Personalized finance advisory through case-based recommender systems and diversification strategies

Musto, Cataldo; Semeraro, Giovanni; Lops, Pasquale; Gemmis, Marco de; Lekkas, Georgios

doi:10.1016/j.dss.2015.06.001

Cited by 61 publications

(32 citation statements)

References 26 publications

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“…As summarized in the literature review of Table 2, previous research on financial robo advisors has mainly focused on designing algorithms with respect to the "optimal" composition of asset classes based on consumers' risk profile and financial goals (Day et al 2018;Kilic et al 2015;Musto et al 2015), usability aspects of the interface (Jung et al 2018), or the interactive role of the company's sales channel and profit orientation to rely on robotic investment advice (Lourenço et al 2020). Robo advisors have been considered predominantly as static tools to replace and optimize the human advisory process (see Lourenço et al 2020 for a notable exception, using an interactive "pension builder" interface), to collect client data and allocate an appropriate portfolio consistent with a clients' risk profile, rather than leveraging the relationship-building potential for the financial services firm or the opportunity to more directly address the lack of broader acceptance of financial robo advisory and lack of a "human touch.…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

Hildebrand

Bergner

2020

J. of the Acad. Mark. Sci.

128

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The current research demonstrates how conversational robo advisors as opposed to static, non-conversational robo advisors alter perceptions of trust, the evaluation of a financial services firm, and consumer financial decision making. We develop and empirically test a novel conceptualization of conversational robo advisors building on prior work in human-to-human communication and interpersonal psychology, showing that conversational robo advisors cause greater levels of affective trust compared to non-conversational robo advisors and evoke a more benevolent evaluation of a financial services firm. We demonstrate that this increase in affective trust not only affects firm perception (in terms of benevolence attributions or a more positively-valenced onboarding experience), but has important implications for investor behavior, such as greater recommendation acceptance and an increase in asset allocation toward conversational robo advisors. These findings have important implications for research on trust formation between humans and machines, the effective design of conversational robo advisors, and public policy in the digital economy.

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Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

Hildebrand

Bergner

2020

J. of the Acad. Mark. Sci.

128

View full text Add to dashboard Cite

show abstract

“…There are several recommender systems with a variety of techniques in individual business applications, such as beauty and make-up [103], property rental [104], stock market [105] and Finance advisory [97]. Moreover, tourism recommender systems create substantial opportunities for tourists to get advice, for instance on their mobile devices, for a variety of attractions, destinations, tour plans, transportation, restaurants and accommodations.…”

Section: Servicementioning

confidence: 99%

New Insights Towards Developing Recommender Systems

Taghavi

Bakhtiyari

Hanachi

2017

The Computer Journal

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Promoting recommender systems in real-world applications requires deep investigations with emphasis on their next generation. This survey offers a comprehensive and systematic review on recommender system development life cycles to enlighten researchers and practitioners. The paper conducts statistical research on published recommender systems indexed by Web of Science to get an overview of the state of the art. Based on the reviewed findings, we introduce taxonomies driven by the following five phases: initiation (architecture and data acquisition techniques), design (design types and techniques), development (implementation methods and algorithms), evaluation (metrics and measurement techniques) and application (domains of applications). A layered framework of recommender systems containing market strategy, data, recommender core, interaction, security and evaluation is proposed. Based on the framework, the existing advanced humanized techniques emerged from computational intelligence and some inspiring insights from computational economics and machine learning are provided for researchers to expand the novel aspects of recommender systems.

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“…Empirical research on robo-advisors has been steadily progressing with technology development. Recent developments in information and communication technology (ICT) and artificial intelligence have defined new terms and become a domain of the robo-advisor [4,5]. Several studies show better portfolio management performance using artificial intelligence than using various existing strategies [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

et al. 2020

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There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

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Personalized finance advisory through case-based recommender systems and diversification strategies

Cited by 61 publications

References 26 publications

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

New Insights Towards Developing Recommender Systems

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

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