Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
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48 results for “threshold crossing”
Perfect spike detection method derived and benchmarked.
problem Missing spikes in neuronal network simulations.
method Time-inverted dynamics to test threshold crossing.
result Method can be faster than alternative perfect tests.
We consider the effects of the global financial crisis through a local Korean financial market around the 2008 crisis. We analyze 185 individual stock prices belonging to the KOSPI (Korea Composite Stock Price Index), cosidering three time periods: the time before, during, and after the crisis. The complex networks gen…
CV inference can be invalid for relatively unstable model comparisons.
problem The validity of cross-validation for model comparison is questioned when models are relatively unstable.
method The study proves that simple, individually stable models can generate relatively unstable comparisons, invalidating CV inference.
result The Lasso and soft-thresholding generate relatively unstable comparisons, invalidating CV inferences.
Predicting into unknown space? Estimating the area of applicability of spatial prediction modelsstat.ML
Method estimates uncertainty in spatial predictions by defining an 'area of applicability'.
problem Uncertainty in predictions for new geographic locations far from training data.
method Proposes a dissimilarity index (DI) based on minimum distance to training data, and defines the 'area of applicability' (AOA) using a threshold on DI.
result Prediction error within the AOA is comparable to the cross-validation error of the model, while cross-validation error does not apply outside the AOA.
Target search optimization by threshold resettingcond-mat.stat-mech
Optimizes search times by resetting agents when a threshold is reached.
problem Improving search efficiency in systems with thresholds.
method Develops a framework for correlated stochastic processes with threshold resetting.
result Optimal resetting can prevent larger losses and is applicable to various stochastic systems.
Study examines local extrema and crossing statistics in financial markets.
problem Understanding local extrema and crossing statistics in financial markets.
method Excursion set theory, numerical computation, theoretical prediction, clustering of geometrical measures, cross-correlation, Singular Value Decomposition.
result Excursion sets reveal statistical coherency and sensitivity to crises in financial markets.
Study examines stock market connections before, during, and after the 2008 financial crisis.
problem Effects of the 2008 global financial crisis on stock market connectivity.
method Generated complex networks from cross-correlation matrices, using threshold networks and minimal spanning trees.
result During the crisis, countries in different zones had varying levels of connectivity.
The paper develops a cross-validation method for improving signal denoising techniques.
problem Improving signal denoising methods for nonparametric regression.
method Develops a general cross-validation framework for signal denoising and applies it to Trend Filtering and Dyadic CART.
result Cross validated versions of Trend Filtering and Dyadic CART achieve nearly optimal convergence rates.
Optimal purchasing policy for mean-reverting items with a finite deadline.
problem Minimizing cost of purchasing and holding mean-reverting items within a fixed time.
method Proved optimal policy as a time-variant threshold function, constructed with dynamic programming.
result Explicit equations for crossing time probability and overshoot expectation.
From Binary Screens to Continuous Compliance: A Shariah Screening Measure for Portfolio Designq-fin.PM
Develops a continuous compliance index for Islamic equity screening.
problem Binary rulebooks lead to inconsistent compliance assessment of firms.
method Integrates six leading financial and business activity standards into a single continuous index.
result Firms with the same pass/fail label can differ significantly in compliance strength.
This work suggests modifications to a previously introduced class of heterogeneous agent models that allow for the inclusion of different types of agent motivations and behaviours in a unified way. The agents operate within a highly simplified environment where they are only able to be long or short one unit of the ass…
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Study develops a method to select penalty parameters for sparse neural networks without cross-validation.
problem Selecting optimal penalty parameters for sparse neural networks without cross-validation.
method Established theoretical foundation to bound the infinite norm of the gradient of the loss function at zero.
result Proposed method effectively selects penalty parameters for sparse neural networks.
New algorithms detect communities in sparse graphs with labeled data.
problem Detecting communities in sparse graphs with limited labeled data.
method Introduces two algorithms: combinatorial and optimization-based, to integrate labeled data with graph structures.
result Detection of communities is feasible throughout the parameter domain with arbitrary labeled data.
Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squaresmath.ST
Study reveals limits of PLS in multi-modal learning with correlated signals.
problem Understanding PLS performance in multi-modal learning with correlated signals.
method Random matrix theory analysis of spiked cross-covariance models.
result Identifies SNR and correlation regimes where PLS fails to recover any signal.
EAST aligns neural network classifiers with user-defined evaluation metrics.
problem Mismatch between neural network training and evaluation metrics leads to suboptimal performance.
method EAST uses dynamic thresholding, soft-set confusion matrix, and annealing to align neural network predictions with target evaluation metrics.
result EAST improves alignment between training objectives and evaluation metrics, outperforming existing methods.
