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.
Designed to compete with fiat currencies, bitcoin proposes it is a crypto-currency alternative. Bitcoin makes a number of false claims, including: solving the double-spending problem is a good thing; bitcoin can be a reserve currency for banking; hoarding equals saving, and that we should believe bitcoin can expand by …
Study uses web search data to analyze tech startups growth.
We analyze the sectoral dynamics of startup venture financing. Based on a dataset of 52000 start-ups and 110000 funding rounds in the United States from 2000 to 2017, and by applying both Principal Component Analysis (PCA) and Tensor Component Analysis (TCA) in sector space, we visualize and measure the evolution of th…
Business cycles affect startup valuations, both directly and indirectly.
Green startups in Italy survive longer than non-green ones.
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…
Enhances VC startup success predictions using graph augmented time series models.
Geoeconomic analysis of venture capital portfolios reveals key emerging tech domains and countries.
Deep learning helps identify promising startups.
We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as we…
YC Bench forecasts startup success in Y Combinator batches with a short-term metric.
In this paper we propose a quadratic programming model that can be used for calculating the term structure of electricity prices while explicitly modeling startup costs of power plants. In contrast to other approaches presented in the literature, we incorporate the startup costs in a mathematically rigorous manner with…
Predicting startup success using Crunchbase data and deep learning.
We consider the problem of evaluating the quality of startup companies. This can be quite challenging due to the rarity of successful startup companies and the complexity of factors which impact such success. In this work we collect data on tens of thousands of startup companies, their performance, the backgrounds of t…
Biotech IPOs in Q1 2021: advanced degrees, clinical trials, and IP key.
This paper compares token and equity financing for startups.
Study reveals which startup valuation factors are most critical.
In the domain of technology startups, biotechnology has often been considered as specific. Their unique technology content, the type of founders and managers they have, the amount of venture capital they raise, the time it takes them to reach an exit as well as the technology clusters they belong to are seen as such un…
A new method to value IPOed companies.
Study predicts startup outcomes like funding, patenting, IPOs using machine learning.
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.
This paper ranks Latin American countries based on AI potential.
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…
Startups is a popular phenomenon that has a significant impact on global economy growth, innovation and society development. However, there is still insufficient understanding about startups, particularly, how to start a new business in the relation to consequent performance. Toward this knowledge, we have performed an…
Tech sector decouples from non-tech sectors post-2015, predicting economic growth.
The paper uses deep learning to detect asset price bubbles in tech stocks.
Study analyzes climate-tech investments across 14 sectors.
Study compares sentiment spillover networks from news and social media in tech companies.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
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…
Study evaluates early-stage cybersecurity firms' performance using Crunchbase data.
Tool uses text mining to define innovative tech fields from abstracts.
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…
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…
Abstracts discuss a common framework for constructing homology theories.
Deep learning models are growing, posing new mathematical challenges.
PHBench predicts Series A funding from Product Hunt launch signals with 7.8% accuracy.
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…
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 …
The hidden Markov model (HMM) is a generative model that treats sequential data under the assumption that each observation is conditioned on the state of a discrete hidden variable that evolves in time as a Markov chain. In this paper, we derive a novel algorithm to cluster HMMs through their probability distributions.…
A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the ent…
The paper tackles financial market dynamics with new tech-driven data.
Paper combines regularization and pruning to reduce FLOPs in DNNs.
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research direction…