Deep learning for decision making and the optimization of socially responsible investments and portfolio

Vo, NNY; He, X; Liu, S; Xu, G

Deep learning for decision making and the optimization of socially responsible investments and portfolio

Vo, NNY He, X

Liu, S

Xu, G

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Publication Type:: Journal Article
Citation:: Decision Support Systems, 2019, 124
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Vo, NNY	en_US
dc.contributor.author	He, X https://orcid.org/0000-0003-1446-9996	en_US
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Decision Support Systems, 2019, 124	en_US
dc.identifier.issn	0167-9236	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135552
dc.description.abstract	© 2019 Elsevier B.V. A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation.ispartof	Decision Support Systems	en_US
dc.relation.isbasedon	10.1016/j.dss.2019.113097	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Information Systems	en_US
dc.title	Deep learning for decision making and the optimization of socially responsible investments and portfolio	en_US
dc.type	Journal Article
utslib.citation.volume	124	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-09-01T00:00:00+1000
pubs.publication-status	Published	en_US
pubs.volume	124	en_US

Abstract:

© 2019 Elsevier B.V. A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.

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http://hdl.handle.net/10453/135552