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.
The paper solves portfolio selection for complex preferences in continuous time.
The paper defines and characterizes conditional nonlinear expectations.
Paper solves a complex portfolio selection problem with time-inconsistent preferences.
We study the dynamic indifference pricing with ambiguity preferences. For this, we introduce the dynamic expected utility with ambiguity via the nonlinear expectation--G-expectation, introduced by Peng (2007). We also study the risk aversion and certainty equivalent for the agents with ambiguity. We obtain the dynamic …
Investor optimizes portfolio under dynamic risk preferences.
Novel representer theorem for metric and preference learning in RKHSs.
The paper explores how investors make decisions under disappointment aversion, finding that they prefer not to invest.
MAXMINLCB optimizes unknown target functions with preference feedback using a Stackelberg game approach.
The purpose of this paper is to analyze and compute the early exercise boundary for a class of nonlinear Black--Scholes equations with a nonlinear volatility which can be a function of the second derivative of the option price itself. A motivation for studying the nonlinear Black--Scholes equation with a nonlinear vola…
The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently face both problems. In this study, we propose a strategy that perform…
Stochastic discount factor (SDF) processes in dynamic economies admit a permanent-transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to analyze the permanent-transitory decomposition of SDF processes. Specifically, …
New MAB model incentivizes user arm-pulling with self-reinforcing preferences.
For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and regression trees are commonly preferred over linear models such as Lasso. This work proposes learning the feature nonlinearities by binning fe…
We analyze and calculate the early exercise boundary for a class of stationary generalized Black-Scholes equations in which the volatility function depends on the second derivative of the option price itself. A motivation for studying the nonlinear Black Scholes equation with a nonlinear volatility arises from option p…
Machine Learning improves macroeconomic forecasting by capturing nonlinearities.
Modeling reinsurance market, we find subgame perfect Nash equilibria.
This paper considers nonlinear regular-singular stochastic optimal control of large insurance company. The company controls the reinsurance rate and dividend payout process to maximize the expected present value of the dividend pay-outs until the time of bankruptcy. However, if the optimal dividend barrier is too low t…
Study Epstein-Zin preferences in mean field portfolio games, proving unique equilibria.
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 consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…
This paper analyzes optimal consumption strategies for loss-averse investors with multiplicative habit formation.
We study risk-sharing economies where heterogenous agents trade subject to quadratic transaction costs. The corresponding equilibrium asset prices and trading strategies are characterised by a system of nonlinear, fully-coupled forward-backward stochastic differential equations. We show that a unique solution generally…
Randomly initialized transformers show extreme token preferences.
We study existence and uniqueness of continuous-time stochastic Radner equilibria in an incomplete market model among a group of agents whose preference is characterized by cash invariant time-consistent monetary utilities. An assumption of "smallness" type is shown to be sufficient for existence and uniqueness. In par…
The purpose of this survey chapter is to present a transformation technique that can be used in analysis and numerical computation of the early exercise boundary for an American style of vanilla options that can be modelled by class of generalized Black-Scholes equations. We analyze qualitatively and quantitatively the…
We reveal a model rank that predicts successful recovery of target functions at overparameterization.
New model improves recommendation systems by analyzing user-item interactions.
Paper proves existence and uniqueness of solutions to nonlocal systems, generalizing stochastic game theory.
The study compares Euclidean and cosine distances in medical drug prescription prediction.
Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user…
New convergence bounds for online learning with heavy-tailed noise.
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
Investors adjust spending based on a social norm, spending less during losses and more during gains.
New decision-theoretic characterization separates belief and decision posteriors.
Two single-timescale algorithms improve TD learning with nonlinear approximations.
We develop theory for nonlinear dimensionality reduction (NLDR). A number of NLDR methods have been developed, but there is limited understanding of how these methods work and the relationships between them. There is limited basis for using existing NLDR theory for deriving new algorithms. We provide a novel framework …
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional …
Optimizes molecular generation for chemist preferences.
New method adapts to user preferences dynamically, improving recommendation models.
Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
Enhances preference learning by incorporating response times into binary choices.
Bayesian optimization learns DM preferences for multi-outcome experiments.
New study shows personalized content recommendations can lead to polarization of user preferences.
Bayesian optimization agent learns user preferences from pairwise comparisons.
New RLHF framework handles general preference oracles without reward functions.
This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…