Russia introduces CryptoRuble, impacting global finance.
problem Impact of government-backed cryptocurrencies on global finance.
method Analyzing motives and implications of CryptoRuble issuance.
result Implications for various stakeholders and future monetary systems.
Study examines cryptocurrency risk spillover effects before and after pandemic.
problem Analyzing risk propagation among cryptocurrencies during extreme events.
method Asymmetric breakpoint approach and network analysis.
result Cryptocurrency risk spillover effect increased during pandemic.
Analyzes changes in cryptocurrency market structure.
problem Understanding shifts in cryptocurrency market dynamics.
method Structural change analysis techniques.
result Identifies key structural changes in the market.
The paper uses AI to analyze on-chain parameters and identify risky cryptocurrencies.
problem Identifying risky cryptocurrencies and understanding their price factors.
method Historical data analysis, AI algorithms, clustering, classification.
result A significant negative correlation between cryptocurrency price and maximum and total supply, and a weak positive correlation with 24-hour trading volume.
This study analyzes cryptocurrencies to reveal their homogeneity and heterogeneity.
problem Exploring the homogeneity and heterogeneity of cryptocurrency market performance and popularities.
method Examined 3607 actively exchanged cryptocurrencies to analyze their prices, volumes, blockchain transactions, coin difficulties, and public opinion.
result Identified strong correlation in market performance and imbalance in popularities and sophistications.
Study uses autoencoders to classify cryptocurrencies over 12 periods.
problem Classify cryptocurrencies based on market behavior.
method Deep convolutional autoencoder neural network.
result Cryptocurrencies transition between classes over time.
Cryptocurrencies show similarities to traditional markets but also have unique characteristics.
problem Understanding the investment potential and characteristics of cryptocurrencies.
method Organized stylized facts and analyzed through empirical asset pricing.
result Cryptocurrencies exhibit similarities to traditional markets but also have distinct characteristics.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
Cryptocurrency market analysis reveals stable properties despite continuous emergence and disappearance of new coins.
problem Lack of comprehensive analysis of the entire cryptocurrency market.
method Analysis of 1,469 cryptocurrencies introduced between April 2013 and June 2017 using ecological modeling.
result Neutral model of evolution can reproduce key empirical observations of the cryptocurrency market.
Study analyzes cryptocurrency market complexity, comparing it to traditional markets.
problem Understanding the dynamics and characteristics of cryptocurrency markets.
method Statistical physics methods and analysis of price fluctuations.
result Cryptocurrency market exhibits complexity similar to traditional markets but with slower information flow.
TDA improves cryptocurrency portfolio management.
problem Traditional methods fail to manage cryptocurrencies effectively.
method Topological Data Analysis (TDA) for identifying investment opportunities.
result TDA-based portfolio management outperforms traditional methods.
Machine learning predicts cryptocurrency price movements.
problem Predicting short-term cryptocurrency price movements.
method Applied machine learning algorithms to cryptocurrency data.
result Machine learning algorithms outperform standard benchmarks in predicting cryptocurrency prices.
Study shows cryptocurrency investor base affects volatility.
problem Investor base changes impact cryptocurrency volatility.
method Proxying investor base with subreddit follower changes, analyzed idiosyncratic volatility.
result Changes in cryptocurrency investor base significantly increase idiosyncratic volatility.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
problem Misunderstanding of cryptocurrency technology and lack of empirical data.
method Analyzing detailed transaction data and summarizing statistics.
result Opportunity for academic research in financial economics.
Letter analyzes cryptocurrency correlations with financial assets.
problem Understanding correlations among cryptocurrencies and financial assets.
method Used a generalized DCC class model to analyze conditional correlations.
result Cryptocurrency correlations are positive but vary over time.
CCI30 is a new cryptocurrency index.
problem None explicitly stated in the abstract.
method Not specified in the abstract.
result Not specified in the abstract.
Clusters cryptocurrency market states via cross correlation analysis.
problem Analyse cryptocurrency market dynamics.
method Cross correlation structure analysis over 5 years.
result Cryptocurrency market clusters into 4 states.
