The paper examines how market trade values and volumes affect price autocorrelation.
problem Understanding the impact of market trade values and volumes on price autocorrelation.
method Derives the dependence of price statistical moments and volatility on trade values and volumes, and assesses statistical moments and correlations by conventional frequency-based probabilities.
result Highlights the impact of market trade randomness on price statistical moments and autocorrelation.
The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…
Study predicts intraday stock trading volume using ML models.
problem Predicting intraday trading volumes in equity markets.
method Used machine learning models with HF predictors.
result Intraday stock trading volume is highly predictable.
Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarke…
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
Market-based portfolio variance measures risks using trade data.
problem Measuring portfolio risks using traditional methods ignores trade volume randomness.
method Uses time series of trades with securities and portfolio to assess variance.
result Portfolio variance can be decomposed into securities' contributions, accounting for trade volume randomness.
This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural …
With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorithms for brain volume quantification are unreliable for the longitudinal assessment of volume changes in this type of setting. Currently most…
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
problem Uncertainty in predicting macroeconomic variables like prices and returns.
method Defines theoretical lower bounds of uncertainty and upper limits on forecast accuracy based on statistical moments and trade volumes.
result Accuracy of forecasts of probabilities of macroeconomic variables doesn't exceed Gaussian approximations.
Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.
problem Limited accuracy of frequency-based asset price statistical moments.
method Derive market-based variance and 3rd statistical moment from trade values and volumes, accounting for trade volume randomness.
result Market-based statistical moments improve price probability forecasts and reliability.
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a viable solution for t…
This work improves multi-task regression performance using approximations of full-conformal prediction.
problem Jointly solve multiple regression tasks with better performance than individual tasks.
method Design an approximating prediction region in RKHS for full-conformal multi-task regression.
result The approximating prediction region improves upon split-conformal prediction on synthetic data.
Dynamic risk assessment method for WUI fires improves upon static frameworks.
problem Static risk assessment methods fail to capture dynamic changes in WUI fire risks.
method Dynamic evaluation matrix, grey incidence analysis, optimization model.
result The proposed method effectively captures dynamic risk evolution patterns.
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous and activates an undirected subgraph spanning over a set o…
Study assesses 'big data' in materials science, highlighting challenges.
problem Understanding what constitutes 'big data' in materials science.
method Selected examples of machine learning models, data quality, and infrastructure requirements.
result Big data presents unique challenges in materials science.
Purpose: Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to address the inverse problem of converting pixel-wise multispectral reflectance measurements to underlying tissue parameters, such as oxygenation. …
Novel IMEX scheme solves financial PDEs with mixed derivatives.
problem Numerical approximations for financial PDEs with mixed derivatives.
method Second order finite volume IMEX Runge-Kutta scheme.
result Achieves true second order convergence with non-regular initial conditions.
CatSIM measures image similarity robustly to small changes.
problem Measuring similarity between images, especially with small perturbations.
method Uses structural similarity image quality paradigm, robust to small location changes.
result Structural similarity between images rated higher when not entirely overlapping.
The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of expert…
Optimizes trading large volumes of volatile assets with fast mean-reverting volatility.
problem Challenges of executing large volumes of illiquid or volatile assets.
method Modeling uncertain volatility and liquidity with fast mean-reverting dynamics, using singular perturbation arguments and high-frequency data.
result Approximately optimal trade execution strategies under fast mean-reversion.
The paper assesses VASPs' solvency using multiple data sources.
problem Insolvency risk in VASPs without systematic auditing.
method Cross-referencing cryptoasset wallets, balance sheets, and supervisory data.
result Inconsistent data between DLT transactions and balance sheets for some VASPs.
Introduces a new price measure and a second-order economic theory for volatility forecasting.
problem Forecasting price volatility in financial markets.
method Develops a new price measure and a second-order economic theory to model price volatility.
result Shows that second-order economic theory improves forecasting of price volatility.
TT-DAC-PS: A deterministic actor-critic approach for optimal trade execution
problem Optimal execution of large stock sell programs
method Twin-Target Deterministic Actor-Critic with Policy Smoothing
result Reduces mean implementation shortfall percentage
Study proposes a new financial market representation for machine learning.
problem Complex analysis of financial time series for machine learning.
method Volume-price-based statistical approach.
result Proposed method outperforms price levels-based method on liquid markets.
Paper uses DRL to optimize trade execution, outperforming VWAP and TWAP.
problem Optimizing returns while minimizing risk in order execution.
method Deep Reinforcement Learning (DRL) for holistic optimization.
result DRL-based approach outperforms VWAP and TWAP in ROI and risk management.
