A Google Trend enhanced deep learning model for the prediction of renewable energy asset price

Abstract

This study investigates the predictive efficiency of various forecasting models for renewable energy asset prices, using oil price and investor sentiment. For renewable energy assets, renewable energy exchange-traded funds (ETFs) are considered in this study. We construct two sentiment indices using the first principal component: a fund-level investor sentiment index based on traditional indices (the Relative Strength Index and the Psychological Line Index) and the Google Trend Index derived from search trend data with keywords related to respective renewable energy ETFs. In this study, we propose a prediction model along with a deep learning framework, integrating both sentiment indices. We predict ETF log returns and conditional volatility using machine learning and deep learning models. To enhance predictive accuracy, we modify both the traditional sentiment and Google Trend indices. The results assert that models incorporating both the modified fund-level investor sentiment and Google Trends indices outperform unmodified indices. This study underscores the effectiveness of integrating multi-source sentiment for improved predictive performance, with a significant contribution by the Google Trend Index. Our model, particularly the CNN-LSTM, outperforms the CNN and BiLSTM models, as validated through Modified Diebold-Mariano tests. In addition to this benchmark, we perform additional benchmarking with forecasting techniques used in the latest ETF study and verify the robustness of our model. The findings of this study will be useful for different stakeholders of the renewable energy sector.