Cryptocurrencies' value tied to liquidity, not intrinsic, making stability uncertain.
problem Cryptocurrencies lack tangible value, leading to price instability.
method Examined using asset flow equations and experimental markets.
result Cryptocurrency prices are influenced by liquidity, not intrinsic value.
This paper models cryptocurrencies using α-stable distributions, outperforming other models.
problem Modeling the highly speculative and leptokurtic nature of cryptocurrencies.
method Used α-stable distribution and compared it with other heavy tailed distributions. Employed maximum likelihood method for estimation. result The α-stable distribution fits cryptocurrency return data better than other models. A new cryptocurrency issuance protocol using proof-of-work without mining electricity.
problem Creating digital currencies without relying on electricity.
method Alternative asset utilization for cryptocurrency creation.
result Supports digital currencies in a proof-of-work manner without mining electricity.
Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
problem Improving diversification in German investor portfolios using cryptocurrencies.
method Portfolio analysis with descriptive statistics, graphical methods, and econometric spanning tests, using a customized EWCI.
result Cryptocurrencies can improve diversification in some windows but not as a normal case.
C2P2 predicts cryptocurrency price movements considering similarities among coins.
problem Predicting cryptocurrency price movements using historical and sentiment data.
method Collective classification using similarity metrics for 21 cryptocurrencies.
result C2P2 outperforms existing methods by 5.1-83% on 21 cryptocurrencies.
DBNs predict cryptocurrency price directions by uncovering causal relationships.
problem Predicting cryptocurrency price movements due to volatility and external factors.
method Dynamic Bayesian Networks (DBN) approach to identify causal relationships among features.
result DBN significantly outperforms baseline models in predicting cryptocurrency prices.
Transformer predicts Ethereum prices using cross-currency correlation and sentiment analysis.
problem Predicting Ethereum cryptocurrency prices with limited data.
method Transformer-based neural network with cross-currency correlation and sentiment analysis.
result Transformer model outperforms other models on some parameters.
Trend following in cryptocurrencies yields high returns, similar to commodities.
problem Investing in cryptocurrencies using trend following strategies.
method A decade of data analysis on cryptocurrency markets and trend following strategies.
result Cryptocurrencies offer strong returns and diversification against traditional equities.
Optimizes cryptocurrency portfolios using MNTS GARCH model.
problem Optimizing cryptocurrency portfolios with non-Gaussian return dynamics.
method Multivariate normal tempered stable (MNTS) GARCH model for non-Gaussian returns, Foster-Hart risk optimization.
result Foster-Hart optimization yields a more profitable portfolio with better risk-return balance.
Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.
problem Evaluating the wealth effect of cryptocurrency prices on real estate.
method Exploiting metaverse LAND and cryptocurrencies to track correlations and causality.
result Cryptocurrency prices Granger cause NFT LAND prices, similar to historical bubbles.
This study examines non-performing assets and cryptocurrencies in Japan.
problem Economic downturn led to non-performing loans, affecting financial institutions.
method Literature analysis to summarize development, issuance, supervision, etc.
result Cryptocurrencies are being regulated in Japan despite non-performing loans.
GRF models predict cryptocurrency VaR better than other methods.
problem Predicting Value at Risk (VaR) for volatile cryptocurrencies.
method Generalized Random Forests (GRF) adapted for quantile prediction.
result GRF models outperform other methods in cryptocurrency VaR predictions.
Cryptocurrencies' prices are influenced by interconnected features, not just their functionality.
problem Understanding the factors influencing cryptocurrency prices over time.
method Correlation networks were used to analyze cryptocurrencies' websites and whitepapers, and two datasets were analyzed to assess potential features.
result Cryptocurrencies are interconnected, and factors other than their functionality contribute to price evolution.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.
Cryptocurrency traders increase stock risk-seeking behavior.
problem Understanding the motivations behind cryptocurrency trading.
method Individual-level brokerage data analysis of stock trading behavior.
result Cryptocurrency traders increase risk-seeking behavior in stocks when engaging in cryptocurrency trading.
