ArticleEntropy (Basel, Switzerland)2020
Forecasting Bitcoin Trends Using Algorithmic Learning Systems.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks.Computational economics · 2022Article
- Conducting Causal Analysis by Means of Approximating Probabilistic Truths.Entropy (Basel, Switzerland) · 2022Article
- Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis.Financial innovation · 2022Article
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1 author.
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Abstract
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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Registered trials
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