Paper models dynamic multivariate functional data with sparse subspace learning.
problem Complex, high-dimensional multivariate functional data with evolving cross-correlations.
method Sparse subspace learning for automatic subspaces formulation and cross-correlation dynamics description.
result Efficient estimation and feature extraction of multivariate functional data.
Exact simulation of correlated binary outcomes using PMF constraints and linear programming.
problem Simulating dependent Bernoulli outcomes with specific means and correlations.
method Formulate the problem over the joint Bernoulli PMF, impose constraints, and solve as a linear program. Use convex-hull characterization and truncated-moment completion scheme for feasibility and simulation.
result Exact simulation framework for correlated binary outcomes, providing a convex-hull characterization and truncated-moment completion scheme.
PLS-SVD struggles with missing data in multimodal datasets, showing a phase transition in performance.
problem Missing data in PLS-SVD for multimodal datasets.
method Replica-symmetric analysis of spiked rectangular random matrices with missing entries.
result PLS-SVD performance transitions from uninformative to informative singular vectors at a critical signal-to-noise threshold.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
The paper proves extremal black holes form at a critical point of gravitational collapse.
problem Formation of extremal black holes in gravitational collapse.
method Constructing smooth families of spherically symmetric solutions to the Einstein-Maxwell-Vlasov system.
result Extremal Reissner-Nordström black holes form at the critical collapse threshold.
A framework for multi-label sentiment analysis in 100 languages with dynamic weighting.
problem Cross-lingual sentiment analysis in multi-label settings with label imbalance.
method Dynamic weighting method, focal loss adaptation, optimal class-specific thresholds.
result State-of-the-art performance in 7 out of 9 metrics across 3 languages.
In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related can…
Explicit moments of decision times for single- and double-threshold drift-diffusion processesq-bio.NC
We derive expressions for the first three moments of the decision time (DT) distribution produced via first threshold crossings by sample paths of a drift-diffusion equation. The "pure" and "extended" diffusion processes are widely used to model two-alternative forced choice decisions, and, while simple formulae for ac…
A novel ABC method for high-dimensional inverse problems using generative modeling and subset simulation.
problem Solving inverse-problems with high-dimensional inputs and expensive forward mappings.
method Joint deep generative modeling, Approximate Bayesian Computation (ABC) with Subset Simulation, and likelihood-free inference.
result Our method delivers promising performance without prior knowledge of the forward or noise distributions.
LoRA fine-tuning creates intruder dimensions that can cause forgetting, and a new law predicts when this happens.
problem Predicting when LoRA fine-tuning creates intruder dimensions that can cause catastrophic forgetting.
method Derived a per-layer critical update strength and an exact secular-equation characterization of the updated spectrum.
result The law localizes the empirical threshold within a factor of two on 82% of layers and separates intruder-bearing from intruder-free layers at deployment.
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classificationcs.LG
Proposes JBNN for multi-label classification with improved efficiency and performance.
problem Multi-label classification with dependencies and heavy computational load.
method Joint Binary Neural Network (JBNN) that synchronously performs multiple binary classifications and captures label relations via joint binary cross entropy (JBCE) loss.
result Significantly better performance and computational efficiency compared to state-of-the-art methods.
Study shows pooling scores for conformal prediction distorts group coverage.
problem Pooling scores for conformal prediction distorts group coverage.
method Derived conservation law and lower bound, demonstrated tension between fairness definitions, quantified trade-off between policies.
result Pooling scores for conformal prediction distorts group coverage.
SNNs optimize cross-market portfolios with neuromorphic computing, reducing computational overhead and improving returns.
problem Complex cross-market portfolio optimization with high-frequency, multi-dimensional datasets.
method Leaky Integrate-and-Fire neuron dynamics, adaptive thresholding, spike-timing-dependent plasticity, lateral inhibition, hierarchical clustering, population-based spike encoding, multiple decoding strategies.
result SNNs deliver superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, with improved computational efficiency.
Method finds optimal binary classification rules under weighted misclassification loss.
problem Optimal binary classification rules for resource-limited settings with cost-sensitive decisions.
method Ensemble learning to derive prediction scores and associated thresholds minimizing weighted misclassification loss.
result Jointly derived score and threshold outperforms methods that derive score first and threshold second.
