IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v25y2020ics2214635019302394.html
   My bibliography  Save this article

Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context

Author

Listed:
  • Bhatia, Ankita
  • Chandani, Arti
  • Chhateja, Jagriti

Abstract

Technological developments have given shape to robotic services which have forayed in the finance and investment industry in the form of Robo-advisors. Robo-advisors are in use in the developed nations since over a decade but have recently entered the developing nations, like India, since early 2015. This research explores the present state of Robo-advisory services in the Indian context and investigates how Robo-advisors can help in mitigating behavioral biases of a retail investor.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:beexfi:v:25:y:2020:i:c:s2214635019302394
    DOI: 10.1016/j.jbef.2020.100281
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635019302394
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbef.2020.100281?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
    ---><---

    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. Deborah M. Gordon, 2011. "The fusion of behavioral ecology and ecology," Behavioral Ecology, International Society for Behavioral Ecology, vol. 22(2), pages 225-230.
    3. M. Keith Chen & Venkat Lakshminarayanan & Laurie R. Santos, 2006. "How Basic Are Behavioral Biases? Evidence from Capuchin Monkey Trading Behavior," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 517-537, June.
    4. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    5. Catherine D'Hondt & Rudy De Winne & Eric Ghysels & Steve Raymond, 2019. "Artificial Intelligence Alter Egos: Who benefits from Robo-investing?," Papers 1907.03370, arXiv.org.
    6. Abraham,Facundo & Schmukler,Sergio L. & Tessada,Jose, 2019. "Robo-Advisors : Investing through Machines," Research and Policy Briefs 134881, The World Bank.
    7. Mikhail Beketov & Kevin Lehmann & Manuel Wittke, 2018. "Robo Advisors: quantitative methods inside the robots," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 363-370, October.
    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. Nourallah, Mustafa, 2023. "One size does not fit all: Young retail investors’ initial trust in financial robo-advisors," Journal of Business Research, Elsevier, vol. 156(C).
    2. Dominik M. Piehlmaier, 2022. "Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    3. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    5. Kumar, Satish & Rao, Sandeep & Goyal, Kirti & Goyal, Nisha, 2022. "Journal of Behavioral and Experimental Finance: A bibliometric overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    6. Sasichakorn Wongsaichia & Phaninee Naruetharadhol & Peerapong Wongthahan & Chavis Ketkaew, 2022. "Ideating A Sustainable Swine Feed Prototype: A Qualitative Approach in Farmers’ Pain Point Identification and Product Development," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    7. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    8. Jinesh Jain & Nidhi Walia & Simarjeet Singh & Esha Jain, 2022. "Mapping the field of behavioural biases: a literature review using bibliometric analysis," Management Review Quarterly, Springer, vol. 72(3), pages 823-855, September.
    9. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    10. Athota, Vidya S. & Pereira, Vijay & Hasan, Zahid & Vaz, Daicy & Laker, Benjamin & Reppas, Dimitrios, 2023. "Overcoming financial planners’ cognitive biases through digitalization: A qualitative study," Journal of Business Research, Elsevier, vol. 154(C).
    11. Ida Ayu Agung Faradynawati & Inga-Lill Söderberg, 2022. "Sustainable Investment Preferences among Robo-Advisor Clients," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    12. Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2023. "On the benefits of robo-advice in financial markets," Working Papers 0734, University of Heidelberg, Department of Economics.
    13. Seiler, Volker & Fanenbruck, Katharina Maria, 2021. "Acceptance of digital investment solutions: The case of robo advisory in Germany," Research in International Business and Finance, Elsevier, vol. 58(C).
    14. Alexia Gaudeul & Caterina Giannetti, 2021. "Fostering the adoption of robo-advisors: A 3-weeks online stock-trading experiment," Discussion Papers 2021/275, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    15. Eman Ismail & Yasser Tawfik Halim & Mohamed Samy EL-Deeb, 2023. "Corporate reputation and shareholder investment: a study of Egypt's tourism listed companies," Future Business Journal, Springer, vol. 9(1), pages 1-15, December.
    16. Bai, Zefeng, 2021. "Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    17. Chaklader, Barnali & Gupta, Brij B. & Panigrahi, Prabin Kumar, 2023. "Analyzing the progress of FINTECH-companies and their integration with new technologies for innovation and entrepreneurship," Journal of Business Research, Elsevier, vol. 161(C).
    18. Zeeshan Ahmed & Shahid Rasool & Qasim Saleem & Mubashir Ali Khan & Shamsa Kanwal, 2022. "Mediating Role of Risk Perception Between Behavioral Biases and Investor’s Investment Decisions," SAGE Open, , vol. 12(2), pages 21582440221, May.
    19. Muskan Sachdeva & Ritu Lehal & Sanjay Gupta & Aashish Garg, 2021. "What make investors herd while investing in the Indian stock market? A hybrid approach," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(1), pages 19-37, September.
    20. Shan, Shan & Umar, Muhammad & Mirza, Nawazish, 2022. "Can robo advisors expedite carbon transitions? Evidence from automated funds," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    21. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    22. Mike K. P. So, 2021. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market," Sustainability, MDPI, vol. 13(3), pages 1-15, January.

