Relevant bibliographies by topics / Robo-advising

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Robo-advising'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 April 2025

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Journal articles on the topic "Robo-advising"

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Salo-Lahti, Marika. "Good or Bad Robots? Responsible Robo-Advising." European Business Law Review 33, Issue 5 (2022): 671–94. http://dx.doi.org/10.54648/eulr2022030.

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FinTech has changed the way financial services are produced and delivered.1 As the Digital Finance Strategy for the EU put it: the future of finance is digital. Digital finance has also helped people and businesses tackle the unprecedented challenges caused by the COVID-19 pandemic. We increasingly rely on digital and remote technologies. The role of regulation is to make sure that these technologies are used in a responsible way.2 Robo-advising is one of the innovations relating to FinTech. This article addresses central challenges and risks in robo-advising, and the regulatory means to tackl
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Ziemiak, Michał P. "Legal Aspects of Robo-Advising in Insurance." Prawo Asekuracyjne 2, no. 107 (2021): 47–62. http://dx.doi.org/10.5604/01.3001.0014.8877.

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Robo-advising, to put it simply, should be understood as a process in which, for instance, giving and submitting recommendations regarding specific services is carried out in an automated algorithm-driven manner. Robo-advising is currently used in the insurance industry, in particular in the USA and Western Europe. Nevertheless, Polish legislation is devoid of regulations relating to robo-advising in insurance. The aim of this article is to present basic legal problems related to this issue as an example of possible automation of the processes of customer needs analysis or insurance contract c
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Gaspar, Raquel M., and Madalena Oliveira. "Robo Advising and Investor Profiling." FinTech 3, no. 1 (2024): 102–15. http://dx.doi.org/10.3390/fintech3010007.

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The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear v
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So, Mike K. P. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market." Sustainability 13, no. 3 (2021): 1306. http://dx.doi.org/10.3390/su13031306.

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Nowadays, we mainly depend on financial consultants or advisors to conduct risk assessments for individual investors before providing them with any investment advice or recommendations. Individual investors should understand the risk level of their investment choices and their investment decisions should match their risk profile. This process is usually conducted in face-to-face meetings. However, during the recent coronavirus disease 2019 pandemic, which has seriously impacted daily life with social distancing, in order to maintain sustainability, contact-free advising, such as robo-advising,
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Thottoli, Mohammed Muneerali, Badria Hamed Alruqaishi, and Arockiasamy Soosaimanickam. "Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?" Contemporary Educational Technology 16, no. 1 (2024): ep485. http://dx.doi.org/10.30935/cedtech/13948.

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<b>Purpose</b>: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could s
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Vijaya, C., Koushik Hati, and M. Thenmozhi. "Developing a Security Risk Assessment based Smart Beta Portfolio Model for Robo Advising." Australasian Business, Accounting and Finance Journal 18, no. 3 (2024): 7–25. http://dx.doi.org/10.14453/aabfj.v18i3.02.

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construction model for Robo Advising investors belonging to different risk categories. This model will cater to the Gen Z tech-savvy retail investors who have become more active and are interested in online investment platforms like Robo Advising. Our study differs from prior studies as it proposes a portfolio construction model for equity investors belonging to different risk categories while traditional approaches map debt portfolios to low risk investors and equity portfolios to high risk investors. Investors are generally risk-averse but prior studies have developed SB portfolios without c
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Dai, Min, Hanqing Jin, Steven Kou, and Yuhong Xu. "Robo-advising: a dynamic mean-variance approach." Digital Finance 3, no. 2 (2021): 81–97. http://dx.doi.org/10.1007/s42521-021-00028-4.

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D’Acunto, Francesco, Nagpurnanand Prabhala, and Alberto G. Rossi. "The Promises and Pitfalls of Robo-Advising." Review of Financial Studies 32, no. 5 (2019): 1983–2020. http://dx.doi.org/10.1093/rfs/hhz014.

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Kim, Seongsu David, Marty Cotwright, and Swarn Chatterjee. "Who Are Robo-Advisor Users?" Journal of Finance Issues 18, no. 2 (2019): 33–50. http://dx.doi.org/10.58886/jfi.v18i2.2225.

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The purpose of this study is to explore the demand for robo-advising services by analyzing the participants’ behavioral characteristics and investment patterns. With the 2015 Financial Industry Regulatory Authority Investor data, we found that robo-advisor users were younger investors with high risk tolerance, whose self-assessment of financial knowledge is comparatively higher than their actual knowledge, and were independent decision-makers. By controlling for those behavioral attributes of robo-advisor users, we also found that robo-advisor users were reluctant to invest in individual stock
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Milo BIANCHI and Marie BRIÈRE. "ROBO-ADVISORS: WHAT WE KNOW AND WHAT WE EXPECT." Bankers, Markets & Investors 168, no. 1 (2022): 35. https://doi.org/10.54695/bmi.168.35.

