On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to …
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arXiv research
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.
169,181 papers · 148 categories
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48 results for “Reverse Application”
Boundary rigidity proven for non-reversible Finsler metrics.
problem Recovering non-reversible Finsler metrics from boundary distance data.
method Sum of reversible Finsler norm and closed 1-form, boundary rigidity results.
result 1-form can be uniquely recovered from boundary distance data.
Reverse Hölder inequalities on the space of Kähler metrics of a Fano variety and effective opennessmath.DG
Reverse Hölder inequalities on Fano metrics with applications to geodesics and singularities.
problem Establishing reverse Hölder inequalities on Kähler metrics of Fano varieties.
method Using log-concavity and properties of Ricci potentials, the inequality is proven for Fano manifolds with log terminal singularities.
result The inequality holds for Fano varieties with log terminal singularities and the constant depends only on p and the dimension of X.
The paper proves a reverse isoperimetric inequality and applies it to analyze surface flows.
problem Analyzing the negative gradient flow of the Willmore energy plus volume.
method Proved a quantitative reverse isoperimetric inequality and applied it to the flow.
result Initial surfaces converge to a round point in finite or infinite time.
Mean-Reversion and Optimizationq-fin.PM
The purpose of these notes is to provide a systematic quantitative framework - in what is intended to be a "pedagogical" fashion - for discussing mean-reversion and optimization. We start with pair trading and add complexity by following the sequence "mean-reversion via demeaning -> regression -> weighted regression ->…
We prove an optimal reverse Poincaré inequality for the heat semigroup generated by the sub-Laplacian on a Carnot group of any step. As an application we give new proofs of the isoperimetric inequality and of the boundedness of the Riesz transform in Carnot groups.
We introduce a general procedure called `reverse engineering' that can be used to construct infinite families of smooth 4-manifolds in a given homeomorphism type. As one of the applications of this technique, we produce an infinite family of pairwise nondiffeomorphic 4-manifolds homeomorphic to CP^2#3(-CP^2).
In this paper, we give a classification of orientation reversing periodic maps on closed surfaces which generalizes the theory of Nielsen for the orientation preserving periodic maps. On one hand, we give a group of data for each orientation reversing periodic map such that two periodic maps with the same data must be …
Note on the classification of the orientation reversing homeomorphisms of finite order of surfacesmath.GT
Classifies orientation-reversing homeomorphisms of even periods on surfaces.
problem Classifying orientation-reversing homeomorphisms of even periods on surfaces.
method Following the approach of [1] and correcting errors in [1] for the case of periods multiple of 4.
result Classification for orientation-reversing homeomorphisms of periods multiple of 4.
New method improves convergence of gradient descent for non-convex, non-reversible Markov chains.
problem Improving convergence of gradient descent for non-convex, non-reversible Markov chains.
method Introducing a new technique that varies the mixing levels of the Markov chains to establish non-ergodic convergence under wider step sizes.
result Established non-ergodic convergence for non-convex problems and non-reversible finite-state Markov chains.
Injectivity of geodesic ray transform on specific Finsler manifolds proven.
problem Injectivity of geodesic ray transform on spherically symmetric reversible Finsler manifolds.
method Reduction to invertibility of generalized Abel transforms using angular Fourier series and Taylor expansions of geodesics.
result Injectivity of geodesic ray transform proven on specified Finsler manifolds.
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et al. (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-rever…
Rex solves the inverse problem for ODE/SDE solvers, improving precision and stability.
problem Inversion of ODE/SDE solvers is inaccurate and impractical for precision applications.
method Rex uses Lawson methods to convert explicit Runge-Kutta schemes into algebraically reversible ones.
result Rex achieves near-machine-precision reconstruction and improves generative models.
Paper proposes mechanism learning to reverse causal inference in ML.
problem Machine learning models learn associational, not causal, relationships.
method Causally weighted Gaussian mixture models (CW-GMMs).
result CW-GMMs can deconfound observational data for reverse causal inference.
Quantum machine learns faster by reverse annealing on AQCs.
problem Training RBMs on AQCs is hard due to low qubit connectivity.
method Embedding RBM nodes to virtual qubits, semantic quantum search, reverse annealing schedule.
result Reverse annealing accelerates RBM training and improves reconstruction scores.
Counting edge bicolorings of graphs on surfaces, with applications in knot theory.
problem Counting edge bicolorings of graphs on surfaces.
method Counting equivalence classes of edge bicolorings under specific relations.
result The equivalence classes of edge bicolorings on surfaces are computed, providing new insights.
