Regulated Bitcoin futures led to higher volatility and trading volume.
problem Estimating the impact of regulated Bitcoin futures on volatility and volume.
method Employed a new causal approach, C-ARIMA.
result Regulated Bitcoin futures increased Bitcoin volatility by more than double.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
problem Maximizing profits in the Bitcoin market using trading signals.
method Comparison of machine learning models (LightGBM, LSTM) and technical analysis strategies (EMA, MACD+ADX).
result LSTM model achieved a 65.23% cumulative return over a year, significantly outperforming other strategies.
Study finds Binance's tether-margined contracts significantly impact bitcoin volatility.
problem Understanding volatility transmission in the crypto market, especially through Binance.
method Analyzing high-frequency realised volatility dynamics and spillovers in bitcoin market pairs.
result Binance's tether-margined contracts are the primary source of volatility and transmit strong flows.
This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, some…
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contrib…
Attempts to accurately measure the monetary velocity or related properties of bitcoin used in transactions have often attempted to either directly apply definitions from traditional macroeconomic theory or to use specialized metrics relative to the properties of the Blockchain like bitcoin days destroyed. In this paper…
Study compares volatility models for Bitcoin, finds GARCH and EGARCH outperform.
problem Evaluating which volatility models best predict Bitcoin spot and option prices.
method Used HIST, EMA ARCH, GARCH, and EGARCH models on Bitcoin spot price series.
result GARCH and EGARCH models outperform other models in both in-sample and out-of-sample forecasts.
Study evaluates 41 ML models for Bitcoin trading performance.
problem Predicting Bitcoin prices for algorithmic trading.
method Examined 21 classifiers and 20 regressors under various market conditions.
result Certain models like Random Forest and Stochastic Gradient Descent outperform others in profit and risk management.
This study compares Bitcoin and Litecoin using cryptocurrency metrics and trading strategies.
problem Valuation and trading strategies for cryptocurrencies.
method Metrics like UTXO, STXO, WAL, CDD, and trading strategies based on PU ratio.
result Bitcoin's superior store-of-value proposition compared to Litecoin validated.
This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.
problem Wash trading in cryptocurrency markets to inflate volume and manipulate market conditions.
method Analysis of 18 million Mt. Gox transactions, exogenous demand shock study.
result Wash trading intensifies in low legitimate trading volume and responds to demand shocks, indicating strategic behavior.
Bitcoin option prices reflect both market maker supply and trader demand, especially from those with insider information.
problem Understanding how market prices of bitcoin options are influenced by both market makers and informed traders.
method Analysis of Deribit options tick-level data to identify supply and demand effects.
result At-the-money option prices are driven by volatility traders, while out-of-the-money options are influenced by both volatility traders and those with insider information.
PreBit predicts Bitcoin price movements using social media and financial data.
problem Predicting extreme price movements of Bitcoin due to its volatility and speculative trading.
method Hybrid model combining FinBERT embeddings of Twitter content with candlestick data and technical indicators.
result The hybrid model can predict significant market movements with a profitable trading strategy.
In recent years cryptocurrency trading has captured the attention of practitioners and academics. The volume of the exchange with standard currencies has known a dramatic increasing of late. This paper addresses to the need of models describing a bitcoin-US dollar exchange dynamic and their use to evaluate European opt…
Study optimizes Bitcoin futures hedging to reduce liquidation risk.
problem Optimizing hedging strategies to minimize liquidation risk in Bitcoin futures.
method Derived a semi-closed form optimal hedging strategy considering spot and futures extreme returns, loss aversion, leverage, and collateral management.
result Optimal strategy reduces both hedged portfolio variance and liquidation probability.
The paper analyzes gold, oil, and bitcoin futures volatility and basis.
problem Understanding the volatility and basis of gold, oil, and bitcoin futures.
method Contract-by-contract analysis of spot and futures prices, trading volume, and open interest data.
result Trading volume positively affects volatility in all three assets, while open interest has a possible negative effect.
Study on cryptocurrency trading patterns using multifractal analysis.
problem Lack of systematic study on temporal structure of cryptocurrency trading.
method Multifractal detrended cross-correlation analysis of price returns, trades, and volume.
result All analyzed quantities exhibit multifractal structure, both univariate and bivariate.
Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts …
Informer model with GMADL loss outperforms benchmarks in high frequency Bitcoin trading.
problem Developing automated trading strategies for high frequency Bitcoin data.
method Informer architecture with RMSE, GMADL, and Quantile loss functions.
result Informer model with GMADL loss function outperforms benchmarks in trading outcomes.
Study on Bitcoin transaction flows and holding times, revealing multifractal and power-law distributions.
problem Characterizing the temporal behavior and variability of Bitcoin transactions and holding times.
method Analysis of Bitcoin transaction data, including holding-time distributions, multiscaling, and multifractality.
result Found multifractal and power-law distributions in Bitcoin transaction flows and holding times, with significant variations in holding times.
New PU ratio predicts long-term Bitcoin returns better than other methods.
problem Lack of convincing proxies for cryptocurrency fundamentals.
method Developed a new market-to-fundamental ratio (PU ratio) using blockchain accounting methods.
result PU ratio effectively predicts long-term Bitcoin returns compared to alternative methods.
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experi…
A feature-rich Bitcoin trading assistant using reinforcement learning.
problem Predicting and analyzing Bitcoin market trends for long-term profits.
method Reinforcement learning with technical indicators and Twitter sentiment scores.
result Average profit of 69% over 685 days, including the Covid-19 period.
HFformer outperforms LSTM in high-frequency trading with multiple signals.
problem Improving high-frequency trading performance using deep learning models.
method Introducing HFformer, a hybrid Transformer model for time series forecasting.
result HFformer achieves higher cumulative PnL than LSTM in backtesting.
Machine learning predicts Bitcoin returns but trading performance drops with costs.
problem Trading Bitcoin predictions with transaction costs.
method XGBoost, LSTM, iTransformer models evaluated in walk-forward protocol; cost-aware execution filter implemented.
result Cost-aware execution filter restores profitability; XGBoost strategy outperforms.
Study identifies Bitcoin arbitrageurs and their trading strategies.
problem Detecting and understanding Bitcoin arbitrageurs on Mt. Gox.
method Analyzing historical trade data from Mt. Gox (2011-2014) to identify and categorize arbitrageurs.
result Expert arbitrageurs have a positive profit margin, while novice users do not.
The paper assesses dimensionality reduction for cryptocurrency link prediction.
problem Establishing a link between cryptocurrencies using dimensionality reduction techniques.
method Used canonical correlation analysis and principal component analysis on log returns and covariates of Bitcoin and Ethereum.
result Performance of dimensionality reduction techniques in forecasting Ethereum returns with Bitcoin features.
Study examines Bitcoin market fragmentation and price formation, revealing market leader-lagger dynamics and trading strategies.
problem Understanding price formation in fragmented Bitcoin markets at sub-second time scales.
method Utilized granular orderbook and trades data, constructed features, and trained linear models to explain market returns.
result Fee regime determines market leadership and profitability of taker strategies, maker strategies tested in real-world trading.
Bitcoin's integration with major financial indices intensifies, suggesting a shift from alternative to integrated asset.
problem Understanding Bitcoin's evolving role in financial markets and its correlation dynamics.
method Rolling-window correlation, static correlation coefficients, and event-study framework on daily data from 2018 to 2025.
result Correlation levels between Bitcoin and major indices reached 0.87 in 2024, indicating a more integrated role.
Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 event…
Proposes deep mixture models for probabilistic price movement forecasting in high-frequency trading.
problem Probabilistic forecasting of price movements in high-frequency trading.
method Deep recurrent neural networks with probabilistic mixture models.
result Outperforms benchmark models in both metric-based and simulated trading scenarios.
A trading system uses LLMs to adapt to volatile crypto markets.
problem Volatility and market sentiment in cryptocurrencies make traditional models ineffective.
method Specialized LLM agents for technical analysis, sentiment evaluation, and decision-making; verbal feedback for continuous improvement.
result Agents outperform buy-and-hold strategy with consistent gains across market phases.
