{"title":"The dynamic impact of network attention on natural resources prices in pre-and post-Russian-Ukrainian war","authors":"","doi":"10.1016/j.resourpol.2024.105271","DOIUrl":null,"url":null,"abstract":"<div><p>Natural resources prices have been greatly affected by the Russian-Ukrainian war. Investors also pay special attention to the price of natural gas and oil. This paper introduces the Thermal optimal path (TOP) method and the Long Short-Term Memory (LSTM) model to investigate the dynamic lead-lag relationship between investor attention and natural resources prices for crude oil and natural gas.</p><p>We find that during the war, the ability of investor attention to lead the prices of crude oil and natural gas become significantly stronger. Adding investor attention to the model significantly improves price prediction in the full sample. In addition, after the sample period is segmented according to the TOP method, the forecasting performance is greatly improved compared to the full sample. These findings indicate that traders and companies can incorporate investor attention into price predictions to further improve their trading strategies.</p></div>","PeriodicalId":20970,"journal":{"name":"Resources Policy","volume":null,"pages":null},"PeriodicalIF":10.2000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Policy","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030142072400638X","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Natural resources prices have been greatly affected by the Russian-Ukrainian war. Investors also pay special attention to the price of natural gas and oil. This paper introduces the Thermal optimal path (TOP) method and the Long Short-Term Memory (LSTM) model to investigate the dynamic lead-lag relationship between investor attention and natural resources prices for crude oil and natural gas.
We find that during the war, the ability of investor attention to lead the prices of crude oil and natural gas become significantly stronger. Adding investor attention to the model significantly improves price prediction in the full sample. In addition, after the sample period is segmented according to the TOP method, the forecasting performance is greatly improved compared to the full sample. These findings indicate that traders and companies can incorporate investor attention into price predictions to further improve their trading strategies.
自然资源价格受到俄乌战争的严重影响。投资者也特别关注天然气和石油的价格。本文引入热最优路径（TOP）方法和长短期记忆（LSTM）模型，研究了投资者注意力与原油和天然气等自然资源价格之间的动态滞后关系。在模型中加入投资者关注度可显著提高对全样本价格的预测。此外，根据 TOP 方法对样本期进行细分后，预测性能比全样本有了很大提高。这些研究结果表明，交易商和公司可以将投资者关注度纳入价格预测，从而进一步改进其交易策略。
期刊介绍：
Resources Policy is an international journal focused on the economics and policy aspects of mineral and fossil fuel extraction, production, and utilization. It targets individuals in academia, government, and industry. The journal seeks original research submissions analyzing public policy, economics, social science, geography, and finance in the fields of mining, non-fuel minerals, energy minerals, fossil fuels, and metals. Mineral economics topics covered include mineral market analysis, price analysis, project evaluation, mining and sustainable development, mineral resource rents, resource curse, mineral wealth and corruption, mineral taxation and regulation, strategic minerals and their supply, and the impact of mineral development on local communities and indigenous populations. The journal specifically excludes papers with agriculture, forestry, or fisheries as their primary focus.
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