Study finds equivalence between MMV and MV preferences with conic constraints.
problem Monotone mean-variance portfolio selection under conic constraints.
method Closed-form solutions for optimal strategies under MMV and MV preferences.
result Optimal strategies coincide with and without the conic constraint.
A new ratio, the Hansen ratio, simplifies mean-variance portfolio theory.
problem Simplifying mean-variance portfolio theory.
method Introducing the Hansen ratio and extending mean-variance theory.
result The Hansen ratio provides a parsimonious description of the mean-variance efficient frontier.
Introduces SMMV preferences to avoid inconsistency in portfolio selection.
problem Monotone mean-variance preferences fail to differentiate strictly dominant payoffs.
method Introduces strictly monotone mean-variance preferences and applies them to portfolio selection problems.
result SMMV preferences provide a more rational basis for assessing prospects and coincide with MV preferences under certain conditions.
New results on financial equilibria in markets with general semimartingales.
problem Existence and uniqueness of mean-variance equilibria in semimartingale markets.
method Analysis of dynamic mean-variance hedging and fixed-point problems.
result First results allowing for general semimartingales and both discrete and continuous time.
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…
Investigates time-inconsistent portfolio selection under MMV preferences.
problem Time-inconsistent optimal strategies for MMV preferences.
method Nash equilibrium controls for MMV and MV preferences, solving FBSDE and HJB equations.
result MMV optimal strategies lead to higher investment amounts than MV strategies, narrowing over time.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
Study on MMV in jump-diffusion models resolves MV's non-monotonicity issues.
problem Non-monotonicity and free cash flow stream problems in MV preferences.
method Explicit solution for MMV preferences in jump-diffusion models, proving non-negative potential measures.
result MMV resolves MV's non-monotonicity and free cash flow stream issues.
Study on reinsurance decisions using mean-variance criterion with irreversible contracts.
problem Optimizing reinsurance premiums and contracts in a Stackelberg game with irreversible contracts.
method Unified singular control framework applied to both discrete and continuous time reinsurance contracts.
result A single once-for-all reinsurance contract is preferred over multiple contracts, and the signing time is crucial.
We study dynamic optimal portfolio allocation for monotone mean--variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui, Li, Wang and Zhu (2012, MAFI) and fully characterize the circumstances under which one can set aside a non-negative cash flow while sim…
Investigates portfolio selection among competitive agents with mean-variance preferences.
problem Optimizing portfolios with multi-agent competition and relative wealth comparison.
method Reformulated as a constrained, non-homogeneous stochastic linear-quadratic control problem; derived optimal feedback strategies; used decoupling techniques and fixed-point theory to solve nonlinear BSDEs.
result Characterized three scenarios based on market and competition parameters: unique Nash equilibrium, no Nash equilibrium, or infinitely many Nash equilibria.
New model recommends stocks considering individual preferences and diversification.
problem Inaccurate stock price predictions and ignoring investment theories.
method Portfolio Temporal Graph Network Recommender (PfoTGNRec) incorporating diversification-enhancing sampling.
result PfoTGNRec outperforms state-of-the-art models in real-world data.
The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.
problem Optimal dynamic portfolio choice for a modified mean-variance utility.
method Complete characterization under minimal assumptions, no restrictions on asset return moments.
result Maximal MMV utility is linked to the monotone Sharpe ratio, with global squared MSR as the nominal yield.
A new, computationally friendly formula for a class of risk-averse preferences.
problem Characterizing a class of risk-averse preferences called uniformly weighted divergence preferences.
method Introducing a new formula that characterizes UWDP as the translation-invariant hull of state-independent expected utility.
result UWDP are the translation-invariant hull of state-independent expected utility over L0. New model considers wealth and time affecting risk aversion in portfolio selection.
problem Optimal investment strategy and consumption process depend on wealth and future income balance.
method Proposed a new mean-variance-utility framework with time and state-dependent risk aversion, solved using game theory.
result Equilibrium investment and consumption policies derived, aligning with investor behavior.
