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.
Novel framework synthesizes stochastic trajectories with anticipated structural breaks.
ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.
We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic "N-1" …
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
Proves and tests methods for learning time-series with breaks.
Bayesian ARMA model with directional shifts captures structural breaks in compositional time series.
Paper detects and estimates breaks in high-dimensional functional time series.
The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…
Anticipatory portfolios use richer models to optimize investments.
The paper examines how markets can anticipate and react to arbitrage opportunities, revealing biases and risks.
This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.
Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body mo…
In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group la…
Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely exchangeab…
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
Classifies patterns of symmetry breaking and vacuum degeneracy in scalar and gauge fields.
This article investigates the evolution of the Mexican stock market integration into the world market. First, we estimate the time-varying Mexican degree of market integration using an international conditional version of the CAPM with segmentation effects. Second, we study the structural breaks in this series. Finally…
The Lucas critique has exposed the problem of the trade-off between changes in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an empirical model…
Derives functional Itô formula for non-anticipative maps of rough paths.
In this paper, we study a class of Anticipated Backward Stochastic Differential Equations (ABSDE) with jumps. The solution of the ABSDE is a triple where is a semimartingale, and are the diffusion and jump coefficients. We allow the driver of the ABSDE to have linear growth on the uniform norm of …
This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics betwe…
Machine learning predicts criminal networks' missing partnerships and future behavior.
New model accurately predicts chemical bond breaking.
Human stablecoin transactions predict political risk in cryptocurrency markets.
Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established unde…
This study extends and evaluates the forecasting performance of the Singular Spectrum Analysis (SSA) technique using a general non-linear form for the re- current formula. In this study, we consider 24 series measuring the monthly seasonally adjusted industrial production of important sectors of the German, French and …
A discrete (finite-difference) analogue of differential forms is considered, defined on simplicial complexes, including triangulations of continuous manifolds. Various operations are explicitly defined on these forms, including exterior derivative and exterior product. The latter one is non-associative. Instead, as ant…
Geometric mechanism mimics physics' symmetry breaking.
We find prominent similarities in the features of the time series for the overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations.
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
In this article we prove that iterated renormalisations of circle diffeomorphisms with breaks, , with given size of breaks, converge to an invariant family of piecewise Moebius maps, of dimension . We prove that this invariant family identifies with a \textit{relative character variety} $χ(…
We consider the pricing of European-style structured credit payoff in a static framework, where the underlying default times are independent given a common factor. A practical application would consist of the pricing of nth-to-default baskets under the Gaussian copula model (GCM). We provide necessary and sufficient co…
Study uncovers tactical line-breaking passes in football using clustering.
We obtain a decomposition of the call option price for a very general stochastic volatility diffusion model extending the decomposition obtained by E. Alòs in [2] for the Heston model. We realize that a new term arises when the stock price does not follow an exponential model. The techniques used are non anticipative. …
AntLer anticipates future learning to improve control performance.
Researchers create BPS monopoles with any desired symmetry breaking.
Paper proposes a VB method for TS-SBP mixture models with reduced computational cost.
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
The paper classifies and proves properties of symmetry breaking operators for specific groups.
Study compares different integrals for optimal portfolio optimization with insider information.
We give a complete classification of intertwining operators (symmetry breaking operators) between spherical principal series representations of G=O(n+1,1) and G'=O(n,1). We construct three meromorphic families of the symmetry breaking operators, and find their distribution kernels and their residues at all poles explic…
Investigates spontaneous symmetry breaking in non-equilibrium systems.
Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments…
Adaptive models improve electricity demand forecasting during lockdown.
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for int…
A simple and elegant arrangement of stock components of a portfolio (market index-DJIA) in a recent paper [1], has led to the construction of crossing of stocks diagram. The crossing stocks method revealed hidden remarkable algebraic and geometrical aspects of stock market. The present paper continues to uncover new ma…
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the …