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.
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related feature…
The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual's perception of the advantages of an innovation and (b) social influence from members of the decision-maker's social net…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, pr…
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
We propose a modelling framework for the optimal selection of crypto assets. Crypto assets differ by two essential features: security (technological) and stability (governance). Investors make choices over crypto assets similarly to how they make choices by using a recommender app: the app presents each investor with a…
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
Study develops smart contract framework for procurement under demand variability.
Model shows AI adoption amplifies financial market risk through prediction, herding, and cognitive dependency.
ADOPT optimizes Adam to converge with any β2 without bounded noise.
GenAI adoption paradoxically lowers ROE for U.S. banks, with spillovers but systemic risk concerns.
Paper develops robust econometric methods for staggered adoption studies.
Study uses exchangeable GPs for staggered-adoption policy evaluation in panel data.
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
This paper analyzes the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can invest both in low-emitting production technologies and trade permits. In the model, technology adoption and allowance price are generated…
Deep learning faces adoption challenges in business analytics.
AI threatens financial stability through misuse and stealth adoption.
New method selects critical DER scenarios for distribution grid investment planning.
Survey of stablecoins to reduce cryptocurrency volatility.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
Blockchain-based exchanges adopt based on token pair volatility and personal use.
A new estimator reduces bias and improves efficiency for staggered adoption studies.
Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit fr…
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of …
We propose and discuss some toy models of stock markets using the same operatorial approach adopted in quantum mechanics. Our models are suggested by the discrete nature of the number of shares and of the cash which are exchanged in a real market, and by the existence of conserved quantities, like the total number of s…
Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant p…
Dark blockchain venues increase miners' profits but raise users' execution risk.
Bitcoin's integration with major financial indices intensifies, suggesting a shift from alternative to integrated asset.
Studies report that firms do not invest in cost-effective green technologies. While economic barriers can explain parts of the gap, behavioural aspects cause further under-valuation. This could be partly due to systematic deviations of decision-making agents' perceptions from normative benchmarks, and partly due to the…
Synthetic experiments are crucial for assessing causal machine learning methods.
Model shows partial compliance can lead to less fair outcomes than expected.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
This paper explores IT governance for CBDC adoption in financial markets.
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing …
Modeling tech transfer to explain convergence in Central and Eastern Europe.
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method. We conduct several conventional sequential estimation procedures separately, and properly integrate their results while maintaining the desired statistical properties. A…
Achieving advancements in automatic recognition of emotions that music can induce require considering multiplicity and simultaneity of emotions. Comparison of different machine learning algorithms performing multilabel and multiclass classification is the core of our work. The study analyzes the implementation of the G…
Simple machine learning models outperform deep learning for sleep scoring.
The paper tackles multi-player information asymmetry bandits in metric spaces.
We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective i…
We propose a new method of discovering causal relationships in temporal data based on the notion of causal compression. To this end, we adopt the Pearlian graph setting and the directed information as an information theoretic tool for quantifying causality. We introduce chain rule for directed information and use it to…
Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-…
A new model improves homogeneity in burn patient reimbursement.
Improved CNN accuracy for encrypted data using approximate activation functions.
This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.
The Klein-Grifone approach to global Finsler geometry is adopted. The nullity distributions of the three curvature tensors of Cartan connection are investigated. Nullity distributions concerning certain relevant special Finsler spaces are considered. Concrete examples are given whenever the situation needs.