Classifies reversible and strongly reversible elements in quaternionic groups.
problem Classifying reversible and strongly reversible elements in quaternionic groups.
method Proves elements are reversible if and only if they are products of skew-involutions (resp. involutions).
result Proves elements are reversible if and only if they are products of skew-involutions (resp. involutions).
The paper classifies reversible and strongly reversible elements in Hermitian isometry groups.
problem Classifying reversible and strongly reversible elements in Hermitian isometry groups.
method Classification through group theory and algebraic manipulation.
result New classification of strongly reversible elements in Sp(n).
The paper classifies reversible and strongly reversible elements in quaternionic hyperbolic spaces.
problem Classifying reversible and strongly reversible elements in quaternionic hyperbolic spaces.
method Analyzing conjugacy classes and using properties of quaternionic hyperbolic spaces and their isometry groups.
result All elements of the isometry group of quaternionic hyperbolic spaces are strongly reversible.
Probabilistic analysis reveals substantial losses for reverse convertible note holders.
problem Substantial losses to reverse convertible note holders due to complex pricing.
method Probabilistic analysis using Law of Total Expectation.
result Note-holders likely suffered substantial losses under various market scenarios.
On Funk's parabolasmath.DG
Study of parabolas in Funk metric on unit disk.
problem Understanding parabolas in Funk metric on a disk.
method Analyzing four types of parabolas due to Funk metric's non-reversibility.
result Two known conics and two irreducible quartics found.
Model explains stock momentum and reversal in China's A-share market.
problem Understanding stock momentum and reversal in China's A-share market.
method Agent-based model with local herding and delayed information diffusion.
result Stronger herding leads to spatially clustered trading and larger price fluctuations.
A new formula reveals symmetries between mean excess and ES functions.
problem Optimizing risk measures in financial models.
method Established a reverse ES optimization formula.
result Reveals elegant symmetries and relationships between mean excess and ES functions.
Improves deep learning models' robustness with reverse adversarial examples.
problem Deep learning models' fragility to adversarial examples.
method Inspired by brain mechanisms, proposes reverse adversarial examples method.
result Average 19.02% accuracy improvement on unseen data transformation.
RML improves generative modeling of complex distributions.
problem Learning complex distributions in applications.
method RML defines a forward process to a known distribution, then learns a reverse Markov process.
result RML efficiently captures complex distributions in simulations and climate data.
Paper predicts recycling bin full events to reduce RVM downtime.
problem Predicting bin full events to increase RVM uptime.
method Hybrid approach combining machine learning and statistical approximation.
result Forecasting leads to less downtime and costs compared to emptying strategies.
This paper classifies reversible and strongly reversible elements in affine groups.
problem Classifying reversible and strongly reversible elements in affine groups.
method Identifying affine transformations and using conjugacy by involutions.
result Classification of reversible and strongly reversible elements in affine groups.
Study finds mean reversion strategies perform well on historical data but fail in recent market conditions.
problem Performance of mean reversion strategies in recent market data.
method Empirical investigation of three mean reversion strategies (PAMR, OLMAR, TCO) on historical S&P 500 data and benchmark datasets.
result Mean reversion strategies may fail in recent market conditions, especially with transaction costs.
Convergence of off-policy TD(0) with linear function approximation for reversible Markov chainsstat.ML
Study shows TD(0) with linear approx. converges for reversible Markov chains.
problem TD(0) divergence with off-policy and function approximation.
method Analyzes standard TD(0) with reversible Markov chains, adapting stochastic approximation framework.
result Establishes convergence with probability one for projected Bellman error = 0.
Enhances RJMCMC efficiency with non-linear transport-based proposals.
problem Designing efficient RJMCMC proposals for complex models.
method Applies non-linear transport-based approach to construct efficient transdimensional jumps.
result Acceptance probability depends only on model probabilities when exact transports are used.
Sharp Talenti-type comparison theorem for p-Laplacian on RCD(K,N) spaces.
problem Understanding the p-Laplacian on RCD(K,N) spaces.
method Proving a Talenti-type comparison theorem.
result Sharp, rigid and stable Talenti-type comparison theorem.
New neural net learns time-reversible symplectic dynamics.
problem Lack of time-reversibility in neural networks for symplectic systems.
method Proposes a new neural network architecture for time-reversible symplectic systems.
result Demonstrates learning of time-reversible symplectic dynamics from data.
