论文标题
在神经网络的背景下，比特币的有效市场假设
The Efficient Market Hypothesis for Bitcoin in the context of neural networks
论文作者
论文摘要
这项研究研究了使用前馈神经网络对比特币的有效市场假设的弱形式。由于近年来加密货币的普及越来越普及，问题出现在比特币中是否可以利用市场效率低下的问题。我们在这里参考的几项研究使用统计测试或机器学习方法在比特币的背景下讨论了此主题，主要依赖于比特币本身的数据。关于市场效率的结果因研究而异。但是，在这项研究中，重点是在神经网络中应用各种与资产相关的输入特征。目的是调查预测准确性在添加股票股票指数（标准普尔500，Russell 2000），货币（EURUSD），10年美国国库券收益率以及金和银生产商指数（XAU）时是否有所提高。如预期的那样，结果表明，更多功能可从54.6％的预测准确性，一项功能提高较高的培训性能，达到61％，具有六个功能。在测试集中，我们观察到，通过我们的神经网络方法，增加了其他资产类别，无法实现预测准确性的提高。一个功能集能够部分优于购买和持有的策略，但是一旦添加了另一个功能，该性能就会再次下降。这使我们得出了一个部分结论，即无法使用神经网络和给定资产类别作为输入来检测到比特币的弱势效率。因此，根据这项研究，我们发现证据表明，在样本期间的有效市场假设方面，比特币市场有效。我们鼓励在这一领域进行进一步的研究，在很大程度上取决于所选的样本周期，输入特征，模型架构和超参数。
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.
