Proposes using Dynamic Mode Decomposition with delays for short-term human motion anticipation.
problem Lack of interpretability and explainability in neural network-based motion anticipation methods.
method Dynamic Mode Decomposition with delays for motion representation and prediction.
result Anticipation errors comparable or better than recurrent neural networks for very short times.
The paper examines how markets can anticipate and react to arbitrage opportunities, revealing biases and risks.
problem The tension between no arbitrage, information efficiency, and risk anticipation in markets.
method Continuous time analysis with model- or event-risk, allowing pre-horizon risk-resolution and Risk-Neutral Equivalent pricing.
result Optimised trading can suppress the anticipation of predictable risk-outcomes, creating an apparent Status Quo Bias.
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
problem Capturing anticipative information in financial markets with Brownian motion and Poisson processes.
method Using Malliavin calculus and filtration enlargement techniques, the paper computes the semimartingale decomposition of the processes.
result The paper provides the exact value of anticipative information in the pure jump case.
Derives functional Itô formula for non-anticipative maps of rough paths.
problem Functional Itô formula for non-anticipative maps of càdlàg rough paths.
method Approximation properties of the signature and Marcus transformation.
result Functional Taylor expansion for sufficiently regular non-anticipative maps.
Anticipatory portfolios use richer models to optimize investments.
problem Optimizing investments with richer models than used for calibration.
method Decision-theoretic definition of anticipation, quadratic geometry, and LQG decomposition.
result Correct anticipation creates value, vacuous anticipation has zero value, and misspecified anticipation is harmful.
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.
problem Improving control performance through online learning is not well understood.
method AntLer uses a probabilistic model to anticipate future learning and optimize control parameters.
result AntLer approximates optimal solutions with high probability.
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
problem The nature of information structures and their impact on non-anticipative aggregation.
method Local reduction of pricing to the natural price filtration, stability properties, and the establishment of new facts.
result Non-anticipative signals can reveal future information, requiring dependence among signals (masking relation) and not independence.
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
problem Maximizing logarithmic utility for an insider with different anticipating techniques.
method Comparison of Russo-Vallois forward and Skorokhod integrals.
result Skorokhod insider outperforms forward insider in logarithmic utility maximization.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
Study compares different integrals for optimal portfolio optimization with insider information.
problem Optimizing portfolios in a financial market with insider information.
method Anticipating stochastic calculus and various integrals (Russo-Vallois forward, Ayed-Kuo, Hitsuda-Skorokhod).
result The Hitsuda-Skorokhod and Ayed-Kuo integrals do not provide a financially meaningful investment strategy.
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…
Automated vehicles learn to predict upcoming maneuvers with high accuracy.
problem Making self-driving cars feel safer by anticipating future actions.
method Machine learning techniques applied to a large dataset of real-world driving.
result Automated vehicles can predict maneuvers up to 5 seconds in advance with high accuracy.
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 1,681 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.
problem Inconsistent performance of LLMs in common sense reasoning tasks.
method Collaborative benchmarking framework evaluating pairs of LLMs and assessors using rejection rate at different tolerance errors.
result Highlights the need to evaluate predictability alongside performance for safer AI systems.
Study shows financial value of weak information converges in discrete vs continuous markets.
problem Analyzing financial value of weak information in discrete vs continuous markets.
method Defined minimal probability measure and financial value of weak information, then showed convergence.
result 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 (Y,Z,ψ) where Y is a semimartingale, and (Z,ψ) 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.
problem Understanding and optimizing strategic interactions in labor markets.
method Formulation of causal strategic classification, theory, and experiments.
result Performatively optimal hiring policies improve employer and labor outcomes, but can also harm labor force utility.
Study optimal portfolios for traders with asymmetric information and delay.
problem Optimizing portfolios for traders with delayed insider information.
method Anticipating stochastic calculus and white noise approach.
result Optimal portfolios maximize expected logarithmic utility under various financial models.
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…
Revisits consumption-investment problem with anticipative noise.
problem Revisits classical consumption-investment problem with anticipative noise.
method Models risky-asset returns through a general α-integral, interpolating between Itô, Stratonovich, and related conventions.
result Derives closed-form optimal policies for logarithmic utility and constant volatilities in a market with n risky assets.
