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Journal articles on the topic 'Robo-advising'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 April 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Rossi, Alberto G., and Stephen Utkus. "The diversification and welfare effects of robo-advising." Journal of Financial Economics 157 (July 2024): 103869. http://dx.doi.org/10.1016/j.jfineco.2024.103869.

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12

Huneberg, Samantha. "The Future of Robo-Advisors in the South African Insurance Industry: Is the South African Regulatory Framework Ready?" South African Mercantile Law Journal 32, no. 2 (2020): 175–204. http://dx.doi.org/10.47348/samlj/v32/i2a1.

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Insurance industries worldwide currently face disruption in many forms. Technology and artificial intelligence are changing the way we know and transact insurance. One way that technology is impacting insurance is through the use of robo-advisors. Robo-advisors provide automated advice to customers based on algorithms built into the software. This means that many people can now access insurance products at the click of a button. The previous dominant role of intermediaries and advisors in the insurance industry are not as significant in the procuring of insurance products as they used to be. R

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13

Caballero-Fernández, Rodrigo, Klender Aimer Cortez-Alejandro, and David Ceballos-Hornero. "Fintech y la transformación de los servicios financieros utilizando robo-advising: Una revisión a la literatura." Vinculatégica EFAN 6, no. 2 (2020): 1087–104. http://dx.doi.org/10.29105/vtga6.2-522.

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Este artículo pretende cumplir con dos objetivos: el primero es realizar una revisión a la literatura relacionada con las plataformas de robo advisor, clasificando los distintos estudios que se han realizado para tener una perspectiva general de dichas investigaciones. El segundo objetivo es mostrar las características principales de plataformas de robo advisor para entender su funcionamiento. La metodología utilizada para el primer objetivo fue por medio de una búsqueda avanzada en scopus donde se clasificaron los diversos estudios por tema. Para el Segundo objetivo se hizo una búsqueda exhau

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14

Méndez-Suárez, Mariano, Francisco García-Fernández, and Fernando Gallardo. "Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 4 (2019): 81. http://dx.doi.org/10.3390/joitmc5040081.

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Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically des

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15

Agarwal, Sumit, and Yeow Hwee Chua. "FinTech and household finance: a review of the empirical literature." China Finance Review International 10, no. 4 (2020): 361–76. http://dx.doi.org/10.1108/cfri-03-2020-0024.

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PurposeThis paper reviews recent advances in the empirical literature of FinTech and household finance.Design/methodology/approachWe survey the effects of FinTech on three different aspects of household finance: payments, lending and portfolio decisions. Specifically, we examine the impact of digital payments, mobile money, FinTech lending, marketplace lending, robo-advising and crowd-funding.FindingsStudies suggest that FinTech has positively benefited households by increasing consumption and borrowing. This allows them to smoothen their consumption across time. Furthermore, there is an impro

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16

Pan, Jingrui, Shancun Liu, Qiang Zhang, and Yaodong Yang. "Discrete Information Acquisition in Financial Markets." Mathematics 13, no. 4 (2025): 666. https://doi.org/10.3390/math13040666.

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We study investors’ information acquisition strategies under arbitrary and discrete sets of information precision and derive conditions for the existence of equilibria. When investors face information choice from general precision sets, despite their homogeneity, the information market can exhibit asymmetric corner equilibria, where some investors acquire low-precision information and others acquire high-precision information. Conversely, in the case of high-precision sets, there is a symmetric and unique interior equilibrium where all informed agents opt for the same precision level. Furtherm

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17

Kabulova, Jelena, and Jelena Stankevičienė. "Valuation of FinTech Innovation Based on Patent Applications." Sustainability 12, no. 23 (2020): 10158. http://dx.doi.org/10.3390/su122310158.

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The financial services sector, perhaps more than any other, is being disrupted by advances in technology. The purpose of this study is to provide comprehensive data and evidence on value of the FinTech innovation event. First, a text-based filtering method for identifying FinTech patent applications is provided. Using machine learning applications, innovations are classified into major technology groups. The methodology for valuation of FinTech innovation is based on data of stock price changes. To assess the value impact, Poisson flow rates and stock price movements were combined. Further, to

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18

Zvyagintseva, Natalya. "Mechanism for Improving the Competitiveness of Brokerage Organizations in the Brokerage Services Market." Bulletin of Baikal State University 33, no. 4 (2023): 658–67. http://dx.doi.org/10.17150/2500-2759.2023.33(4).658-667.

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The article concretizes the problems that arise among professional participants of the securities market in the implementation of brokerage activities and hinder the development of competition in the market of brokerage services. Taking into account the current trends in the development of competitive relations in the brokerage services market, the methods of solving the identified problems in the algorithm «trend — problem — potential solution» through the prism of a systemic vision are proposed. Various directions for improving the competitiveness of brokerage organizations are formulated, i

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19

Shakir, Aamir, and Nitin Simha Vihari. "PERCEPTION AND AWARENESS ANALYSIS OF FINANCIAL TECHNOLOGY (FINTECH) SERVICES AMONGST THE EXPATRIATES IN THE UNITED ARAB EMIRATES." Journal of Information System and Technology Management 7, no. 27 (2022): 63–75. http://dx.doi.org/10.35631/jistm.727005.

