From the 2 of 12 linked papers with an AI index.
12 papers
Are Three Matrices All You Need To Beat the Market? Observable Matrix Dynamics for Portfolio Optimization
Igor Halperin
The paper proposes a dynamic portfolio management framework that relies only on three matrices derived from daily price, volume, and market cap data— a return‑correlation distance…
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
The paper proposes a physics‑informed reinforcement learning framework that learns optimal, cost‑aware portfolio allocation policies from historical data by solving a projected Ham…
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…