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his paper examines how robo-advisory services could solve some fundamental problems in individual investors’ decision making and traditional financial advice, and promote financial inclusion. We show the role that artificial intelligence plays, and should play, in robo-advice. We emphasize the importance of recent discoveries in Explainable Artificial Intelligence (XIA) and the importance of developing customer/robo interactions. We examine how far the personalization of robo-advice recommendations can go, given the trade-off between the goal of personalizing advice and the risks associated wi
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Dissertations / Theses on the topic "Robo-advising"

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Jones, Jentry Indigo. "Behavioral Household Finance and Robo-Advising." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1084.

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Cette recherche étudie le comportement des ménages investisseurs qui utilisent un robo-advisor français à travers quatre articles empiriques. Dans la première étude, les investisseurs qui épargnent régulièrement épargnent moins à moyen et long terme que ceux qui n'épargnent que sporadiquement, ce qui remet en question l'universalité de ce conseil financier populaire. Dans la deuxième étude, les investisseurs actualisent le risque de leur portefeuille au fil du temps d'une manière qui ne reproduit pas toujours les résultats obtenus avec des portefeuilles statiques, ce qui remet en question la m
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Rodrigues, Alessandra Alves. "A mean-variance look at robo-advising." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19635.

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Mestrado em Finanças<br>Nos últimos anos, a indústria de gestão de riquezas enfrentou desafios significativos e tendências impactantes, tais como a diminuição da confiança dos clientes nos serviços financeiros tradicionais, novos encargos regulatórios e aumento da concorrência. Neste contexto, a ascensão de gestores de investimento automatizados, conhecidos como "roboadvisors" e a nova combinação de ciência e capital humano tem desafiado a indústria de gestão de capital a encontrar novas formas de criar valor para beneficiar o cliente. Sobre esse assunto, esse projeto contribui para uma anális
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Jacobson, Ryan Jonathan. "Robo-Advising on South African Exchange Traded Funds utilizing Prospect Theory." Master's thesis, Faculty of Commerce, 2019. https://hdl.handle.net/11427/31844.

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Robo-advising is an emerging trend in markets around the world. The term has come to refer almost exclusively to automated advisory services for financial investments or wealth management. Currently, in the South African market, financial services firms offer their own robo-advising platforms that only provide automated advice about their own products. This paper investigates the possibility of a roboadvising platform existing outside of these financial institutions. The paper reviews the preconditions that make robo-advising possible. Namely, risk profiling, portfolio allocation, availability
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WERNER, JONATAN, and JONAS SJÖBERG. "Prospect Theory in the Automated Advisory Process." Thesis, KTH, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199087.

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With robo-advisors and regulation eventually changing the market conditions of thefinancial advisory industry, traditional advisors will have to adapt to a new world of asset management. Thus, it will be of interest to traditional advisors to further explore the topic of how to automatically evaluate soft aspects such as client preferences and behavior, and transform it into portfolio allocations while retaining stringency and high quality in the process. In this thesis, we show how client preferences and behavioral aspects can be translated into quantitative parameters, suitable for an asset
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Smith, Jacques. "Constructing low cost core-satellite portfolios with multiple risk constraints: practical applications to Robo advising in South Africa using active, passive and smart-beta strategies." Master's thesis, Faculty of Commerce, 2020. http://hdl.handle.net/11427/32985.

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Risk and tracking error budgeting was originally adopted by large institutional investors, including pension funds, plan sponsors, foundations, and endowments. More recently, risk and tracking error budgeting have gained popularity among financial advisors, multi-managers, fund of funds managers, high net worth individuals as well as retail investors. These techniques contribute to the portfolio optimisation process by limiting the extent to which a portfolio can deviate from its benchmark with regards to risk and tracking error. This is an ambitious paper that attempts to determine the optima
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Brink, Erik, and Herfors David. "Automatiserad rådgivning i Sverige : En studie om potentiella användare av automatiserad rådgivning i Sverige." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34400.

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Becker, Tobias Niklas. "The integrability of ESG investing into robo advising." Master's thesis, 2019. http://hdl.handle.net/10362/73601.

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Environmental, Social and Governance (ESG) investing and passive asset management are two distinct trends in financial markets. This research examines whether robo advisors can combine these two trends by integrating ESG into their passively managed portfolios. On the example of the ESG portfolio of a German robo advisor, the research finds that ESG is integrable into robo advising, with limitations. A backtest with a threshold-based rebalancing strategy was performed over the sample period 05/31/2011 – 10/31/2018. In comparison to its Non-ESG counterpart, the ESG portfolio does not over
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Book chapters on the topic "Robo-advising"

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D’Acunto, Francesco, and Alberto G. Rossi. "Robo-Advising." In The Palgrave Handbook of Technological Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65117-6_26.

