Research
On-device research index

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.

168,742 papers · 148 categories

Trend · papers per month

23466992 · Jun 202019922001200920172026
48 results for hand movement

In this article we discuss the distribution of asset price movements by the market potential function. From the principle of free energy minimization we analyze two different kinds of market potentials. We obtain a U-shaped potential when market reversion (i.e. contrarian investors) is dominant. On the other hand, if t…

2014-03-13abs ↗pdf ↗

Paper uses deep Q-network to correct eye movement artifacts in OCT volumes.

problem Unsupervised correction of inter-frame misalignments in OCT volumes due to eye movement artifacts.
method Dueling deep Q-network trained to maximize reward signals based on intensity-based image similarity metrics.
result Average normalized mutual information and correlation coefficient of 0.985 and 0.914, respectively.

Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.

problem Explaining and forecasting price movements in equity markets using OFI.
method Systematic approach to combine OFIs from multiple levels into an integrated variable, testing multi-asset models with and without cross-impact terms.
result Lagged cross-asset OFIs improve future return forecasting but not contemporaneous price impact.

Framework uses optimal transport for neural architecture search.

problem Optimizing neural architectures in deep learning.
method Semi-discrete optimization using optimal transport.
result Gradient flow and minimizing movement scheme converge to reaction-diffusion equations.

Paper reduces movement primitive dimensionality in parameter space.

problem High dimensionality of movement primitives makes policy optimization expensive.
method Investigates dimensionality reduction in parameter space, identifying principal movements.
result Dimensionality reduction in parameter space is more effective than in configuration space.

We recast the Calabi flow in DeGiorgi's language of minimizing movements. We establish the long time existence of minimizing movements for K-energy with arbitrary initial condition. Furthermore we establish some a priori regularity of these solutions, and that sufficiently regular minimizing movements are smooth soluti…

2012-08-13abs ↗pdf ↗

Taureau uses Twitter sentiment analysis to predict stock market movement.

problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.

The study models market price movement based on investors' expectations.

problem Understanding the dynamics of investors' expectations and market price movement.
method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.
result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.

The paper presents a method to reduce arm motion complexity for prosthetics and robotics.

problem Reducing the complexity of human arm motions for robotic and prosthetic control.
method Data-driven techniques including DTW, DBA, Ward's distance, batch-DTW, and fPCA.
result Representative motion clusters and averages for different arm DOF levels.

Paper proves minimizing movements match smooth droplet flow in 3D.

problem Consistency of minimizing movements with smooth mean curvature flow.
method Proved minimizing movements coincide with smooth droplet flow.
result Minimizing movements and smooth mean curvature flow are consistent in 3D.

MPANF improves naive forecast by incorporating directional information.

problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.

The study introduces a new stickiness parameter for stock prices using a non-linear model.

problem Understanding how closely individual stocks follow a stock index's price movements.
method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.
result Defined a stickiness parameter for stock price returns using a novel model.

Bayesian method infers local rules for collective animal movement.

problem Learn local rules governing long-term group behaviors.
method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.
result Recover true costs and find value of collective movement.

NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.

problem Sparse and nonstationary movement speed data from urban roads.
method Nonstationary Temporal Matrix Factorization (NoTMF) model.
result NoTMF outperforms baseline models in forecasting urban road movement speeds.

New algorithm reduces dynamic regret in time-varying movement costs.

problem Dynamic regret in online convex optimization with time-varying movement costs.
method Introduced a novel algorithm for time-varying movement costs, achieving comparator-adaptive dynamic regret bound.
result Established first comparator-adaptive dynamic regret bound of O~((M2+MPT)(T+tλt))\widetilde{\mathcal{O}}(\sqrt{(M^2+MP_T)(T+\sum_t λ_t)}).

Alternative proof of weak solutions to mean curvature flow using minimizing movements.

problem Existence of weak solutions to mean curvature flow and volume preserving mean curvature flow.
method Proposes a new existence proof using a minimizing movements scheme and a novel proxy for distance.
result Unconditional convergence towards a De Giorgi solution for the minimizing movements scheme.

The Autoencoder Reconstruction Ratio detects increased asset co-movements.

problem Detecting changes in asset co-movements for risk management.
method Uses a deep sparse denoising autoencoder to measure asset returns with latent variables.
result Lower ARR values indicate periods of market weakness and increased volatility.