A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops exact and invariant study-based decompositions for network meta-analysis.
Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynam…
Cash managers make daily decisions based on predicted monetary inflows from debtors and outflows to creditors. Usual assumptions on the statistical properties of daily net cash flow include normality, absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set from…
Fine particulate matter (PM) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of P…
Social media reduces individual investors' disposition effect through negative information.
Natural analogs of Lie brackets on affine bundles are studied, based on natural examples from differential geometry and analytical mechanics. In particular, a close relation to Lie algebroids and, by a sort of duality, to affine analogs of Poisson structures is established as well as affine versions of the complete lif…
Deep learning models outperform MICE in large survey imputation but with hyperparameter tuning.
Study complex lines in symplectic geometry, generalizing previous results.
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the sam…
Study shows how COVID-19 pandemic affected China's crude oil futures market efficiency.
Study shows the corrected Akaike criterion is inadmissible for estimating Kullback-Leibler discrepancy.
RL methods applied to option pricing using modified QLBS and RLOP models.
Data-driven decision-making often overestimates benefits due to the winner's curse.
Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top or hypothesis testing may be written in this form. We propose a general framework to handle these classes of problems and show which known methods (both known and …
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures. We propose a number of hypotheses on the effects of specific intermediate-layer network architectures on the representation capacity of DNNs. In order to prove the hy…
Study shows -NN regressor consistency in complex survey designs.
Study shows benefits of transfer learning with neural networks.
Hopfield networks outperform deep-learning methods in portfolio optimization.
Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
A new method for compressive classification using bridge regression.
The paper explores how to handle uncertain evidence in probabilistic models.
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract info…
Evidence acquisition costs influence disclosure behavior and preference.
Nostradamus links climate and stock market performance.
Modern supervised machine learning algorithms involve hyperparameters that have to be set before running them. Options for setting hyperparameters are default values from the software package, manual configuration by the user or configuring them for optimal predictive performance by a tuning procedure. The goal of this…
This study examine the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using t…
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical ou…
This article introduces a framework to estimate the value of evidence-based decision making.
Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular applica…
The Frenet frame generalizes the Park transform for multi-phase circuits.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samp…
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis…
It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of def…
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
Method estimates Bayesian evidence from posterior samples using normalizing flows.
Overcomplete representations and dictionary learning algorithms kept attracting a growing interest in the machine learning community. This paper addresses the emerging problem of comparing multivariate overcomplete representations. Despite a recurrent need to rely on a distance for learning or assessing multivariate ov…
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.
In the modern era, abundant information is easily accessible from various sources, however only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting…
The paper addresses XVA valuation under market crises using a renewal process.
We consider the game-theoretic scenario of testing the performance of Forecaster by Sceptic who gambles against the forecasts. Sceptic's current capital is interpreted as the amount of evidence he has found against Forecaster. Reporting the maximum of Sceptic's capital so far exaggerates the evidence. We characterize t…
A new gradient flow framework for distributionally robust optimization.
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…
Proposes a method to identify causal relationships using background knowledge.
Two simulation-based methods improve optimal sampling design in systems biology.