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arXiv research

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.

168,657 papers · 148 categories

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0111 · Apr 201519922001200920172026
4 results for “mean-variance-skewness-kurtosis”
Convex scalarizations of the mean-variance-skewness-kurtosis problem in portfolio selectionq-fin.PM

We solve a portfolio selection problem with four objectives, finding convex scalarizations for part of the Pareto front.

problem Portfolio selection with four objectives: mean, variance, skewness, and kurtosis.
method Linearly scalarize MVSK objectives into a convex polynomial FλF_λ over the probability simplex, compute optimizers for each λλ.
result Identify a set of hyper-parameters for which the scalarization is convex, allowing computation of part of the Pareto front.
portfolio selectionmean-variance-skewness-kurtosisconvex scalarizationPareto frontsparse optimization
2023-02-21abs ↗pdf ↗
A Study of Correlations in the Stock Marketq-fin.ST

We study the various sectors of the Bombay Stock Exchange(BSE) for a period of 8 years from April 2006 - March 2014. Using the data of daily returns of a period of eight years we make a direct model free analysis of the pattern of the sectorial indices movement and the correlations among them. Our analysis shows signif…

2015-04-22abs ↗pdf ↗
Sparse High-Order Portfolios via Proximal DCA and SCAq-fin.PM

Paper solves high-order portfolio optimization with cardinality constraint.

problem Solving non-convex cardinality constrained high-order portfolio optimization.
method Transformed cardinality constraint into penalty term, proposed pDCA, pDCAe, and SCA algorithms.
result Proposed algorithms achieve high utility and sparse solutions efficiently.
portfolio optimizationcardinality constraintnon-convex optimizationDC propertyproximal DCA
2020-08-29abs ↗pdf ↗
Yau's Affine-Normal Descent for Large-Scale Unrestricted Higher-Moment Portfolio Optimizationq-fin.PM

Algorithm tackles large-scale portfolio optimization with higher moments, improving computational efficiency.

problem Optimizing portfolios with higher moments (variance, skewness, kurtosis) for large asset universes is computationally infeasible.
method Developed a structure-exploiting algorithm based on Yau's affine-normal descent, working directly with return matrix.
result Algorithm avoids explicit higher-order tensors and exploits quartic structure for efficient computation.
portfolio optimizationhigher momentsYau's affine-normal descentlarge-scale computationasymmetry
2026-04-28abs ↗pdf ↗