A new trading strategy using reinforcement learning for statistical arbitrage.
problem Traditional statistical arbitrage models rely on model assumptions and price deviations from a long-term mean.
method Empirical reversion time metric, reinforcement learning framework, and state space optimization.
result Optimal mean reversion strategy identified through reinforcement learning.
Algebraic method reveals criterion for quaternionic Möbius group reversibility.
problem Characterizing reversibility in quaternionic Möbius group elements.
method Purely algebraic approach using matrix entries and conjugacy invariants.
result Explicit criterion for reversibility in terms of matrix entries.
A Finsler space is said to be geodesically reversible if each oriented geodesic can be reparametrized as a geodesic with the reverse orientation. A reversible Finsler space is geodesically reversible, but the converse need not be true. In this note, building on recent work of LeBrun and Mason, it is shown that a geodes…
HDT improves MCMC on graphs with history-dependent sampling.
problem Efficient sampling from target distributions on general graphs with low computational overhead.
method History-driven target (HDT) framework that replaces the original target distribution with a history-dependent one.
result Near-zero variance performance and scalability to large graphs with memory-efficient implementation.
Sharp stability results for reverse isoperimetric inequalities in 2D.
problem Reverse isoperimetric inequalities in the plane.
method Stability analysis of -convex bodies and convex bodies with smooth boundaries.
result Sharp stability results for reverse isoperimetric inequalities, including inradius and Cheeger inequalities.
DDS samples from noisy data by reversing diffusion, providing theoretical guarantees.
problem Sampling from unnormalized densities.
method Denoising diffusion process, score matching, optimal control, Schrödinger bridges.
result DDS provides theoretical guarantees for sampling.
We develop the basics of a theory of almost isometries for spaces endowed with a quasi-metric. The case of non-reversible Finsler (more specifically, Randers) metrics is of particular interest, and it is studied in more detail. The main motivation arises from General Relativity, and more specifically in spacetimes endo…
Paper optimizes stock option forecasting using ML models and improved trading strategies.
problem Improving accuracy of stock option predictions and trading decisions.
method Application of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Quasi-Reversibility Method (QRM).
result Optimized stock option investment results through improved trading strategies and model combination.
Conditional diffusion models can approximate target distributions well with Gaussian-mixture reverse kernels.
problem Approximating target distributions in conditional diffusion models.
method Using finite Gaussian mixtures with ReLU-network logits as reverse kernels, reducing the problem to static conditional density approximation.
result The resulting neural reverse-kernel class is dense in conditional KL divergence under exact terminal matching.
Method recovers asset return distributions from option prices.
problem Recovering implied physical densities from option prices.
method Non-parametric method based on Distribution Matching.
result Complete recovery of physical probability distributions.
Reverse Experience Replay improves Deep Q-learning for sparse rewards.
problem Sparse rewards and reward-maximizing tasks in Deep Q-learning.
method Sampling transitions in reverse order for training.
result Significantly increased performance in tasks with limited experience and memory capacity.
New knots not rationally concordant to their reverses found.
problem Identifying knots not rationally concordant to their reverses.
method Infinite family of knots constructed, rational knot concordance group analyzed.
result Infinite rank subgroup in rational knot concordance group.
The paper classifies reversible elements in Seifert-fibered spaces and braid groups.
problem Classifying reversible elements in Seifert-fibered spaces and braid groups.
method Classification of reversible elements in Fuchsian groups, application to Seifert-fibered groups, and analysis of 3-torsion elements.
result Classification and analysis of reversible and 3-torsion elements in Seifert-fibered spaces and braid groups.
Reverse annealing boosts quantum matrix factorization performance.
problem Improving quantum matrix factorization performance.
method Combining forward and reverse annealing for nonnegative/binary matrix factorization.
result Combination of forward and reverse annealing significantly improves performance.
Paper proposes a new method to solve Schrödinger Bridge Problem using kernel regression.
problem Schrödinger Bridge Problem in the context of entropic optimal transport.
method Forward-reverse iterative Monte Carlo procedure using kernel regression.
result Developed a provably convergent algorithm for approximating Schrödinger potentials.
New method learns distribution shifts caused by predictive models in social computing.
problem Learning distribution shifts due to predictive models in social computing.
method Reverse causal model with microfoundation for agents' actions.
result Effective in minimizing performative prediction risk.