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.
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.
Paper proposes a novel trading strategy combining clustering and reinforcement learning for multi-period portfolio management.
problem Developing an effective trading strategy for multi-period portfolio management.
method The paper integrates clustering techniques with reinforcement learning to categorize and manage stocks across multiple trading periods.
result The proposed strategy outperforms conventional techniques in various metrics, achieving an average return of 151% over 360 trading periods.
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 …
This paper analyzes correlations in patterns of trading of different members of the London Stock Exchange. The collection of strategies associated with a member institution is defined by the sequence of signs of net volume traded by that institution in hour intervals. Using several methods we show that there are signif…
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.
Co-trading networks reveal dynamic market structures and improve covariance estimation.
problem Modeling high-dimensional stock covariances in US equity markets.
method Co-trading-based pairwise similarity measure for constructing dynamic networks, spectral clustering, robust covariance estimator.
result Co-trading networks capture time-evolving stock dependencies and improve portfolio performance.
The paper identifies clusters in the World Trade Network using communicability distances.
problem Identifying clusters in the World Trade Network.
method Uses Estrada and vibrational communicability distances to find clusters maximizing a modularity function.
result Identifies specific distance thresholds that maximize modularity, revealing unique relationships between countries.
In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure. In this paper, we consider a class of binary matrices, arising in many applications, which exhibit both row and column cluster structure, and our goal is …
Algorithm maps trade-off between clustering fidelity and representation size.
problem Optimizing trade-off between clustering fidelity and representation size.
method Introduces primal Deterministic Information Bottleneck (DIB) problem for discrete search spaces.
result Shows richer Pareto frontier over Lagrangian relaxation.
This work optimizes clustering with adaptive queries to minimize disagreements.
problem Minimizing disagreements in clustering with adaptive similarity queries.
method Active learning algorithms and information-theoretical bounds.
result Achieves an almost optimal trade-off between queries and clustering error.
VolTS uses stats & ML to forecast stock market trends based on volatility.
problem Capturing profitable trading opportunities from market dynamics.
method Combines statistical analysis with machine learning; k-means++ clustering, Granger causality test.
result Effective at identifying profitable trading opportunities through volatility clusters and Granger causality.
New method clusters financial time series into volatility regimes.
problem Finding the number of volatility regimes in nonstationary financial time series.
method Change point detection and clustering of segment distributions.
result Optimized trading strategy based on learned volatility regimes.
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
ExKMC improves explainable k-means clustering by balancing accuracy and simplicity.
problem Limited explainable methods for unsupervised learning.
method Develops ExKMC, a new algorithm that uses a decision tree with k′ leaves to explain k-means clustering, trading explainability for accuracy. result ExKMC produces a low-cost clustering that outperforms existing methods.
We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph cluste…
A new method clusters data from multiple sources using a mixture of multilayer SBMs.
problem Aggregating multiple clustering results from different data sources.
method Uses a mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices.
result Identifies and clusters observations based on their specificities within components.
Proposes a variational framework for fair clustering.
problem Ensuring fairness in clustering algorithms.
method Integrates fairness term with clustering objectives, using variational approach.
result Derives tight upper bound for optimization, enabling scalable solution.
Two machine learning methods detect insider trading from investor activity data.
problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.
The paper models trades in dark pools using Hawkes processes.
problem Modeling clustered trades in dark pools.
method Developed a non-Markovian Hawkes process with time-dependent baseline intensity.
result Obtained closed-form formulas for the Hawkes process.
This paper detects fraudulent trading in the NFT market.
problem Fraudulent activities like wash trading in the NFT market.
method Unsupervised learning using K-means clustering on market data.
result Identified groups of traders with suspicious behavior.
ADEC addresses feature randomness and drift in autoencoder-based clustering.
problem Clustering autoencoders learn unreliable pseudo-labels, distorting latent space and feature randomness.
method Adversarial training to balance reconstruction loss and clustering objective.
result ADEC outperforms state-of-the-art autoencoder-based clustering methods.
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
problem Economic shocks impact the efficiency and robustness of international pesticide trade networks.
method Simulations were used to quantify efficiency and robustness under different economic shocks. Three strategies were tested: descending, random, and ascending node removal.
result The international pesticide trade networks became more efficient and robust except for clustering coefficient. Import-oriented economies were more vulnerable to shocks.
New approach for fair graph clustering using semidefinite relaxation.
problem Ensuring equitable representation in network analysis.
method Semidefinite relaxation approach for NP-hard optimization problem.
result Optimal accuracy-fairness trade-off achieved.
We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that…
DynAE improves deep clustering by dynamically shifting from reconstruction to centroid construction.
problem Lack of clear cost functions in unsupervised learning for capturing variations and similarities.
method Dynamic Autoencoder (DynAE) that gradually eliminates reconstruction in favor of centroid construction.
result DynAE achieves state-of-the-art results in deep clustering compared to other methods.