    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. Mike K. P. So, 2021. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
    2. Bai, Zefeng, 2021. "Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    3. Viktar Fedaseyeu & Vitaliy Strohush, 2012. "Loans from the Government, Overinvestment by Households, and Asset Bubbles," Working Papers 443, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. R. Andergassen, 2003. "Rational destabilising speculation and the riding of bubbles," Working Papers 475, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Anne Lavigne, 2006. "Gouvernance et investissement des fonds de pension privés aux Etats-Unis," Working Papers halshs-00081401, HAL.
    6. Dash, Saumya Ranjan & Maitra, Debasish, 2018. "Does sentiment matter for stock returns? Evidence from Indian stock market using wavelet approach," Finance Research Letters, Elsevier, vol. 26(C), pages 32-39.
    7. Florian Meier, 2020. "The Age of Cheap Money and Passive Investing: Are Pro Forma Earnings Value Relevant?," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 9(2), pages 1-1.
    8. Glaser, Markus, 2003. "Online Broker Investors: Demographic Information, Investment Strategy, Portfolio Positions, and Trading Activity," Sonderforschungsbereich 504 Publications 03-18, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    9. Philip A. Stork, 2011. "The intertemporal mechanics of European stock price momentum," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 28(3), pages 217-232, August.
    10. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    12. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    13. Stijn Claessens & M. Ayhan Kose, 2013. "Financial Crises: Explanations, Types and Implications," CAMA Working Papers 2013-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Raphaëlle Bellando & Sébastien Ringuedé, 2007. "Compétition entre fonds et prise de risque excessive : une application empirique au cas des OPCVM actions de droit français," Post-Print halshs-00226341, HAL.
    15. Luisa Corrado & Marcus Miller & Lei Zhang, 2007. "Bulls, bears and excess volatility: can currency intervention help?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 261-272.
    16. Laureti, Carolina & Szafarz, Ariane, 2023. "Banking regulation and costless commitment contracts for time-inconsistent agents," Economic Modelling, Elsevier, vol. 129(C).
    17. Hamza Bahaji, 2011. "Incentives from stock option grants: a behavioral approach," Post-Print halshs-00681607, HAL.
    18. Mathias Drehmann & Jörg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
    19. Schmidt, Ulrich & Friedl, Andreas & Lima de Miranda, Katharina, 2015. "Social comparison and gender differences in risk taking," Kiel Working Papers 2011, Kiel Institute for the World Economy (IfW Kiel).
    20. Scheffknecht, Lukas & Geiger, Felix, 2011. "A behavioral macroeconomic model with endogenous boom-bust cycles and leverage dynamcis," FZID Discussion Papers 37-2011, University of Hohenheim, Center for Research on Innovation and Services (FZID).

    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:eee:beexfi:v:25:y:2020:i:c:s2214635019302394. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.