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.
Revisits consumption-investment problem with anticipative noise.
The paper studies how noisy labels impact decision-making in machine learning.
New algorithm speeds up RNN time series prediction by filtering noise.
Proposes using Dynamic Mode Decomposition with delays for short-term human motion anticipation.
Study optimal portfolios for traders with asymmetric information and delay.
The paper examines how markets can anticipate and react to arbitrage opportunities, revealing biases and risks.
We develop a Markovian approximation for SVV models to compute hedging strategies.
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
Automatic speech recognition can potentially benefit from the lip motion patterns, complementing acoustic speech to improve the overall recognition performance, particularly in noise. In this paper we propose an audio-visual fusion strategy that goes beyond simple feature concatenation and learns to automatically align…
Derives functional Itô formula for non-anticipative maps of rough paths.
Anticipatory portfolios use richer models to optimize investments.
Finding efficient decoders for quantum error correcting codes adapted to realistic experimental noise in fault-tolerant devices represents a significant challenge. In this paper we introduce several decoding algorithms complemented by deep neural decoders and apply them to analyze several fault-tolerant error correctio…
ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.
We study optimal investment in an asset subject to risk of default for investors that rely on different levels of information. The price dynamics can include noises both from a Wiener process and a Poisson random measure with infinite activity. The default events are modelled via a counting process in line with large p…
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.
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.
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
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…
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.
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 …
Study compares employers with and without anticipating strategic labor force responses.
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 …
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…
Metric learning enhances combinatorial coverage metrics' ability to predict classification errors.
Machine learning uncovers hidden correlations in granular material behavior.
Novel framework synthesizes stochastic trajectories with anticipated structural breaks.
Efficient algorithm for contextual bandits with first-order guarantees.
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…
The paper introduces a knowledge score for GPR predictions to assess their reliability.
New MKABSDEs help calculate initial margins in financial contracts.
ResUNet-CMB neural network reconstructs CMB effects from noisy data.
Machine learning predicts criminal networks' missing partnerships and future behavior.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
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…
A dynamical model is introduced for the formation of a bullish or bearish trends driving an asset price in a given market. Initially, each agent decides to buy or sell according to its personal opinion, which results from the combination of its own private information, the public information and its own analysis. It th…
Episodes of market crashes have fascinated economists for centuries. Although many academics, practitioners and policy makers have studied questions related to collapsing asset price bubbles, there is little consensus yet about their causes and effects. This review and essay evaluates some of the hypotheses offered to …
This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests o…
Paper presents a GAN-based method to automate robust hedging.