Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America. In contrast, the Asia/Pacific region the biggest is receives the most information. According to the minimum spanning tree, the GSPC is located at…
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48 results for “Directional Information Flow”
A new method for optimization in probability space using Newton's flows.
problem Optimization in probability space with information metrics.
method Information Newton's flows, including Fisher-Rao and Wasserstein-2 metrics, with Newton's Langevin dynamics and variational methods.
result Effective numerical implementation and convergence results for the proposed method.
Unified framework maps financial market dynamics using TE and KM, revealing directional information flow.
problem Challenges in traditional correlation analysis of financial markets, especially during crises.
method Combines Transfer Entropy (TE) and Kramers-Moyal (KM) expansion to analyze dynamic interactions among major indices.
result Increased directional information flow during crises, highlighting gold-dollar and oil-equity linkages.
Enhances multi-modular models by directing information flow between components.
problem Improving predictive performance in multi-modular models with misspecification.
method Introduces Semi-Modular Inference (SMI) with an influence parameter to control information flow between modules.
result SMI allows for tunable and directed information flow, improving prediction in some settings.
We investigated financial market data to determine which factors affect information flow between stocks. Two factors, the time dependency and the degree of efficiency, were considered in the analysis of Korean, the Japanese, the Taiwanese, the Canadian, and US market data. We found that the frequency of the significant…
Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networksphysics.geo-ph
DCGANs generate drainage networks quickly from samples.
problem High computational costs in generating large numbers of drainage networks.
method DCGANs trained with connectivity-informed directional information.
result Connectivity-informed DCGANs outperform other methods in reproducing accurate drainage networks.
The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
Order-flow entropy predicts price magnitude without directionality.
problem Predicting price magnitude in financial markets.
method Real-time order-flow entropy computed from a 15-state Markov transition matrix.
result Order-flow entropy predicts the magnitude of intraday returns with high accuracy.
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
problem Understanding information transmission and market dynamics in Chinese stock sectors.
method Daily closing price data of 28 sectors from 2000 to 2017, transfer entropy, maximum spanning arborescence (MSA).
result The composite sector is an information source, and the non-bank financial sector is an information sink.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
A first attempt at obtaining market--directional information from a non--stationary solution of the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. We demonstrate that the concept of price impact is poorly applicable to market dynamics. Inste…
Paper develops models to forecast private equity fund cash flows.
problem Limited literature on illiquid alternative asset cash flow forecasting.
method Develops benchmark model and two novel approaches (direct vs. indirect) using LSTM/GRU models and macroeconomic indicators.
result Direct model performs better and aligns with actual cash flows, but indirect model's performance is less clear.
Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Directionstat.ML
This paper reviews information theory in open-world machine learning.
problem Lack of a unified theoretical foundation for open-world machine learning.
method Synthesis of information theoretic approaches.
result Established a pathway toward provable and trustworthy open world intelligence.
We show that a mean curvature flow starting from a compact, smoothly embedded hypersurface M remains unique past singularities, provided the singularities are of mean convex type, i.e., if around each singular point, the surface moves in one direction. Specifically, the level set flow of M does not fatten if all singul…
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. …
To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the infor…
Causal Compressionstat.ML
We propose a new method of discovering causal relationships in temporal data based on the notion of causal compression. To this end, we adopt the Pearlian graph setting and the directed information as an information theoretic tool for quantifying causality. We introduce chain rule for directed information and use it to…
Deep learning methods improve subsurface flow modeling efficiency.
problem Efficiently modeling subsurface flow with uncertain parameters.
method Two categories of deep-learning based inverse modeling methods: surrogate-based and direct.
result Deep-learning methods significantly accelerate subsurface flow modeling.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
problem Accurate inflation forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index.
method Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model that uses bidirectional information flow between levels and informative constraints on RNN parameters.
result BiHRNN significantly outperforms traditional RNN models in forecasting accuracy.
New Ricci flow method for directed graphs with balancing factor.
problem Analyzing asymmetry in directed networks.
method Rigorous formulation of Ricci flow on directed weighted graphs with balancing factor.
result Existence and uniqueness of discrete Ricci flow solutions.
Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fieldsphysics.ao-ph
New neural network enforces mass conservation for better ice flow predictions.
problem Reliably project future sea level rise by improving ice sheet model inputs.
method Proposes divergence-free neural networks (dfNNs) enforcing local mass conservation.
result dfNNs yield more reliable ice flux estimates compared to other models.
In this paper, we consider the interpretability of the foundational Laplacian-based semi-supervised learning approaches on graphs. We introduce a novel flow-based learning framework that subsumes the foundational approaches and additionally provides a detailed, transparent, and easily understood expression of the learn…
The purpose of this article is to provide a general overview of curvature functional in Finsler geometry and use its information to introduce the gradient flow on Finsler manifolds. For this purpose, we first prove that the space of Finslerian metrics is a Riemannian manifold. Then it is given a decomposition for the t…
Bitcoin's attention is linked to Google Trends data, not general uncertainty.
problem Bitcoin's correlation with Google Trends data was previously misunderstood.
method Analyzed bidirectional relationships between Bitcoin returns and Google Trends attention over six days.
result Information flows from Bitcoin volatility to Google Trends attention, not the other way.
Proposes a new model for traffic flow on directed graphs.
problem Modeling advection on directed graphs for traffic flow.
method Reformulates graph advection operator as finite difference scheme; proposes DGAMGP model.
result Effective modeling of traffic flow and uncertainty as an advective process.
