IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2024i1p57-d1554695.html
   My bibliography  Save this article

Contextual Analysis of Financial Time Series

Author

Listed:
  • Nadezhda Yarushkina

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Aleksey Filippov

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Anton Romanov

    (Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

Abstract

The evaluation of the financial state of small and medium-sized companies is a pressing issue today. This article introduces a novel method to evaluate a company’s financial state, implemented as a module within a decision support system. This component uses fuzzy logic and knowledge engineering methods. The article describes an ontological model that provides the framework for data analysis and financial time-series modeling. The ontological framework simplifies the representation of the trends in the financial indicators under analysis. Integrating an ontology and a set of fuzzy rules makes it possible to develop control systems based on fuzzy inference. This approach provides the analysis and interpretation of results. The experimental results validate the accuracy and effectiveness of the proposed method.

Suggested Citation

  • Nadezhda Yarushkina & Aleksey Filippov & Anton Romanov, 2024. "Contextual Analysis of Financial Time Series," Mathematics, MDPI, vol. 13(1), pages 1-17, December.
  • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:57-:d:1554695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/1/57/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/1/57/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pierpaolo Angelini, 2024. "Financial Decisions Based on Zero-Sum Games: New Conceptual and Mathematical Outcomes," IJFS, MDPI, vol. 12(2), pages 1-28, June.
    2. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    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. Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    2. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    3. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    4. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    5. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    6. Ming‐Lang Tseng & Hien Minh Ha & Thi Phuong Thuy Tran & Tat‐Dat Bui & Chih‐Cheng Chen & Chun‐Wei Lin, 2022. "Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2082-2106, July.
    7. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    8. Anderson, Brian S., 2022. "What executives get wrong about statistics: Moving from statistical significance to effect sizes and practical impact," Business Horizons, Elsevier, vol. 65(3), pages 379-388.
    9. Feng, Cai (Mitsu) & Botha, Elsamari & Pitt, Leyland, 2024. "From HAL to GenAI: Optimizing chatbot impacts with CARE," Business Horizons, Elsevier, vol. 67(5), pages 537-548.
    10. Syed, Tahir Abbas & Aslam, Haris & Bhatti, Zeeshan Ahmed & Mehmood, Fahad & Pahuja, Aseem, 2024. "Dynamic pricing for perishable goods: A data-driven digital transformation approach," International Journal of Production Economics, Elsevier, vol. 277(C).
    11. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    12. Md. Emam Hossain & Subarna Biswas, 2024. "Technology acceptance model for understanding consumer’s behavioral intention to use artificial intelligence based online shopping platforms in Bangladesh," SN Business & Economics, Springer, vol. 4(12), pages 1-61, December.
    13. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. Shahsuzan Zakaria & Suhaily Maizan Abdul Manaf & Mohd Talmizie Amron & Mohd Taufik Mohd Suffian, 2023. "Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies," Information Management and Business Review, AMH International, vol. 15(4), pages 420-432.
    15. Watson, Graeme J. & Desouza, Kevin C. & Ribiere, Vincent M. & Lindič, Jaka, 2021. "Will AI ever sit at the C-suite table? The future of senior leadership," Business Horizons, Elsevier, vol. 64(4), pages 465-474.
    16. Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    17. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
    18. Gianluca Gabrielli & Alice Medioli & Paolo Andrei, 2022. "Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2022(2), pages 89-112.
    19. Cong Cheng & Mengxin Zhang, 2023. "Conceptualizing Corporate Digital Responsibility: A Digital Technology Development Perspective," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    20. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.

    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:13:y:2024:i:1:p:57-:d:1554695. 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.