arXiv research

Deeper neural networks learn lower frequency functions faster, according to a new principle.

This paper contributes to the literature on international stock market comovements and contagion. The novelty of our approach lies in application of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock markets in a time-frequency domain. While major part of…

The paper studies frequency monotonicity for solutions of nonlinear equations under Ricci flow.

We propose a new framework for measuring connectedness among financial variables that arises due to heterogeneous frequency responses to shocks. To estimate connectedness in short-, medium-, and long-term financial cycles, we introduce a framework based on the spectral representation of variance decompositions. In an e…

Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable charac…

SRMD uses random features for efficient time-frequency analysis.

Lower bounds for eigenvalues on manifolds with negative Ricci curvature.

The paper proposes a mixed-frequency quantile regression model for VaR and ES forecasting.

Lower bounds for eigenvalues on manifolds with negative Ricci curvature.

Estimates for $p$-capacities on symmetric manifolds.

The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filters and data, called band-limiting, during training. The frequency dom…

A new algorithm for CTMAB minimizes regret with sampling costs.

Study analyzes fluctuations in Mexican financial market index.

Estimates chirp signal frequencies using probabilistic models.

We prove sharp bounds for the growth rate of eigenfunctions of the Ornstein-Uhlenbeck operator and its natural generalizations. The bounds are sharp even up to lower order terms and have important applications to geometric flows.

We consider a Nash equilibrium between two high-frequency traders in a simple market impact model with transient price impact and additional quadratic transaction costs. Extending a result by Schöneborn (2008), we prove existence and uniqueness of the Nash equilibrium and show that for small transaction costs the high-…

The paper develops methods to estimate frequencies in large discrete data sets with improved coverage and robustness.

Improved LSTM cell for high-frequency trading forecasts.

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t…

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised feature trans…

Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.

Study on HFTs' interactions with a large trader using mean field game theory.

The paper analyzes how deep neural networks handle noisy labels and finds disparate impacts.

ResNets can approximate input distances under certain conditions, but existing theory is flawed.

In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and aro…

Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the da…

This note proves a Gaussian version of a Pólya-Szegö conjecture using rearrangement techniques.

An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we stud…

Differentially private weighted sampling improves privacy while maintaining utility.

A guide to using low-pass graph filters for network data.

We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized …

Revisits Gaussian process model with spherical harmonics for scalable deep learning.

Method reduces categorical data to lower dimensions using density matrices.

AaSP improves audio self-supervised learning by addressing aliasing issues.

While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel. This paper presents such a…

We examine the performance of six estimators of the power-law cross-correlations -- the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the cross-periodogram estimator and the local cross-Whittle e…

Deep RL strategy improves natural gas trading performance.

Generative adversarial network improves audio inpainting for long gaps.

This paper revisits the fractional cointegrating relationship between ex-ante implied volatility and ex-post realized volatility. We argue that the concept of corridor implied volatility (CIV) should be used instead of the popular model-free option-implied volatility (MFIV) when assessing the fractional cointegrating r…

Unified model estimates landslide hazard combining susceptibility, intensity, and frequency.

Winterization of Texas power system profitable but risky, estimated at $11.74bn over 30 years.

HyFAD improves time series imputation by combining time and frequency diffusion.

Less frequent retraining improves forecast accuracy in retail demand forecasting.

Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …

This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained duri…

We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency $ε$, an extra observation is gathered by the agent for free. We prove that, no matter how small $ε$ is the agent can ensure a regret uniformly bounded in time. More precisely, we construct an algorithm with a…

Improved KAN model explains brain dynamics through edge learning and synaptic strength.