Machine learning for robo-advisors: Testing for neurons specialization

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dc.contributor.authorSemko, Roman
dc.date.accessioned2020-10-22T15:19:12Z
dc.date.available2020-10-22T15:19:12Z
dc.date.issued2019
dc.description.abstractThe rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.en_US
dc.identifier.citationSemko R. Machine learning for robo-advisors: Testing for neurons specialization [electronic resource] / Semko, R. // Investment Management and Financial Innovations. - 2019. - Vol. 16, Issue 4. - P. 205-214.en_US
dc.identifier.urihttp://doi.org/10.21511/imfi.16(4).2019.18
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/18298
dc.language.isoenuk_UA
dc.relation.sourceInvestment Management and Financial Innovations.en_US
dc.statusfirst publisheduk_UA
dc.subjectwealth managementen_US
dc.subjectrobo-advisoren_US
dc.subjectfintechen_US
dc.subjectmachine learningen_US
dc.subjectneural networken_US
dc.subjectportfolio optimizationen_US
dc.subjectarticleen_US
dc.titleMachine learning for robo-advisors: Testing for neurons specializationen_US
dc.typeArticleuk_UA
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