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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.

169,051 papers · 148 categories

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4.2%8.3%12.5%16.7% · Apr 199519922001200920172026
48 results for return constraints

In this work, we consider the optimal portfolio selection problem under hard constraints on trading amounts, transaction costs and different rates for borrowing and lending when the risky asset returns are serially correlated. No assumptions about the correlation structure between different time points or about the dis…

2014-10-29abs ↗pdf ↗

In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we anal…

2016-09-18abs ↗pdf ↗

The paper analyzes portfolio optimization with two risk measures.

problem Optimizing a portfolio with two coherent risk measures.
method Analytical results for a static portfolio optimization problem with two risk measures.
result Characterization of optimal portfolios and explicit formulas for Gaussian returns.

Paper examines the relationship between maximizing and minimizing expected return in portfolio optimization.

problem Investment risk and return optimization in portfolio problems.
method Lagrange undetermined multiplier method and replica analysis.
result Derived mean square error and correlation coefficient of optimal portfolios as functions of risk tolerance.

Study consumption-investment problem in markets with rank-based returns.

problem Consumption-investment problem in markets with rank-based returns.
method Derives an HJB equation with Neumann boundary conditions for the value function and proves a corresponding verification theorem.
result Explicit solutions for unconstrained, open market constraints, and fully invested cases.

The signal-noise ratio of a portfolio of p assets, its expected return divided by its risk, is couched as an estimation problem on the sphere. When the portfolio is built using noisy data, the expected value of the signal-noise ratio is bounded from above via a Cramer-Rao bound, for the case of Gaussian returns. The bo…

2014-09-21abs ↗pdf ↗

Paper studies portfolio investment under volatility uncertainty and short-sale constraints, improving risk-adjusted returns.

problem Investment portfolio optimization under volatility uncertainty and short-sale constraints.
method Sublinear expectation model to handle volatility uncertainty, constructing SLE-MUV model.
result Pareto frontier of SLE-MUV model is a continuous convex curve with polynomial analytical expression.

A declining CVaR glidepath framework for TDF design with Chilean pension system application

problem Designing Target-Date Funds around an explicit return objective while controlling risk
method Propose a framework for designing TDFs with a declining CVaR constraint
result Key feature: conservative evaluation of each glidepath

In the world of modern financial theory, portfolio construction has traditionally operated under at least one of two central assumptions: the constraints are derived from a utility function and/or the multivariate probability distribution of the underlying asset returns is fully known. In practice, both the performance…

2014-12-24abs ↗pdf ↗

Investing in cryptocurrencies can improve portfolio risk-return profile, especially with diversification strategies.

problem Investing in cryptocurrencies and evaluating their potential for portfolio allocation strategies.
method Investigated different types of investors, various portfolio construction rules, and incorporated liquidity constraints.
result Cryptocurrencies can improve the risk-return profile of portfolios, especially with diversification strategies.

Study optimal consumption and portfolio strategies with no-borrowing constraint in financial markets.

problem Maximizing utility from consumption under constraints in a stochastic environment.
method Lagrange duality and singular control problem to solve dynamic no-borrowing constraint.
result Retrieve optimal portfolio and consumption plans via dual singular control problem.

Dynamic promotion optimization for e-commerce platforms within financial constraints.

problem Balancing promotional costs with incremental revenue for sustainable growth.
method Knapsack Problem formulation for dynamic optimization, Retrospective Estimation, online-dynamic calibration.
result Significant increase in target outcome while staying within financial constraints.

MPC outperforms reactive budgeting in non-stationary return environments.

problem Optimizing budget allocation under non-stationary returns.
method Receding-horizon Model Predictive Control (MPC) compared to reactive policies.
result MPC consistently outperforms reactive budgeting when return dynamics are predictable.

The estimation of asset return distributions is crucial for determining optimal trading strategies. In this paper we describe the constrained mixture model, based on a mixture of Gamma and Gaussian distributions, to provide an accurate description of price trends as being clearly positive, negative or ranging while acc…

2011-03-14abs ↗pdf ↗

Paper analyzes an algorithm for maximizing non-concave functions with budget constraints.

problem Maximizing non-concave functions with budget constraints under DR-submodularity.
method Generalized Sequential algorithm for online monotone DR-submodular function maximization.
result First competitive ratio bound matches known tight bound for linear objective functions.

