Clusters of financial market states identified over 2006-2019.
problem Understanding the statistical properties of financial markets.
method Clustering analysis of correlation matrices constructed from sliding epochs.
result Financial markets can be classified into distinct states with transitions indicating precursors to catastrophic events.
ClusterLOB clusters market events to identify different trading behaviors.
problem Understanding market microstructure and participant behavior in financial markets.
method ClusterLOB uses K-means++ algorithm to cluster market events based on six time-dependent features.
result ClusterLOB identifies three distinct trading behaviors: directional, opportunistic, and market-making participants.
Study clusters Indian stocks using polyspectral means for nuanced market insights.
problem Analyzing temporal patterns and financial relationships in Indian stock market.
method k-means clustering algorithm applied to polyspectral means of stock data.
result Identified five distinctive clusters of stocks with varying ownership structures.
We propose a novel method to quantify the clustering behavior in a complex time series and apply it to a high-frequency data of the financial markets. We find that regardless of used data sets, all data exhibits the volatility clustering properties, whereas those which filtered the volatility clustering effect by using…
DynMSA detects market clusters for better portfolio allocation.
problem Identifying stable market clusters for effective portfolio management.
method Combining Random Matrix Theory with modularity optimization and spectral clustering.
result DynMSA outperforms baseline models in intra- and inter-cluster correlation differences.
High-speed clustering detects financial market states from intraday data.
problem Detecting and understanding intraday financial market states.
method High-speed maximum likelihood clustering algorithm applied to correlation matrices of intraday market microstructure features.
result State signature vectors enable real-time state detection and provide low-dimensional descriptors.
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
Proposes ICC method for dynamic portfolio optimization.
problem Non-stationarity in market conditions makes traditional portfolio optimization ineffective.
method Inverse Covariance Clustering (ICC) to identify market states and integrate into dynamic optimization.
result ICC-PO generates portfolios with higher Sharpe Ratios and greater robustness.
Develops a new framework to measure network connectedness across and within markets.
problem Lack of flexible methods to measure network connectedness and its evolution.
method Allows network nodes to be connected in clusters, with shocks orthogonal across clusters and correlated within clusters.
result Demonstrates the effectiveness of the new framework in a detailed empirical analysis of equity markets.
By analyzing a large data set of daily returns with data clustering technique, we identify economic sectors as clusters of assets with a similar economic dynamics. The sector size distribution follows Zipf's law. Secondly, we find that patterns of daily market-wide economic activity cluster into classes that can be ide…
Proposes a new clustering method based on expectiles for non-spherical clusters.
problem Inability of K-means to handle non-spherical clusters. method Uses expectiles to define cluster centers and searches for clusters via a greedy algorithm.
result Outperforms K-means and spectral clustering on asymmetric shaped clusters. This study uses moving average cluster entropy to analyze financial market dynamics.
problem Understanding long-range dependence in financial markets.
method Moving average cluster entropy approach applied to ARFIMA and FBM processes.
result Long-range positive correlation in financial markets is linked to the cluster entropy behavior.
Market entropy analysis reveals horizon dependence in asset prices.
problem Quantifying horizon dependence of asset prices in high-frequency data.
method Cluster entropy approach to quantify price dynamics over different temporal horizons.
result Systematic dependence of cluster entropy and Market Dynamic Index on temporal horizon.
Review of financial market data clustering and networks.
problem Understanding correlations, hierarchies, and networks in financial markets.
method Compilation and synthesis of research from various fields.
result A comprehensive resource for financial market analysis.
Clusters asset classes to identify lead-lag relationships in market regimes.
problem Understanding lead-lag relationships between different asset classes.
method Defining macroeconomic regimes by clustering indices and investigating lead-lag relationships.
result Unravels market features and highlights informative market trends or risks.
New algorithm recovers stock market sectors using SPC and f-SPC.
problem Recovering hierarchical structure of stock market interactions.
method Super-Paramagnetic Clustering (SPC) and Fast Super-Paramagnetic Clustering (f-SPC) algorithms.
result f-SPC solutions converge to maximum entropy phase and perform better for high-dimensional data.
New model prices crypto options by clustering market regimes and using implied volatility.
problem Inaccurate option pricing for volatile crypto markets.
method Time-regime clustering with Implied Stochastic Volatility Model (ISVM).
result MR-ISVM overcomes complexity and adapts to market dynamics.
Algorithm classifies market regimes using time series signatures.
problem Classifying different market conditions from time series data.
method Utilizes path signatures and a metric structure for clustering.
result Established a connection between regime separation and point clustering.
Study electricity price correlations to reveal grid structure.
problem Understanding the locational structure of electricity grids.
method Clustering methods and correlation functions for spiky time series.
result Reconstructed locational structure of the grid.
Study uses contrastive learning to analyze market order behavior.
problem Understanding diverse market order behaviors.
method Self-supervised learning with triplet loss for order representation.
result Identified distinct behavior types using K-means clustering.
Model simulates financial time series with volatility clustering and cross correlations.
problem Simulate financial time series with volatility clustering and cross correlations.
method Introduced an Ising model with interactions between financial time series.
result Simulated financial time series exhibit volatility clustering and cross correlations.
New clustering method for financial data with known cluster number.
problem Clustering financial data with known number of clusters.
method Introduced a covariance-based dissimilarity measure for multifractional Brownian motions.
result Asymptotically consistent clustering algorithms for multifractional Brownian motions.
Paper proposes a new algorithm for clustering financial market regimes.
problem Rapid and automated detection of distinct market regimes.
method Wasserstein k-means algorithm for clustering financial time-series.
result Wasserstein k-means algorithm outperforms traditional clustering methods.
