利用人工智能对冲比特币及其衍生品利润的比较分析

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications Pub Date : 2024-10-11 DOI:10.1016/j.physa.2024.130159

Qing Zhu , Jianhua Che , Shan Liu

{"title":"利用人工智能对冲比特币及其衍生品利润的比较分析","authors":"Qing Zhu , Jianhua Che , Shan Liu","doi":"10.1016/j.physa.2024.130159","DOIUrl":null,"url":null,"abstract":"<div><div>Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under <span><math><mi>T</mi></math></span>+0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under <span><math><mi>T</mi></math></span>+1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the <span><math><mi>T</mi></math></span>+1 trading was found to have higher volatility, profits, and risk than <span><math><mi>T</mi></math></span>+0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130159"},"PeriodicalIF":2.8000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of profits from Bitcoin and its derivatives using artificial intelligence for hedge\",\"authors\":\"Qing Zhu , Jianhua Che , Shan Liu\",\"doi\":\"10.1016/j.physa.2024.130159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under <span><math><mi>T</mi></math></span>+0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under <span><math><mi>T</mi></math></span>+1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the <span><math><mi>T</mi></math></span>+1 trading was found to have higher volatility, profits, and risk than <span><math><mi>T</mi></math></span>+0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.</div></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":\"654 \",\"pages\":\"Article 130159\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S037843712400668X\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S037843712400668X","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

由于个人投资者和投资机构在如何选择比特币及其新衍生品和交易所交易基金（ETF）之间存在差异，本文使用比特币和ProShares比特币策略ETF（BITO）数据以及混合变异模式分解和双向门控周期单元模型来研究比特币及其新衍生品ETF之间的相互联系，并从中提出可操作的建议。除了使用比特币和 BITO 进行金融模拟交易外，该研究还扩展到其他主要 ETF。研究发现(1) 比特币数据可用于预测和描述 BITO；(2) 在 T+0 交易下，比特币的波动性、盈利性和风险性高于 BITO；(3) 在 T+1 交易下，比特币的波动性、盈利性和风险性低于 BITO；然而，T+1 交易的波动性、盈利性和风险性高于 T+0 交易。因此，本研究为预测和描述新的 ETF 搭建了一座从理论到实践的桥梁。与以往研究不同的是，本研究利用人工智能和定量金融模拟探索了比特币和 BITO 之间的关系，拓展了对比特币市场的实践和理论认识。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Comparative analysis of profits from Bitcoin and its derivatives using artificial intelligence for hedge

Because there is a discrepancy between how individual investors and investment institutions choose Bitcoin and its new derivatives and Exchange-Traded Funds (ETFs), this paper used Bitcoin and ProShares Bitcoin Strategy ETF (BITO) data and a mixed variational mode decomposition and bidirectional gated cycle unit model to examine the interconnections between Bitcoin and its new derivative ETFs, from which actionable recommendations were developed. As well as conducting financial simulation trading using Bitcoin and BITO, the study expanded to examine other major ETFs. It was found that: (1) Bitcoin data could be employed to forecast and describe BITO; (2) under

T

+0 trading, Bitcoin was more volatile, profitable, and risky than BITO; and (3) under

T

+1 trading, Bitcoin was less volatile, profitable, and risky than BITO; however, the

T

+1 trading was found to have higher volatility, profits, and risk than

T

+0 trading. This study, therefore, builds a bridge from theory to practice for the prediction and description of new ETFs. Different from previous studies, this study explored the relationships between Bitcoin and BITO using Artificial Intelligence and quantitative financial simulations, which extends the practical and theoretical understanding of the Bitcoin market.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Physica A: Statistical Mechanics and its Applications 物理-物理：综合

CiteScore

7.20

自引率

9.10%

发文量

852

审稿时长

6.6 months

期刊介绍： Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.