3 citations · 5 across the 15 of their papers we have counts for
8 papers · 1 filter
Are Three Matrices All You Need To Beat the Market? Observable Matrix Dynamics for Portfolio Optimization
Igor Halperin
We present a simple framework for dynamic portfolio management that uses nothing but daily prices, trading volumes, and market capitalizations. Its state is three fixed-size matric…
Observable Matrix Dynamics of Stocks
Igor Halperin
The Observable Matrix Dynamics (OMD) approach monitors the time development of complex non-linear systems through the trajectory of a fixed-size distance matrix and its spectrum. W…
SciPhy Reinforcement Learning for Portfolio Optimization
Igor Halperin, Andrey Itkin
This paper introduces a dynamic portfolio optimization framework for large institutional investors using Scientific Physics-Informed Reinforcement Learning (SciPhyRL). Formulated i…
Learning as Observable Matrix Dynamics: Diffusive Relaxations versus Phase Transitions
Igor Halperin
Observable Matrix Dynamics (OMD) is a diagnostic framework that probes the dynamics of high-dimensional internal representations of inputs by a neural network via a fixed-size $N \…
I-BBS: Coordinate-Free Inference of Latent Sub-Manifolds Using Random Distance Matrix Theory
Igor Halperin
Bogomolny, Bohigas and Schmit (BBS) found that the spectrum of the pairwise distance matrix on N points sampled from a smooth d-dimensional manifold encodes a signature of the unde…
Frustrated Dynamics of Distance Matrices
Igor Halperin
We introduce the Frustrated Distance Matrix (FDM) model, a dynamic extension of the static distance-matrix ensemble on S^2 analyzed by Bogomolny, Bohigas, and Schmit (BBS). Its ent…
Frustrated Fields: Statistical Field Theory for Frustrated Brownian Particles on 2D Manifolds
Igor Halperin
We develop a statistical field theory that describes the large-N limit of a system of Brownian particles with quenched random pairwise interactions on a compact two-dimensional Rie…
Order Out of Noise and Disorder: Fate of the Frustrated Manifold
Igor Halperin
We study Langevin dynamics of Brownian particles on two-dimensional Riemannian manifolds, interacting through pairwise potentials linear in geodesic distance with quenched rand…