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

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 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.

Deep learning for misinformation detection on online social networks: a survey and new perspectives

“…The term deep learning (DL) was first introduced to the machine learning community by Dechter (1986) and to artificial neural networks based on a Boolean threshold by Aizenberg (1999). In the field of ML in artificial intelligence, DL is an emerging technique which is used in various applications including computer vision (Wang and Yeung 2013), speech recognition (Hinton et al 2012), natural language processing (Young et al 2018), anomaly detection (Du et al 2017), portfolio optimization (Vo et al 2019), healthcare monitoring (Islam et al 2018b), personality mining (Vo et al 2018), novelty detection in robot behavior, traffic monitoring, visual data processing, social network analysis, etc. Nowadays, it is becoming increasingly used for processing data and creating patterns to assist the decision-making process.…”

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

“…Results show that their proposed model outperformed multiple layer perceptron (MLP), SVM, and KNN in finding the patterns in the financial time series. Vo et al [41] used a multivariate bidirectional-LSTM (MB-LSTM) to develop a deep responsible investment portfolio (DRIP) model for the prediction of stock returns for socially responsible investment portfolios. They applied the deep reinforcement learning (DRL) model to retrain neural networks.…”

Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities

“…The second piece of the prediction procedure is based on well founded mathematical error measurement tools which inform us about the quality of the model. Previous articles have selected several indicators to measure the quality of the model’s forecasts of financial market developments [16] , [17] . The most classic are the Mean Absolute Percentage Error(MAPE), R, Theil U and the Root Mean Squared Error(RMSE).…”

“…It squares the deviations to give more weight to large errors and to exaggerate errors. In this article, we follow the approach of [17] and choose the RMSE to measure the predictive accuracy of our model. The Root Mean Squared Error (RMSE) as the evaluation metrics for the absolute value prediction is given by: where N is the total number of value price in the closing price data and and y i are respectively the predicted and actual price.…”