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 paper evaluates investment risks in LATAM AI startups using DCF method.
Study examines tech stocks' reactions to Facebook data leak scandal.
Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.
Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this pape…
Tech sector decouples from non-tech sectors post-2015, predicting economic growth.
Thanks to the recent availability of comprehensive and detailed online databases of startup companies, it has become possible to more directly investigate startup ecosystems i.e. startup populations in specific regions. In this paper, we analyze the emergence of 20+ such ecosystems in Europe and the USA, with a specifi…
The paper uses deep learning to detect asset price bubbles in tech stocks.
LightAutoML automates ML for a large financial services company.
Defines data science as a natural ecosystem with challenges and missions.
Study analyzes climate-tech investments across 14 sectors.
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
Debt-financed collateral in DeFi increases stability risks.
Study compares sentiment spillover networks from news and social media in tech companies.
This paper explains tax policy for crypto assets in a rapidly evolving tech landscape.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
SFC aims to protect the Amazon with a digital currency and smart contracts.
Newsroom in online ecosystem is difficult to untangle. With prevalence of social media, interactions between journalists and individuals become visible, but lack of understanding to inner processing of information feedback loop in public sphere leave most journalists baffled. Can we provide an organized view to charact…
Mirzakhani obtained the asymptotic growth, when , of the number of curves in the mapping class group orbit of some given simple curve and with length at most . Years later she extended this result from simple to arbitrary curves. Here we give a short and relative low-tech argument showing how to derive t…
The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.
The green area of economy is the key of healthy living. It is necessary to convene economic and ecologic framework to establish a market attentive to drastic reduction of emissions damaging our climate and landscapes in rural areas, to the protection of biological diversity of the planet, to stop producing nuclear wast…
Study quantifies systemic risk in DeFi using network analysis.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
Tool uses text mining to define innovative tech fields from abstracts.
The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implemen…
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation. Furthermore, we…
Study examines European banks' digital transformation strategies.
We construct a statistical indicator for the detection of short-term asset price bubbles based on the information content of bid and ask market quotes for plain vanilla put and call options. Our construction makes use of the martingale theory of asset price bubbles and the fact that such scenarios where the price for a…
Study examines machine learning competitions' impact on AI development.
Dual risk models are popular for modeling a venture capital or high tech company, for which the running cost is deterministic and the profits arrive stochastically over time. Most of the existing literature on dual risk models concentrated on the optimal dividend strategies. In this paper, we propose to study the optim…
Enhances VC startup success predictions using graph augmented time series models.
Study uses web search data to analyze tech startups growth.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." In this manuscript we introduce the SciML software ecosystem as a tool for mixing the information of physical laws and scientific models with data-driven machine learning approache…
Study maps research streams in biodiversity finance, identifies key areas.
Geoeconomic analysis of venture capital portfolios reveals key emerging tech domains and countries.
The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms wi…
Abstracts discuss a common framework for constructing homology theories.
Deep learning models are growing, posing new mathematical challenges.
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
The dual risk model is a popular model in finance and insurance, which is often used to model the wealth process of a venture capital or high tech company. Optimal dividends have been extensively studied in the literature for a dual risk model. It is well known that the value function of this optimal control problem do…
Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.
This study compares CeFi and DeFi, finding some DeFi assets are not truly decentralized.
Kernelmethods library simplifies kernel-based ML in Python.
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic processes. We introduce a novel non-parametric approach to represent random variables which splits apart dependency and distribution without losi…
We present a methodology for clustering N objects which are described by multivariate time series, i.e. several sequences of real-valued random variables. This clustering methodology leverages copulas which are distributions encoding the dependence structure between several random variables. To take fully into account …