arXiv research

We consider a financial market model with a single risky asset whose price process evolves according to a general jump-diffusion with locally bounded coefficients and where market participants have only access to a partial information flow. For any utility function, we prove that the partial information financial marke…

Study on pricing rules for income streams with partial insider information.

Semi-supervised learning improves with partial label information.

CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.

The question addressed in this paper is the performance of the optimal strategy, and the impact of partial information. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. We focus on the optimal strategy with a logarithmic utility functi…

Study market efficiency under partial information using SDEs and optimization.

The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.

We analyse the optimal exercise of an executive stock option (ESO) written on a stock whose drift parameter falls to a lower value at a change point, an exponentially distributed random time independent of the Brownian motion driving the stock. Two agents, who do not trade the stock, have differing information on the c…

Enhances reinforcement learning with partial state information.

New method quantifies redundant information using information bottleneck.

Study proves value of non-Markovian games with partial, asymmetric info.

In this paper, we study the mean-variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, …

Inference for normal and Monte Carlo distributions using minimum relative entropy.

This paper concerns the recursive utility maximization problem under partial information. We first transform our problem under partial information into the one under full information. When the generator of the recursive utility is concave, we adopt the variational formulation of the recursive utility which leads to a s…

The paper extends utility maximization by integrating partial information and robust VaR constraints.

The paper analyzes how investors' wealth can decline collectively under partial information.

Framework benchmarks optimizers on multiple criteria.

The paper analyzes extreme risk measures with limited distributional information.

New BED method handles online inference for partially observed dynamical systems.

This paper studies the question of filtering and maximizing terminal wealth from expected utility in a partially information stochastic volatility models. The special features is that the only information available to the investor is the one generated by the asset prices, and the unobservable processes will be modeled …

We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes …

Optimizes fund manager's wealth with partial information on market risk.

The paper uses belief propagation to analyze rankings and partial orders from partial information.

The paper develops methods to estimate POMDPs from partial information.

Proposes a new GAN architecture for generating data conditioned on partial information.

Modeling market dynamics with informed and uninformed traders and fads.

RID framework quantifies and regularizes task-relevant knowledge in distillation.

The econophysics approach to socio-economic systems is based on the assumption of their complexity. Such assumption inevitably lead to another assumption, namely that underlying interconnections within socio-economic systems, particularly financial markets, are nonlinear, which is shown to be true even in mainstream ec…

IDS optimizes regret in stochastic partial monitoring with linear rewards.

Study portfolio optimization with partial info and drawdown constraints using deep learning.

In this paper we study the Föllmer-Schweizer decomposition of a square integrable random variable $ξ$ with respect to a given semimartingale $S$ under restricted information. Thanks to the relationship between this decomposition and that of the projection of $ξ$ with respect to the given information flow, we characteri…

Investors pay for additional asset information based on utility maximization.

Abstract: Determines thermoelastic coefficients from boundary data.

In this paper we prove that, in the category of chain complexes, partial algebras can be functorially replaced by quasi-isomorphic algebras. In particular, partial algebras contain all of the important homological and homotopical information that genuine algebras do. Applying this result to McClure's partial algebra in…

We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6(d+1) k^{3/2} \sqrt{n \log(k)}$, matching the best known information-theoretic upper bound. The same algorithm also achieves…

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…

This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have \emph{partial} and \emph{noisy} access to the hidden state sequence as side information. This access can be seen as "partial labeling" of …

There has been rapid progress recently on the application of deep networks to the solution of partial differential equations, collectively labelled as Physics Informed Neural Networks (PINNs). In this paper, we develop Physics Informed Extreme Learning Machine (PIELM), a rapid version of PINNs which can be applied to s…

Introduces a new model for mapping matrices to matrices, subsuming linear regression.

The paper explores the tradeoffs between fairness measures in machine learning.

A measure of neural complexity quantifies how hard it is to access information across neurons.

Study Nash equilibrium between broker and informed trader in dealer and lit markets.

In this paper, we consider a financial market with assets exposed to some risks inducing jumps in the asset prices, and which can still be traded after default times. We use a default-intensity modeling approach, and address in this incomplete market context the problem of maximization of expected utility from terminal…

New algorithms for efficient learning with partial information, reducing regret.

The paper calculates bounds for risk metrics and entropies under partial information constraints.

Proposes SPCA to incorporate structural constraints in model identification.

The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a partial information setting, in which the learner can only observe $r \le d$ att…

We consider an investor faced with the utility maximization problem in which the risky asset price process has pure-jump dynamics affected by an unobservable continuous-time finite-state Markov chain, the intensity of which can also be controlled by actions of the investor. Using the classical filtering theory, we redu…