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机器学习预测加密货币指数及成分价格

文章 arXiv papers · 作者: Reaz Chowdhury et al.
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总结

论文使用机器学习方法预测加密货币指数及其九种成分加密货币的收盘价。文中称，研究比较了多种算法和模型，以找出表现最佳的方法，并将结果与相关研究进行比较。研究旨在分析波动市场中的价格变化，并探索其在交易和加密货币投资组合管理中的潜在用途。

作者报告称，表现最佳的方法取得了优于或可与既有研究相比的结果。然而，所提供的介绍没有说明算法、数据来源、预测期限、评估指标或具体结果。文中也没有说明是否在扣除成本后将预测作为交易信号进行评估，也没有说明其在不断变化的市场条件下表现如何。因此，文本明确了预测任务并作出宽泛的比较主张，但细节不足以判断预测是否可靠或是否具有实际交易价值。

核心观点

  • 研究预测加密货币指数及九种成分资产的收盘价。
  • 研究比较了多种机器学习模型，以选出首选预测方法。
  • 作者将报告结果与相关预测研究进行了比较。
  • 所提供的介绍没有说明模型细节、数据、评估指标和交易测试。

标签

加密资产机器学习统计学投资组合构建
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# Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning


# Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning









At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。