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
problem Evolution of cryptocurrency interactions
method Construct directed and weighted networks from Granger causal relationships between cryptocurrency log-returns.
result Normalized returns exhibit heavy-tailed distributions.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Network analysis reveals changing cryptocurrency market leaders.
problem Understanding evolving cryptocurrency market leaders and their influence.
method Hourly-resolution data and Kendall's Tau correlation for network analysis.
result Pearson's correlation underestimates market dynamics; FTT and FTX were key during the 2021 bull run.
Cryptocurrencies have heavy-tailed return distributions, requiring diversification.
problem Cryptocurrency returns do not follow Gaussian distributions.
method Applied econophysics and entropy measures to analyze returns.
result Portfolio diversification reduces return uncertainty.
Deep learning predicts cryptocurrency price movements with 78% accuracy.
problem Predicting price formation in cryptocurrency markets with high volatility and illiquidity.
method Applied deep learning to predict mid-price changes on live tick-level cryptocurrency data.
result Achieved 78% accuracy in predicting mid-price movement of Bitcoin vs USD.
A time-varying network for cryptocurrencies reveals community structure and diversification benefits.
problem Investing in cryptocurrencies requires understanding their risk and market segmentation.
method Developed a dynamic covariate-assisted spectral clustering method to estimate community structure based on return cross-predictability and technological similarities.
result Investors can achieve better risk diversification by investing in cryptocurrencies from different communities.
Cryptocurrency time-series predictability is low, resembling Brownian noise.
problem Low predictability of cryptocurrency exchange rates.
method Complexity and model predictions of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP exchange rates.
result Simpler models outperform complex ones in cryptocurrency forecasting.
Cryptocurrencies show varying levels of efficiency over time, forming clusters with younger ones mimicking older ones.
problem Determining the efficiency of cryptocurrencies over time.
method Permutation entropy and statistical complexity over sliding time-windows of price log returns.
result 37% of cryptocurrencies are efficient over 80% of the time, while 20% are efficient in less than 20% of the time.
CryptoGAT improves cryptocurrency price prediction by treating it as a graph problem.
problem Cryptocurrency price prediction challenges due to extreme volatility.
method CryptoGAT, a Graph Attention Network, redefines cryptocurrency prediction as a cross-asset graph problem.
result CryptoGAT outperforms state-of-the-art methods in cryptocurrency price prediction.
Worldcoin aims to make cryptocurrency transparent and accessible.
problem Lack of transparency and regulation in cryptocurrency markets.
method Hypothetical cryptocurrency model to establish transparency and equal access.
result Established a model for a regulated and trusted cryptocurrency infrastructure.
Unified Bayesian framework predicts cryptocurrency market dynamics and volatility.
problem Predicting cryptocurrency market trends and volatility.
method Bayesian framework based on potential field theory and Gaussian Process.
result Attractors and repellers from the potential field are reliable market indicators.
Cryptocurrencies are becoming more linked in their returns and volatilities.
problem Understanding the increasing interconnectivity of cryptocurrencies.
method Examined market linkages using returns and volatilities, applied various methodologies.
result Significant increase in market linkages for both returns and volatilities.
Study compares price patterns of cryptocurrencies and stocks using machine learning.
problem Investor behavior in cryptocurrencies vs. stocks.
method Machine learning models (LR, RF, SVM) classify price time series of cryptocurrencies and stocks.
result Cryptocurrencies and stocks have distinct price patterns, explained by various statistical features.
DAM improves cryptocurrency trend forecasting using multimodal data.
problem Simplistic merging of sentiment data in cryptocurrency trend forecasting.
method Dual Attention Mechanism (DAM) integrating financial metrics and sentiment analysis.
result DAM outperforms conventional models by up to 20% in prediction accuracy.
This paper predicts and forecasts cryptocurrency prices using machine learning.
problem High volatility and complexity of cryptocurrency prices.
method Applied machine learning techniques to predict and forecast cryptocurrency index and constituent prices.
result Best machine learning approach outperformed previous works.
The paper evaluates criteria for selecting cryptocurrencies based on historical data.
problem High risk of cryptocurrencies due to volatility.
method Characterized returns and risks using historical data in short time windows (7 and 15 days). Analyzed the importance of criteria using various methods.
result Importance of criteria for selecting cryptocurrencies is analyzed and evaluated.