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.
Surveying machine learning methods for economic forecasting.
Survey finds LLMs match human economic expectations closely.
LLMs can predict CFO responses to economic surveys
Generative AI predicts economic activity from corporate transcripts.
Survey analyzes economic research on cryptocurrencies using hybrid methods.
Survey of large language models in financial prediction and trading.
Quantifying the improvement in human living standard, as well as the city growth in developing countries, is a challenging problem due to the lack of reliable economic data. Therefore, there is a fundamental need for alternate, largely unsupervised, computational methods that can estimate the economic conditions in the…
In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems.…
Econophysics embodies the recent upsurge of interest by physicists into financial economics, driven by the availability of large amount of data, job shortage in physics and the possibility of applying many-body techniques developed in statistical and theoretical physics to the understanding of the self-organizing econo…
DRL enhances economic modeling with deep learning methods.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite …
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the …
This paper surveys NLP techniques for predicting stock market movements.
Paper develops NN models for diabetes screening using NHANES data.
Research explores how interconnected systems synchronize and how to control their behavior.
Survey of machine learning methods for time series forecasting.
The economic crisis in Argentina around year 2002 provides a unique opportunity for Econophysics studies. The available data on individual income are analyzed to show that they correspond to non stationary states. However, the rather restricted size of the data survey imposes difficulties that must be overcome through …
We empirically investigate distributions of individual consumption expenditure f or four commodity categories conditional on fixed income levels. The data stems from the Family Expenditure Survey carried out annually in the United Kingdom. W e use graphical techniques to test for normality and lognormality of these dis…
Bayesian model uses mobile data to assess business resilience after hurricanes.
This survey explores causal inference in banking, finance, and insurance.
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.
Survey of deep causal models for industrial applications.
Survey on causal inference methods for observational data.
Survey of robust clustering methods for hotspot detection.
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
DEPLOYERS models multi-country economic systems using ABM.
The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during the last two years, multiple so called stablecoins have surfaced---cryptocurrencies focused on maintaining stable exchange rates. In this paper, we systematically explore and analyze the stabl…
We present a stochastic agent-based model for the distribution of personal incomes in a developing economy. We start with the assumption that incomes are determined both by individual labour and by stochastic effects of trading and investment. The income from personal effort alone is distributed about a mean, while the…
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyzed…
We analyze three sets of income data: the US Panel Study of Income Dynamics PSID), the British Household Panel Survey (BHPS), and the German Socio-Economic Panel (GSOEP). It is shown that the empirical income distribution is consistent with a two-parameter lognormal function for the low-middle income group (97%-99% of …
Spatial machine learning improves poverty targeting in Indonesia.
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applica…
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a p…
Geospatial framework assesses climate risks for California's banking and exposed sectors.
Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inf…
Current economic theories miss most of economic dynamics.
Machine learning detects survey validity from user behavior.
Survey updates knowledge on homogeneous Einstein spaces.
Survey on submanifolds in nearly Kähler spaces.
Survey of minimal genus problem progress.
The paper improves Lasso inference methods for survey data.
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
Survey on L^2-invariants for 3-manifolds.
Survey on DDVV-type inequalities, their history, and recent developments.