Mansour Davoudi, Mina Ghavipour, Morteza Sargolzaei-Javan, Saber Dinparast
{"title":"去中心化存储加密货币：识别有效实体和预测未来价格趋势的创新网络模型","authors":"Mansour Davoudi, Mina Ghavipour, Morteza Sargolzaei-Javan, Saber Dinparast","doi":"10.1007/s10614-024-10664-8","DOIUrl":null,"url":null,"abstract":"<p>Cryptocurrencies, recognized for their transformative impact on both emerging economies and the global financial landscape, are increasingly integral to investment strategies due to their widespread adoption and significant market volatility driven by socio-political news. This study analyzes the price trends of four major cryptocurrencies in decentralized storage—Filecoin, Arweave, Storj, and Siacoin—using a novel approach that combines network analysis, textual analysis, and market analysis. By constructing a network of relevant entities, summarizing pertinent news articles, assessing sentiment with the FinBert model, and evaluating financial market data through transformer encoders, our methodology provides a comprehensive analysis of factors influencing cryptocurrency prices. The integration of these analyses enables us to predict the price trends of the examined cryptocurrencies with accuracies of 76% for Filecoin, 83% for Storj, 61% for Arweave, and 74% for Siacoin, highlighting the model's effectiveness in navigating the complexities of the cryptocurrency market.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends\",\"authors\":\"Mansour Davoudi, Mina Ghavipour, Morteza Sargolzaei-Javan, Saber Dinparast\",\"doi\":\"10.1007/s10614-024-10664-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Cryptocurrencies, recognized for their transformative impact on both emerging economies and the global financial landscape, are increasingly integral to investment strategies due to their widespread adoption and significant market volatility driven by socio-political news. This study analyzes the price trends of four major cryptocurrencies in decentralized storage—Filecoin, Arweave, Storj, and Siacoin—using a novel approach that combines network analysis, textual analysis, and market analysis. By constructing a network of relevant entities, summarizing pertinent news articles, assessing sentiment with the FinBert model, and evaluating financial market data through transformer encoders, our methodology provides a comprehensive analysis of factors influencing cryptocurrency prices. The integration of these analyses enables us to predict the price trends of the examined cryptocurrencies with accuracies of 76% for Filecoin, 83% for Storj, 61% for Arweave, and 74% for Siacoin, highlighting the model's effectiveness in navigating the complexities of the cryptocurrency market.</p>\",\"PeriodicalId\":50647,\"journal\":{\"name\":\"Computational Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Economics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1007/s10614-024-10664-8\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s10614-024-10664-8","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends
Cryptocurrencies, recognized for their transformative impact on both emerging economies and the global financial landscape, are increasingly integral to investment strategies due to their widespread adoption and significant market volatility driven by socio-political news. This study analyzes the price trends of four major cryptocurrencies in decentralized storage—Filecoin, Arweave, Storj, and Siacoin—using a novel approach that combines network analysis, textual analysis, and market analysis. By constructing a network of relevant entities, summarizing pertinent news articles, assessing sentiment with the FinBert model, and evaluating financial market data through transformer encoders, our methodology provides a comprehensive analysis of factors influencing cryptocurrency prices. The integration of these analyses enables us to predict the price trends of the examined cryptocurrencies with accuracies of 76% for Filecoin, 83% for Storj, 61% for Arweave, and 74% for Siacoin, highlighting the model's effectiveness in navigating the complexities of the cryptocurrency market.
期刊介绍：
Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
