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

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lalatendu Mishra , Balaji Dinesh , P.M. Kavyassree , Nachiketa Mishra
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引用次数: 0

摘要

本研究利用石油价格和投资者情绪调查了各种可再生能源资产价格预测模型的预测效率。对于可再生能源资产,本研究考虑了可再生能源交易所交易基金(ETF)。我们利用第一个主成分构建了两个情绪指数:一个是基于传统指数(相对强弱指数和心理线指数)的基金级投资者情绪指数,另一个是根据与各可再生能源 ETF 相关关键词的搜索趋势数据得出的谷歌趋势指数。在本研究中,我们提出了一个预测模型和一个深度学习框架,将这两个情绪指数整合在一起。我们使用机器学习和深度学习模型预测 ETF 的对数收益率和条件波动率。为了提高预测准确性,我们修改了传统的情感指数和谷歌趋势指数。结果表明,包含修改后的基金级投资者情绪指数和谷歌趋势指数的模型优于未修改的指数。这项研究强调了整合多源情感以提高预测性能的有效性,其中谷歌趋势指数做出了重大贡献。我们的模型(尤其是 CNN-LSTM)优于 CNN 和 BiLSTM 模型,这一点已通过修改后的 Diebold-Mariano 测试得到验证。除此基准外,我们还对最新 ETF 研究中使用的预测技术进行了额外的基准测试,并验证了我们模型的稳健性。本研究的结果将对可再生能源领域的不同利益相关者有所帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
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.
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