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.
A new framework for systematic graph neural network data augmentation.
Neuro-symbolic agent learns systematic generalisation from formal instructions.
Paper analyzes systematic jump risk around the clock using news narratives.
Study uses TV news to measure climate risks affecting clean energy firms.
Economic factors significantly influence stock returns, as shown by attribution analysis.
In this paper, we measure systematic risk with a new nonparametric factor model, the neural network factor model. The suitable factors for systematic risk can be naturally found by inserting daily returns on a wide range of assets into the bottleneck network. The network-based model does not stick to a probabilistic st…
Paper predicts stock volatility using ESG news, showing deep learning's effectiveness.
We study the effects of non-systematic and systematic mortality risks on the required initial capital in a pension plan, in the presence of financial risks. We discover that for a pension plan with few members the impact of pooling on the required capital per person is strong, but non-systematic risk diminishes rapidly…
Analyzes news graphs to predict financial market dislocations.
The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to …
Detects systematic anomalies in consumer complaints using NLP.
New method to derive integrable systems from existing Lax systems.
Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and v…
This paper studies systematic exploration for reinforcement learning with rich observations and function approximation. We introduce a new model called contextual decision processes, that unifies and generalizes most prior settings. Our first contribution is a complexity measure, the Bellman rank, that we show enables …
Link prediction is a popular research topic in network analysis. In the last few years, new techniques based on graph embedding have emerged as a powerful alternative to heuristics. In this article, we study the problem of systematic biases in the prediction, and show that some methods based on graph embedding offer le…
New algorithm improves asset ranking for better cross-sectional portfolios.
We introduce a novel systematic construction for integrable (3+1)-dimensional dispersionless systems using nonisospectral Lax pairs that involve contact vector fields. In particular, we present new large classes of (3+1)-dimensional integrable dispersionless systems associated to the Lax pairs which are polynomial and …
ChatGPT improves momentum strategies by analyzing news data.
The study uses financial events to predict stock market movements.
New maxfaces with Enneper ends found.
NIFTY dataset for financial forecasting models.
Machine learning models predict brain age with systematic bias, corrected in this study.
Paper tackles division difficulty, proposing new methods to improve accuracy.
Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usab…
This short note provides a systematic construction of market models without unbounded profits but with arbitrage opportunities.
The study identifies impactful news articles based on liquidity changes, improving asset return prediction.
CLSVAE repairs systematic errors in images with minimal labeled data.
The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.
New formulas derived for anomaly cancellation using modular forms and E8 bundles.
Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explanation methods provide explanations through feature importance scores, which identify features that are important for each individual input. …
An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For exam…
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
The paper introduces Relative Bias to quantify LLM bias systematically.
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
Paper finds new realizable data for maps with three branch points.
Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most st…
Systematic review of ML models for detecting social media deception.
Paper develops an AI-driven framework for systematic investing.
Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to…
In this paper we generate and systematically classify all prime planar knotoids with up to 5 crossings. We also extend the existing list of knotoids in and add all knotoids with 6 crossings.
26 different concrete representations of the space of vector valued distributions on a smooth manifold of dimension n are presented systematically, most of them new. In the particular case of representations as module homomorphisms acting on sections of the dual bundle resp. on n-forms, the continuity of these homomorp…
Proposes a new risk model using stable laws to manage company-wide losses.
Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we …
Spotlight method finds hidden errors in deep learning models.
This paper does not contain any new results, it is just an attempt to present, in a systematic way, one construction which establishes an interesting relationship between some ideas and notions well-known in the theory of integrable systems on Lie algebras and a rather different area of mathematics studying projectivel…
The abstract reviews Markov models in life insurance surplus.