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.
We consider an incomplete market with a nontradable stochastic factor and a continuous time investment problem with an optimality criterion based on monotone mean-variance preferences. We formulate it as a stochastic differential game problem and use Hamilton-Jacobi-Bellman-Isaacs equations to find an optimal investmen…
In the paper, we consider three quadratic optimization problems which are frequently applied in portfolio theory, i.e, the Markowitz mean-variance problem as well as the problems based on the mean-variance utility function and the quadratic utility.Conditions are derived under which the solutions of these three optimiz…
Naive investors make riskier choices than optimal strategies in continuous-time finance.
This note finds closed-form solutions for mean-risk portfolios using a specific type of mixture distribution.
We give an algebraic definition of a Markowitz market and classify markets up to isomorphism. Given this classification, the theory of portfolio optimization in Markowitz markets without short selling constraints becomes trivial. Conversely, this classification shows that, up to isomorphism, there is little that can be…
Investigates mean-variance portfolio selection in non-Markovian markets.
Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.
We introduce a solution scheme for portfolio optimization problems with cardinality constraints. Typical portfolio optimization problems are extensions of the classical Markowitz mean-variance portfolio optimization model. We solve such type of problems using a method similar to column generation. In this scheme, the o…
This paper optimizes portfolio selection for multivariate affine and quadratic Volterra models with rough volatilities.
By Markowitz geometry we mean the intersection theory of ellipsoids and affine subspaces in a real finite-dimensional linear space. In the paper we give a meticulous and self-contained treatment of this arch-classical subject, which lays a solid mathematical groundwork of Markowitz mean-variance theory of efficient por…
Markowitz's celebrated mean--variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes known parameters has led to p…
The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.
Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.
This paper studies a continuous-time market where an agent, having specified an investment horizon and a targeted terminal mean return, seeks to minimize the variance of the return. The optimal portfolio of such a problem is called mean-variance efficient à la Markowitz. It is shown that, when the market coefficients a…
This paper proposes a new method to optimize portfolio allocation with transaction costs using Wiener chaos expansion.
Proposes a new framework to optimize portfolios with reduced estimation errors.
Optimal capital allocation between different assets is an important financial problem, which is generally framed as the portfolio optimization problem. General models include the single-period and multi-period cases. The traditional Mean-Variance model introduced by Harry Markowitz has been the basis of many models use…
This paper studies a robust continuous-time Markowitz portfolio selection pro\-blem where the model uncertainty carries on the covariance matrix of multiple risky assets. This problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dy…
Continuous-time mean-variance portfolio selection model with nonlinear wealth equations and bankruptcy prohibition is investigated by the dual method. A necessary and sufficient condition which the optimal terminal wealth satisfies is obtained through a terminal perturbation technique. It is also shown that the optimal…
New methods incorporate alpha signals into portfolio construction, improving performance.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
New method improves portfolio allocation using local Gaussian correlation.
We consider a group of mean-variance investors with mimicking desire such that each investor is willing to penalize deviations of his portfolio composition from compositions of other group members. Penalizing norm constraints are already applied for statistical improvement of Markowitz portfolio procedure in order to c…
Signed network models reduce portfolio risk by considering negative edges in financial markets.
This paper presents practical methods for portfolio selection in investments.
Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.
The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.
Efficiently solves large portfolio optimization problems by reducing and sparsifying covariance matrices.
The portfolio optimisation problem, first raised by Harry Markowitz in 1952, has been a fundamental and central topic to understanding the stock market and making decisions. There has been plenty of works contributing to development of the mean-variance optimisation (MVO) so far. In this paper, one kind of them, namely…
Robustifies Markowitz portfolios to reduce transaction costs and improve performance.
We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaus…
Enhances portfolio construction with tailored regime forecasts for individual assets.
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (…
Investors benefit from long horizons in a market with mean-reverting equity returns.
New method improves portfolio selection by filtering noisy covariance matrices.
Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal from a financial point of v…
New approach to portfolio optimization shows entropy regularization is ineffective.
The vector of periodic, compound returns of a typical investment portfolio is almost never a convex combination of the return vectors of the securities in the portfolio. As a result the ex post version of Harry Markowitz's "standard mean-variance portfolio selection model" does not apply to compound return data. We pro…
A metaheuristic approach solves portfolio optimization with constraints.
Motivated by recent advances in the spectral theory of auto-covariance matrices, we are led to revisit a reformulation of Markowitz' mean-variance portfolio optimization approach in the time domain. In its simplest incarnation it applies to a single traded asset and allows to find an optimal trading strategy which - fo…
The paper proposes a new portfolio allocation method combining RMT and machine learning.
Paper tackles P vs NP problem in portfolio optimization with cardinality constraints and Black-Scholes derivatives.
This study investigates how Decision-Focused Learning improves stock return predictions for better portfolio optimization.
A new method for efficient portfolio optimization using graph structures.
BPASGM uses sparse graphical models to optimize portfolio selection.
It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this paper we address the problem by regularizing the mean-variance objective function with a weighted elastic net penalty. We show that the…
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
Optimizes high-dimensional portfolios using joint shrinkage.