Bitcoin price forecasting with neuro-fuzzy techniques

Atsalakis, George S.; Atsalaki, Ioanna G.; Pasiouras, Fotios; Zopounidis, Constantin

doi:10.1016/j.ejor.2019.01.040

Cited by 187 publications

(110 citation statements)

References 38 publications

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“…In so, further shortcomings in the documentation render it impossible to reproduce and verify the empirical analyses at all. These include, not explicitly reporting the analyzed time range [41,42], data split [43], or machine learning setup (e.g., layer structure, activation function, loss function, learning function) [44][45][46]. Furthermore, inconsistencies in the reporting prohibit reproducing the empirical tests.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning for Bitcoin Pricing — A Structured Literature Review

Jaquart¹,

Dann²,

Martin³

2020

WI2020 Zentrale Tracks

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Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.

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Section: Discussionmentioning

confidence: 99%

“…weekly intervals [35]) in combination with advanced machine learning models and a large number of features might result in an insufficient number of data points in the sample [49]. Furthermore, test splits of three percent or less, corresponding to 60 observations or less, limit the generalizability of the reported results [41,46].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Jaquart¹,

Dann²,

Martin³

2020

WI2020 Zentrale Tracks

View full text Add to dashboard Cite

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“…Furthermore, the investment returns achieved by a trading simulation, based on the signals of the proposed model, are 71.21% higher than the ones achieved through a naïve buy-and-hold strategy. As did Atsalakis et al [ 5 ], we also compare our trading systems results to a simple buy-and-hold strategy.…”

Section: Introductionmentioning

confidence: 88%

“…Their paper demonstrates that ARIMA model enduring power of volatile Bitcoin price prediction. Atsalakis et al [ 5 ] proposed a computational intelligence technique that uses a hybrid neuro-fuzzy controller to forecast the direction in the change of the daily price of Bitcoin. The proposed methodology outperforms two other computational intelligence models, the first being developed with a simpler neuro-fuzzy approach, and the second being developed with artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Bitcoin Trends Using Algorithmic Learning Systems

Cohen

2020

Entropy

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This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

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“…Vector Autoregressive model and multivariate vector autoregressive model [2,16,17,18,19,20]. With the development of big data technology and artificial intelligence, a growing body of empirical studies has applied machine learning models to classification and prediction.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Price Prediction based on Multiple Market Sentiment

Wang¹,

Chen²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult.To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions.

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Bitcoin price forecasting with neuro-fuzzy techniques

Cited by 187 publications

References 38 publications

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Forecasting Bitcoin Trends Using Algorithmic Learning Systems

Cryptocurrency Price Prediction based on Multiple Market Sentiment

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