We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of corr…
Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.
problem Identifying and quantifying insider trading in cryptocurrency markets.
method Algorithmic identification of insider volume spikes, conservative profit bounds calculation, social-media verification.
result Median returns above 100%, upper-quartile returns exceeding 2000% for insider profits.
AI helps assess nature-related financial risks for financial institutions.
problem Challenges in evaluating nature-related risks due to large data volume and complexity.
method Uses AI to address data gaps, uncertainty, and complex systems.
result Potential AI solutions for two use cases: beef supply and water utility.
This paper studies the trading volumes and wealth distribution of a novel agent-based model of an artificial financial market. In this model, heterogeneous agents, behaving according to the Von Neumann and Morgenstern utility theory, may mutually interact. A Tobin-like tax (TT) on successful investments and a flat tax …
Paper calculates loan loss after default using Bayesian model.
problem Determining loan loss after borrower default.
method Bayesian scheme considering repayment period, volumes, moments, and parameters.
result Allows setting LGD less than or equal to 1 for accurate estimates.
Study shows how firms adapt to systemic risk during crises, revealing key players and trade volume predictors.
problem Understanding systemic risk in local production networks during crises.
method Analyzing Hungarian production network dynamics from 2015 to 2022 using a null model and empirical data.
result Firms' adaptive behavior during crises leads to more resilient economies, with trade volume being a significant predictor.
Framework scores DeFi users based on liquidity and trading behavior.
problem Distinguishing between liquidity provision and active trading in DeFi.
method Rule-based decomposition, deep residual neural network, pool-level context.
result Deep residual neural network improves user scoring and risk assessment.
This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some po…
When executing their orders, investors are proposed different strategies by brokers and investment banks. Most orders are executed using VWAP algorithms. Other basic execution strategies include POV (also called PVol) -- for percentage of volume --, IS -- implementation shortfall -- or Target Close. In this article ded…
Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of …
ReLU networks don't exponentially distort curve lengths as previously thought.
problem Understanding how neural networks distort curve lengths with depth.
method Analyzing expected length distortion of ReLU networks with random initialization.
result Expected length distortion does not grow with depth, and shrinks slightly.
Paper introduces active Bayesian method for assessing black-box classifiers efficiently.
problem Need to assess performance of black-box classifiers reliably with limited labels.
method Develops inference strategies and proposes active Bayesian framework for efficient instance selection.
result Significant gains in performance assessment with fewer labels compared to traditional methods.
Study finds no statistically significant trading edge in MNQ futures signals from OHLCV data.
problem Testing intraday momentum signals from OHLCV data in MNQ futures under realistic execution constraints.
method 947 trading days of five-minute data, 14 signal families evaluated, strict institutional criteria applied.
result No signal satisfies all criteria simultaneously, gross edge insufficient to overcome costs.
A benchmarking framework for studying data geometry.
problem Generalization and approximation error bounds in deep learning.
method Repurposing and extending dSprites and COIL-20 with additional transformation dimensions and dense, axis-aligned sampling.
result Near-ground-truth accuracy in curvature, reach, and volume estimation.
Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.
problem Understanding dynamics of informal currency markets.
method Modeling bid/ask intentions using Limit Order Book, applying Avellaneda-Stoikov model with Market Maker.
result Market Maker improves market quality and bid/ask dynamics.
Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
Although compelling assessments have been examined in recent years, more studies are required to yield a better understanding of the several methods where assessment techniques significantly affect student learning process. Most of the educational research in this area does not consider demographics data, differing met…
Framework evaluates the impact of prior knowledge in deep learning models.
problem Mitigating data-driven model shortcomings like data dependence and generalization ability.
method Model-agnostic framework inspired by interpretable machine learning, assessing data volume and estimation range effects.
result Complex relationship between data and knowledge, including dependence, synergistic, and substitution effects.
Bayesian neural networks improve cosmic parameter estimation from modified gravity simulations.
problem Estimating cosmological parameters from large-scale structure data with modified gravity.
method Implement Bayesian neural networks (BNNs) with two cases: single BLL and FullB, trained on dark matter only particle mesh N-body simulations. result BNNs yield well-calibrated uncertainty estimates and accurately predict cosmological parameters for Ωm and σ8. Bayesian networks improve product risk assessment by handling uncertainty and causality.
problem Limited handling of uncertainty and inability to incorporate causal explanations in existing methods.
method Bayesian Networks (BNs) for improved systematic product risk assessment.
result BN approach provides more powerful and flexible risk assessments.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
Study factors affecting liquidity on decentralized exchanges, introducing new metrics.
problem Understanding and predicting liquidity on decentralized exchanges (DEXs).
method Analyzes platform, blockchain, token pair, and liquidity pool factors; introduces new metrics.
result Identifies how various factors affect liquidity through concentration and total value locked.