Physics-informed model reduces RBC simulation costs.
problem Computational infeasibility of direct numerical simulations for turbulent systems.
method Combines CNN and recurrent architecture, penalized with PDEs, uses conformal prediction.
result Significant reduction in computational cost for long-term simulations.
This paper uses Hawkes processes to forecast high-frequency order flow imbalance.
problem Forecasting the asymmetry in high-frequency order flow events.
method Hawkes processes accounting for lagged dependence between bid and offer events.
result Hawkes process with a Sum of Exponential's kernel gives the best forecast of order flow imbalance.
For a bounded domain of class , the properties are studied of fields of `good directions', that is the directions with respect to which can be locally represented as the graph of a continuous function. For any such domain there is a canonical smooth field of good direct…
Circular Directional Flow Decomposition of Networksphysics.soc-ph
CDFD analyzes circularity and directionality in weighted directed networks.
problem Analyzing circularity and directionality in weighted directed networks.
method CDFD framework separates flow into circular and acyclic components.
result CDFD yields a normalized circularity index capturing flow in cycles and directionality.
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
problem Understanding information dynamics in Bitcoin markets during the 2017 crash.
method Analysis of high-frequency market-microstructure observables using information theoretic measures.
result Temporal changes in information sharing across markets, including predictability, memory, and synchronous coupling.
The study characterizes heat flow and concentration on directed graphs with a lower Ricci curvature bound.
problem Understanding heat flow and concentration on directed graphs with a specific curvature bound.
method Characterization via gradient estimate and transportation inequality for the heat semigroup.
result Concentration of measure inequality for directed graphs with positive Ricci curvature.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
problem Clustering directed networks without label supervision.
method DIGRAC uses a graph neural network with a novel imbalance loss for directed flow imbalance.
result DIGRAC outperforms 10 state-of-the-art methods on directed graph clustering.
The set of directions from a quadratic differential that diverge on average under Teichmuller geodesic flow has Hausdorff dimension exactly equal to one-half.
Attention mechanism combines bottom-up and top-down signals in neural networks.
problem Combining robust perception with bottom-up and top-down signals.
method Attention mechanism over modulated recurrent neural networks.
result Bidirectional information flow leads to improved performance in various tasks.
Cryptocurrencies in the Balance Sheet: Insights from (Micro)Strategy -- Bitcoin Interactionsq-fin.GN
Study finds significant BTC co-movements with equity markets, highlighting dynamic risk management needs.
problem Understanding the impact of corporate Bitcoin holdings on equity markets.
method Dataset of 39 firms, daily returns analysis, Pearson correlations, single factor model regressions, transfer entropy.
result BTC has a significant positive beta with equity markets, with BTC as the dominant information driver.
New method recovers curvature from heat diffusion data.
problem Recovering Riemannian curvature from heat diffusion.
method Information-theoretic approach using relative entropy.
result Local curvature determined by heat diffusion.
Paper proves short-time existence for network flow, providing detailed insights.
problem Short-time existence for the flow of a network of curves in the plane.
method Direct PDE approach, handling singularities at vertices using self-similar expanding solutions.
result Substantially more detailed information about network resolution into a regular one.
While market is a social field where information flows over the interacting agents, there have been not so many methods to observe the spreading information in the prices comprising the market. By incorporating the entropy transfer in information theory in its relation to the Granger causality, the paper proposes a tre…
Proposes a framework to adjust quotes for informational risk in markets with informed traders and price-revealing quotes.
problem Informational risk in markets with informed traders and price-revealing quotes.
method Proposes a tractable framework to adjust quotes considering adverse selection and price reading.
result Market makers can adjust their quotes to better manage informational risk.
Geodesic flows with specific integrals are linked to special 4-webs.
problem Characterizing geodesic flows with commuting quadratic integrals.
method Characterization through geodesic 4-webs and geometric hypothesis.
result Metrics of Stäckel type for geodesic flows with specific integrals.
We develop an adversarial-reinforcement learning scheme for microswimmers in statistically homogeneous and isotropic turbulent fluid flows, in both two (2D) and three dimensions (3D). We show that this scheme allows microswimmers to find non-trivial paths, which enable them to reach a target on average in less time tha…
A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. In our previous work[2], we established that it is the share execution flow () and…
SFM matches flows on statistical manifolds for better discrete generation.
problem Discrete generation on statistical manifolds with strong prior assumptions.
method Statistical Flow Matching (SFM) on manifold of categorical distributions using Fisher information metric.
result SFM achieves higher sampling quality and likelihood than other models.
The Chern-Ricci flowmath.DG
Survey on Chern-Ricci flow for complex manifolds.
problem Understanding and solving open problems in Chern-Ricci flow.
method Parabolic flow of Hermitian metrics on complex manifolds.
result Open problems and new directions in Chern-Ricci flow highlighted.
Study examines how information flows in Indian stock market during crises.
problem Understanding information diffusion in financial networks during market turbulence.
method Applied communicability, a measure of ease of information flow, to financial networks.
result Approximately 70% and 80% of stock pairs exhibit significant changes in communicability during crises.
Paper confirms Thom's conjecture for nonlinear evolutions on manifolds.
problem Thom's gradient conjecture for nonlinear evolution equations.
method Extending and settling the conjecture in infinite dimensional problems using Łojasiewicz, L. Simon, and Kurdyka-Mostowski-Parusinski's foundational works.
result Uniqueness of the limiting direction and characterization of convergence rates for both classical and infinite dimensional settings.
INDEQS: A Graph-Based Neural Controlled Differential Equation Framework for Forecasting
problem Forecasting time series with neural networks
method Incorporating prior knowledge of a directed graph
result Outer informedness consistently improves forecasting accuracy