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.
A new method reduces high-dimensional state space for dynamic choice models.
A new method reduces complexity in estimating dynamic choice models.
Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…
Dynamic assortment problem on two-sided platform with unknown parameters
Paper tackles RLHF with DCPPO method, proving near-optimal suboptimality.
Quantizes contact structures using dynamical methods.
Study optimal portfolio choice with risk control for log-returns.
We study a stylized dynamic assortment planning problem during a selling season of finite length . At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the selle…
New formulations capture aversion to ambiguity about volatility.
Defines -expectation of distributions and its applications.
This study examines the collateral choice option and its valuation and hedging.
Adaptive algorithm improves convergence rate of Langevin dynamics.
Bayesian DL model improves DCMs for better predictive and inferential performance.
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
This paper solves the dynamic portfolio choice problem. Using an explicit solution with a power utility, we construct a bridge between a continuous and discrete VAR model to assess portfolio sensitivities. We find, from a well analyzed example that the optimal allocation to stocks is particularly sensitive to Sharpe ra…
A method for dynamic portfolio choice with uncertain parameters using Pontryagin projection.
Simple algorithms identify best items or full rankings from choice-based feedback.
When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequ…
Statistical physics method analyzes minority game dynamics in financial markets.
The Epps effect varies under different sampling schemes, affecting correlation emergence rates.
The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.
Investors' strategic trading affects asset prices, modeled as a game.
The paper analyzes how wealth affects investment strategies in incomplete markets.
We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as…
This survey is an introduction to asymptotic methods for portfolio-choice problems with small transaction costs. We outline how to derive the corresponding dynamic programming equations and simplify them in the small-cost limit. This allows to obtain explicit solutions in a wide range of settings, which we illustrate f…
Improves predictions by integrating forward-looking views into dynamic factor models.
New algorithm tackles dynamic assortment optimization with knapsack constraints.
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messages to its users and is penalized when users abandon the platform due to marketing fatigue. We propose a novel sequential choice model to ca…
We solve a version of the optimal trade execution problem when the mid asset price follows a displaced diffusion. Optimal strategies in the adapted class under various risk criteria, namely value-at-risk, expected shortfall and a new criterion called "squared asset expectation" (SAE), related to a version of the cost v…
POSL predicts dynamic convection volumes in hemodiafiltration patients.
SGBD algorithm improves robustness in Bayesian sampling.
There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or `green' benefits. Some consumers might value these benefits while others do not. However, as evidenced by myriad failed attempts of environmental p…
Derives equations of motion for systems with angular momentum on Finsler geometries.
This paper presents several models addressing optimal portfolio choice, optimal portfolio liquidation, and optimal portfolio transition issues, in which the expected returns of risky assets are unknown. Our approach is based on a coupling between Bayesian learning and dynamic programming techniques that leads to partia…
We develop a robust framework for pricing and hedging of derivative securities in discrete-time financial markets. We consider markets with both dynamically and statically traded assets and make minimal measurability assumptions. We obtain an abstract (pointwise) Fundamental Theorem of Asset Pricing and Pricing--Hedgin…
New model optimizes assortment and pricing with dynamic customer arrivals.
Identifies most probable flows for Kunita SDEs in fluid dynamics.
We investigate a class of binary choice models with social interactions. We propose a unifying perspective that integrates economic models using a utility function and psychological models using an impact function. A general approach for analyzing the equilibrium structure of these models within mean-field approximatio…
A model simulates how different types of traders react to macroeconomic news.
Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue. One of the key challenges in this problem is to model the customer su…
The goal of dynamic time warping is to transform or warp time in order to approximately align two signals together. We pose the choice of warping function as an optimization problem with several terms in the objective. The first term measures the misalignment of the time-warped signals. Two additional regularization te…
In this paper we derive a scaling limit for an infinite dimensional limit order book model driven by Hawkes random measures. The dynamics of the incoming order flow is allowed to depend on the current market price as well as on a volume indicator. With our choice of scaling the dynamics converges to a coupled SDE-ODE s…
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the gap between deep learning and dynamical systems, offering a novel perspective. However, deciphering the inner working of these models is still a…
We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (…
We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…
New probability path model improves flow matching forecasting performance.