Application of grey relational analysis and artificial neural networks on corporate social responsibility (CSR) indices

ABSTRACT

This research examines return predictability based on minimized forecast errors of CSR Indices through the grey relational analysis (GRA) and three types of artificial neural networks (ANN) model, namely: back-propagation perceptron (BPN); recurrent neural network (RNN); and radial basis function neural network (RBFNN), to capture non-linear characteristics of CSR indices for better forecasting accuracy. The study finds that the BPN model has the lowest forecast error, outperforming the RNN and RBFNN models. The model is also consistently better in using the 33% testing data. On the other hand, both the RNN and the RBFNN models preferred the 50% testing data. Based on the GRA rankings, the US Dollar Index and the S&P 500 index are the 1st and 2nd ranking variable, respectively. For the BPN and RNN models, the study experienced the lowest mean absolute error and root mean square errors when using the All Variables group.

KEYWORDS:

• CSR indices
• grey relational analysis
• artificial neural network

JEL:

• G10
• G17

Disclosure statement

No potential conflict of interest was reported by the author(s).