arXiv research

Study S-shaped utility maximization with VaR constraint and unobservable drift.

Develops deep learning methods for solving S-shaped utility maximisation problems.

We solve S-shaped utility portfolio selection with SD constraints using algorithms and neural networks.

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…

Dynamic risk constraints help limit risky behavior in financial portfolios.

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

Study optimal consumption for loss-averse agents considering past spending peaks.

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…

This paper analyzes optimal consumption strategies for loss-averse investors with multiplicative habit formation.

Within the framework of the cumulative prospective theory of Kahneman and Tversky, this paper considers a continuous-time behavioral portfolio selection problem whose model includes both running and terminal terms in the objective functional. Despite the existence of S-shaped utility functions and probability distortio…

In this study, we extend the optimal execution problem with convex market impact function studied in Kato (2014) to the case where the market impact function is S-shaped, that is, concave on $[0, \bar {x}_0]$ and convex on $[\bar {x}_0, \infty )$ for some $\bar {x}_0 \geq 0$. We study the corresponding Hamilton-Jacobi-…

The aim of this work consists in the study of the optimal investment strategy for a behavioural investor, whose preference towards risk is described by both a probability distortion and an S-shaped utility function. Within a continuous-time financial market framework and assuming that asset prices are modelled by semim…

We develop a tractable model of realization utility that studies the role of reference-dependent S-shaped preferences in a dynamic investment setting with reinvestment. Our model generates both voluntarily realized gains and losses. It makes specific predictions about the volume of gains and losses, the holding periods…

Investigates optimal PPI strategies in jump-diffusion models to mitigate downside risk.

Novel framework for portfolio selection considering utility and risk.

This paper formulates and studies a general continuous-time behavioral portfolio selection model under Kahneman and Tversky's (cumulative) prospect theory, featuring S-shaped utility (value) functions and probability distortions. Unlike the conventional expected utility maximization model, such a behavioral model could…

The most commonly accepted model for investors' preferences is expected utility theory. More recently, other theories have emerged and pose new challenges to mathematics. The present paper treats preferences of cumulative prospect theory (CPT), where an "S-shaped" utility function is considered (i.e. convex up to a cer…

The highly detailed international trade data among all countries in the world during 1971-2000 shows that the kinds of export goods and the logarithmic GDP (gross domestic production) of a country has an S-shaped relationship. This indicates all countries can be divided into three stages accordingly. First, the poor co…

Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.

Hierarchical geodesic model for analyzing shapes on manifolds.

The paper analyzes how behavioral investors make portfolio decisions using Markowitz Stochastic Dominance criteria.

A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and selec…

Decision maker's preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function fr…

This paper presents a simple model to measure the relative economic growth of economic systems. The model considers S-Shaped patterns of economic growth that, represented with a linear model, measure how an economic system grows in comparison with another one. In particular, this model introduces an approach which indi…

New method reconstructs 3D shapes from 2D images using Kendall's shape space.

At the heart of technology transitions lie complex processes of social and industrial dynamics. The quantitative study of sustainability transitions requires modelling work, which necessitates a theory of technology substitution. Many, if not most, contemporary modelling approaches for future technology pathways overlo…

The paper defines approximate fibrations in higher topos theory.

The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a simple kernel such as the Gaussian, however, limits its practical applicability. We provide a scheme for learning a kernel parameterized by …

Study on investment strategy for agents with periodic preferences and discounting.

The present paper is devoted to geometric optimization problems related to the Neumann eigenvalue problem for the Laplace-Beltrami operator on bounded subdomains $Ω$ of a Riemannian manifold $(\mathcal{M},g)$. More precisely, we analyze locally extremal domains for the first nontrivial eigenvalue $μ_2(Ω)$ with respect …

The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.

Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more co…

Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear funct…

This paper finds a unique partition of a sample space for estimating continuous distributions.

Curve diffusion flow straightens curves with endpoints on intersecting lines.

In this paper, we consider a class of plane curves called log-aesthetic curves and their generalization which are used in computer aided geometric design. We consider these curves in the framework of the similarity geometry and characterize them as invariant curves under the integrable flow on plane curves which is gov…

Introduces intrinsic Riemannian cross-covariance for manifold-valued random objects.

New framework models non-conservative stochastic processes without energy conservation constraints.

Study compares ZBDT model to BDT for financial derivatives valuation.

Investors optimize their portfolios within a Wasserstein ball to match a benchmark's risk profile.

Paper provides new bounds for risk aggregation and sharing.

Reconstruction-based learning produces uninformative features for perception tasks.

Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks have dominated the state of the art in this task. However, since this problem presents a challenging environment due to high variability in th…

The paper provides high-probability bounds on false discovery proportions in conformal inference.

The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal w…

The universe's shape and size are determined in general cosmological models.

ES-VAE models skeletal pose trajectories by removing nuisance factors.

The paper uses 3D shapes to reveal sundial design adjustments based on latitude.