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.
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
Shapley value improves model interpretation but not causal inference.
problem Improving model interpretability without losing predictive power.
method Analyzed Shapley value in Bayesian networks, linking it to conditional independence.
result Eliminating high Shapley value variables does not harm predictive performance, but low Shapley value variables can.
Study examines European banks' digital transformation strategies.
problem Lack of a common framework for open banking innovation in banking sector.
method Qualitative analysis of partnerships and API development.
result European banks are diversifying and boosting customer relationship management.
Paper finds funding rates on BitMEX predict Bitcoin inverse swap contracts.
problem Understanding the relationship between BitMEX funding rates and Bitcoin derivatives.
method Examined Heteroskedasticity of funding rates, established Granger causality, developed GARCH models for prediction.
result Funding rates on BitMEX predict Bitcoin inverse swap contracts.
The paper brings order to the diverse cognitive fallacies.
problem The proliferation of cognitive fallacies in human reasoning.
method Formally establishing implication relationships (IRs) between fallacies.
result A systematic approach to categorize and understand cognitive fallacies.
This study investigates porosity and topological properties of TPMS using machine learning.
problem Understanding the relationships between porosity and topological properties of TPMS.
method Application of machine learning techniques to analyze porosity and shape factor of TPMS.
result Conjectures suggesting polynomial relationships between porosity and shape factor of TPMS.
This article surveys our ongoing project about the relationship between invariants extending the classical Rohlin invariant of homology spheres and those coming from 4-dimensional (Yang-Mills) gauge theory. The main conjecture towards which this project is directed is that the Rohlin invariant and the gauge theoretic i…
Neural network predicts nonlinear safety behavior based on personality traits.
problem Predicting construction workers' unsafe behaviors based on personality traits.
method Developed a forecasting model using neural network algorithms.
result Nonlinear relationship exists between personality traits and safety behavior.
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.
We study the relationship between many natural conditions that one can put on a diffeological vector space: being fine or projective, having enough smooth (or smooth linear) functionals to separate points, having a diffeology determined by the smooth linear functionals, having fine finite-dimensional subspaces, and hav…
Investigates how diversification preferences relate to risk attitudes.
problem Connecting diversification preferences to risk attitudes.
method Analyzes diversification preferences for various pairs of risks under different conditions.
result Diversification preferences for certain pairs of risks imply specific levels of risk aversion.
Survey explores geometric aspects of policy optimization in control systems.
problem Understanding the geometric relationships between control design and optimization.
method Geometric perspective on policy optimization, focusing on parameterization and topology.
result Implications of policy geometry on stability and performance of local search algorithms.
Study examines UK firms' financial performance linked to corporate governance.
problem Impact of corporate governance on UK firms' financial performance.
method Cross-sectional regression analysis of 252 firms in 2014.
result Corporate governance mechanisms have mixed effects on financial performance.
This paper highlights the size-dependency of income distributions, i.e. the income distribution curves versus the population of a country systematically. By using the generalized Lotka-Volterra model to fit the empirical income data in the United States during 1996-2007, we found an important parameter λ can scale wi…
Curvature improves label space encoding for better class representation.
problem Inconsistent class distances in one-hot encoding.
method Introducing curvature using a metric tensor.
result Better representation of ancestral and convergent relationships.
We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion…
New method extracts features from large datasets using transport operators.
problem Feature extraction for complex, large datasets.
method Transport operators on graphs to generalize diffusion processes.
result Flexibility and ability to quantify new relationships in datasets.
This research unifies concepts of fading memory in RNNs.
problem Unclear relationships between fading memory concepts in RNNs.
method Unified language and new proofs for fading memory concepts.
result Clarified relationships between fading memory concepts.
We use customer demand data for fashion articles on Myntra, and derive a fashionability or style quotient, which represents customer demand for the stylistic content of a fashion article, decoupled with its commercials (price, offers, etc.). We demonstrate learning for assortment planning in fashion that would aim to k…
Private learning can be used to efficiently solve online learning problems.
problem The relationship between differentially private learning and online learning efficiency.
method Derive an efficient black-box reduction from differentially private learning to online learning from expert advice.
result An efficient differentially private learner implies an efficient online learner.
We investigate the joint dynamics of spot and implied volatility from an empirical perspective. We focus on the equity market with the SPX Index our underlying of choice. Using only observable quantities, we extract the instantaneous variance curves implied by the market and study their daily variations jointly with sp…
The relationship between price volatilty and a market extremum is examined using a fundamental economics model of supply and demand. By examining randomness through a microeconomic setting, we obtain the implications of randomness in the supply and demand, rather than assuming that price has randomness on an empirical …
The paper classifies links based on signature and crossing number properties.
problem Understanding the relationship between signature and crossing number of knots and links.
method Refined theorems and comprehensive classification of links with specific properties.
result Identification of all links where signature + crossing number = 2, closures of positive 3-braids.
Paper investigates differentiable fuzzy implications and their suitability for learning.
problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.
Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and a…
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph part…
We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…
Study shows realizable learnability doesn't imply agnostic learnability for distributions.
problem Learnability and robustness of distribution classes.
method Analyzes the relationship between learnability and robustness for distribution learning.
result Realizable learnability does not imply agnostic learnability for distributions.
This study proposes a framework for identifying profitable trading opportunities based on volatility and causal relationships.
problem Identifying profitable trading opportunities in financial markets.
method A combination of Gaussian Mixture Model (GMM), Granger Causality Test (GCT), Peter-Clark Momentary Conditional Independence (PCMCI) test, Dynamic Time Warping (DTW), and K-Nearest Neighbours (KNN) for identifying and executing trades.
result The proposed volatility-based trading strategy outperformed a Buy-and-Hold strategy, yielding a total return of 15.38%.
TransINT embeds KGs by preserving implication rules, outperforming existing methods.
problem Embedding KGs while preserving relation implications for better access and analysis.
method Isomorphic intersections of linear subspaces with shared parameters for missing facts.
result Significant performance improvement in link prediction and triple classification.
Study finds optimal board gender diversity for emissions performance.
problem Association between board gender diversity and emissions performance.
method Panel regressions, machine learning, explainable AI.
result Optimal board gender diversity for emissions performance is approximately 35%.
Study reveals how initial weights influence convergence in deep ReLU networks.
problem Understanding the dynamics and generalization of deep ReLU networks.
method Teacher-student setting, gradient analysis, and activation assumptions.
result Initial weights close to teacher nodes lead to faster convergence, and fan-out weights of other nodes converge to zero in over-parameterized cases.
Pruning neural networks adds differential privacy noise, preserving data utility.
problem Achieving differential privacy in neural networks without sacrificing data utility.
method Proving equivalence between pruning and adding differential privacy noise to hidden-layer activations.
result Pruning can be a more effective alternative to adding differential privacy noise for neural networks.
Bayesian machine learning methods improve nowcasting with mixed frequency data.
problem Handling frequency mismatches and predicting short-term economic indicators.
method Developed Gaussian process (GP) methods for MIDAS regressions.
result Gaussian process-MIDAS methods offer gains in predictive accuracy.
ProbE model improves relational implication detection to 0.8143.
problem Improving inference of relational data to extract more useful information.
method Formal probabilistic model of relational implication using estimators based on empirical distribution.
result ProbE model outperforms existing approaches, achieving 0.8143 AUC on evaluation dataset.
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would li…
Investigates long-range dependencies in language and music.
problem Measuring long-range dependence in sequence data.
method Statistical framework based on long memory stochastic processes.
result Testable implications about long memory in data and learned representations.
As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assura…
Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.
problem The classic relationship between IR and its determinants does not account for turnover costs.
method Mathematical derivations and simulations considering volatility of information coefficient and portfolio turnover.
result Turnover-adjusted IR is lower and managers can improve performance by limiting turnover.
Accurately determining dependency structure is critical to discovering a system's causal organization. We recently showed that the transfer entropy fails in a key aspect of this---measuring information flow---due to its conflation of dyadic and polyadic relationships. We extend this observation to demonstrate that this…
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent work showed that knowledge about the real underlying causal structure of a data generation process has implications for various machine learni…
Diversification represents the idea of choosing variety over uniformity. Within the theory of choice, desirability of diversification is axiomatized as preference for a convex combination of choices that are equivalently ranked. This corresponds to the notion of risk aversion when one assumes the von-Neumann-Morgenster…
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structu…
Study scaling laws of reward model overoptimization in reinforcement learning.
problem Reward model overoptimization hinders true performance in reinforcement learning.
method Synthetic setup with fixed gold reward model; optimization using RL or best-of-n sampling; analysis of scaling laws. result Scaling laws of reward model overoptimization differ based on optimization method and scale smoothly with model parameters.
The paper examines how closed curves on surfaces intersect and how this intersection determines the curves.
problem Determining closed curves on surfaces based on their intersections.
method Constructing and studying k-equivalent curves, analyzing intersections with other curves. result Curves are determined by their intersections with all other curves, but non-simple curves require infinitely many intersections to distinguish.
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…
Deep learning improves trip prediction accuracy in transportation planning.
problem Traditional models fail to accurately predict person and vehicle trips due to complexity and dynamics.
method Developed and trained a deep learning model using NHTS data.
result Deep learning model achieved 98% accuracy for person trip prediction and 96% for vehicle trip estimation.