Back to Search
Start Over
Non-fundamental, non-parametric Bitcoin forecasting
- Source :
- Physica A, Physica A, Elsevier, 2019, 531, pp.121727-. ⟨10.1016/j.physa.2019.121727⟩
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Bitcoin is the largest cryptocurrency in the world, but its lack of quantitative qualities makes fundamental analysis of its intrinsic value difficult. As an alternative valuation and forecasting method we propose a non-parametric model based on technical analysis. Using simple technical indicators, we produce point and density forecasts of Bitcoin returns with a feedforward neural network. We run several models over the full period of April 2011–March 2018, and four subsamples, and we find that backpropagation neural networks dominate various competing models in terms of their forecast accuracy. We conclude that the dynamics of Bitcoin returns is characterized by predictive local non-linear trends that reflect the speculative nature of cryptocurrency trading.
- Subjects :
- [PHYS]Physics [physics]
Statistics and Probability
Cryptocurrency
Computer science
Nonparametric statistics
Condensed Matter Physics
01 natural sciences
010305 fluids & plasmas
Intrinsic value (finance)
Technical analysis
0103 physical sciences
Econometrics
010306 general physics
Valuation (finance)
Subjects
Details
- ISSN :
- 03784371
- Volume :
- 531
- Database :
- OpenAIRE
- Journal :
- Physica A: Statistical Mechanics and its Applications
- Accession number :
- edsair.doi.dedup.....883f3c6b7420cf87ca944e855a03b945