The Efficient Market Hypothesis for Bitcoin in the context of neural
networks
- URL: http://arxiv.org/abs/2208.07254v1
- Date: Sat, 25 Jun 2022 09:04:37 GMT
- Title: The Efficient Market Hypothesis for Bitcoin in the context of neural
networks
- Authors: Mike Kraehenbuehl, Joerg Osterrieder
- Abstract summary: This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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