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.
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…
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…
This study compares Markowitz and Single-Index models for Malaysian stocks.
A new portfolio optimization method using the Sherman-Morrison identity.
Hybrid approach combines Markowitz's theory with reinforcement learning for optimal portfolio management.
We briefly review the approach to optimization of portfolios according to the theory of Markowitz and propose a further modification that can improve the outcome of the optimization process. The modification takes account of the entropic contribution from the time series used to compute the parameters in the Markowitz …
This paper compares modern portfolio theories and applies them to real-world portfolio selection.
Quantum computing optimizes ESG portfolios efficiently.
We study the Markowitz portfolio selection problem with unknown drift vector in the multidimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering theory is used to learn the posterior distribution about the drift …
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…
Improved Markowitz method handles uncertainty in return forecasts.
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
Article proposes a profitable intraday trading strategy for Chinese stocks.
We derive properties of the cdf of random variables defined as saddle-type points of real valued continuous stochastic processes. This facilitates the derivation of the first-order asymptotic properties of tests for stochastic spanning given some stochastic dominance relation. We define the concept of Markowitz stochas…
Markowitz simplified portfolio returns assuming constant trade volumes.
Robustifies Markowitz portfolios to reduce transaction costs and improve performance.
The paper proposes a new portfolio allocation method combining RMT and machine learning.
Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.
Improved portfolio optimization using machine learning and hierarchical clustering.
The paper revisits Markowitz's pseudodistance on pseudo-Riemannian manifolds.
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…
The paper analyzes how behavioral investors make portfolio decisions using Markowitz Stochastic Dominance criteria.
Study Gromov hyperbolic domains in Minkowski space, proving equivalence to boundary properties.
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…
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
The asymptotic distribution of the Markowitz portfolio is derived, for the general case (assuming fourth moments of returns exist), and for the case of multivariate normal returns. The derivation allows for inference which is robust to heteroskedasticity and autocorrelation of moments up to order four. As a side effect…
New method corrects Markowitz variance for trading volume fluctuations.
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…
We introduce a representation theory for risk operations on locally compact groups in a partition of unity on a topological manifold for Markowitz-Tversky-Kahneman (MTK) reference points. We identify (1) risk torsion induced by the flip rate for risk averse and risk seeking behaviour, and (2) a structure constant or co…
Improved portfolio optimization using Kendall-like correlation coefficients.
A new portfolio optimization model minimizes maximum drawdown, offering faster and more robust solutions.
Naive investors make riskier choices than optimal strategies in continuous-time finance.
Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two dimensional space of utility and risk. This is a rather general pattern. The modern portfolio theor…
Study analyzes portfolio performance of crypto and traditional assets.
New algorithm optimizes adaptive return level for Markowitz portfolios.
This paper optimizes portfolio selection for multivariate affine and quadratic Volterra models with rough volatilities.
The emergence of robust optimization has been driven primarily by the necessity to address the demerits of the Markowitz model. There has been a noteworthy debate regarding consideration of robust approaches as superior or at par with the Markowitz model, in terms of portfolio performance. In order to address this skep…
In this study, we have investigated empirically the effects of market properties on the degree of diversification of investment weights among stocks in a portfolio. The weights of stocks within a portfolio were determined on the basis of Markowitz's portfolio theory. We identified that there was a negative relationship…
We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of p…
Network-based strategy for optimal cryptocurrency portfolios identified.
This paper bridges Markowitz planning and deep reinforcement learning for portfolio optimization.
Paper connects two portfolio methods, HRP and Minimum Variance, revealing their underlying similarity.
This paper presents practical methods for portfolio selection in investments.
The focal point of this paper is the issue of "drawdown" which arises in recursive betting scenarios and related applications in the stock market. Roughly speaking, drawdown is understood to mean drops in wealth over time from peaks to subsequent lows. Motivated by the fact that this issue is of paramount concern to co…
The main purpose of this study is the determination of the optimal length of the historical data for the estimation of statistical parameters in Markowitz Portfolio Optimization. We present a trading simulation using Markowitz method, for a portfolio consisting of foreign currency exchange rates and selected assets fro…
The paper describes a method to infer the signal-to-noise ratio in portfolio optimization.
This paper compares three portfolio designs for Indian stocks.
ACGAN improves portfolio allocation by learning trends and uncertainty.