Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Graph Ricci flow reveals hidden hierarchies in stock market correlations.
problem Detecting hidden structures in the complex stock market graph.
method Using graph Ricci curvature and flow techniques to analyze the NASDAQ 100 index.
result Algorithm detects hidden hierarchies, community behavior, and clustering in financial markets.
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.
Graph Neural Networks improve volatility prediction in financial markets.
problem Traditional models struggle with complex, non-linear interdependencies in financial markets.
method Temporal Graph Attention Network (Temporal GAT) combines GCNs and GATs to capture dynamic graph structures.
result Temporal GAT outperforms traditional GARCH models in volatility forecasting, especially for short- to mid-term predictions.
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
problem Cross-market return predictability and asymmetry between U.S. and Chinese markets.
method Directed bipartite graph capturing time-ordered linkages, hypothesis testing for edge selection, regularized and ensemble machine learning models.
result U.S. returns predict Chinese intraday returns, but not vice versa, revealing asymmetry.
Graph Signal Processing improves stock market volatility forecasting.
problem Forecasting realized volatility in a global stock market context.
method Integrating Graph Signal Processing into the HAR model.
result The proposed model outperforms HAR-type benchmarks.
Investigation of the market graph attracts a growing attention in market network analysis. One of the important problem connected with market graph is to identify it from observations. Traditional way for the market graph identification is to use a simple procedure based on statistical estimations of Pearson correlatio…
DGRCL integrates dynamic and static graph relations for financial market prediction.
problem Capturing the evolving nature of stock markets while considering both temporal changes and static relational structures.
method Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, including Embedding Enhancement (EE) and Contrastive Constrained Training (CCT) modules.
result DGRCL significantly outperforms state-of-the-art TGL baselines on NASDAQ and NYSE datasets.
Graph auto-encoders predict stock market instability by measuring graph structure changes.
problem Forecasting stock market instability and volatility.
method Use graph auto-encoders to reconstruct graph structure and measure changes.
result Higher GAE reconstruction error correlates with higher volatility.
This research introduces dynamic portfolio cuts using a spectral approach for graph-theoretic diversification.
problem Traditional methods for estimating asset-return covariance assume statistical time-invariance, failing to capture the nonstationary nature of asset price movements.
method Introduces graph spectral estimators that account for nonstationarity, partitioning the market graph into time-evolving clusters for dynamic portfolio cuts.
result Demonstrates the advantages of the proposed framework over traditional methods through numerical case studies using real-world price data.
SRR detects early signs of financial crises using multi-layer graphs.
problem Predicting systemic financial transitions from evolving market interactions.
method Systemic Risk Radar (SRR) models financial markets as multi-layer graphs.
result Graph-derived features provide useful early-warning signals compared to feature-based models.
The paper explores learning graphs in financial markets using Laplacian constraints.
problem Learning undirected graphical models for financial assets.
method Alternating Direction Method of Multipliers for graph learning.
result Laplacian matrix as a model for financial assets' precision matrix.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
A diversified portfolio is created by solving the MIS problem in large market graphs, outperforming conventional methods.
problem Finding the maximum independent set (MIS) in large-scale market graphs is computationally challenging.
method Solved the MIS problem using a quantum-inspired algorithm (Simulated Bifurcation) and a combinatorial optimization solver.
result The SB-based solver optimized MIS portfolios, achieving a Sharpe ratio of 1.16 and outperforming major indices.
GAT-AGNN learns stock trends using graph and attention mechanisms.
problem Predicting dynamic stock trends in a complex market.
method Sequential graph structure with attention mechanisms.
result GAT-AGNN outperforms state-of-the-art methods in stock trend prediction.
Study detects anomalies in financial markets using GNN and nonextensive entropy.
problem Detecting anomalies in global financial markets with many correlated assets.
method Used Graph Neural Networks (GNN) with nonextensive entropy to measure uncertainty.
result Anomalies are statistically different for nonextensive entropy parameters before, during, and after a crisis.
Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we pres…
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
It is assumed that under suitable economic and information-theoretic conditions, market exchange rates are free from arbitrage. Commodity markets in which trades occur over a complete graph are shown to be trivial. We therefore examine the vector space of no-arbitrage exchange rate ensembles over an arbitrary connected…
Paper uses HGNN to predict stock types from relationships and temporal data.
problem Predicting stock types from complex market data.
method Integrates stock relationships and temporal data using HGNN.
result Effective prediction of stock types with HGNN model.
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we empl…
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
problem Discerning temporal correlations among financial entities.
method Combines time series data and discrete features into a temporal graph, using a memory-based temporal graph neural network.
result MTRGL outperforms traditional methods in temporal graph link prediction and pair trading.
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.
The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this questio…
GraphCNNpred predicts stock market indices using deep learning.
problem Predicting stock market trends with diverse datasets.
method Graph-based CNN model for feature extraction.
result Improves prediction performance by 4% to 15% in F-measure.
