arXiv research

Study examines how risk tolerance impacts long-term investment returns.

ADGAN improves risk tolerance prediction by aligning cross-domain data.

Algorithms optimize fair portfolios for diverse risk-tolerant consumers.

Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.

New method reduces sample complexity for robust learning.

We analyze a nonlinear equation proposed by F. Black (1968) for the optimal portfolio function in a log-normal model. We cast it in terms of the risk tolerance function and provide, for general utility functions, existence, uniqueness and regularity results, and we also examine various monotonicity, concavity/convexity…

We study a portfolio optimization problem for competitive agents with CRRA utilities and a common finite time horizon. The utility of an agent depends not only on her absolute wealth and consumption but also on her relative wealth and consumption when compared to the averages among the other agents. We derive a closed …

We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market pri…

The study analyzes how large language models form and express investor risk profiles.

In complex systems like financial market, risk tolerance of individuals is crucial for system resilience.The single-security price limit, designed as risk tolerance to protect investors by avoiding sharp price fluctuation, is blamed for feeding market panic in times of crash.The relationship between the critical market…

We present turnpike-type results for the risk tolerance function in an incomplete market setting under time-monotone forward performance criteria. We show that, contrary to the classical case, the temporal and spatial limits do not coincide. We also show that they depend directly on the left- and right-end of the suppo…

A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.

Simplified NFT games discussed with methods for extracting value.

Split conformal prediction provides finite-sample guarantees for black-box models without distributional assumptions.

Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex nonsmooth losses (such as modern deep networks), the population risk is generally significantly more well-behaved from an optimization point of vie…

Bayesian Robust Optimization for Imitation Learning (BROIL) balances risk and reward.

In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and…

In the general framework of a semimartingale financial model and a utility function $U$ defined on the positive real line, we compute the first-order expansion of marginal utility-based prices with respect to a ``small'' number of random endowments. We show that this linear approximation has some important qualitative …

Optimizes pension fund management under funding risks.

Study uses AI agents to improve equity portfolio management.

We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…

Study $N$-player and mean-field games in Itô-diffusion markets with competitive or homophilous interactions.

Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.

Paper proposes FTT-NAS to create fault-tolerant CNNs for edge devices.

Investors with anxiety about drawdowns may use stop-loss and trailing stops as optimal selling strategies.

We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercis…

The paper analyzes risk measures and optimal reserve allocation strategies.

Paper introduces Lambda EVaR, a new risk measure.

We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …

The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…

DeepDyve uses simpler neural networks to verify DNNs for faults.

Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released…

Study improves portfolio optimization for Indonesian banks using robust methods.

Develops Thompson Sampling algorithms for mean-variance bandits.

We study the sensitivity of the expected utility maximization problem in a continuous semi-martingale market with respect to small changes in the market price of risk. Assuming that the preferences of a rational economic agent are modeled with a general utility function, we obtain a second-order expansion of the value …

This paper deals with multidimensional dynamic risk measures induced by conditional $g$-expectations. A notion of multidimensional $g$-expectation is proposed to provide a multidimensional version of nonlinear expectations. By a technical result on explicit expressions for the comparison theorem, uniqueness theorem and…

Study risk sharing with Lambda VaR under diverse beliefs.

Study optimal investment decisions for diverse risk-tolerant agents.

We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input. Whereas previous work focuses on arbitrarily strict threat models, i.e., $ε$-perturbations, we consider arbitrary valid inputs and propose an information-based characteristic for evaluating tolerance to diverse …

Framework for private, noise-tolerant, and efficient learning algorithms.

Fault-tolerant neural networks inspired by biological error correction codes.

PCL framework optimizes climate risk management across three clusters.

The paper optimizes reinsurance under uncertain dependence among insurers.

Paper addresses fault-tolerance in distributed machine learning with stochastic gradient descent.

In recent years, the evaluation of the minimal investment risk of the quenched disordered system of a portfolio optimization problem and the investment concentration of the optimal portfolio has been actively investigated using the analysis methods of statistical mechanical informatics. However, the work to date has no…

Efficiently learns halfspaces with malicious noise, near-optimal label complexity.

This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone…

This paper reviews quantum machine learning from NISQ to fault tolerance.