A Fund Selection Robo-Advisor with Deep-learning Driven Market Prediction

Gu, Chen-Sheng; Hsieh, Hong-Po; Wu, Chung-Shu; Chang, Ray-I; Ho, Jan-Ming

doi:10.1109/smc.2019.8914183

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…An advanced RA is composed of many such application types to address the high decision-making complexity in investment advisory. Based on the three types of AI presented by Davenport and Ronanki (2018), the following AI-based RA application types could be identified: intelligent CA (Day, Lin, & Chen, 2018; Hildebrand & Bergner, 2021), intelligent CS (Tertilt & Scholz, 2018; Xue, Zhu, Liu, & Yin, 2018) and PA (Day & Lin, 2019; Gu, Hsieh, Wu, Chang, & Ho, 2019). The characteristics of each are the following.…”

Section: Existing Research On Robo-advisorymentioning

confidence: 99%

Implications of AI-based robo-advisory for private banking investment advisory

Dietzmann

Jaeggi

Alt

2023

JEBDE

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PurposeAI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.Design/methodology/approachThe present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.FindingsThe study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.Originality/valueThe research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

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Section: Existing Research On Robo-advisorymentioning

confidence: 99%

Implications of AI-based robo-advisory for private banking investment advisory

Dietzmann

Jaeggi

Alt

2023

JEBDE

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“…These models usually use market data related to the quotations of investment assets. Solutions taking into account macroeconomic variables [ 48 ], recommendations based on financial social networks [ 49 ], and social media sentiments [ 50 ] are also analyzed. To the best of the authors’ knowledge, only in one case did the prediction concern the acceptance of robo-advisory [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Orzeszko,

Piotrowski

2024

PLoS ONE

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The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by banks from various communication channels. In the research carried out in the work, an attempt was made to obtain an answer to the question whether the data collected by banks can also be used to determine the degree of consumer interest in this type of service. This is important because the identification of customers interested in the service will allow banks to direct a properly prepared message to a selected group of addressees, increasing the effectiveness of their promotional activities. The aim of the article is to construct and examine the effectiveness of predictive models of consumer acceptance of robo-advisory services provided by banks. Based on the authors’ survey on the use of artificial intelligence technology in the banking sector in Poland, in this article we construct tree-based models to predict customers’ attitudes towards using robo-advisory in banking services using, as predictors, their socio-demographic characteristics, behaviours and attitudes towards modern digital technologies, experience in using banking services, as well as trust towards banks. In our study, we use selected machine learning algorithms, including a decision tree and several tree-based ensemble models. We showed that constructed models allow to effectively predict consumer acceptance of robo-advisory services.

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Exploring Multi-Time Context Vector and Randomness for Stock Movement Prediction

Seo

Yang

2022

2022 IEEE International Conference on Big Data (Big Data)

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A Fund Selection Robo-Advisor with Deep-learning Driven Market Prediction

Cited by 8 publications

References 23 publications

Implications of AI-based robo-advisory for private banking investment advisory

Implications of AI-based robo-advisory for private banking investment advisory

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Exploring Multi-Time Context Vector and Randomness for Stock Movement Prediction

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