Executive summary 

We are pleased to publish our first paper as a Working Party using data science techniques to look at sustainability and climate change-related issues. In this paper, we summarise the first stage of our analysis, where we introduce data science techniques to construct a time series analysis of the Standard & Poor’s (S&P) Green Bond Index. Scope of this paper This aim of this paper is to lay out the foundations for a time series analysis on green, social and sustainability (GSS) bond indices, and is not intended to be a definitive guide. We have deliberately excluded stationarity and restricted this paper to a univariate analysis. We will include stationarity and expand our examination to a multivariate analysis in subsequent papers. For the purposes of this paper, we have focussed the S&P Green Bond Index and performed various univariate time series analyses using a range of models, which include neural networks. 

This paper focusses on using a rolling window approach of one prior day’s index value to predict today’s index value. In particular, this paper discusses (arranged as per the following Sections): 

• Section 2: Introduction o Background to GSS bonds and a brief explanation on the analysis covered in this paper.
• Section 3: Data
• Insight into the data used in our analysis along with summary information on the train / validation / test splits. 
• Section 4: Summary of models used
• A high-level summary of model architectures used in our analysis (i.e. neural networks and a decision tree) with supplemental, background information, grouped into five model categories.
• Section 5: Training the models o Background information to the loss history, Adam optimiser, regularisation techniques, and hyperparameter optimisation techniques used in our analysis.
• Section 6: Results
• Summary tables and graphs of the best performing model per model category. 
• Section 7: Conclusions and next steps o Summary of conclusions from our analysis and potential areas of analysis for subsequent papers.