Green Bond Index Prediction Based on CEEMDAN-LSTM - Wang, Jiaqi; Tang, Jiulin; Guo, Kun (Frontiers in Energy Research, 2022)
Green bonds, which are designed to finance for environmental-friendly or sustainable projects, have attracted more and more investors’ attention. However, the study in this field is still relatively limited, especially in forecasting the market’s future trends. In this paper, a hybrid model combining Complete Ensemble Empirical Mode Decomposition (CEEMDAN) and Long Short-Term Memory (LSTM) is introduced to predict CUFE-CNI Green Bond Index. In order to evaluate the performance of our model, we also use Empirical Mode Decomposition (EMD) to decompose the green bond index. Our empirical result suggests that, compared with EMD-LSTM and LSTM models, CEEMDAN-LSTM is the most accurate model in green bond index forecasting. Meanwhile, we find that indices from energy commodity markets and green stock markets are both effective predictors, which also provides ground on the correlations between the green bond market and other financial markets.