Investigates optimal portfolios with risk-free assets, minimizing investment risk.

problem Investment risk minimization with budget and return constraints.
method Replica analysis and exploration of implications of a risk-free asset.
result Implications of a risk-free asset on optimal portfolio and investment risk.

Paper optimizes financial trading strategies under uncertain market conditions.

problem Guaranteeing robust positive expected profits in financial systems.
method Transformed semi-infinite constraints into structured policies and proposed a novel graphical approach.
result Demonstrated superior risk-adjusted returns and downside risk compared to conventional strategies.

Robust MCVaR portfolio optimization using RKHS for risk management.

problem Minimizing portfolio risk while achieving higher returns under uncertainty.
method Introduces a robust MCVaR model with ellipsoidal support and RKHS uncertainty set for chance constraint.
result Robust model outperforms nominal and market portfolios in various market conditions.

Deep learning models improve stock market portfolio returns.

problem Optimizing portfolio returns using deep learning methods.
method Deep neural networks (feedforward and LSTM) applied to stock market excess returns forecasting.
result Deep learning models deliver significant gains in portfolio certainty equivalent returns and Sharpe ratios.

CB-APM uses analyst consensus as a bottleneck to interpret stock returns.

problem Tackles the challenge of understanding and predicting stock returns using professional beliefs.
method Embeds analyst consensus as a structural bottleneck, treating it as a sufficient statistic for market information.
result CB-APM portfolios exhibit strong monotonic return gradients and robust across different economic conditions.

Dynamic tracking error framework shows similar performance but varying volatility across different constraints.

problem Differences in governance parameters between Total Portfolio Approach and Strategic Asset Allocation.
method Portfolio simulations using U.S. equity and bond data from 2000 to 2026, spanning 2004 to 2026.
result Realized tracking error volatility varies 12-fold across different constraints, with costs highest during crises.

This paper uses entropy to derive stock price dynamics and option valuation.

problem Deriving stock price dynamics and option valuation from information constraints.
method Develops an entropic inference framework to derive stochastic processes from information constraints, representing price changes through two channels: continuous and jump.
result The derived dynamics is the Merton jump diffusion, with Geometric Brownian Motion as the no jump limit.

Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.

problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.

Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.

problem Optimizing investment portfolios with high inflation and bounded leverage constraints.
method Formulated an optimal control problem, established a closed-form solution, and developed a novel LFNN approach.
result The LFNN strategy outperforms a passive benchmark by about 200 bps with a high probability of success.

The study examines portfolio optimization with quadratic transaction costs, complicating the optimization process.

problem Portfolio optimization with quadratic transaction costs is more challenging than with linear costs.
method Introduced numerical algorithms to solve the optimization problem with quadratic transaction costs.
result Quadratic transaction costs significantly impact the expected returns of optimized portfolios.

Algorithm safely learns from sub-optimal baseline policies while satisfying constraints.

problem Safe reinforcement learning with constraints when baseline policy is sub-optimal.
method Iterative policy optimization alternating between return maximization, baseline distance minimization, and constraint projection.
result Consistently outperforms baselines, achieving 10x fewer constraint violations and 40% higher reward.

Optimizes multi-period portfolios with tail-risk constraints using neural networks.

problem Maximizing expected return while managing tail-risk constraints over multiple periods.
method Recurrent neural network approach to approximate optimal policy.
result Validated in financial and insurance models, capturing long-term risk dynamics.

The thesis models financial returns using mixtures of generalized normal distributions.

problem Estimation issues in financial return analysis.
method Mixtures of generalized normal distributions (MGND), ECM/GEM algorithms, constrained mixture models (CMGND), GND-HMMs.
result Enhanced accuracy and interpretability in financial return modeling.

New algorithm reduces regret and constraint violation in constrained bandit problems.

problem Optimizing under budget and stochastic constraints in resource-constrained settings.
method Lyapunov optimization methodology, tLyOn{ t LyOn} algorithm.
result Achieves O(KBlogB)O(\sqrt{K B\log B}) regret and zero constraint-violation for large BB.