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.
AlphaForge mines and dynamically combines alpha factors for better investment performance.
RVRAE combines deep learning and dynamic factor models for better stock returns prediction.
Unified framework combines views and optimization for better portfolio management.
Enhanced AI analysis predicts S&P 500 stock dynamics using various financial metrics.
Improves predictions by integrating forward-looking views into dynamic factor models.
Modeling dynamic user interests using neural matrix factorization.
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
PRISM-VQ combines financial priors with vector quantization for better stock prediction.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
DISTANA improves weather prediction by inferring hidden factors from temperature data.
Study on liquidity dynamics in Uniswap v3 pools using statistical methods.
Prediction of dynamical time series with additive noise using support vector machines or kernel based regression has been proved to be consistent for certain classes of discrete dynamical systems. Consistency implies that these methods are effective at computing the expected value of a point at a future time given the …
Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm h…
We prove that a topological contact isotopy uniquely defines a topological contact Hamiltonian. Combined with previous results from [MS11], this generalizes the classical one-to-one correspondence between smooth contact isotopies and their generating smooth contact Hamiltonians and conformal factors to the group of top…
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix factorizations for exploration and time-varying community detection in time-evolving graph sequences. The matrix factori…
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
Study reveals which startup valuation factors are most critical.
We investigate the impact of Knightian uncertainty on the optimal timing policy of an ambiguity averse decision maker in the case where the underlying factor dynamics follow a multidimensional Brownian motion and the exercise payoff depends on either a linear combination of the factors or the radial part of the driving…
A new method combines predictors and their lags using supervised PCA for dynamic forecasting.
Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factor…
Conditions for hyperbolic and relatively hyperbolic extensions of free groups using automorphisms with fixed points.
New method linearizes nonlinear coupled oscillators on graphs.
A new model for dynamic covariance recovery in neuroimaging data.
Paper addresses xVA models for market-implied skew and smile.
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent fac…
Dynamic factor analysis reveals insights into Philippine stock market dynamics.
We propose the factorized action variational autoencoder (FAVAE), a state-of-the-art generative model for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and tr…
Deep neural network learns meaningful factors to predict stock returns.
Proposes iVDFM for identifying latent factors in multivariate time series.
The study examines the dynamic behavior of RMSprop and Adam algorithms.
New method detects global factors near BBP phase transition in high-dimensional data.
We develop theoretical foundations of Resonator Networks, a new type of recurrent neural network introduced in Frady et al. (2020) to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures. Given a composite vector formed by the Hadamard product between a discrete set of high-dim…
An ideal cognitively-inspired memory system would compress and organize incoming items. The Kanerva Machine (Wu et al, 2018) is a Bayesian model that naturally implements online memory compression. However, the organization of the Kanerva Machine is limited by its use of a single Gaussian random matrix for storage. Her…
New method improves sales forecasting accuracy using tensor factorization.
Paper predicts international trade flows using machine learning and factorization models.
Dynamic risk factor model improves portfolio performance in high dimensions.
Model for dynamic relational data with regime changes.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
A new multi-factor model improves commodity pricing accuracy.
Mainstream financial econometrics methods are based on models well tuned to replicate price dynamics, but with little to no economic justification. In particular, the randomness in these models is assumed to result from a combination of exogenous factors. In this paper, we present a model originating from game theory, …
There is broad interest in creating RL agents that can solve many (related) tasks and adapt to new tasks and environments after initial training. Model-based RL leverages learned surrogate models that describe dynamics and rewards of individual tasks, such that planning in a good surrogate can lead to good control of t…
New algorithm reduces pricing error by a factor of T^2/3.
Improved model predicts wildfire spread on slopes.
Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech-source separation etc). Despite having received extensive study, it is poorly understood whether or not there exist natural algorithms that…
Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…
The paper solves multi-period portfolio selection with constraints using a dynamic factor model.
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural Networks (RNNs) and Predictive State Representations (PSRs), and inherit advantages from both types of models. Like many successful RNN archi…
Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence modelling is particularly important for managing credit risk, for instance, where joint defaults are a major cause of concern. Often, the d…