Paper solves a complex portfolio selection problem with time-inconsistent preferences.
problem Time-inconsistent preferences in portfolio selection.
method Unified framework with minimal assumptions, proving existence and uniqueness of solution.
result Existence and uniqueness of square-integrable solution for the integral equation.
The paper solves an insurance problem using mean-variance and rank-dependent utility theory.
problem Formulating and solving an insurance problem with rank-dependent utility and mean-variance premium principle.
method Formulated as a non-concave maximization problem, then turned into a concave quantile optimization problem, solved using calculus of variations.
result An optimal insurance contract is derived and numerically computed.
Research tackles investor confusion in ESG rankings, offering tailored strategies.
problem Widespread confusion among investors regarding ESG rankings.
method Developed ESG ensemble strategies, integrated ESG scores into RL model, proposed Double-Mean-Variance model, introduced ESG-adjusted CAPMs.
result Optimized portfolios that balance financial returns and ESG-focused outcomes.
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…
Investigates portfolio optimization with and without gearing constraints.
problem Improving portfolio weights for better alignment with expected returns.
method Extends the alpha-weight angle bound to include gearing constraints and uses theoretical arguments and simulations.
result Equally weighted portfolios are not preferable to mean-variance portfolios even with poor forecast ability and a badly conditioned covariance matrix.
In this paper, we consider equilibrium strategies under Volterra processes and time-inconsistent preferences embracing mean-variance portfolio selection (MVP). Using a functional Itô calculus approach, we overcome the non-Markovian and non-semimartingale difficulty in Volterra processes. The equilibrium strategy is the…
RL models outperform traditional methods in certain market conditions.
problem Traditional portfolio management methods rely on accurate forecasts and do not incorporate specific investor preferences.
method Deep reinforcement learning with specific investor preferences incorporated into reward functions, realistic transaction costs modelled.
result RL models can significantly outperform traditional methods in upward trending markets, but not in sideways trending markets.
Paper uses inverse optimization to measure risk preference from investment portfolios.
problem Measuring subjective risk preference in investment portfolios.
method Inverse optimization on mean-variance framework.
result Quantified risk preference parameters validated with existing measures.
Study dynamic asset allocation in incomplete markets using game theory and nonlocal BSDEs.
problem Dynamic mean-variance asset allocation in general incomplete markets with non-exponential discounting.
method Game-theoretic approach, decomposition into myopic and hedging strategies, nonlocal BSDEs, fixed-point theorem.
result Well-posedness of solutions to BSDEs, existence of equilibrium control policy.
A new approach to MV portfolio optimization with jumps and RL.
problem Continuous-time Mean-Variance portfolio optimization with jumps.
method Jump-diffusion process, Reinforcement Learning, time-inconsistent control (TIC), Actor-Critic RL algorithm.
result The proposed RL model is profitable in real-world market data.
Optimizes risk sharing with multiple models under uncertainty.
problem Risk sharing with multiple models under ambiguity.
method Constructs a mean-variance criterion using chi-squared divergence, adapts monotone preferences, and uses dual representation.
result Characterizes optimal risk sharing contract and agent's wealth process.
This paper analyzes a game between insurer and reinsurer under ambiguity and risk aversion, optimizing reinsurance and investment strategies.
problem Optimizing reinsurance and investment strategies in a game between insurer and reinsurer under ambiguity and risk aversion.
method Stackelberg game, α-maxmin mean-variance criterion, Heston's stochastic volatility, Hamilton-Jacobi-Bellman equations, Riccati differential equations. result Excess-of-loss reinsurance is optimal for the insurer, and the equilibrium strategies are determined by specific equations.