Scheme for online state discovery in financial markets using feature correlations and clustering.
problem Discovering temporal states in high-frequency financial data without human intervention.
method Unbiased Fourier estimator for feature correlations, high-speed clustering algorithm, state space enumeration.
result Feature cluster configuration is a candidate for system state representation.
UNMIX identifies hidden buyers in darknet markets by clustering anonymized IDs.
problem Identifying hidden buyers in darknet markets where IDs are anonymized.
method UNMIX, a hidden buyer identification model using Dirichlet Hawkes Process.
result UNMIX successfully groups transactions from one hidden buyer into one cluster.
Study finds price-based clustering outperforms AI and human methods in stock market analysis.
problem Investigates if AI can improve stock clustering compared to traditional methods.
method Compares price-based, human-informed, and AI-driven clustering methods using synthetic factor models.
result Price-based clustering reduces RMSE by 15.9% relative to GICS and 14.7% relative to LLM embeddings.
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.
In this study, we establish a network structure of the Korean stock market, one of the emerging markets, with its minimum spanning tree through the correlation matrix. Base on this analysis, it is found that the Korean stock market doesn't form the clusters of the business sectors or of the industry categories. When th…
Modeling financial crises and cryptocurrency shocks using copulae clustering.
problem Detecting financial crises and shock events in stock and cryptocurrency markets.
method Copulae clustering based on probability distribution distances.
result Successfully detected all past crises and shock events in stock and cryptocurrency markets.
New model explains asymmetric volatility feedback and clustering effects in stock market.
problem Explaining asymmetric feedback and clustering effects in stock market volatility.
method Proposed an asymmetric ARCH model calibrated with historical data.
result Volatility in short time scales is influenced by past return signs (leverage effect), while long-term clustering is dominant.
Proposes a method to assess clustering stability in financial time series.
problem Assessing the validity of clustering in financial data.
method Empirical framework with data perturbations.
result Provides insights into assets' clustering behavior through multi-view analysis.
The paper proposes a method to cluster data and estimate regression parameters using VI for financial forecasting.
problem Learning relationships between input and output with different parameters in different regions of the input space.
method Cluster-based regression using Variational Inference (VI).
result The approach can predict the expected value and full distribution of predicted output.
New method detects and clusters market regimes in multidimensional data.
problem Detecting and clustering market regimes in complex data structures.
method Non-parametric online market regime detection and clustering using path-wise two-sample tests and maximum mean discrepancy.
result Successfully detected and clustered market regimes in various data structures.
Study financial markets using synchronization measures and clustering algorithms.
problem Analyze high-frequency trading dynamics and market states.
method Ordinal pattern series, information-theoretic synchronization measure, clustering algorithms, Markov model.
result Identify two coherent seasons of centralized and decentralized synchronicity.
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, w…
Study shows how wealth distribution leads to volatility clustering in speculative markets.
problem Volatility clustering in financial markets.
method Agent-based model of financial markets with heterogeneous wealth distribution and round-trip trading.
result Heterogeneous wealth distribution induces volatility clustering through market wealth redistribution.
A hybrid approach detects financial market regime switches using PCA and k-means.
problem Detecting regime switches in financial markets for trend forecasting.
method Dimensionality reduction with PCA and clustering with k-means.
result Trading strategies based on detected regimes show improved performance.
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.
Improved market state classification for risk assessment.
problem Classifying financial market states for better risk assessment.
method Modified selection criteria for market states, clustering optimization, and visualization of correlation matrices.
result Statistically significant results in SP 500 and Nikkei 225 markets.
Paper uses clustering to diversify stocks, reducing risk.
problem Diversifying stock portfolios to reduce risk.
method Applied correlation clustering to a stock correlation matrix.
result Portfolio diversification using clustering reduces risk.
Study of 2D Ising model reveals patterns in financial markets.
problem Understanding stylized facts in financial markets using statistical physics.
method 2D Ising model with spin interactions; analysis of spin clusters, persistence, and dynamics.
result Microscopic mechanisms explain stylized facts like sharp peaks in returns and heavy-tailed distributions.
A new frequency-based index estimates sub-cluster connections.
problem Determining the strength of connections between similar sub-clusters.
method Frequency coefficient based on the SST poverty index for clustering.
result Estimation of connection strength reveals natural homogeneous clusters.
Unified approach for clustering financial multiplex networks.
problem Lack of methods to capture interconnections between assets over time.
method Tensor-based unified local and global clustering coefficients for multiplex networks.
result Unified clustering coefficients effectively describe dependencies between assets over time.
Study financial market graphs with Laplacian constraints.
problem Learning undirected graphs in financial markets.
method Proposes algorithms to estimate graphs accounting for financial data properties.
result Guidelines for estimating graphs in financial markets.
Solves financial volatility clustering using Minkowski metric in GARCH(1,1) model.
problem Financial volatility clustering and long memory process.
method Minkowski metric applied to GARCH(1,1) model.
result Equivalent to dark volatility or hidden risk fear field.
Study of U.S. stock market dynamics using Boltzmann Machine model.
problem Understanding market correlation structure and instabilities.
method Boltzmann Machine model with binary variables, exact and approximate learning algorithms.
result Binarization preserves market correlation structure and heavy positive tail in couplings.
A new log-volatility factor model reduces dimensionality and identifies cluster contributions to volatility clustering.
problem Understanding the sources of volatility clustering in financial markets.
method Introduced a new factor model using Directed Bubble Hierarchical Tree (DBHT) to identify the number of factors and integrated non-parametric proxy to study volatility clustering.
result Clusters contribute to volatility clustering locally, while the market contributes globally.
The study uses DCC for financial market analysis, revealing hidden correlations.
problem Identifying hidden nonlinear correlations in financial markets.
method Agglomerative hierarchical clustering with distance correlation coefficient.
result DCC reveals more information than Pearson correlation for financial data.