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 proposes AI for stock market forecasting using external knowledge.
StockAgent uses AI to simulate real-world stock trading, analyzing external factors and profitability.
In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional netw…
New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.
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…
Model shows PoS networks can be captured by external finance, leading to centralization.
Study shows stock price interactions increase during crises due to external stimulus.
Study confirms sparse coding in whole brain using MRI data.
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
Spatio-temporal (ST) data for urban applications, such as taxi demand, traffic flow, regional rainfall is inherently stochastic and unpredictable. Recently, deep learning based ST prediction models are proposed to learn the ST characteristics of data. However, it is still very challenging (1) to adequately learn the co…
To meet the Basel II regulatory requirements for the Advanced Measurement Approaches, the bank's internal model must include the use of internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. Quantification of operational risk cannot be base…
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse problem. Physical model based techniques are popular for guided wave based damage localization. The performance of these techniques depend on …
Margin trading in which investors purchase shares with money borrowed from brokers is blamed to be a major cause of the 2015 Chinese stock market crash. We propose a cascading failure model and examine how an increase in margin trading increases share price vulnerability. The model is based on a bipartite graph of inve…
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
Proposes MD-LiNA for multi-domain latent factor causal discovery.
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
In this paper, we prove the global risk optimality of the hedging strategy of contingent claim, which is explicitly (or called semi-explicitly) constructed for an incomplete financial market with external risk factors of non-Gaussian Ornstein-Uhlenbeck (NGOU) processes. Analytical and numerical examples are both presen…
A new matrix factorization method for high-dimensional data.
We consider an optimal investment and consumption problem for a Black-Scholes financial market with stochastic volatility and unknown stock appreciation rate. The volatility parameter is driven by an external economic factor modeled as a diffusion process of Ornstein-Uhlenbeck type with unknown drift. We use the dynami…
We prove that the marginal densities of a global probability mass function in a primal normal factor graph and the corresponding marginal densities in the dual normal factor graph are related via local mappings. The mapping depends on the Fourier transform of the local factors of the models. Details of the mapping, inc…
We consider a large, homogeneous portfolio of life or disability annuity policies. The policies are assumed to be independent conditional on an external stochastic process representing the economic-demographic environment. Using a conditional law of large numbers, we establish the connection between claims reserving an…
The constant growth of the e-commerce industry has rendered the problem of product retrieval particularly important. As more enterprises move their activities on the Web, the volume and the diversity of the product-related information increase quickly. These factors make it difficult for the users to identify and compa…
The waiting time needed for a stock market index to undergo a given percentage change in its value is found to have an up-down asymmetry, which, surprisingly, is not observed for the individual stocks composing that index. To explain this, we introduce a market model consisting of randomly fluctuating stocks that occas…
Robust data fusion via subsampling improves model performance for rare data types.
This paper optimizes portfolio management in incomplete markets with stochastic factors, considering periodic wealth evaluations.
We calculate in the strong coupling and large N limit the energy emitted by an accelerated external charge in SU(N) Yang-Mills theory, using the AdS/CFT correspondence. We find that the energy is a local functional of the trajectory of the charge. It coincides up to an overall factor with the Lienard formu…
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
BeMF improves recommendation reliability in recommender systems.
Method predicts NAFLD risk with high accuracy and distribution-free coverage guarantees.
Study factors affecting liquidity on decentralized exchanges, introducing new metrics.
Backtests of structured strategies lose much of their predictive power in live trading.
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
The value of stocks, indices and other assets, are examples of stochastic processes with unpredictable dynamics. In this paper, we discuss asymmetries in short term price movements that can not be associated with a long term positive trend. These empirical asymmetries predict that stock index drops are more common on a…
Modeling dynamic user interests using neural matrix factorization.
New estimator improves ATT estimation efficiency with external controls.
Method estimates model performance on external samples from limited statistical characteristics.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
A method for logistic regression inference using both internal and external data.
Study long-term asset liquidation behavior with external flows.
Study examines remittances in Nepal, linking external demand and domestic monetary conditions.
The paper uses filtering techniques to predict rating transitions.
Framework for estimating treatment effects using external control data.
This paper studies a portfolio optimization problem in a discrete-time Markovian model of a financial market, in which asset price dynamics depend on an external process of economic factors. There are transaction costs with a structure that covers, in particular, the case of fixed plus proportional costs. We prove that…
Study identifies negative data externalities affecting model performance on specific groups.
Local mappings relate dual and primal factor graphs for efficient marginal probability estimation.
Survey of methods to incorporate external knowledge into stock price prediction.
Understanding generalization in reinforcement learning (RL) is a significant challenge, as many common assumptions of traditional supervised learning theory do not apply. We focus on the special class of reparameterizable RL problems, where the trajectory distribution can be decomposed using the reparametrization trick…