This is a follow up of our previous paper - Trybuła and Zawisza \cite{TryZaw}, where we considered a modification of a monotone mean-variance functional in continuous time in stochastic factor model. In this article we address the problem of optimizing the mentioned functional in a market with a stochastic interest rat…
A new model selects low-carbon mutual funds considering ESG criteria, risk, and investor preferences.
problem Aligning financial investments with a low-carbon economy.
method Tri-criterion portfolio selection model using a preference-based multi-objective genetic algorithm (ev-MOGA).
result The model successfully incorporates carbon risk exposure and loss-adverse attitudes into portfolio construction.
For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time se…
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.
Optimal insurance contracts are designed to screen risk preferences and risk types under asymmetric information.
problem Designing optimal insurance contracts under asymmetric information and risk types.
method Constructing a menu of contracts that maximizes mean-variance utilities, subject to truth-telling constraints.
result Equilibrium contracts exhibit nonlinear pricing with decreasing risk loadings, inducing self-selection.
The model of rational decision-making in most of economics and statistics is expected utility theory (EU) axiomatised by von Neumann and Morgenstern, Savage and others. This is less the case, however, in financial economics and mathematical finance, where investment decisions are commonly based on the methods of mean-v…
Comonotonic allocations are restored under certain constraints, improving risk-sharing.
problem Feasibility constraints can distort optimal risk-sharing allocations.
method Identified componentwise convex-order solidity as a sufficient condition to restore comonotonic allocations.
result Componentwise convex-order solidity ensures comonotonic improvements under feasible constraints.
Paper proposes a new portfolio model for better investment decisions.
problem Traditional portfolio models fail to adapt to nonstationary markets.
method Developed a mean-detrended cross-correlation portfolio model (M-DCCP model).
result The M-DCCP model outperforms traditional models in constructing optimal portfolios.
Robo-advisor uses ML to optimize investment performance.
problem Maximizing investment performance with historical data.
method Inverse optimization and deep reinforcement learning.
result Robo-advisor consistently outperformed S&P 500.
Paper solves a control problem with robust methods.
problem Monotone mean-variance problems with stochastic coefficients.
method Finding saddle point through BSDEs with unbounded coefficients.
result Optimal control and value match mean-variance problems.
The choice of admissible trading strategies in mathematical modelling of financial markets is a delicate issue, going back to Harrison and Kreps (1979). In the context of optimal portfolio selection with expected utility preferences this question has been a focus of considerable attention over the last twenty years. We…
Deep learning improves portfolio optimization efficiency.
problem Efficient frontier calculation in high-dimensional finance problems.
method Deep neural networks for portfolio optimization with added constraints.
result A new projected feedforward network outperforms classical methods.
This study evaluates shrinkage estimators for improving mean and covariance in portfolio optimization.
problem Estimation errors in expected returns and covariance matrix in mean-variance model.
method Examined five shrinkage estimators for expected returns and eleven for covariance matrix across six datasets.
result GMV model with Ledoit Wolf COV2 outperforms traditional methods in most scenarios.
New method for portfolio management learns from past wealth evolution.
problem Optimizing portfolio selection based on past performance.
method Simulated annealing clustering for asset selection, considering past wealth evolution.
result Strategy effectively learns from past performance and performs well in practice.
The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.
problem Optimal trading strategies with random market coefficients.
method Backward stochastic differential equations (BSDEs) to find optimal strategies.
result MMV and MV problems share the same optimal portfolio and value under random coefficients.
The paper analyzes optimal investment strategies for life insurance contracts using mean-variance optimization.
problem Optimal portfolio choice for equity holders in life insurance contracts.
method Mean-variance optimization, explicit formulas, Hamilton-Jacobi-Bellman equations, numerical analysis.
result Equity holders increase investment in risky assets during economic downturns.
New method solves continuous time mean-variance model for consistent investment strategy.
problem Time-consistent optimal strategy for continuous time mean-variance model.
method Developed a new Bellman principle method.
result Obtained a time-consistent dynamic optimal strategy.
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…
The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff. However, standard formulations do not take into account {\em risk}. In online decision making systems, risk is a primary concern. In this regard, the mean-variance risk measure is one of the most co…
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.
The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.
problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.
method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.
result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.