Hierarchical graph learning for calendar spread strategies in commodity futures markets
problem Developing machine-learning methods for calendar spread strategies in commodity futures markets
method Proposing a hierarchical graph learning approach
result Outperforming benchmark models in both prediction and trading performance
Statistical physics of complex systems exploits network theory not only to model, but also to effectively extract information from many dynamical real-world systems. A pivotal case of study is given by financial systems: market prediction represents an unsolved scientific challenge yet with crucial implications for soc…
Graph neural networks detect collusion patterns across markets.
problem Detecting and predicting collusion in different national markets.
method Two-phase approach using GNNs for zero-shot learning and OOD generalization.
result GNNs outperform NNs in detecting complex collusive patterns.
The investigations of financial markets from a complex network perspective have unveiled many phenomenological properties, in which the majority of these studies map the financial markets into one complex network. In this work, we investigate 30 world stock market indices through their visibility graphs by adopting the…
Graph-based framework predicts ADR signals from clinical data.
problem Detecting ADRs in post-market surveillance using clinical data.
method Developed a Drug-disease graph with Graph Neural Network for ADR signal prediction.
result Improved AUROC and AUPRC performance (0.795 and 0.775) compared to other algorithms.
CATNet predicts CAT bond spreads using graph-based deep learning.
problem Complex, relational data in CAT bonds not well captured by traditional models.
method CATNet applies R-GCN to CAT bond primary market as a graph.
result CATNet outperforms Random Forest and XGBoost benchmarks.
Graph neural networks improve financial modeling of complex data.
problem Complex financial data and market volatility.
method Review and categorize GNN models for financial graphs.
result GNN models enhance performance in financial tasks.
We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
problem Predicting the movement of financial indices like S&P500.
method Transfer learning, causal graph, multidisciplinary knowledge, VAE network.
result 74.3% accuracy, 67% F1-score, 0.42 Matthew correlation on 12 years test period.
Our model predicts stock market intervals using chaotic fusion and graph convolutional networks.
problem Uncertainty in financial market predictions without quantified uncertainty.
method Bi-level chaotic fusion, graph convolutional networks, volatility-aware gating, temporal dependencies.
result Significant improvements in prediction intervals and coverage compared to existing methods.
Paper uses GNNs to efficiently detect profitable triangular arbitrage opportunities.
problem Detecting profitable triangular arbitrage opportunities in dynamic markets.
method Formulate the problem as a graph-based optimization task and use a GNN architecture to capture complex relationships.
result GNN-based method achieves higher average yield with reduced computational time compared to traditional methods.
In this paper we study the effect of network structure between agents and objects on measures for systemic risk. We model the influence of sharing large exogeneous losses to the financial or (re)insuance market by a bipartite graph. Using Pareto-tailed losses and multivariate regular variation we obtain asymptotic resu…
SAMBA predicts stock returns efficiently using Mamba and graph neural networks.
problem Accurate stock price predictions for financial returns.
method SAMBA integrates Mamba architecture with graph neural networks to achieve near-linear computational complexity.
result SAMBA significantly outperforms state-of-the-art models in prediction accuracy.
A novel approach using graph learning and synthetic long positions for statistical arbitrage in options markets.
problem Exploiting statistical arbitrage opportunities in options markets using machine learning.
method Two-stage graph learning approach: first stage defines a novel prediction target isolating pure arbitrages via synthetic bonds; second stage proposes SLSA positions.
result Statistically significant outperformance of GL baselines and consistent positive returns with an average P&L-contract information ratio of 0.1627.
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
problem Predicting stock price trends in volatile financial markets.
method Constructs a bi-typed MKG with hybrid-relations and uses DanSmp, a dual attention network, to learn momentum spillover signals.
result DanSmp improves stock prediction accuracy using the MKG.
Model forecasts market structure from financial networks using machine learning.
problem Predicting market correlation structure from financial networks.
method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).
result Model improves market structure forecasting by up to 40% over benchmarks.
System constructs public competitor graph from financial reports.
problem Time-consuming and expert-laden manual extraction of corporate relationships.
method Financial report processing to generate reliable knowledge graph of corporate relationships.
result More than 83% of S\&P 500 companies' competition relationships retrieved.
NETpred uses graph models to predict multiple market indices.
problem Predicting multiple market indices with high accuracy.
method NETpred constructs a heterogeneous graph of related indices and stocks, selects representative nodes, and uses semi-supervised learning to predict index labels.
result NETpred outperforms state-of-the-art methods by 3%-5% in F-score on various datasets.
Proposes a THGNN for dynamic financial time series prediction.
problem Challenges in predicting stock market price movements.
method Temporal and heterogeneous graph neural network (THGNN) approach.
result Significantly improved prediction performance compared to state-of-the-art methods.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
GeomHerd predicts herding behavior before market prices move, using Ricci curvature of agent interaction graphs.
problem Quantifying herding behavior in markets that lags behind actual price movements.
method Develops a geometric framework to track coordination on agent interaction graphs, bypassing lag in price-correlation statistics.
result GeomHerd anticipates herding long before market baselines, with significant lead times in predictions.
I find a topological arrangement of stocks traded in a financial market which has associated a meaningful economic taxonomy. The topological space is a graph connecting the stocks of the portfolio analyzed. The graph is obtained starting from the matrix of correlation coefficient computed between all pairs of stocks of…