Modeling lead-lag relationship between two text corpora for improved topic modeling.
problem Recognizing the relationship between multiple text corpora for better topic modeling.
method Proposed a jointly dynamic topic model and embedding extension for large-scale text corpus.
result The proposed model can well recognize the lead-lag relationship between two text corpora and improve topic learning.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
Novel method models dynamic brain graphs from time series data.
problem Generating hypotheses for dynamic brain states.
method Conditionally weighted superposition of static graphs.
result Improves f1-scores by 22-28% on average over baselines.
New method evaluates financial graphs for stock trend forecasting.
problem Lack of dynamic stock relationship graphs and evaluation methods.
method SPNews dataset and novel evaluation methods independent of downstream tasks.
result Evaluation methods can differentiate between various financial relationship graphs.
Paper explores two methods for optimal portfolio selection in financial markets.
problem Optimal portfolio selection for financial markets with jumps.
method Maximum principle and dynamic programming approach.
result Relationship between two methods and their adjoint processes.
New framework for dynamic causal graph modeling and effect estimation.
problem Dynamic changes in causal relationships over time.
method Score-based causal discovery with autoregressive model structure.
result Dynamic causal graph with time-varying causal relations.
Proposes C2RM to mine cross-cryptocurrency relationships for better Bitcoin price prediction.
problem Limited consideration of historical relationships and interactions between cryptocurrencies for Bitcoin price prediction.
method C2RM module using Dynamic Time Warping for lead-lag relationship extraction and aggregation.
result Improves existing price prediction methods by significant performance improvement.
Study on cryptocurrency market dynamics and correlations over time.
problem Understanding the dynamics and correlations of cryptocurrency market over time.
method Evolutionary correlation analysis, turning point algorithm, inverse relationship between market size and collective dynamics, time-varying consistency of relationships, examination of volatility structure.
result Increased uniformity in volatility during market crashes, termed 'volatility dispersion'.
Quantizes contact structures using dynamical methods.
problem Quantizing contact structures in a flat connection.
method Constructs a dynamical quantization using a flat connection on a Hilbert tractor bundle.
result Determines a contact tractor connection whose parallel sections determine a distinguished choice of Reeb dynamics.
This study examines lead-lag relationships in Chinese futures markets using high-frequency data.
problem Understanding high-frequency trading dynamics and information flow in futures markets.
method High-frequency tick-by-tick data analysis of lead-lag relationships between different maturity futures contracts.
result The near-month futures lead longer-dated contracts by one tick, with a negative feedback effect on the leading asset.
New method predicts dynamic relationships in terrorist networks.
problem Dynamic co-evolution of multiplex graphs and nodal attributes in terrorism networks.
method Time-varying stochastic latent factor models with neural network Gaussian processes.
result Superior performance in predicting unobserved dynamic relationships.
New metric measures dynamical richness without relying on accuracy.
problem Lack of a reliable metric for measuring dynamical richness.
method Developed a computationally efficient, performance-independent metric based on low-rank bias.
result Metric recovers neural collapse as a special case and captures known transitions without accuracy.
Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundame…
This paper investigates factors influencing SGD minima.
problem Understanding the factors that influence the minima found by SGD.
method Examined learning rate, batch size, Hessian, and gradient covariance; used stochastic differential equations to model SGD.
result The ratio of batch size to learning rate is a main factor in SGD dynamics.
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.
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
Study shows gaps in Bitcoin order book are linked to returns but only in the short term.
problem Understanding the relationship between gaps and returns in Bitcoin order books.
method Examined the dynamics of gaps and returns in a Bitcoin order book without considering long-term causation.
result The causal relationship between gaps and returns is limited to instantaneous causation.
BSG learns dynamic network spillovers and uncertainty quantification.
problem Identifying indirect spillovers and systemic risk in dynamic networks.
method Bayesian Spillover Graphs using FEVD and Bayesian time series models.
result Significant performance gains over baselines in identifying source and sink nodes.
Method discovers local independence in systems with continuous variables.
problem Applying Context-Specific Independence (CSI) to continuous variables is impractical.
method Neural contextual decomposition (NCD) learns partition of joint outcome space.
result NCD successfully discovers local independence in synthetic and real-world systems.
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
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.
Spacetimeformer learns spatiotemporal relationships from data alone.
problem Forecasting multivariate time series with distinct spatial relationships.
method Transformers with dynamic graph connections learning interactions between space, time, and value.
result Competitive results on various time series prediction benchmarks.
Meta-causal states group equivalent qualitative causal dynamics, useful for analyzing system changes.
problem Qualitative changes in causal relationships due to agent actions or environmental tipping points.
method Propose meta-causal states to group causal models based on equivalent qualitative behavior and parameterize specific mechanisms.
result Meta-causal states can be inferred from observed agent behavior and disentangled from unlabeled data.