Study develops a multi-pair trading strategy using graph clustering and machine learning.
problem Improving risk-adjusted returns and reducing transaction costs in US equities market.
method Statistical arbitrage, graph clustering algorithms, Kelly criterion, machine learning classifiers.
result Optimal signal detection and risk management techniques outperformed benchmarks.
Cluster-DP improves differential privacy in randomized experiments by clustering data.
problem Reducing variance in causal effect estimation from differentially private data.
method Cluster-DP leverages a given cluster structure to improve the privacy-variance trade-off.
result Selecting higher-quality clusters decreases the variance penalty without compromising privacy guarantees.
This study uses hierarchical structure methods (minimal spanning tree, (MST) and hierarchical tree, (HT)) to examine the hierarchical structures of the United State (US) foreign trade by using the real prices of their commodity export and import move together over time. We obtain the topological properties among the co…
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the t…
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Fragmented exchanges arise due to speed advantages in high-activity regions.
problem Fragmentation of distributed securities exchanges due to speed advantages in high-activity regions.
method Economic model and Monte Carlo simulations of a decentralized exchange with two miner clusters.
result Speed advantage increases with infrastructure asymmetry between regions.
Study reveals patterns in trader clusters over time, improving investment predictions.
problem Managing diverse trader risk in financial services.
method Clustered trader data analyzed using Ewens' Sampling Distribution and Aggregating Algorithm (AA). Statistically Validated Networks (SVN) applied for improved results.
result Temporal distributions of trader clusters follow Ewens' Sampling Distribution, and AA can be improved with SVN.
Modeling price clustering in financial markets using discrete distributions.
problem Price clustering phenomenon in financial markets.
method Discrete price model based on mixture of double Poisson distributions with dynamic volatility and proportions.
result Higher instantaneous volatility weakens price clustering at ultra-high frequencies.
Model explains stylized facts in financial log returns through agent behavior.
problem Understanding stylized facts in financial log returns.
method Agent-based model with three types of traders.
result Model produces log returns with stylized facts like leptokurtosis and volatility clustering.
A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading…
The paper addresses Qini curve estimation under clustered network interference.
problem Qini curves can be biased when interference is ignored in clustered network settings.
method Proposes three estimation strategies for clustered network interference.
result Identifies the most appropriate approach based on bias-variance trade-offs.
The team predicts foreign exchange rates using clustering and attention models.
problem Complexity and unexpected events in foreign exchange markets.
method Clustering and attention models applied to historical data.
result Improved event-driven price prediction for oversold scenarios.
New method uses synthetic data to validate financial agent classification.
problem Validation of machine learning methods for financial agent classification.
method Agent-based model to generate synthetic data for validation.
result Unsupervised clustering may give incorrect results for financial agents.
The paper uses clustering and integer programming to optimize stock selection for investment funds.
problem Maximizing profits and minimizing risk in stock markets.
method Data-oriented analysis and clustering techniques with integer programming.
result Reconstructed NASDAQ 100 index fund example demonstrates effectiveness.
This paper studies the topological properties of the World Trade Web (WTW) and its evolution over time by employing a weighted network analysis. We show that the WTW, viewed as a weighted network, displays statistical features that are very different from those obtained by using a traditional binary-network approach. I…
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
This paper optimizes multi-currency AMMs to reduce forex trading costs.
problem Lack of direct liquid markets for currency pairs.
method Constant-mean AMM architecture, hierarchical agglomerative clustering algorithm.
result Optimized multi-currency pools reduce trading costs by ~13%.
FBC clusters data fairly without needing cluster count.
problem Fairness in clustering groups of different sensitive groups.
method Developed a Bayesian model-based clustering method with a fair prior and efficient MCMC algorithm.
result Reasonably infers the number of clusters and achieves a fair utility trade-off.
Analyzes NFT market trends, trade networks, and visual features.
problem Understanding the structure and evolution of NFT market.
method Data analysis of 6.1 million trades of 4.7 million NFTs.
result NFTs form tight clusters and collections contain visually homogeneous objects.
In an illiquid stock, traders can collude and place orders on a predetermined price and quantity at a fixed schedule. This is usually done to manipulate the price of the stock or to create artificial liquidity in the stock, which may mislead genuine investors. Here, the problem is to identify such group of colluding tr…
An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering technique which integrates such constraints. Motivated by the recently proposed 1-spectral clustering for the unconstrained problem, our method is…
Method determines latent dimensionality in international trade flows.
problem Finding meaningful low-dimensional latent features in high-dimensional international trade data.
method Proposes a latent dimension determination method based on clustering of nonnegative RESCAL decompositions.
result Validates the latent features against empirical economic facts.