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.
Model predicts real-time job applicant numbers for regional economic analysis.
Proposes a taxonomy for economic policies.
Money analyzed as a multidimensional tensor for better economic policy.
LLMs can memorize economic data and recall exact values before their training cutoff.
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…
Data describing historical economic growth are analysed. Included in the analysis is the world and regional economic growth. The analysis demonstrates that historical economic growth had a natural tendency to follow hyperbolic distributions. Parameters describing hyperbolic distributions have been determined. A search …
Historical economic growth in Latin America is analysed using the data of Maddison. Unified Growth Theory is found to be contradicted by these data in the same way as it is contradicted by the economic growth in Africa, Asia, former USSR, Western Europe, Eastern Europe and by the world economic growth. Paradoxically, U…
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…
New framework detects time-varying economic persistence.
AI-driven tax policies improve economic equality and productivity.
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
Generative Networks outperform traditional methods in PiT ESG generation.
Google Trends data improves economic forecasts of private consumption.
The paper analyzes tech specialization and diversification at various scales.
New framework for interpretable firm characteristics factors.
Examines financial risks' impact on EU-15 economic growth.
This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been …
Novel framework analyzes economic shifts in data-poor economies.
Based on the assumption that economic complexity is characterised by the interactions of economic agents (who) constantly change their actions and strategies in response to the outcome they mutually create, this paper presents how network models can be used a proxies for the mapping, quantification and analysis of Roma…
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
DRL enhances economic modeling with deep learning methods.
The study examines tail dependence between global economic uncertainty and BRICS currencies using high-frequency data.
A scalable method for econometric inference using machine learning for big data.
Current economic theories miss most of economic dynamics.
Cohort analysis speeds up Bitcoin blockchain data queries.
Peru's abundant natural resources and friendly trade policies has made the country a major economic player in both South America and the global community. Consequently, exports are playing an increasingly important role in Peru's national economy. Indeed, growing from 13.1% as of 1994, exports now contribute approximat…
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…
Study improves stock return prediction by switching between economic states, outperforming traditional methods.
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on the indexing of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic con…
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
I argue that the current financial crisis highlights the crucial need of a change of mindset in economics and financial engineering, that should move away from dogmatic axioms and focus more on data, orders of magnitudes, and plausible, albeit non rigorous, arguments.
In the same way as the Hilbert Program was a response to the foundational crisis of mathematics, this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent fina…
A simple but useful method of reciprocal values is introduced, explained and illustrated. This method simplifies the analysis of hyperbolic distributions, which are causing serious problems in the demographic and economic research. It allows for a unique identification of hyperbolic distributions and for unravelling co…
The optimal approach is to theorize after examining data, not before.
Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers propo…
We develop a complexity measure for large-scale economic systems based on Shannon's concept of entropy. By adopting Leontief's perspective of the production process as a circular flow, we formulate the process as a Markov chain. Then we derive a measure of economic complexity as the average number of bits required to e…
Modeling business cycles via collective risk fluctuations in economic agents' risk space.
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
The paper uses machine learning to predict the impact of the Ukraine crisis on financial markets.
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 …
The paper argues for a social-economic approach to AI development.
The paper gives picture of enrichment to economic and financial system analysis using agent-based models as a form of advanced study for financial economic data post-statistical-data analysis and micro-simulation analysis. Theoretical exploration is carried out by using comparisons of some usual financial economy syste…
FinML-Chain integrates blockchain data for financial machine learning.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.