ArticleFinancial innovation2021
Forecasting and trading cryptocurrencies with machine learning under changing market conditions.
Article in Financial innovation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting.Frontiers in artificial intelligence · 2025Review
- Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?Financial innovation · 2023Article
- Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets.Financial innovation · 2023Article
- A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting.Financial innovation · 2023Article
- Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets.Financial innovation · 2023Article
- Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets.Financial innovation · 2023Article
- Diversification evidence of bitcoin and gold from wavelet analysis.Financial innovation · 2023Article
- Markets in crypto-assets regulation: Does it provide legal certainty and increase adoption of crypto-assets?Financial innovation · 2023Article
- The linkage between Bitcoin and foreign exchanges in developed and emerging markets.Financial innovation · 2023Article
- Industry return lead-lag relationships between the US and other major countries.Financial innovation · 2023Article
- Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality.Financial innovation · 2023Article
- The transaction behavior of cryptocurrency and electricity consumption.Financial innovation · 2023Article
- Empirical evidence on the ownership and liquidity of real estate tokens.Financial innovation · 2023Article
- Effect of blockchain technology initiatives on firms' market value.Financial innovation · 2023Article
- The predictive power of Bitcoin prices for the realized volatility of US stock sector returns.Financial innovation · 2023Article
- Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm.Financial innovation · 2023Article
- A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System.Annals of data science · 2022Review
- Impact of COVID-19 effective reproductive rate on cryptocurrency.Financial innovation · 2022Article
- Predicting cash holdings using supervised machine learning algorithms.Financial innovation · 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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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study examines the predictability of three major cryptocurrencies-bitcoin, ethereum, and litecoin-and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.