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Alekseenko, Aleksandr. "Shall Regulators Introduce Special Rules for Robo Advising Services?" In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87687-6_19.

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Kashif, Mohammad, Arokiaraj David, Md Sikandar Azam, and Sanjeet Kumar. "Utilization of Robo-Advisory Tools in Decision Support Systems." In Robo-Advisors in Management. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2849-1.ch005.

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Decision Support Systems are essential tools for helping business organizations navigate the decision-making processes. Through the use of technology, Decision Support Systems significantly improve decision-making processes by offering thorough, data-driven insights by utilizing robo advisory tools. Automated, algorithm-driven platforms are known as robo advisory tools. Robo advisers are renowned for being easily accessible, reasonably priced, and able to offer financial services to a larger range of clients. The integration of robo advising tools with Decision Support Systems provides decision-makers with advanced algorithms and data analytics functionalities. With technology and regulations changing constantly, using robo advising tools in Decision Support Systems is becoming a trend. This chapter has emphasized how critical it is to recognize the constraints, legal framework, and compliance issues related to the use of robo advisors.
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Kaur, Ravneet, and Kapil Kumar Aggarwal. "Navigating the Future." In Advances in Human Resources Management and Organizational Development. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7668-3.ch012.

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Artificial intelligence is revolutionizing various industries, including travel, education, health, and investment. Robo advisory models (RAs) have increased assets under management (AUM) due to price, transparency, and competitiveness. Although not widely used, robo-advisors are becoming more cost-effective than traditional human advice services. This study explores the current state of robo-advisors and their potential to transform wealth management by facilitating effective decision-making processes and introducing a new era of financial advising services.
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"New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes." In Machine Learning and Data Sciences for Financial Markets. Cambridge University Press, 2023. http://dx.doi.org/10.1017/9781009028943.003.

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"Robo-Advising: Less AI and More XAI? Augmenting Algorithms with Humans-in-the-Loop." In Machine Learning and Data Sciences for Financial Markets. Cambridge University Press, 2023. http://dx.doi.org/10.1017/9781009028943.004.

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"Introduction to Part I. Robo-advising as a Technological Platform for Optimization and Recommendations." In Machine Learning and Data Sciences for Financial Markets. Cambridge University Press, 2023. http://dx.doi.org/10.1017/9781009028943.002.

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Haberly, Daniel, and Dariusz Wójcik. "Asset Management as a Digital Platform Industry." In Sticky Power. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198870982.003.0009.

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While contemporary technological disruption is increasingly conceptualized in terms of the logic and paradoxes of the digital platform economy, discussions of FinTech have only engaged to a limited extent with these debates—particularly from an economic geographic standpoint. This chapter fills this gap by extending the Global Financial Network (GFN) framework to problematize the organizational and geographic logic of the digital platform economy in finance, and applying it to examine the impact of the digital platform model on asset management. It shows that asset management is being profoundly disrupted by what we dub digital asset management platforms—or DAMPs—which encompass services including index fund and ETF provision, robo-advising, and analytics and trading support. Like other digital platforms, DAMPs do not so much leverage technology to enhance their competitiveness within markets, as to radically restructure the market itself. Also, like other platforms, their rise has produced a winner-take-all paradox of centralization through democratization that defies predictions of technology-enabled industry decentralization. However, the logic and implications of the rise of DAMPs diverges, in other respects, from nonfinancial digital platforms, as finance has long possessed an informational intensity and regulatory and organizational fluidity characteristic of the digital platform economy. Consequently, the digital platform model has mostly developed endogenously in asset management through incremental innovation by major financial firms—in a process that has reinforced the position of leading incumbent asset management centers, and above all New York—rather than being introduced from the outside by upstart technology firms and clusters.
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Goswami, Divya, and Balraj Verma. "Robo-Finance Unveiled." In Innovative Technologies for Increasing Service Productivity. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2019-8.ch014.

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The study aims to correlate financial investment advice with investors, using Scopus and VOS viewer to analyze prominent authors and journals. Gomber emerged as a leading writer, with journals like The Journal of Management Information Systems and Journal of Behavioral and Experimental Finance standing out. These journals have gained significant citations for research on financial advice. Future studies should focus on cognitive biases, financial technology, roboadvisors, financial behavior, and AI. Conducting longitudinal research on market efficiency, decision-making dynamics, information asymmetries, and investor intelligence can enhance financial advice quality. This research provides a foundation and suggestions for future studies in financial advising, especially for investors.
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Conference papers on the topic "Robo-advising"

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Wang, Haoran, and Shi Yu. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00063.

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Bukhanov, D. V. "Robo-Advising As A Key Factor Of The Russian Stock Market Development." In Proceedings of the II International Scientific Conference GCPMED 2019 - "Global Challenges and Prospects of the Modern Economic Development". European Publisher, 2020. http://dx.doi.org/10.15405/epsbs.2020.03.158.

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