arXiv research

FactorGCL uses hypergraph learning to predict stock returns by mining hidden factors.

Study proposes a new method for deep portfolio optimization using residual factors.

The aim of our work is to propose a natural framework to account for all the empirically known properties of the multivariate distribution of stock returns. We define and study a "nested factor model", where the linear factors part is standard, but where the log-volatility of the linear factors and of the residuals are…

We add size factor to CAPM and normalize residuals by Volatility Index.

Replicates deep learning strategy for trading factor residuals, finds strong performance.

In dealing with high-dimensional data sets, factor models are often useful for dimension reduction. The estimation of factor models has been actively studied in various fields. In the first part of this paper, we present a new approach to estimate high-dimensional factor models, using the empirical spectral density of …

A model explains stock returns and volatility using multifractal and rough components.

RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.

A well known result on pseudodifferential operators states that the noncommutative residue (Wodzicki residue) of a pseudodifferential projection vanishes. This statement is non-local and implies the regularity of the eta invariant at zero of Dirac type operators. We prove that in a filtered algebra the value of a proje…

Batch normalization makes deep residual networks train faster.

Effective Gram matrix predicts deep network generalization.

This work improves fair tensor decomposition using a kernel criterion.

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio.…

SMART-FAN-Lasso fine-tunes neural networks for high-dimensional nonparametric regression.

Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise, Sigma = (sigma^2)*I. The maximum likelihood solution for the model is an eigenvalue problem on the sample covariance matrix. In this paper we consider the situa…

The paper proves geometric and spectral alignment for deep neural networks.

We study conformal $Spin$-subgeometry of submanifolds in a semi-Riemannian $Spin$-manifold, focusing on conformal $Spin$-manifolds $(M,[h])$ and their Poincaré-Einstein metrics $(X,g_+)$. Our approach is based on the spectral theory of Dirac operator in the ambient $Spin$-manifold, and associated spinor valued meromorp…

We study the stability and convergence of training deep ResNets with gradient descent. Specifically, we show that the parametric branch in the residual block should be scaled down by a factor $τ=O(1/\sqrt{L})$ to guarantee stable forward/backward process, where $L$ is the number of residual blocks. Moreover, we establi…

Machine learning helps estimate risk premiums of stocks without knowing their factors.

Dual model predicts electricity spot prices using neural networks and wavelets.

Study various series of groups and their Lie algebras in split extensions.

Residual networks' depth is mathematically equivalent to expanding an implicit ensemble size.

ResNets promote smoother interpolations than MLPs, enhancing generalization.

Spatial Adapter adds structured spatial representation to frozen predictors.

Variational auto-encoders (VAEs) are a popular and powerful deep generative model. Previous works on VAEs have assumed a factorized likelihood model, whereby the output uncertainty of each pixel is assumed to be independent. This approximation is clearly limited as demonstrated by observing a residual image from a VAE …

It is commonly believed that the correlations between stock returns increase in high volatility periods. We investigate how much of these correlations can be explained within a simple non-Gaussian one-factor description with time independent correlations. Using surrogate data with the true market return as the dominant…

DeCom predicts post-COVID RSV timing and intensity with NPI consideration.

New method for factor analysis using nuclear and $\ell_0$ norms.

We propose a novel exponentially-modified Gaussian (EMG) mixture residual model. The EMG mixture is well suited to model residuals that are contaminated by a distribution with positive support. This is in contrast to commonly used robust residual models, like the Huber loss or $\ell_1$, which assume a symmetric contami…

The study improves stock market valuation using volatility and earnings data.

Improved covariance matrix forecasting for S&P 500 using factor models and shrinkage.

Belief propagation and its variants are popular methods for approximate inference, but their running time and even their convergence depend greatly on the schedule used to send the messages. Recently, dynamic update schedules have been shown to converge much faster on hard networks than static schedules, namely the res…

Develops a framework for identifying mispriced assets through attention factors for statistical arbitrage.

This paper improves bond market making by adjusting hit-ratios for client flow quality.

We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requir…

Momentum ResNets improve ResNets' memory efficiency.

A major contributing factor to the recent advances in deep neural networks is structural units that let sensory information and gradients to propagate easily. Gating is one such structure that acts as a flow control. Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforwa…

Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.

New method for evaluating and learning in complex decision-making scenarios.

The existing graph neural networks (GNNs) based on the spectral graph convolutional operator have been criticized for its performance degradation, which is especially common for the models with deep architectures. In this paper, we further identify the suspended animation problem with the existing GNNs. Such a problem …

The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statisti…

The paper tackles noisy functional data by exploring a multivariate perspective.

Deep linear ResNets converge globally with certain transformations.

A method to reduce bias in model-based policy evaluation by shifting operators.

A simple strategy prevents negative transfer in transfer learning.

MRC-LSTM predicts Bitcoin prices using CNN and LSTM.

Study reveals a hidden cost in derivatives markets through option-implied discount factors.

Temporal aggregation reveals latent default correlation from monthly data.