arXiv research

Clusters of financial market states identified over 2006-2019.

Study clusters Indian stocks using polyspectral means for nuanced market insights.

ClusterLOB clusters market events to identify different trading behaviors.

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.

Crowded trades cluster investors, affecting stock price stability.

Proposes ICC method for dynamic portfolio optimization.

Develops a new framework to measure network connectedness across and within 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.

This study uses moving average cluster entropy to analyze financial market dynamics.

Clusters asset classes to identify lead-lag relationships in market regimes.

New model prices crypto options by clustering market regimes and using implied volatility.

Algorithm classifies market regimes using time series signatures.

Study uses contrastive learning to analyze market order behavior.

Paper proposes a new algorithm for clustering financial market regimes.

UNMIX identifies hidden buyers in darknet markets by clustering anonymized IDs.

Clusters cryptocurrency market states via cross correlation analysis.

Study finds price-based clustering outperforms AI and human methods in stock market analysis.

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.

Market dynamic is quantified in terms of the entropy $S(τ,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(τ,n)$ is defined according to Shannon as $\sum P(τ,n)\log P(τ,n),$ with $P(τ,n)$ the probability for the cluster t…

We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…

The paper proposes a method to cluster data and estimate regression parameters using VI for financial forecasting.

New method detects and clusters market regimes in multidimensional data.

Study financial markets using synchronization measures and clustering algorithms.

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…

A hybrid approach detects financial market regime switches using PCA and k-means.

Improved market state classification for risk assessment.

Study of 2D Ising model reveals patterns in financial markets.

We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical …

Study financial market graphs with Laplacian constraints.

Unified approach for clustering financial multiplex networks.

We study the structure of locational marginal prices in day-ahead and real-time wholesale electricity markets. In particular, we consider the case of two North American markets and show that the price correlations contain information on the locational structure of the grid. We study various clustering methods and intro…

The study uses DCC for financial market analysis, revealing hidden correlations.

We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach we call Fast Super-Paramagnetic Clustering (f-SPC). The methods…

This article investigates the correlation structure of the global crude oil market using the daily returns of 71 oil price time series across the world from 1992 to 2012. We identify from the correlation matrix six clusters of time series exhibiting evident geographical traits, which supports Weiner's (1991) regionaliz…

The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.

The team predicts foreign exchange rates using clustering and attention models.

We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that …

Proposes a method to incorporate current market conditions in VaR and stress testing.

Study examines local extrema and crossing statistics in financial markets.

The article detects market regimes from covariance matrices using VLSTAR and clustering models.

The paper monitors stock market relationships using network analysis and statistical control charts.

This paper detects fraudulent trading in the NFT market.

Modeling price clustering in financial markets using discrete distributions.

Detects lead-lag clusters in US equity market time series.

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial ti…