The aim of this paper is to study the relationship between Hamiltonian dynamics and constrained variational calculus. We describe both using the notion of Lagrangian submanifolds of convenient symplectic manifolds and using the so-called Tulczyjew's triples. The results are also extended to the case of discrete dynamic…
Study uses geometric algebra to analyze credit cycles, revealing dangerous feedback loops.
problem Understanding and predicting dangerous feedback loops in credit cycles.
method Represent economic states as multi-vectors in Clifford algebra, focusing on bivector elements for rotational coupling.
result Geometric relationship between unemployment and credit contraction shifts from simple correlation to dangerous rotational dynamics during crises.
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-co…
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial f…
Microstructure of market dynamics is studied through analysis of tick price data. Linear trend is introduced as a tool for such analysis. Trend arbitrage inequality is developed and tested. The inequality sets limiting relationship between trend, bid-ask spread, market reaction and average update frequency of price inf…
Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.
problem Historical gas fee volatility caused economic disequilibria and stakeholder challenges.
method Time-varying Granger causality analysis using data on active wallets and transaction volume.
result Dynamic bidirectional causal relationships between transaction fees and economic subsystems across Ethereum.
Recent advances in employing neural networks on graph domains helped push the state of the art in link prediction tasks, particularly in recommendation services. However, the use of temporal contextual information, often modeled as dynamic graphs that encode the evolution of user-item relationships over time, has been …
New technique identifies lead-lag relationships in FX market during pandemic.
problem Identifying lead-lag relationships in financial markets, especially during crises.
method Dynamic Programming technique for finding optimal lead-lag path, using a loose metric.
result The proposed technique gives the best results in identifying statistically significant paths and closest forecasts.
Study examines how economic policy uncertainty impacts stock markets.
problem Dynamic relationship between economic policy uncertainty and stock markets.
method Used symmetric thermal optimal path (TOPS) method.
result Different interaction patterns observed in emerging and developed markets.
Network analysis reveals distinct financial relationships among Euro Area banks.
problem Understanding complex interbank relationships in the Euro Area.
method Multi-layer network approach using granular financial data.
result A more complete picture of the Euro Area interbank market topology.
Study on memory effects in RNNs learning temporal data.
problem Understanding memory effects in RNNs for temporal data learning.
method Mathematical analysis of continuous-time linear RNNs, focusing on approximation and optimization dynamics.
result Long-term memory requires a large number of neurons and slows down training.
ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.
problem Detecting and quantifying causal influence ranges in complex systems.
method Bayesian data assimilation and assimilative causal inference (ACI) to trace causes back from observed effects.
result Mathematically rigorous formulations of forward and backward causal influence ranges (CIRs) for nonlinear dynamical systems.
Extremely accurate prediction of dynamical system bifurcations using control inputs.
problem Predicting complex bifurcation structures in dynamical systems.
method Extending extreme learning machines with control inputs to model system dynamics.
result The model can nearly reproduce the entire structure of bifurcations using only a few parameter values.
MEG models for dynamic networks estimate dependencies and shared latent space relationships.
problem Modeling dynamic networks with shared latent space relationships and dependencies.
method MEG combines mutually exciting point processes and latent space models to estimate node-specific parameters and unobserved edges.
result MEG models can estimate intensities for unobserved edges, useful for anomaly detection in real-world applications.
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
The paper monitors stock market relationships using network analysis and statistical control charts.
problem Detecting abnormal changes in the financial market network structure.
method Network construction using distance methods, hierarchical clustering, and Shewhart control charts.
result Abnormal changes in financial market relationships can be detected using statistical process control.
The value of an asset in a financial market is given in terms of another asset known as numeraire. The dynamics of the value is non-stationary and hence, to quantify the relationships between different assets, one requires convenient measures such as the means and covariances of the respective log returns. Here, we dev…
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To…
Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.
Survey on computational models in dynamical systems, including new universality concepts.
problem Understanding the relationship between computational models and dynamical systems.
method Review of recent works on Turing universality, Topological Kleene Field Theories, and dynamical bordisms.
result Introduction of new perspectives on computability through dynamical systems.
Model predicts future term connections in biomedical research.
problem Capturing temporal dynamics and unobserved connections in biomedical term relationships.
method Variational inference model for positive-unlabeled learning on dynamic graphs.
result Model effectively predicts term relationships in real-world datasets.
Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.
problem Challenges in causal effect estimation for dynamic treatment regimes with long follow-up times.
method Combining outcome regression models with deep Bayesian models for high-dimensional features.
result Stable and accurate dynamic causal effect estimation from observational data, especially with long-term follow-up.
Energy consumption in Ecuador has increased significantly during the last decades, affecting negatively the financial position of the country since large energy consumption subsidies are provided in its internal market and Ecuador is mostly a crude oil exporter and oil derivatives importer country. This research seeks …
We analyze the relationships between game theory and quantum mechanics and the extensions to statistical physics and information theory. We use certain quantization relationships to assign quantum states to the strategies of a player. These quantum states are contained in a density operator which describes the new quan…
This note explores the consequences of nonlinear price impact functions on price dynamics within the chartist-fundamentalist framework. Price impact functions may be nonlinear with respect to trading volume. As indicated by recent empirical studies, a given transaction may cause a large (small) price change if market d…