DIET-SNN optimizes SNNs for faster, lower-energy image classification.
problem High inference latency and inefficient input encoding in SNNs.
method End-to-end backpropagation to optimize membrane leak and firing threshold.
result Achieves top-1 accuracy of 69% on ImageNet with 5 timesteps and 12x less compute energy.
In active learning, the user sequentially chooses values for feature and an oracle returns the corresponding label . In this paper, we consider the effect of feature noise in active learning, which could arise either because itself is being measured, or it is corrupted in transmission to the oracle, or the o…
Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphscs.LG
Zero-Copy Architecture Detects Cross-Company Financial Signals Instantly.
problem Financial models miss cross-company disruptions due to static data.
method Heterogeneous Rust-Python streaming architecture that maps cross-company attention as a continuous-time graph.
result Zero-copy parsing and inference process delivers real-time cross-company signal detection.
A new test statistic speeds up MMD while maintaining power.
problem Efficiently testing two distributions without permutations.
method Cross-MMD statistic based on sample-splitting and studentization.
result Cross-MMD has a limiting standard Gaussian distribution under the null.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
problem Optimizing a single-asset portfolio with transaction costs and signal autocorrelation.
method Formulated an optimization problem to minimize trades while maintaining signal integrity and achieving maximum return.
result Locally optimal solution minimizes trades and achieves maximum return, with a quantifiable improvement based on threshold and autocorrelation removed.
Sandpile Economics: Theory, Identification, and Evidencephysics.soc-ph
Sandpile Economics explains how economies can be prone to large crises from small shocks.
problem Capitalist economies' recurrent crises disproportionate to shocks.
method Formal framework interpreting instability as geometric fragility of production networks.
result Curvature of production networks predicts medium-run output dynamics and resilience.
Study builds an EWS for early detection of stock market turbulence.
problem Early detection of stock market turbulence.
method SWARCH filtering probabilities, two-peak method, LSTM network.
result 96.6% test-set accuracy and 2.4 days of forewarned period.
Paper offers a fast method to assess DeFi liquidation risk.
problem Assessing liquidation risk in DeFi stablecoin lending.
method Modeling collateral exchange rate as zero-drift geometric Brownian motion.
result Derives an exact formula for liquidation probability.
Identifying the covariation between the diffusion parts and the co-jumps given discrete observationsmath.PR
In this paper we consider two semimartingales driven by diffusions and jumps. We allow both for finite activity and for infinite activity jump components. Given discrete observations we disentangle the {\it integrated covariation} (the covariation between the two diffusion parts, indicated by IC) from the co-jumps. Thi…
Do Prediction Markets Match Option Prices? Bitcoin Threshold Evidence from Binance and Polymarketq-fin.TR
Prediction markets and crypto options show persistent pricing gaps.
problem Comparing prediction markets and crypto options for identical payoffs.
method Comparing Polymarket Yes prices with Binance call option prices.
result Mean pricing gap of 5.6 percentage points across 214 hourly observations.
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
problem Early onset of herd immunity contradicts R value estimates from early stage growth.
method Utilizes Vasicek's bond pricing formula and de Finetti's Theorem approach.
result Reduces modeling discrepancy to simple convexity formulas.
Regression Trees and Random forest based feature selection for malaria risk exposure predictionstat.ML
This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshol…
New approach predicts stock price synchronization using RNNs and LSTMs.
problem Forecasting synchronization of stock prices in the Indian market.
method Utilizing recurrence plots and CRQA for non-linear analysis, RNNs and LSTMs for prediction.
result Accuracy of 0.98 and F1 score of 0.83 in predicting stock price synchronization.
The paper analyzes trace regression with low-rank matrices under various regularization methods.
problem Estimating low-rank matrices with near-optimal error bounds under unknown regularization parameters.
method General spikiness notion, restricted strong convexity of sampling operator, cross-validation for parameter selection.
result Cross-validated estimators select near-optimal penalty parameters and outperform theory-inspired approaches.
Unified formula for training dynamics of linear networks combining lazy and balanced regimes.
problem Training dynamics of linear networks in two distinct setups: lazy and balanced/active.
method Unified formula for the evolution of the learned matrix, combining lazy and balanced regimes.
result Unified formula allows for rapid convergence and low rank bias, proving a complete phase diagram.
Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature …
Optimal iterative thresholding algorithms improve upon hard and soft thresholding.
problem Optimizing sparsity or rank constraints in optimization problems.
method Developed the notion of relative concavity for thresholding operators, finding a new class of operators that are optimal.
result A new class of thresholding operators, including thresholding and reciprocal thresholding, achieves the strongest convergence guarantee.