IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i4p180-d535829.html
   My bibliography  Save this article

Cognitive User Interface for Portfolio Optimization

Author

Listed:
  • Yuehuan He

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada)

  • Oleksandr Romanko

    (Algorithmics, SS&C Technologies, 185 Spadina Ave, Toronto, ON M5T 2C6, Canada)

  • Alina Sienkiewicz

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada)

  • Robert Seidman

    (Algorithmics, SS&C Technologies, 185 Spadina Ave, Toronto, ON M5T 2C6, Canada)

  • Roy Kwon

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada)

Abstract

This paper describes the development of a chatbot as a cognitive user interface for portfolio optimization. The financial portfolio optimization chatbot is proposed to provide an easy-to-use interface for portfolio optimization, including a wide range of investment objectives and flexibility to include a variety of constraints representing investment preferences when compared to existing online automated portfolio advisory services. Additionally, the use of a chatbot interface allows investors lacking a background in quantitative finance and optimization to utilize optimization services. The chatbot is capable of extracting investment preferences from natural text inputs, handling these inputs with a backend financial optimization solver, analyzing the results, and communicating the characteristics of the optimized portfolio back to the user. The architecture and design of the chatbot are presented, along with an implementation using the IBM Cloud, SS&C Algorithmics Portfolio Optimizer, and Slack as an example of this approach. The design and implementation using cloud applications provides scalability, potential performance improvements, and could inspire future applications for financial optimization services.

Suggested Citation

  • Yuehuan He & Oleksandr Romanko & Alina Sienkiewicz & Robert Seidman & Roy Kwon, 2021. "Cognitive User Interface for Portfolio Optimization," JRFM, MDPI, vol. 14(4), pages 1-15, April.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:4:p:180-:d:535829
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/4/180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/4/180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cocca, Teodoro, 2016. "Potential and Limitations of Virtual Advice in Wealth Management," Journal of Financial Transformation, Capco Institute, vol. 44, pages 45-57.
    2. Przegalinska, Aleksandra & Ciechanowski, Leon & Stroz, Anna & Gloor, Peter & Mazurek, Grzegorz, 2019. "In bot we trust: A new methodology of chatbot performance measures," Business Horizons, Elsevier, vol. 62(6), pages 785-797.
    3. Claude Montmarquette & Nathalie Viennot-Briot, 2019. "The Gamma Factors and the Value of Financial Advice," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 387-411, May.
    4. Rese, Alexandra & Ganster, Lena & Baier, Daniel, 2020. "Chatbots in retailers’ customer communication: How to measure their acceptance?," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    5. Oleksandr Romanko & Helmut Mausser, 2016. "Robust scenario-based value-at-risk optimization," Annals of Operations Research, Springer, vol. 237(1), pages 203-218, February.
    6. Oleksandr Romanko & Helmut Mausser, 2016. "Robust scenario-based value-at-risk optimization," Annals of Operations Research, Springer, vol. 237(1), pages 203-218, February.
    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. Tao Zhang & Chao Feng & Hui Chen & Junjie Xian, 2022. "Calming the customers by AI: Investigating the role of chatbot acting-cute strategies in soothing negative customer emotions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2277-2292, December.
    2. 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).
    3. Mark Anthony Camilleri & Ciro Troise, 2023. "Live support by chatbots with artificial intelligence: A future research agenda," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 61-80, March.
    4. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    5. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    6. Virginie Gabrel & Cécile Murat & Aurélie Thiele, 2018. "Portfolio optimization with pw-robustness," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 267-290, September.
    7. Huang, Yu-Shan (Sandy) & Dootson, Paula, 2022. "Chatbots and service failure: When does it lead to customer aggression," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    8. Massilva Dekkal & Manon Arcand & Sandrine Prom Tep & Lova Rajaobelina & Line Ricard, 2024. "Factors affecting user trust and intention in adopting chatbots: the moderating role of technology anxiety in insurtech," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 699-728, September.
    9. Dai, Zhifeng & Wang, Fei, 2019. "Sparse and robust mean–variance portfolio optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1371-1378.
    10. Tran, Anh D. & Pallant, Jason I. & Johnson, Lester W., 2021. "Exploring the impact of chatbots on consumer sentiment and expectations in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    11. Le, Hoang Tran Phuoc Mai & Park, Jungkun & Lee, Sangwoo, 2023. "Emotion and trust in virtual service assistant design for effective service recovery," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    12. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    13. Kumar, Prashant & Utkarsh,, 2023. "Effects of in-store information quality and store credibility on consumer engagement in green retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    14. Rodríguez-López, María Eugenia & Higueras-Castillo, Elena & Rojas-Lamorena, Álvaro J. & Alcántara-Pilar, Juan Miguel, 2024. "The future of TV-shopping: predicting user purchase intention through an extended technology acceptance model," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    15. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 2020. "Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    16. Dehghani, Milad & William Kennedy, Ryan & Mashatan, Atefeh & Rese, Alexandra & Karavidas, Dionysios, 2022. "High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology," Journal of Business Research, Elsevier, vol. 149(C), pages 393-411.
    17. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    18. Soderlund, Magnus & Oikarinen, Eeva-Liisa & Tan, Teck Ming, 2021. "The happy virtual agent and its impact on the human customer in the service encounter," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    19. Li, Meichan & Wang, Rui, 2023. "Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    20. Xuequn Wang & Xiaolin Lin & Bin Shao, 2023. "Artificial intelligence changes the way we work: A close look at innovating with chatbots," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 339-353, March.

    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:jjrfmx:v:14:y:2021:i:4:p:180-:d:535829. 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.