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.
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
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. …
The paper examines how markets can anticipate and react to arbitrage opportunities, revealing biases and risks.
Proposes a new regularization technique for neural networks using elliptic operators.
By employing the technique of enlargement of filtrations, we demonstrate how to incorporate information about the future trend of the stochastic interest rate process into a financial model. By modeling the interest rate as an affine diffusion process, we obtain explicit formulas for the additional expected logarithmic…
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…
By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen …
Derives functional Itô formula for non-anticipative maps of rough paths.
Metric learning enhances combinatorial coverage metrics' ability to predict classification errors.
Anticipatory portfolios use richer models to optimize investments.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
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…
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.
Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstr…
AntLer anticipates future learning to improve control performance.
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
Study compares different integrals for optimal portfolio optimization with insider information.
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…
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…
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts. The field of Learning from Demonstration (LfD) has sought to enable machines to infer predictive models of human behavior for autonomous robot control. However, humans exhibit heterogenei…
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between N…
Behavior of systems that are functions of anticipated behavior of other systems, whose own behavior is also anticipatory but homeostatic and determined by hierarchical ordering, which changes over time, of sets of possible environments that are not co-possible, is proven to be highly non-linear and sensitively dependen…
PredictaBoard benchmarks LLM score predictors to assess their ability to anticipate errors.
Study shows financial value of weak information converges in discrete vs continuous markets.
We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a p…
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 …
Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in \emph{default}, which, loosely speaking means that the company has been hav…
Study compares employers with and without anticipating strategic labor force responses.
Study optimal portfolios for traders with asymmetric information and delay.
Revisits consumption-investment problem with anticipative noise.
Study proposes a new method for deep portfolio optimization using residual factors.
Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to …
Novel framework synthesizes stochastic trajectories with anticipated structural breaks.
Paper predicts stock prices using ML and human intelligence.
Study on implied volatility of Asian options with stochastic volatility.
Adversarial techniques learn invariant representations across multiple domains.
We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of discipli…
Derives exact gradients for linear optics with single photons.
The paper introduces a knowledge score for GPR predictions to assess their reliability.
New MKABSDEs help calculate initial margins in financial contracts.
The paper analyzes implied volatility for European and Asian options under stochastic volatility Bachelier model.
Machine learning predicts criminal networks' missing partnerships and future behavior.
Model financial dynamics using 2-manifold geometries, revealing the torus as best for cyclical data.
Study uses FinBERT for financial sentiment analysis to predict stock movement.
Anticipatory model generates music with control over events.
We find prominent similarities in the features of the time series for the (model earthquakes or) 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 observation…