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.
Quantum computing offers financial industry new optimization and risk management tools.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
Neural model learns company embeddings from data and news.
GenAI offers financial benefits but requires risk management.
Computable contracts simplify financial transactions and reduce legal costs.
Study finds environmental liability insurance reduces industrial carbon emissions.
Study analyzes COFCO's acquisition of Mengniu Dairy, revealing financial and non-financial impacts.
Novel financial time-series data representation improves industry sector classification.
In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international en…
AI agent predicts industry and product/service codes for companies.
Study on financial impacts of zombie outbreak on economy.
A clustering procedure, based on the Hausdorff distance, is introduced and tested on the financial time series of the Dow Jones Industrial Average (DJIA) index.
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within indiv…
Paper reviews and compares methods for handling imbalanced data.
The informational context is regularly questioned in a transitional economic regime like the one implemented in China or Vietnam. This article investigates this issue and the predictive power of fundamental analysis in such context and more precisely in a Chinese context with an analysis of 3 different industries (medi…
This paper assesses risks in DeFi investments.
Time-varying neural network improves stock return prediction.
Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we …
SHIFT simulates realistic financial markets for research and industry.
In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective …
UniFinEval benchmarks financial models across text, images, and videos.
Financial market created for wellbeing indices to mitigate socioeconomic risks.
Artificial Intelligence (AI) is an important driving force for the development and transformation of the financial industry. However, with the fast-evolving AI technology and application, unintentional bias, insufficient model validation, immature contingency plan and other underestimated threats may expose the company…
Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that …
RAG-IT automates financial analysis using LLMs and specialized datasets.
Financial markets have been extensively studied as highly complex evolving systems. In this paper, we quantify financial price fluctuations through a coupled dynamical system composed of phase oscillators. We find a Financial Coherence and Incoherence (FCI) coexistence collective behavior emerges as the system evolves …
New risk theory for 'Pay-for-Performance' models.
Quantum-inspired tensor network speeds up financial risk assessment.
iConViz helps banks manage default contagion risk in networked loans.
Photonic chip speeds up option pricing with GAN for financial efficiency.
Quantum computing promises to revolutionize finance, especially in optimization and modeling.
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…
Recent developments in the literature on financial architecture suggest that banks and markets not only coexist, but also coevolve in ways that are non-neutral from the viewpoint of optimality. This article aims to analyse the concrete mechanisms of this coevolution by focusing on a very relevant case study: Belgium (t…
Large language models learn company embeddings from SEC filings.
This work uses the stocks of the 197 largest companies in the world, in terms of market capitalization, in the financial area in the study of causal relationships between them using Transfer Entropy, which is calculated using the stocks of those companies and their counterparts lagged by one day. With this, we can asse…
Identifies key industrial sectors in S&P 500 states.
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…
QuantBench benchmarks AI methods for quantitative investment.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
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…
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
We take a closer look at the life and legacy of Micheal Milken. We discuss why Michael Milken, also know as the Junk Bond King, was not just any other King or run-of-the-mill Junk Dealer, but "The Junk Dealer". We find parallels between the three parts to any magic act and what Micheal Milken did, showing that his acco…
The paper proposes a new portfolio optimization model that includes VaR risk measure.
We consider a general discrete-time financial market with proportional transaction costs as in [Kabanov, Stricker and Rásonyi Finance and Stochastics 7 (2003) 403--411] and [Schachermayer Math. Finance 14 (2004) 19--48]. In addition to the usual investment in financial assets, we assume that the agents can invest part …
This paper assesses the role of financial performance in explaining firms' investment dynamics in the wine industry from the three European Union (EU) largest producers. The wine sector deserves special attention to investigate firms' investment behavior given the high competition imposed by the latecomers. More precis…
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hi…
Deep learning solves and estimates complex financial models.
Financial institutions face new model risks with AI, requiring enhanced model risk management.