比特币市场的智能预测

IF 7.4 2区 经济学 Q1 BUSINESS, FINANCE
Gil Cohen, Avishay Aiche
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引用次数: 0

摘要

本文探讨了人工智能(AI)在预测比特币价格走势方面的有效性。为此,我们开发了两种不同的交易策略,并将它们的表现与传统的买入并持有(B&H)策略进行了比较。在2018年1月至2023年9月期间,我们发现,由ChatGPT 01-Preview优化的策略将多个技术指标和情绪分析整合到一个加权综合指数中,取得了944.85%的优异总回报。第二个策略采用了极端梯度提升(XGBoost)技术,取得了 189.05 % 的总回报。人工智能策略比 XGBoost 策略的超额回报率高出 755.8%,这凸显了人工智能的显著优势,尤其是在利用社交媒体等多种数据源预测比特币价格走势方面,比单纯依赖经济数据更加有效。这两种交易策略的表现都明显优于传统的 B&H 策略,后者同期的回报率为 73.08%。此外,我们还发现,在比特币价格波动较大的时期,人工智能具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent forecasting in bitcoin markets
This paper examines the effectiveness of Artificial Intelligence (AI) in predicting Bitcoin's price movements. To achieve this, we developed two distinct trading strategies and compared their performance against each other and the traditional Buy and Hold (B&H) strategy. Over the period from January 2018 to September 2023, we found that the strategy optimized by ChatGPT 01-Preview, which integrates multiple technical indicators and sentiment analysis into a weighted composite index, delivered an exceptional total return of 944.85 %. The second strategy, that is using Extreme Gradient Boosting (XGBoost) technique achieved a total return of 189.05 %. The AI strategy's excess return of 755.8 % over the XGBoost strategy highlights the significant advantage of AI particularly in utilizing diverse data sources, such as social media, to predict Bitcoin's price trends more effectively than relying solely on economic data. Both trading strategies significantly outperformed the traditional B&H strategy, which returned 73.08 % over the same period. Furthermore, we found that AI has an advantage during periods of high Bitcoin price volatility.
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来源期刊
Finance Research Letters
Finance Research Letters BUSINESS, FINANCE-
CiteScore
11.10
自引率
14.40%
发文量
863
期刊介绍: Finance Research Letters welcomes submissions across all areas of finance, aiming for rapid publication of significant new findings. The journal particularly encourages papers that provide insight into the replicability of established results, examine the cross-national applicability of previous findings, challenge existing methodologies, or demonstrate methodological contingencies. Papers are invited in the following areas: Actuarial studies Alternative investments Asset Pricing Bankruptcy and liquidation Banks and other Depository Institutions Behavioral and experimental finance Bibliometric and Scientometric studies of finance Capital budgeting and corporate investment Capital markets and accounting Capital structure and payout policy Commodities Contagion, crises and interdependence Corporate governance Credit and fixed income markets and instruments Derivatives Emerging markets Energy Finance and Energy Markets Financial Econometrics Financial History Financial intermediation and money markets Financial markets and marketplaces Financial Mathematics and Econophysics Financial Regulation and Law Forecasting Frontier market studies International Finance Market efficiency, event studies Mergers, acquisitions and the market for corporate control Micro Finance Institutions Microstructure Non-bank Financial Institutions Personal Finance Portfolio choice and investing Real estate finance and investing Risk SME, Family and Entrepreneurial Finance
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