Machine learning for robo-advisors: Testing for neurons specialization
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Date
2019
Authors
Semko, Roman
Journal Title
Journal ISSN
Volume Title
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Abstract
The 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.
Description
Keywords
wealth management, robo-advisor, fintech, machine learning, neural network, portfolio optimization, article
Citation
Semko 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.