This study analyzes how cryptocurrency networks adapt to financial disruptions.
problem Understanding how cryptocurrency networks respond to financial crises.
method Vertex centrality measures to assess network stability and resilience.
result Different cryptocurrencies experienced shifts in their network roles during the FTX crisis.
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.
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.
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.
The paper analyzes cryptocurrency trading networks using pairwise and high-order dependencies.
problem Understanding information flows and dependencies in cryptocurrency markets.
method Defined a cryptocurrency trading network using weekly log returns, analyzed using Granger causality and O-information.
result High-order dependencies reveal that stable coins play a major role in high-order effects.
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.
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.
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representati…
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …
Study improves cryptocurrency price prediction using neural networks and technical indicators.
problem Improving cryptocurrency price prediction accuracy.
method Integrates technical indicators, Transformer neural network, and BiLSTM.
result Demonstrates superior performance in predicting cryptocurrency prices.
Paper uses ANFIS to predict cryptocurrency prices.
problem Predicting cryptocurrency prices for seven days.
method Adaptive Network Based Fuzzy Inference System (ANFIS) with hybrid and backpropagation algorithms.
result The method can predict cryptocurrency prices in a short time.
Study shows institutional investments significantly impact cryptocurrency market evolution.
problem Limited understanding of institutional investments' role in cryptocurrency market evolution.
method Quantitative analysis of 1324 cryptocurrencies' investments from 2014-2022.
result Institutional investments correlate with cryptocurrency market capitalization.
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
problem Predicting price movements in volatile cryptocurrency markets.
method Discretisation-aware Bayesian Networks with three methods and multiple bin counts.
result Equal interval with two bins provides best predictive performance.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15…
Network-based strategy for optimal cryptocurrency portfolios identified.
problem Challenges in predicting cryptocurrency prices in a volatile market.
method Network methods to identify decorrelated cryptocurrencies, Markowitz Portfolio Theory.
result Network-based portfolios outperform benchmarks with high expected returns.
A new GCN model detects cryptocurrency fraud by considering network evolution and balance theory.
problem Detecting fraud in evolving signed cryptocurrency trust networks.
method Motif-aware temporal GCN using balance theory and learnable weights.
result The model outperforms existing methods on bitcoin datasets.
Study shows cryptocurrency market impact on DeFi returns stronger than other drivers.
problem Understanding drivers of DeFi returns and their relative importance.
method Investigated four drivers: cryptocurrency market exposure, network effect, investor attention, and valuation ratio. Designed a new market index, DeFiX.
result Cryptocurrency market impact on DeFi returns is stronger than other drivers and provides superior explanatory power.
Study evaluates deep learning models for cryptocurrency price prediction.
problem Accurate cryptocurrency price forecasting models are needed due to market volatility.
method Reviewed and evaluated deep learning models including LSTM, CNN, and Transformer.
result Convolutional LSTM with multivariate approach provides best prediction accuracy.
Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two data…
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.
Study uses RNN for real-time crypto price prediction and trading optimization.
problem High volatility in cryptocurrency markets makes traditional forecasting models unreliable.
method Data collection, preprocessing, model refinement, and backtesting.
result Improved accuracy in real-time crypto price prediction and optimized trading strategies.
Inspection-L detects illicit cryptocurrency transactions using GNNs and self-supervised learning.
problem Detect illicit cryptocurrency transactions for anti-money laundering.
method Graph Neural Network (GNN) framework based on self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN) with supervised learning algorithms.
result Inspection-L outperforms state-of-the-art methods in key classification metrics.
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.
This paper optimizes cryptocurrency portfolios by clustering price correlations and improving risk-return profiles.
problem Volatility and regulatory uncertainty in cryptocurrency markets make portfolio construction challenging.
method The paper combines network analysis, price forecasting, and portfolio theory to identify stable groups of correlated cryptocurrencies.
result Predictive consensus-clustering portfolios maintain positive and stable performance up to a 14-day horizon, with favourable gain-loss asymmetry and tighter tail-risk control.
Detects and traces masterminds behind cryptocurrency pump-and-dump schemes.
problem Identifying and tracing the entities organizing cryptocurrency manipulation.
method Collects real-time data from social networks and cryptocurrency markets, constructs temporal attributed graphs, and uses GNN to identify masterminds.
result Achieves higher F1 scores and precision than state-of-the-art fraud detection methods, detects 438 masterminds.
Neural nets analyze crypto markets for multi-timeframe trading.
problem High-frequency trading in cryptocurrency markets.
method Multi-timeframe trend analysis and high-frequency direction prediction networks.
result Positive risk-adjusted returns through machine learning.
