Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio

Nhi N.Y.Vo, Xuezhong (Tony) He (), Shaowu Liu and Guandong Xu

Published Paper Series from Finance Discipline Group, UTS Business School, University of Technology, Sydney

Abstract: 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.

Keywords: Socially responsible investment; Portfolio optimization; Multivariate analytics; Deep reinforcement learning; Decision support systems (search for similar items in EconPapers)
Pages: 11 pages
Date: 2019-01-01
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (38)

Published in: He, X., Li, Y. and Zheng, M., 2019, "Heterogeneous Agent Models in Financial Markets: A Nonlinear Dynamics Approach", International Review of Financial Analysis, 2, 135-149.

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Persistent link: https://EconPapers.repec.org/RePEc:uts:ppaper:2019-3

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