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用神经网络和跨资产输入检验比特币市场效率

文章 arXiv papers · 作者: Mike Kraehenbuehl et al.
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总结

本研究使用前馈神经网络检验比特币市场有效假说的弱式。研究考察在比特币收益之外，加入其他资产的收益或相关特征是否能改善预测。额外输入包括股票指数、EUR/USD汇率、US国债收益率，以及黄金和白银生产商指数。研究比较训练表现与测试集预测，并考察相对于买入并持有策略的表现。

训练准确率从单个特征时的54.6%升至六个特征时的61%，但新增特征并未提高测试集准确率。一组特征的表现部分优于买入并持有策略，而再加入一个特征后，该表现有所下降。因此，作者认为在样本期内，他们的模型和所选输入未揭示可利用的弱式无效性，同时也说明这只是部分结论。结果取决于所选时期、特征、架构和超参数；它既不能证明比特币始终有效，也不能排除其他方法。

核心观点

  • 本研究使用前馈神经网络检验比特币市场的弱式有效性。
  • 研究比较仅使用比特币收益与加入股票、货币、国债收益率和金属相关指数作为输入的效果。
  • 随着特征增加，训练准确率提高，但测试准确率没有提高。
  • 一组特征的表现部分优于买入并持有策略，再加入一个特征后表现减弱。
  • 有关市场有效性的结论仅适用于所研究的样本、输入和模型配置。

标签

加密资产机器学习统计学股票外汇固定收益
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# The Efficient Market Hypothesis for Bitcoin in the context of neural networks


# The Efficient Market Hypothesis for Bitcoin in the context of neural networks









This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market inefficiencies could be exploited in Bitcoin. Several studies we refer to here discuss this topic in the context of Bitcoin using either statistical tests or machine learning methods, mostly relying exclusively on data from Bitcoin itself. Results regarding market efficiency vary from study to study. In this study, however, the focus is on applying various asset-related input features in a neural network. The aim is to investigate whether the prediction accuracy improves when adding equity stock indices (S&P 500, Russell 2000), currencies (EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index (XAU), in addition to using Bitcoin returns as input feature. As expected, the results show that more features lead to higher training performance from 54.6% prediction accuracy with one feature to 61% with six features. On the test set, we observe that with our neural network methodology, adding additional asset classes, no increase in prediction accuracy is achieved. One feature set is able to partially outperform a buy-and-hold strategy, but the performance drops again as soon as another feature is added. This leads us to the partial conclusion that weak market inefficiencies for Bitcoin cannot be detected using neural networks and the given asset classes as input. Therefore, based on this study, we find evidence that the Bitcoin market is efficient in the sense of the efficient market hypothesis during the sample period. We encourage further research in this area, as much depends on the sample period chosen, the input features, the model architecture, and the hyperparameters.

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。