Simple linear models reveal complex cryptocurrency networks.
problem Understanding complex causal networks in cryptocurrency markets.
method Multivariate linear models to infer financial networks from cryptocurrency price series.
result Simple linear models can create informative cryptocurrency networks reflecting economic intuition.
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.
Study cryptocurrency market complexity using multifractal and cross-correlation analyses.
problem Understanding the complexity and dynamics of cryptocurrency markets, especially during the COVID-19 pandemic.
method Multifractal formalism, cross-correlation analyses, network representation.
result Cryptocurrency market dynamics exhibit multifractal and intermittent bifractality, with topology changes during significant events.
Coding collaborations link crypto returns, revealing systemic transparency.
problem Cryptocurrencies' market behavior overlooked due to isolated code approach.
method Analyzed 4% of developers contributing to multiple cryptocurrencies.
result First coding event linking two cryptocurrencies synchronizes their returns.
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.
We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introdu…
Study on cryptocurrency market correlations at various time scales.
problem Understanding the hierarchical structure of cryptocurrency market dynamics.
method Analysis of MST and TMFG for 25 liquid cryptocurrencies at different time horizons.
result Cryptocurrency market correlations decrease with finer time scales and show a growing hierarchical structure with coarser scales.
Neural Hawkes method estimates cryptocurrency market microstructure and causality.
problem Estimating non-parametric Hawkes processes in high dimensions.
method Physics-informed neural networks for solving integral equations.
result Robust estimation of Hawkes processes in high dimensions.
Predicts cryptocurrency prices with deep state-space model.
problem Predicting day-ahead crypto-currency prices.
method Proposes a deep state-space model combining state-space formulation and deep neural networks.
result The deep state-space model outperforms state-of-the-art and classical methods in accuracy.
Study examines dynamic relationship between BRICS stocks and cryptocurrencies.
problem Understanding the impact of BRICS stock markets on cryptocurrency markets.
method Time-varying parameter vector autoregression model (TVP-VAR).
result Three out of five BRICS stock markets are primary sources of shocks affecting the financial network.
This study analyzes cryptocurrency market crashes using complex network analysis.
problem Identifying and understanding dynamics of cryptocurrency market crashes.
method Complex network analysis of cryptocurrency market during pre-crash, crash, and post-crash periods.
result Network density and clustering coefficient spike during crashes, indicating uninformed panic sell-off.
This study analyzes cryptocurrency market dynamics using a novel q-dependent detrended cross-correlation method.
problem Capturing correlations at varying fluctuation amplitudes and time scales in complex systems.
method Extends traditional metrics with q-dependent detrended cross-correlation coefficient ρ(q,s) and qMSTs. result Significant shifts in network structures during major disruptions, leading to decentralized correlations.
Predicts cryptocurrency pump probability using sequence-based neural networks.
problem Detecting pump-and-dump schemes in cryptocurrency markets.
method Developed a sequence-based neural network (SNN) that encodes historical P&D events into sequences for prediction.
result SNN improves prediction accuracy by leveraging positional attention to extract useful information.
The paper uses machine learning to predict cryptocurrency price changes.
problem Predicting significant price changes in cryptocurrency markets.
method Autoencoder-CNN-GANs algorithm for filtering and predicting price fluctuations.
result The model achieves predictive performance in real-time price sequences.
This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.
problem Understanding how cryptocurrencies integrate with traditional financial markets and the impact of market stress on cross-asset spillovers.
method Combining rolling correlation networks, community structure, market-specific and system-wide Turbulence Indices, and VAR-based connectedness analysis.
result Cross-asset integration is episodic, with network structure and turbulence playing a role in transmission during stress periods.
FinBERT-BiLSTM predicts cryptocurrency prices using sentiment analysis.
problem Predicting volatile cryptocurrency market prices.
method Hybrid model combining Bi-LSTM and FinBERT for sentiment analysis.
result Enhanced forecasting accuracy for volatile financial markets.
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
MFIN networks improve crypto trading with multiple features.
problem Selecting and processing multiple features for effective trading.
method End-to-end framework using Multi-Factor Inception Networks (MFINs).
result MFINs learn uncorrelated, higher-Sharpe strategies not captured by traditional factors.
Paper proposes a new reinforcement learning framework for cryptocurrency market making.
problem Improving profit and stability in cryptocurrency market making.
method Event-based reinforcement learning environment, training two policy-based agents with neural networks and various reward functions.
result Improved profit and stability demonstrated over time-based approach.
This study diversifies stock and crypto portfolios using network analysis.
problem Balancing returns and volatility in diversified portfolios.
method Community detection in network representations of assets, using Louvain and Affinity propagation algorithms.
result Opposite trends in crypto and traditional asset markets.
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.
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.