Research graph
References from Prediction of robo-advisory acceptance in banking services using tree-based algorithms. Local targets link to admitted publications; unresolved targets remain external evidence.
Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management &
2019 · External reference
Robo advisors, algorithmic trading and investment management: wonders of fourth industrial revolution in financial markets
2020 · External reference
The promises and pitfalls of robo-advising.
10.1093/rfs/hhz014 · 2019 · External reference
Whose algorithm says so: the relationships between type of firm, perceptions of trust and expertise, and the acceptance of financial robo-advice
10.1016/j.intmar.2019.10.003 · 2020 · External reference
Fintech and banking: What do we know
10.1016/j.jfi.2019.100833 · 2020 · External reference
The financial advice puzzle: the role of consumer heterogeneity in the advisor choice
10.1016/j.jretconser.2019.102014 · 2020 · External reference
Robo-advisors: A substitute for human financial advice?
10.1016/j.jbef.2020.100275 · 2020 · External reference
Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications
10.1016/j.techfore.2022.121824 · 2022 · External reference
Joint Committee discussion paper on automation in financial advice [Internet].
2015 · External reference
Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending
10.1287/isre.2021.1009 · 2021 · External reference
Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach
10.1016/j.jbef.2021.100461 · 2021 · External reference
Joint Committee Report on the results of the monitoring exercise on ‘automation in financial advice’.
2018 · External reference
Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions
10.1016/j.jbef.2020.100297 · 2020 · External reference
The evolution of the financial technology ecosystem: an introduction and agenda for future research on disruptive innovations in ecosystems
10.1016/j.techfore.2019.119779 · 2020 · External reference
Robo-advisors and wealth management
10.3905/jai.2018.20.3.079 · 2018 · External reference
10.1007/978-3-319-95999-3_20
10.1007/978-3-319-95999-3_20 · 2019 · External reference
Robo-advisor configuration: An investigation of user preferences and the performance-control dilemma
2020 · External reference
Not transparent and incomprehensible: A qualitative user study of an AI-empowered financial advisory system.
10.1016/j.dim.2023.100041 · 2023 · External reference
Consumer acceptance and adoption of AI robo-advisors in fintech industry.
10.3390/math11061311 · 2023 · External reference
Acceptance of digital investment solutions: The case of robo advisory in Germany.
10.1016/j.ribaf.2021.101490 · 2021 · External reference
Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model
10.22146/jieb.v38i3.6242 · 2023 · External reference
Artificial intelligence in financial services: a qualitative research to discover robo-advisory services
10.1108/qrfm-10-2020-0199 · 2021 · External reference
Bergner A. Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making
10.1007/s11747-020-00753-z · 2021 · External reference
Designing a robo-advisor for risk-averse, low-budget consumers.
10.1007/s12525-017-0279-9 · 2018 · External reference
Robo-advisor acceptance: Do gender and generation matter?
10.1371/journal.pone.0269454 · 2022 · External reference
Factors influencing acceptance of robo-advisors for wealth management in Malaysia.
10.1080/23311916.2023.2188992 · 2023 · External reference
Demographic and Socio-Economic Factors as Barriers to Robo-Advisory Acceptance in Poland.
10.17951/h.2022.56.3.109-126 · 2022 · External reference
The potential use of robo-advisors among the young generation: Evidence from Italy.
10.1016/j.frl.2022.103046 · 2022 · External reference
Acceptance of robo-advisors: Effects of financial experience, affective reactions, and self-enhancement motives.
10.1002/cfp2.1047 · 2019 · External reference
Huang Ch-H. Revisiting the development trajectory of the digital divide: a main path analysis approach
10.1016/j.techfore.2022.121607 · 2022 · External reference
Understanding consumer’s adoption of financial robo-advisors at the outbreak of the COVID-19 crisis in Malaysia.
2021 · External reference
Modeling the robo-advisor ecosystem: Insights from a simulation study.
