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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.

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191382572763 · Jun 202019922001200920172026
48 results for utility function

New method for fair resource allocation in AI-aware networks with unknown utility functions.

problem Fair resource allocation in AI-aware communication networks with unknown utility functions.
method Distributed, data-driven bilevel optimization approach to learn surrogate utility functions.
result The proposed algorithm learns from data to autotune surrogate utility functions for unknown utility functions.

The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility through the notion of a utility density function. According…

2007-09-05abs ↗pdf ↗

Investigates conditions for risk or utility functionals to be sensitive to large losses.

problem Conditions for risk or utility functionals to be sensitive to large losses.
method Analyzes sensitivity to large losses for various risk and utility functionals.
result Value at Risk and Expected Shortfall generally fail to be sensitive to large losses, but expected utility functionals and certain adjusted versions are sensitive.

A framework for eliciting utility functions from investor preferences.

problem Hard elicitation of specific utility functions in portfolio selection.
method Preference-fitting method using probability-wealth pairs and PHARA approximation.
result Fitted utility function converges to the optimal one as more data is used.

Introduces new performance measures using scaled utility functions.

problem Performance measurement in financial contexts.
method Certainty equivalents defined via scaled utility functions, well-posed portfolio optimization problem under generic conditions.
result Link between portfolio dynamics, benchmark process, and utility function choice in the long-run setting.

Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.

problem Portfolio optimization under expected utility criterion for large portfolios.
method Analytical expressions for optimal portfolios under hyperbolic return distributions and various utility functions.
result The two-fund separation holds true for a broad class of utility functions.

A new method extends Bayesian optimization to more models and utilities.

problem Extending Bayesian optimization to a broader class of models and utilities.
method Likelihood-free Bayesian Optimization (LFBO) which directly models the acquisition function without separate inference.
result LFBO outperforms state-of-the-art black-box optimization methods on real-world problems.

A key issue in the estimation of energy hedges is the hedgers' attitude towards risk which is encapsulated in the form of the hedgers' utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses this issue by estimating an…

2011-03-30abs ↗pdf ↗

Study on robust utility maximization with nonconcave utility functions under projective determinacy.

problem Investor's optimal investment strategy under model ambiguity and nonconcave utility.
method Projective functions of the path and sets of priors, upper-semicontinuous utility.
result Existence of optimal investment strategy under PD.

Gambles are random variables that model possible changes in monetary wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but thei…

2014-05-03abs ↗pdf ↗

New insights into risk aversion for complex decision models.

problem Understanding risk aversion in non-monotone decision models.
method Characterization of probabilistic risk aversion for generalized rank-dependent functions.
result Probabilistic risk aversion is determined by the distortion function, which is convex or scaled quantile-spread mixtures.

We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product d…

2017-11-01abs ↗pdf ↗

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

problem Learn linear utility functions from pairwise comparison queries.
method Analyzes passive and active learning settings, considering noise-free and noisy query responses.
result Efficient learnability of linear utilities in passive learning, but not for utility parameters without strong assumptions.

We present an optimal investment theorem for a currency exchange model with random and possibly discontinuous proportional transaction costs. The investor's preferences are represented by a multivariate utility function, allowing for simultaneous consumption of any prescribed selection of the currencies at a given term…

2008-11-24abs ↗pdf ↗

Study optimal portfolio strategies with time-varying discount rates.

problem Optimizing portfolio decisions with a non-constant discount rate.
method Introduced subgame perfect strategies to handle time inconsistency, using fixed point iteration to find the utility-weighted discount rate.
result Subgame perfect strategies are equivalent to optimal strategies under certain utility function assumptions.

Novel framework for portfolio selection considering utility and risk.

problem Maximizing utility subject to risk constraints with various utility and risk functionals.
method General framework accommodating non-concave utilities and non-convex risk measures. Characterization of well-posedness using a simple either-or criterion.
result Minimal condition for well-posedness: either utility or risk must be sensitive to large losses.

We pursue an inverse approach to utility theory and consumption & investment problems. Instead of specifying an agent's utility function and deriving her actions, we assume we observe her actions (i.e. her consumption and investment strategies) and ask if it is possible to derive a utility function for which the observ…

2011-01-18abs ↗pdf ↗

Investor optimizes investment strategy under model uncertainty and random utility.

problem Optimizing investment under model ambiguity and random utility.
method Proves existence of optimal strategy using primal methods, with assumptions on market and utility function.
result Existence of optimal investment strategy proven.

RUMBoost combines RUMs and deep learning for better choice modelling.

problem Creating interpretable and robust discrete choice models.
method Gradient Boosted Regression Trees for utility functions, with constraints for interpretability and monotonicity.
result RUMBoost outperforms ML and RUM benchmarks in predictive performance and interpretability.

We give a general formulation of the utility maximization problem under nondominated model uncertainty in discrete time and show that an optimal portfolio exists for any utility function that is bounded from above. In the unbounded case, integrability conditions are needed as nonexistence may arise even if the value fu…

2013-07-13abs ↗pdf ↗

This paper solves a utility maximization problem under utility-based shortfall risk constraint, by proposing an approach using Lagrange multiplier and convex duality. Under mild conditions on the asymptotic elasticity of the utility function and the loss function, we find an optimal wealth process for the constrained p…

2015-01-29abs ↗pdf ↗

The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.

problem Learning optimal policies from observed data with asymmetric counterfactual utilities.
method The approach involves identifying and minimizing the maximum expected utility loss using statistical decision theory and solving intermediate classification problems.
result One can learn minimax loss decision rules from observed data.

Optimal portfolios are formed by combining momentum, size, and volatility characteristics, enhancing utility for all investors.

problem Estimation error in forming optimal portfolios from characteristics.
method Maximizing an in-sample loss function that is more concave than the utility function, linking weights to characteristics.
result Optimal portfolios with significantly higher certainty equivalents than benchmarks for all investors.

We study the utility indifference price of a European option in the context of small transaction costs. Considering the general setup allowing consumption and a general utility function at final time T, we obtain an asymptotic expansion of the utility indifference price as a function of the asymptotic expansions of the…

2014-01-14abs ↗pdf ↗

Algorithm improves recommendation subset selection in the presence of biases.

problem Maximizing submodular functions for recommendation in the presence of social biases.
method Algorithm for submodular maximization with fairness constraints.
result Algorithm provably outputs subsets with near-optimal utility and proportional representation.

New algorithm tackles unknown utility network resource allocation.

problem Maximizing network utility with unknown agent utilities.
method Modeling as a bandit problem, proposing algorithms for resource allocation.
result Proposed algorithms are optimal when all agents have the same utility.

Deep learning solves dynamic programming with recursive utility.

problem Challenges in solving high-dimensional discrete-time dynamic programming problems with recursive utility.
method Certainty Equivalent Learning (CEL) algorithm that learns certainty-equivalent value directly with neural networks.
result Accurate value and policy approximations in high-dimensional problems, comparable to VFI in some cases.