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.
This work develops a high precision fault diagnosis classifier using XAI insights.
Visualizes board connections for socially responsible investing insights.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the glob…
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine l…
Industrial-scale podcast recommender system optimizes long-term listening journeys.
Study refines trend-following strategy to improve adaptability.
Develops a deep multi-factor model for factor investing with clear financial insights.
Survey of deep causal models for industrial applications.
Hybrid framework injects TSLM insights into GRLM for robust time-series reasoning.
Study analyzes COFCO's acquisition of Mengniu Dairy, revealing financial and non-financial impacts.
Generative AI predicts economic activity from corporate transcripts.
This paper explores how NLP enhances insurance data analysis.
Tourism is one of the most important economic activities in the world: for many countries it represents the single largest product in their export basket. However, it is a product difficult to chart: "exporters" of tourism do not ship it abroad, but they welcome importers inside the country. Current research uses socia…
Big data transforms accounting and auditing, enhancing insights but posing challenges.
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks wit…
The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develo…
UniFinEval benchmarks financial models across text, images, and videos.
FL improves insurance claims loss prediction without sharing data.
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good …
This paper develops a federated approach to learn Granger causality in interdependent industrial clients.
PIML enhances machine learning for subsurface energy systems.
Study examines machine learning competitions' impact on AI development.
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notio…
Conversion of raw data into insights and knowledge requires substantial amounts of effort from data scientists. Despite breathtaking advances in Machine Learning (ML) and Artificial Intelligence (AI), data scientists still spend the majority of their effort in understanding and then preparing the raw data for ML/AI. Th…
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
This research tackles unsupervised topic extraction in noisy social media data.
Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective experiments or computer simulations. Obtaining insight into the problem or performing subsequent optimizations requires hundreds of thousands of evaluations of the objective function which is most often a …
Study on cyber insurance viability using statistical models.
Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Following [1] a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) app…
New model predicts multiple future trends from merchant transactions.
China and EU race to develop hydrogen for energy transition.
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Shai is a 10B model for asset management tasks, outperforming baselines.
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Signifi…
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit …
Improves industry classification for diversified companies.
Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about)…
Amazon SageMaker Model Monitor detects drift in deployed ML models.
Efficiently identifies promising hyperparameters for online learning models.
Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and adv…
Study explores fairness in financial deep learning through multi-scale trust quantification.
Develops MIS, a probabilistic model for multi-industry classification.
Study finds environmental liability insurance reduces industrial carbon emissions.
FinRobot AI agent for equity research provides comprehensive insights.
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates e…