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.
Investor clusters analyzed in Helsinki Stock Exchange IPOs.
Study finds dividend policy has no significant effect on IPO stock prices.
Biotech IPOs in Q1 2021: advanced degrees, clinical trials, and IP key.
Public debt is one of the important economic variables that quantitatively describes a nation's economy. Because bankruptcy is a risk faced even by institutions as large as governments (e.g. Iceland), national debt should be strictly controlled with respect to national wealth. Also, the problem of eliminating extreme p…
We describe a project, called the "Discretization in Geometry and Dynamics Gallery", or DGD Gallery for short, whose goal is to store geometric data and to make it publicly available. The DGD Gallery offers an online web service for the storage, sharing, and publication of digital research data.
The paper develops a model to predict IPO events in private equity investments.
We study a practical optimization problems for venture capital investments and/or Research and Development (R&D) investments. The first problem is that, given the amount of the initial investment and the reward function at the initial public offering (IPO) market, the venture capitalist wants to maximize overall discou…
This paper discusses the potential impacts of the so-called `initial coin offerings', and of several developments based on distributed ledger technology (`DLT'), on corporate governance. While many academic papers focus mainly on the legal qualification of DLT and crypto-assets, and most notably in relation to the pote…
Paper introduces CageBO for optimizing complex public policy problems.
Private estimation with public data reduces sample complexity.
Policy shifts between Trump and Biden impact ESG investments, creating volatility.
Improves zeroth-order optimization for private machine learning with public data.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
New method for private learning with public features improves convergence rates.
regvis.net offers a visual survey of regulatory visualization.
A digital euro protocol offers complete privacy and offline transactions using Groth-Sahai proofs.
Research uses Twitter data to analyze public perception of city logistics.
Study finds billing codes at IPO boost digital health companies' financial performance.
In this paper we study continuous-time stochastic control problems with both monotone and classical controls motivated by the so-called public good contribution problem. That is the problem of n economic agents aiming to maximize their expected utility allocating initial wealth over a given time period between private …
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal r…
New algorithm clusters LLM inputs efficiently with quality control.
New method accurately reconstructs Russell 3000 index, revealing crowded portfolios.
This paper identifies and addresses biases in medical imaging research.
Study predicts startup outcomes like funding, patenting, IPOs using machine learning.
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…
Study detects unlawful insider trading using SHAP and CF, identifying key features.
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…
missForestPredict fills missing data for prediction models quickly and accurately.
New privacy-preserving learning model for mixtures of private and public data.
LLMs show biases in investment analysis, leading to unreliable recommendations.
SentiCite analyzes citations for sentiment and nature, improving on existing methods.
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a larg…
Dynamic pricing policy converges to Nash equilibrium with low regret.
Global convergence for robust regression problems via IRLS with enhancements.
GD with large init shows incremental learning in matrix factorization.
Study analyzes misinformation on social media during COVID-19.
Domains in infinite jets present the simplest class of diffieties with boundary. In this note some basic elements of geometry of these domains are introduced and an analogue of the C-spectral sequence in this context is studied. This, in particular, allows cohomological interpretation and analysis of initial data, boun…
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
Social media hype can misprice IPO stocks, leading to short-term gains but long-term losses.
Implementing large-scale information and communication technology (IT) projects carries large risks and easily might disrupt operations, waste taxpayers' money, and create negative publicity. Because of the high risks it is important that government leaders manage the attendant risks. We analysed a sample of 1,355 publ…
This paper offers a financial economic perspective on the optimal time (and age) at which the owner of a Variable Annuity (VA) policy with a Guaranteed Living Withdrawal Benefit (GLWB) rider should initiate guaranteed lifetime income payments. We abstract from utility, bequest and consumption preference issues by treat…
Study improves prediction of UK road accidents' severity using AI.
This paper examines risks and uncertainties of changing data sources in machine learning for official statistics.
As researchers and practitioners of applied machine learning, we are given a set of requirements on the problem to be solved, the plausibly obtainable data, and the computational resources available. We aim to find (within those bounds) reliably useful combinations of problem, data, and algorithm. An emphasis on algori…
Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that…
We define and make initial study of Lie groupoids equipped with a compatible homogeneity (or graded bundle) structure, such objects we will refer to as weighted Lie groupoids. One can think of weighted Lie groupoids as graded manifolds in the category of Lie groupoids. This is a very rich geometrical theory with numero…
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same…