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.
Machine learning infers time-reversible dynamics from data.
Researchers tackle the globalization problem of locally cosymplectic Hamiltonian dynamics.
We apply numerical dynamic programming techniques to solve discrete-time multi-asset dynamic portfolio optimization problems with proportional transaction costs and shorting/borrowing constraints. Examples include problems with multiple assets, and many trading periods in a finite horizon problem. We also solve dynamic…
In this paper, we analyze dynamic programming as a novel approach to solve the problem of maximizing the profits of a bank. The mathematical model of the problem and the description of a bank's work is described in this paper. The problem is then approached using the method of dynamic programming. Dynamic programming m…
Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…
Paper models non-linear dynamics from time series data.
We introduce a dynamical-systems approach for the study of the Sard problem in sub-Riemannian Carnot groups. We show that singular curves can be obtained by concatenating trajectories of suitable dynamical systems. As an applications, we positively answer the Sard problem in some classes of Carnot groups.
Develops a new method for risk diversification using dynamic risk measures.
Extends optimal transport to dynamic and martingale settings.
Choosing a portfolio of risky assets over time that maximizes the expected return at the same time as it minimizes portfolio risk is a classical problem in Mathematical Finance and is referred to as the dynamic Markowitz problem (when the risk is measured by variance) or more generally, the dynamic mean-risk problem. I…
New algorithm improves convergence for non-convex problems with boundaries.
In this paper we analyze a dynamic recursive extension of the (static) notion of a deviation measure and its properties. We study distribution invariant deviation measures and show that the only dynamic deviation measure which is law invariant and recursive is the variance. We also solve the problem of optimal risk-sha…
Paper analyzes convergence of dynamic policy gradient for MDPs, improving performance in finite-time problems.
Paper unifies subspace identification and DMD for dynamical systems.
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-b…
Equivalence of convex optimization, saddle-point problems, and variational inequalities is a well-established concept. The variational inequality (VI) is a static problem which is studied under dynamical settings using a framework called the projected dynamical system, whose stationary points coincide with the static s…
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
In this paper, we consider the problem of optimization of a portfolio consisting of securities. An investor with an initial capital, is interested in constructing a portfolio of securities. If the prices of securities change, the investor shall decide on reallocation of the portfolio. At each moment of time, the prices…
Two heuristics solve dynamic multiple travelling salesmen problems.
Community-based system dynamics improves ML fairness by involving excluded stakeholders.
Universal online optimization for dynamic environments using uniclass prediction.
We present a solution to an optimal stopping problem for a process with a wide-class of novel dynamics. The dynamics model the support/resistance line concept from financial technical analysis.
HRM-Agent learns to navigate dynamic mazes using reinforcement learning.
Study minimax rates for online learning with time-varying dynamics.
Efficiently tunes hyperparameters with dynamic accuracy method.
Algorithm selects best model based on state, reducing costs.
Model dynamic customer sensitivities across categories.
This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem wit…
Problem of global integration of geometric structures arising in the theory of dynamical systems admitting the normal shift is considered. In the case when such integration is possible the problem of globalization for shift maps is studied.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
DPDP combines neural heuristics with DP for vehicle routing problems.
MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.
We consider an optimal stopping problem where a constraint is placed on the distribution of the stopping time. Reformulating the problem in terms of so-called measure-valued martingales allows us to transform the marginal constraint into an initial condition and view the problem as a stochastic control problem; we esta…
Algorithm learns dynamics from past observations.
We consider the -means clustering problem in the dynamic streaming setting, where points from a discrete Euclidean space can be dynamically inserted to or deleted from the dataset. For this problem, we provide a one-pass coreset construction algorithm using space $\tilde{O}(k\cdot \mathrm{pol…
Geometric Hydrodynamics tackles open problems in fluid dynamics.
Model stock price dynamics using semi-Markov processes.
A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical system's evolution and/or limit points in the associated state space can correspond to numerical solutions to certain mathematical optimiza…
The regression of multiple inter-connected sequence data is a problem in various disciplines. Formally, we name the regression problem of multiple inter-connected data entities as the "dynamic network regression" in this paper. Within the problem of stock forecasting or traffic speed prediction, we need to consider bot…
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
New estimator learns symmetric dynamics from few observations.
A neural network approach solves dynamic portfolio optimization without dynamic programming.
Formula for the force field of Newtonian dynamical systems admitting the normal shift of hypersurfaces in Riemannian manifolds is considered. Problem of globalization for geometric structures associated with this formula is studied.
Deep learning solves dynamic programming with recursive utility.
New model predicts dynamic volatility in uncertain financial markets.
Dynamic reinsurance aims to minimize surplus risk using martingale transport.
Paper adds Fisher Information to mean field optimization for faster convergence.
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide…