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

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. •A Deep Responsible Investment Portfolio framework to integrate deep and reinforcement learning and portfolio optimization.•A multivariate model for forecasting long-term returns, fully tested on real-life datasets with 100 stocks over 30 years.•The first report (to the best of our knowledge) leverages deep learning and ESG ratings into a portfolio optimization model.