statistical Parameters Based on Fuzzy Measures," Mathematics, MDPI, vol. 8(11), pages 1-20, November.
    12. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
    13. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," 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. 14(1), pages 549-568, March.
    14. Gia Sirbiladze & Tariel Khvedelidze, 2023. "Associated Statistical Parameters’ Aggregations in Interactive MADM," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    15. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    16. Zuzana Virglerova & Eva Ivanova & Jan Dvorsky & Jaroslav Belas & Tomáš Krulický, 2021. "Selected factors of internationalisation and their impact on the SME perception of the market risk," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1011-1032, December.
    17. Tianxiang Zhan & Fuyuan Xiao, 2021. "A Fast Evidential Approach for Stock Forecasting," Papers 2104.05204, arXiv.org, revised Jul 2021.
    18. Yin, Likang & Deng, Yong, 2018. "Toward uncertainty of weighted networks: An entropy-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 176-186.
    19. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security," Agriculture, MDPI, vol. 12(11), pages 1-27, November.
    20. da Fonseca, André Luís A. & Campos, Roberta D., 2021. "The cultural intertwining of consumption and entrepreneurship: A selective review of qualitative studies," Journal of Business Research, Elsevier, vol. 135(C), pages 149-162.

    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:jmathe:v:11:y:2023:i:13:p:3033-:d:1189422. 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.