Metric learning enhances combinatorial coverage metrics' ability to predict classification errors.
problem Dataset dependence of combinatorial coverage metrics in anticipating classification errors.
method Metric learning to improve latent space separation of data classes.
result Metric learning increases SDCCMs' ability to distinguish between correctly and incorrectly classified data.
Novel framework synthesizes stochastic trajectories with anticipated structural breaks.
problem Synthesizing forward-looking, time-evolving stochastic trajectories with anticipated structural breaks.
method Anticipatory Neural Jump-Diffusion (ANJD) flow, AVNSG for dynamic spectral whitening.
result The framework effectively captures non-commutative moments and high-order stochastic texture.
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.
problem Uncertainty in probabilistic predictions from Gaussian process regression models.
method A knowledge score quantifying the reduction of uncertainty in GPR predictions.
result The knowledge score improves prediction accuracy in various tasks.
New MKABSDEs help calculate initial margins in financial contracts.
problem Calculating initial margins in financial contracts with dependencies.
method Introduced MKABSDEs, provided existence and uniqueness, applied to CVaR, used deterministic and Monte-Carlo methods for numerical approximations.
result MKABSDEs provide a new way to solve for initial margins in financial contracts.
Machine learning predicts criminal networks' missing partnerships and future behavior.
problem Predicting and understanding criminal networks' properties and future behavior.
method Combining graph representation learning and machine learning methods.
result Outstanding accuracy in predicting missing criminal partnerships and future behavior.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
problem Optimal portfolio optimization under default risk when traditional techniques are not applicable.
method Alternative approach using forward integration to avoid Jacod density hypothesis.
result Weaker intensity hypothesis is the appropriate condition for optimality in logarithmic utility.
Anticipatory model generates music with control over events.
problem Controlling symbolic music generation.
method Interleaving event and control sequences to predict future events.
result Anticipatory model matches autoregressive models in performance and can infill control tasks.
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…
Paper presents a GAN-based method to automate robust hedging.
problem Addressing uncertainty in market data generating process.
method Adversarial approach inspired by GANs for automated robustification.
result Automated robustification of hedging objective through three modular components.
'There is no terror in the bang, only is the anticipation of it' - Alfred Hitchcock. Yet there is everything in correctly anticipating the bang a movie would make in the box-office. Movies make a high profile, billion dollar industry and prediction of movie revenue can be very lucrative. Predicted revenues can be used …
Proposes a new regularization technique for neural networks using elliptic operators.
problem Improving model behavior in underrepresented data regions.
method Modifies the empirical risk minimization objective to minimize an elliptic operator over the data domain.
result The proposed regularization technique anticipates error behavior outside the training set using existing elliptic operator theory.
New measures detect asymmetries, non-linearity in stock returns.
problem Detecting asymmetries and non-linearity in stock returns.
method Proposed non-linear, local, invariant dependence measures; nonparametric estimator proven.
result Measures show tail asymmetry, non-linearity, risk buildup during market distress.
The paper studies how noisy labels impact decision-making in machine learning.
problem The impact of noisy labels on decision-making in machine learning.
method Introducing a notion of regret, studying standard approaches, and estimating individual-level mistakes.
result Standard approaches can lead to unforeseen mistakes for individuals, revealing the need for anticipation.
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…
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
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" …
ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.
problem Inaccurate foresight in non-Markovian environments due to state-based methods' limitations.
method Lifted state space into a signature-augmented manifold, using a self-consistent field approach to anticipate future path-law.
result ARL achieves deterministic evaluation of expected returns with reduced computational complexity and variance.
Paper presents a DRL framework for detecting and anticipating financial crises.
problem Detecting and adapting to financial crises using deep reinforcement learning.
method Two sub-networks, one for past performances and standard deviations, the other for contextual features. Adversarial training for robustness.
result Framework substantially outperforms traditional methods in detecting and anticipating crises.
The involutory birack counting invariant is an integer-valued invariant of unoriented tangles defined by counting homomorphisms from the fundamental involutory birack of the tangle to a finite involutory birack over a set of framings modulo the birack rank of the labeling birack. In this first of an anticipated series …
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…
Study compares ML algorithms for predicting stock market directional bias.
problem Predicting the direction of stock market movements.
method Examined and contrasted logistic regression, decision tree, random forest, and a deep neural network.
result All models consistently reach above 50% in directional bias forecasting.