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.
S-DIDML integrates structural DID with ML for causal inference in high-dimensional data.
DID measures similarity invariant to diffeomorphisms.
In 2003, S.-s. Chern began a study of almost-complex structures on the 6-sphere, with the idea of exploiting the special properties of its well-known almost-complex structure invariant under the exceptional group . While he did not solve the (currently still open) problem of determining whether there exists an int…
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of data, naturally the samples are collected and stored distributively in local computational nodes. Thus, there is a growing need to develop algo…
Bayesian methods improve DiD analysis for ATT estimation.
A survey of finite group actions on symplectic 4-manifolds is given with a special emphasis on results and questions concerning smooth or symplectic classification of group actions, group actions and exotic smooth structures, and homological rigidity and boundedness of group actions. We also take this opportunity to in…
Proposes a new DiD method for learning optimal treatment policies.
We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to evolutionary shocks and recovers more slowly from them now than i…
Cluster jackknife improves inference for staggered DID methods.
Historical economic growth in Asia (excluding Japan) is analysed. It is shown that Unified Growth Theory is contradicted by the data, which were used (but not analysed) during the formulation of this theory. Unified Growth Theory does not explain the mechanism of economic growth. It explains the mechanism of Malthusian…
Historical economic growth in countries of the former USSR is analysed. It is shown that Unified Growth Theory is contradicted by the data, which were used, but not analysed, during the formulation of this theory. Unified Growth Theory does not explain the mechanism of economic growth. It explains the mechanism of Malt…
Meta-learner estimates heterogeneous DiD effects robustly.
Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
Standard economic theory makes an allowance for the agency problem, but not the compounding of moral hazard in the presence of informational opacity, particularly in what concerns high-impact events in fat tailed domains (under slow convergence for the law of large numbers). Nor did it look at exposure as a filter that…
This version withdrawn by arXiv administrators because the submitter did not have the right to agree to our license at the time of submission.
In this paper, we prove that any polarized K-stable manifold is CM-stable. This extends what I did for Fano manifolds in my 2012 paper.
The paper revisits and improves on a Bayesian relevance vector machine method for small sample sizes.
TMLE improves IPM estimation for ecological population dynamics.
Paper uses non-Euclidean analysis to classify brain structure variations.
What would you do if you were invited to play a game where you were given \$25 and allowed to place bets for 30 minutes on a coin that you were told was biased to come up heads 60% of the time? This is exactly what we did, gathering 61 young, quantitatively trained men and women to play this game. The results, in a nut…
Isomorphism found between filtered calculus and crossed products.
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…
With this work it is analyzed the import and export of horticultural products between Portugal and the other world countries. It is used data about Portuguese international trade of vegetables from 2006 to 2010. The data were obtained from the INE (Statistics Portugal), gently given by the AICEP (Trade & Investment Age…
Digital transformation boosts corporate financial asset allocation, especially short-term.
This paper examines SVB's failure and its impact on bank stocks.
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. However, some of those dependencies are generated by causal structures involving varia…
Combines structured inference and targeted learning to tackle causal inference challenges.
Research aims to predict fallen angel bonds' bankruptcy using machine learning.
New braid representations using virtual knot theory.
Study examines how COVID-19 affected stock and crypto market efficiency.
Current paper addresses topology issues in PBSHM to enable transfer learning.
Andreas Maurer in the paper "A vector-contraction inequality for Rademacher complexities" extended the contraction inequality for Rademacher averages to Lipschitz functions with vector-valued domains; He did it replacing the Rademacher variables in the bounding expression by arbitrary idd symmetric and sub-gaussian var…
While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement f…
Improved TreNet for trend prediction in time series data.
Paper examines the structure of stochastic gradients in deep learning.
Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (…
Panoptic trades options without oracles on Ethereum.
Study provides selective inference method for latent block models.
Machine learning classifies surface wave dispersion curves from ambient noise.
How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regr…
A new protocol corrects confounding effects to measure alignment-induced activation shifts accurately.
In this paper we study the group theoretic structures of colored HOMFLY polynomials in a specific limit. The group structures arise in the perturbative expansion of Chern-Simons Wilson loops, while the limit is . The result of the paper is twofold. First, we explain the emergence of Kadomsev-Pe…
Derives matrix Harnack inequalities for semilinear heat equations on manifolds.
Cryptos remained resilient after SVB's collapse, contrary to expectations.
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets w…
Nowadays, when crashes and crises are rather frequent events, an effective monitoring system for the international financial market is needed. Modern nonlinear methods, such as Recurrence Quantification Analysis (RQA), demonstrate the ability to reveal the regularities of the system behavior. Thus, they can be useful f…
What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results. Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually att…