{"title":"用于预测可再生能源资产价格的谷歌趋势增强型深度学习模型","authors":"Lalatendu Mishra , Balaji Dinesh , P.M. Kavyassree , Nachiketa Mishra","doi":"10.1016/j.knosys.2024.112733","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112733"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Google Trend enhanced deep learning model for the prediction of renewable energy asset price\",\"authors\":\"Lalatendu Mishra , Balaji Dinesh , P.M. Kavyassree , Nachiketa Mishra\",\"doi\":\"10.1016/j.knosys.2024.112733\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":\"308 \",\"pages\":\"Article 112733\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124013674\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124013674","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A Google Trend enhanced deep learning model for the prediction of renewable energy asset price
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.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.