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The objective of this study is to analyse the perception and awareness of the Expatriates and start-up businesses of the presence and value proposition of FinTech Services such as Payment/Banking, Lending and Crowdfunding in UAE. Data was collected using a survey-based questionnaire and respondents were chosen using convenient sampling method. Research design is a Cross-Sectional design. Data was collected between February to April 2020. The target audiences were Expats between the ages of 20-40 who have been living in UAE for at least 3 years. The level of awareness of most respondents in the

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20

Belyaeva, E. S., P. V. Kovalenko, and A. A. Sotnikov. "Digital technologies in the system of remote service." Вестник Северо-Кавказского федерального университета, no. 2 (95) (2023): 34–42. http://dx.doi.org/10.37493/2307-907x.2023.2.4.

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Currently one of the main trends, which was largely influenced by the pandemic, is the digital transformation of all spheres of society. The financial sector is highly subject to digital modernization. In this area, both banking products and services that form the final consumer experience and affect their loyalty undergo digital modernization. Therefore, the introduction of digital technologies and digital transformation of business processes is currently a priority for banks. It contributes to their active expansion, reduces the costs, improves the quality and efficiency of banking services

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21

Eshtokin, Sergey V. "Problems of the implementation and use of chat-bots, robo-advising and other products of the era of Industry 4.0 in the activities of Russian banks." Market economy problems, no. 4 (2020): 151–64. http://dx.doi.org/10.33051/2500-2325-2020-4-151-164.

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Предмет/тема. В статье рассматриваются проблемные вопросы внедрения чат-ботов, робоэдвайзинга и некоторых других элементов искусственного интеллекта (ИИ) в деятельность банковской системы РФ в условиях вступления в эпоху Индустрии 4.0. Цели/задачи. Целью статьи является выявление актуальных проблем, препятствующих эффективному внедрению различных элементов ИИ в банковскую сферу РФ. Методология исследования предполагала идентификацию и классификацию сильных и слабых сторон внедрения чат-ботов и других элементов ИИ на основе сбора практической информации по данным банковских и других финансовых

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22

Gryzunova, Natalia, Igor Keri, and Olga Romanchenko. "Financial engineering of business processes and investment of industrial companies in the innovation system of megacity." SHS Web of Conferences 92 (2021): 04009. http://dx.doi.org/10.1051/shsconf/20219204009.

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Research background: The UN usually defines cities with more than 1 million inhabitants as megacities. As of 2019, over 55% of the world’s population lives in metropolitan areas. Nowadays, rapid population growth and urban growth have created many socio-economic problems, but also created enormous potential for opportunities. On the one hand, risks, depletion of urban resources, industrial isomorphism, environmental pollution and environmental damage, and, of course, we must not forget about the global virus that is adjusting urbanization processes. On the one hand, risks, depletion of urban r

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23

D'Acunto, Francesco, and Alberto G. Rossi. "Robo-Advising." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3578259.

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D'Acunto, Francesco, and Alberto G. Rossi. "Robo-advising." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3545554.

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Bianchi, Milo, and Marie Briere. "Robo-Advising for Small Investors." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3751620.

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26

Liang, Jiawen, Cathy Yi‐Hsuan Chen, and Bowei Chen. "Robo-Advising Under Rare Disasters." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4204014.

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27

Bianchi, Milo, and Marie Briere. "Robo-Advising for Small Investors." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3751620.

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28

Orgeldinger, Jörg. "Digital transformation with robo-advising and AI in asset management: A critical appraisal." Journal of AI, Robotics & Workplace Automation, March 1, 2024. http://dx.doi.org/10.69554/vaup4384.

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After the two revolutions in finance over the last century — the efficient market mathematical finance revolution in the 1950s and the behavioural finance revolution in the 1970s —now the third finance revolution with ‘machine finance’ has begun. Robo-advising combines artificial intelligence (AI) and financial expertise to offer accessible and personalised financial guidance. It analyses data, optimises portfolios and provides lower-cost investment strategies. Robo-advisors automate tasks such as portfolio rebalancing and offer efficiency and rational decision making; however, concerns regard

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29

Capponi, Agostino, Sveinn Ólafsson, and Thaleia Zariphopoulou. "Personalized Robo-Advising: Enhancing Investment Through Client Interaction." Management Science, September 13, 2021. http://dx.doi.org/10.1287/mnsc.2021.4014.

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Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework in which a robo-advisor interacts with a client to solve an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff adapts to the client’s risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. We show that the optimal investment strategy includes both myopic and intertempora

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30

Chernavin, N. "Platform for developing stock trading strategies based on user-defined conditions." Tsifrovaya economika, June 24, 2024, 61–66. http://dx.doi.org/10.33276/de-2024-02-07.

