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.
QLSTM improves speech recognition by considering internal quaternion dependencies.
Trade networks for maize, rice, soy, and wheat are more vulnerable to shocks.
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characteriz…
Investigates optimal strategies for market makers using internal liquidity.
For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external drivi…
Proposes a Coulomb-like model for international trade flows, fitting real-world data.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks-- Internal Covariate Shift-- the current solution has certain drawbacks. Specifically, BN depends on batch statistics for layerwise input normalization during training which makes the estimates of …
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
The \emph{World Trade Web} (WTW), the network defined by the international import/export trade relationships, has been recently shown to display some important topological properties which are tightly related to the Gross Domestic Product of world countries. While our previous analysis focused on the static, undirected…
Oil prices affect Russian banks' stability, with negative impacts from decreases.
Extremal dependence between international stock markets is of particular interest in today's global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, o…
In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility…
Bayesian hierarchical tensor factorization model for international trade flows
Paper offers a dual formulation for consumption problem with multiplicative habit.
In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the dependence between frequencies of different risk categories and between severities of different risk categories as well …
We present a broad agenda for meaningful banking regulation reform aiming the creation of evolutive competitive environment to maximize the effectiveness of international financial system through the introduction of fair competition process among the banks in free market capitalism. We assume that the international fin…
Investment risk on a regulated market is influenced by gold prices and oil trading.
Internal Lagrangians derived from variational principles.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of food trade networks are still poorly documented, especially from a multi-network perspective. In particular,…
U-Net trained to recover acoustic interference striations from distorted data.
Are expansions and recessions more likely to end as their magnitude increases? In this paper we apply parametric hazard models to investigate this issue in a sample of 16 countries from 1881 to 2000. For the total sample we find evidence of positive magnitude dependence for recessions, while for expansions we are not a…
Paper proposes a new MAR model for global economic forecasting.
Examines international taxation's impact on Georgian businesses.
Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions …
Optimized execution model using interbank and internal liquidity.
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
The geometrical theory of partial differential equations in the absolute sense, without any additional structures, is developed. In particular the symmetries need not preserve the hierarchy of independent and dependent variables. The order of derivatives can be changed and the article is devoted to the higher--order in…
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
Spain uses DEA to select international markets for exports.
Improved speech recognition model with better performance.
Introduces internal Lagrangians for differential equations and connects them to presymplectic structures.
New findings show that some knotted surfaces remain distinct even after many stabilizations.
Extended symmetries and anomalies in compactified 6d SCFTs on various internal manifolds.
This paper develops a non-Bayesian methodology to analyze the time-varying structure of international linkages and market efficiency in G7 countries. We consider a non-Bayesian time-varying vector autoregressive (TV-VAR) model, and apply it to estimate the joint degree of market efficiency in the sense of Fama (1970, 1…
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models and requirements on evaluation processes formulated in the Commission Delegated Regulation. The…
Study improves stock price prediction by integrating international markets using deep learning.
A conceptor-based approach helps robots recognize human internal states.
Quaternion neural networks improve speech recognition efficiency.
Mathematical model predicts international trade and global economy dynamics.
Random feature maps improve forecasting with cheaper computation.
Study uses AI to predict changes in international public finances based on US markets.
New stabilization methods for exotic surfaces in 4-manifolds.
LLMs prefer Bitcoin under crisis frames, affecting financial decisions.
Bilateral trade relationships in the international level between pairs of countries in the world give rise to the notion of the International Trade Network (ITN). This network has attracted the attention of network researchers as it serves as an excellent example of the weighted networks, the link weight being defined …
Neural networks need guidance to learn appropriate internal representations.