Study examines liquidation, leverage, and optimal margin requirements in Bitcoin futures markets.
problem Understanding and optimizing margin requirements in Bitcoin futures markets.
method Empirical analysis using generalized extreme value theory and BitMEX data.
result Margin requirements need to be significantly higher to reduce daily margin calls.
The study identifies key factors affecting cryptocurrency prices, including market beta, trading volume, and volatility.
problem Understanding the factors influencing cryptocurrency prices and their dynamics over time.
method ARDL technique and error-correction models applied to weekly data of Bitcoin, Ethereum, Dash, Litecoin, and Monero from 2010-2018.
result Cryptomarket-related factors are significant determinants of cryptocurrency prices in both short- and long-run, while attractiveness matters only in the long-run.
Paper models Bitcoin market dynamics using 1+1D field theory.
problem Understanding stylized facts in Bitcoin markets.
method Collects order-book datasets, applies KPZ-like stochastic equations.
result Predicts order book dynamics with high precision.
Deep model predicts Bitcoin price movements without retraining.
problem Stationary modelling of high-frequency Bitcoin price movements.
method Deep recurrent model based on order flow.
result Model maintains stability during volatile periods.
2024 saw Bitcoin ETF approval, offering regulated exposure.
problem Understanding unique liquidity risks in Bitcoin ETFs.
method Analyzed premium/discount patterns in first four months.
result Premium/discount behavior differs from traditional ETFs.
We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitc…
Reinforcement learning crypto agent achieves high returns on Bitcoin derivatives.
problem Maximizing returns on volatile cryptocurrency markets.
method Online transfer learning with an echo state network and recurrent reinforcement learning.
result Achieves a total return of 350%, net of transaction costs, over five years.
Study examines Bitcoin's volatility and returns using stochastic volatility model.
problem Characterizing Bitcoin as a financial asset and its volatility patterns.
method Asymmetric stochastic volatility model applied to Bitcoin data from 2013-2019.
result Bitcoin shows weak post-holiday effects and no asymmetry effect in returns and volatility.
Paper forecasts extreme Bitcoin volatility spikes using whale transactions and CryptoQuant data.
problem Forecasting extreme volatility spikes in Bitcoin market.
method Proposes Synthesizer Transformer model for forecasting.
result Model outperforms state-of-the-art models in forecasting extreme volatility spikes.
New blockchain metrics improve cryptocurrency trading and prediction.
problem Improving trading and prediction in the volatile cryptocurrency market.
method Developed blockchain metrics based on public data from Bitcoin mining nodes.
result Blockchain metrics provide statistical advantage in trading Bitcoin assets.
New model explains price dynamics of Bitcoin with psychological factors.
problem Understanding price variations in cryptocurrency markets with psychological factors.
method Extended agent-based model with heterogeneous psychological parameters.
result Model shows diverse dynamics based on psychological correlation.
MRC-LSTM predicts Bitcoin prices using CNN and LSTM.
problem Predicting Bitcoin price with high volatility and complex factors.
method Combines MRC and LSTM, focusing on multi-scale features and long-term dependencies.
result MRC-LSTM significantly outperforms other models in Bitcoin price prediction.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
This study compares Bitcoin and S&P 500 returns using a new GTS distribution method.
problem Analyzing the daily return distributions and tail probabilities of Bitcoin and S&P 500.
method Used advanced Fast Fractional Fourier transform (FRFT) to fit the seven-parameter General Tempered Stable (GTS) distribution.
result Bitcoin has heavier tails and higher prevalence of high returns compared to S&P 500.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
problem Predicting price movements of cryptocurrencies using deep learning.
method Used deep learning algorithms (MLP, CNN, LSTM, ALSTM) on hourly and daily data of Bitcoin and Ethereum.
result Unrestricted model with trading and social media indicators outperforms restricted model.
This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed wi…
Study compares statistical and machine learning models for detecting crypto trading anomalies.
problem Detecting outliers in cryptocurrency limit order books for market dynamics analysis.
method Comprehensive comparative analysis of 13 diverse models using a unified testing environment.
result Empirical Covariance (EC) model outperforms standard Buy-and-Hold by 6.70%.