2018 · External reference
The adoption of robo-advisory among millennials in the 21st century: trust, usability and knowledge perception.
10.3390/su15076016 · 2023 · External reference
One size does not fit all: young retail investors’ initial trust in financial robo-advisors
10.1016/j.jbusres.2022.113470 · 2023 · External reference
No trust, no use: how young retail investors build initial trust in financial robo-advisors
10.1108/jfra-12-2021-0451 · 2023 · External reference
Trust me, I am a Robo-advisor
10.1057/s41260-022-00284-y · 2023 · External reference
Digital financial advice solutions–evidence on factors affecting the future usage intention and the moderating effect of experience
10.1016/j.jeconbus.2021.106009 · 2021 · External reference
The utilization of robo-advisors by individual investors: An analysis using diffusion of innovation and information search frameworks.
10.1891/jfcp-18-00078 · 2020 · External reference
Artificial Intelligence Alter Egos: Who might benefit from robo-investing
10.1016/j.jempfin.2020.10.002 · 2020 · External reference
Robo-investment aversion.
10.1371/journal.pone.0239277 · 2020 · External reference
Artificial intelligence and customers’ intention to use robo-advisory in banking services. Equilibrium
2023 · External reference
Exploring the Innovation Diffusion of Big Data Robo-Advisor.
2022 · External reference
10.1109/asonam.2018.8508269
10.1109/asonam.2018.8508269 · External reference
Importance of Event Binary Features in Stock Price Prediction.
10.3390/app10051597 · 2020 · External reference
Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms.
10.3390/su12030849 · 2020 · External reference
10.1109/asonam.2018.8508854
10.1109/asonam.2018.8508854 · External reference
10.1109/iccns58795.2023.10193396
10.1109/iccns58795.2023.10193396 · External reference
10.1109/smc.2019.8914183
10.1109/smc.2019.8914183 · External reference
Group Recommendation Based on Financial Social Network for Robo-Advisor.
10.1109/access.2018.2871131 · 2018 · External reference
Robo-advisor using genetic algorithm and BERT sentiments from tweets for hybrid portfolio optimization
10.1016/j.eswa.2021.115060 · 2021 · External reference
Technology acceptance prediction of robo-advisors by machine learning.
10.1016/j.iswa.2023.200197 · 2023 · External reference
Forecasting currency covariances using machine learning tree-based algorithms with low and high prices.
10.5604/01.3001.0015.5582 · 2021 · External reference
Unresolved reference
2009 · External reference
Unresolved reference
2019 · External reference
10.1007/978-1-0716-1418-1
10.1007/978-1-0716-1418-1 · 2021 · External reference
10.1201/9781315139470
10.1201/9781315139470 · 2017 · External reference
Unresolved reference
External reference
Random Forests, Machine Learning
2001 · External reference
10.1007/978-1-4419-9326-7_2
10.1007/978-1-4419-9326-7_2 · 2012 · External reference
A comparison of random forest variable selection methods for classification prediction modeling
10.1016/j.eswa.2019.05.028 · 2019 · External reference
10.1007/978-3-642-21551-3_33
10.1007/978-3-642-21551-3_33 · 2012 · External reference
Bagging predictors.
10.1007/bf00058655 · 1996 · External reference
Analyzing bagging.
10.1214/aos/1031689014 · 2002 · External reference
Forecasting cryptocurrencies volatility using statistical and machine learning methods: a comparative study.
10.1016/j.asoc.2023.111132 · 2024 · External reference
A decision-theoretic generalization of online-learning and an application to boosting
10.1006/jcss.1997.1504 · 1997 · External reference
Khoshgoftaar TM, Van Hulse J, Napolitano A. RUSBoost: A hybrid approach to alleviating class imbalance
10.1109/tsmca.2009.2029559 · 2010 · External reference
10.1145/1143844.1143970
10.1145/1143844.1143970 · External reference
Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems.
10.1007/s11269-017-1595-8 · 2017 · External reference
Unresolved reference
2014 · External reference