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The article is devoted to the research of investment robo-advising and auto-trading platforms in the Russian market. The author analyzes the tariffs and functionalities of different platforms. Based on the conducted analy-sis, the author proposes new concepts and a working scheme for implementing robo-advising and auto-trading. The implementation of the author's proposals should allow reducing the cost of providing investment services and also expanding their functionality towards automatically generating trading strategies based on user-defined con- ditions. Within the proposed scheme, specia

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31

Bianchi, Milo, and Marie Briere. "Robo-Advising: Less AI and More XAI?" SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3825110.

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D'Acunto, Francesco, Nagpurnanand Prabhala, and Alberto Rossi. "The Promises and Pitfalls of Robo-Advising." SSRN Electronic Journal, 2017. http://dx.doi.org/10.2139/ssrn.3122577.

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33

D'Acunto, Francesco, Nagpurnanand Prabhala, and Alberto G. Rossi. "The Promises and Pitfalls of Robo-Advising." SSRN Electronic Journal, 2018. http://dx.doi.org/10.2139/ssrn.3165339.

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34

Dai, Min, Hanqing Jin, Steven Kou, and Yuhong Xu. "Robo-Advising: A Dynamic Mean-Variance Approach." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3721478.

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35

Alsabah, Humoud, Agostino Capponi, Octavio Ruiz Lacedelli, and Matt Stern. "Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*." Journal of Financial Econometrics, January 3, 2020. http://dx.doi.org/10.1093/jjfinec/nbz040.

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Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of

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36

Ge, Ruyi, Zhiqiang (Eric) Zheng, Xuan Tian, and Li Liao. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending." Information Systems Research, July 20, 2021. http://dx.doi.org/10.1287/isre.2021.1009.

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We study the human–robot interaction of financial-advising services in peer-to-peer lending (P2P). Many crowdfunding platforms have started using robo-advisors to help lenders augment their intelligence in P2P loan investments. Collaborating with one of the leading P2P companies, we examine how investors use robo-advisors and how the human adjustment of robo-advisor usage affects investment performance. Our analyses show that, somewhat surprisingly, investors who need more help from robo-advisors—that is, those encountered more defaults in their manual investing—are less likely to adopt such s

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37

Yan, Yuxing. "Robo Advising Using Python: A New Finance Course." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4167003.

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Capponi, Agostino, Sveinn Olafsson, and Thaleia Zariphopoulou. "Personalized Robo-Advising: Enhancing Investment through Client Interactions." SSRN Electronic Journal, 2019. http://dx.doi.org/10.2139/ssrn.3453975.

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Capponi, Agostino, Octavio Ruiz Lacedelli, and Matt Stern. "Robo-Advising As a Human-Machine Interaction System." SSRN Electronic Journal, 2018. http://dx.doi.org/10.2139/ssrn.3228685.

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Lee, Yongjae, Jang Ho Kim, and Woo Chang Kim. "On the Viability of Robo-Advising for Individual Investors." SSRN Electronic Journal, 2015. http://dx.doi.org/10.2139/ssrn.2679303.

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Hao, Rubin, Conghui Hu, Xin Xu, and Yu Zhang. "Beyond Performance: The Financial Education Role of Robo-Advising." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4230191.

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Rossi, Alberto G., and Stephen P. Utkus. "Who Benefits from Robo-advising? Evidence from Machine Learning." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3552671.

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43

Liang, Gechun, Moris Simon Strub, and Yuwei Wang. "Predictable Forward Performance Processes: Infrequent Evaluation and Robo-Advising Applications." SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3944223.

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44

Keffert, Henk. "Robo-Advising: Optimal Investment with Mismeasured and Unstable Risk Preferences." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4352621.

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Keffert, Henk. "Robo-advising: Optimal investment with mismeasured and unstable risk preferences." European Journal of Operational Research, December 2023. http://dx.doi.org/10.1016/j.ejor.2023.12.002.

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46

Rossi, Alberto G., and Stephen P. Utkus. "The Needs and Wants in Financial Advice: Human versus Robo-advising." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3759041.

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47

D'Acunto, Francesco, and Alberto G. Rossi. "New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes." SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3778244.

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48

Rossi, Alberto G., and Stephen P. Utkus. "The Needs and Wants in Financial Advice: Human versus Robo-advising." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3759041.

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49

Cui, Xiang-Yu, Duan Li, Xiao Qiao, and Moris S. Strub. "Risk and Potential: An Asset Allocation Framework with Applications to Robo-Advising." Journal of the Operations Research Society of China, June 16, 2022. http://dx.doi.org/10.1007/s40305-022-00400-0.

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50

D'Acunto, Francesco, and Alberto G. Rossi. "FinTech and Robo-Advising: The Transformation Role of AI in Personal Finance." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4100784.

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