Algebraic definition classifies Markowitz markets simplifying portfolio optimization.
problem Complexity in Markowitz market classification.
method Algebraic definition and isomorphism classification.
result Classification shows little beyond portfolio optimization insights.
Develops tests for Markowitz stochastic dominance spanning using saddle points.
problem Determining if adding securities or relaxing investment constraints improves investment opportunity sets.
method Derives properties of cdfs, defines Markowitz stochastic dominance spanning, constructs non-parametric tests based on subsampling.
result Rejects market portfolio Markowitz efficiency and finds evidence of outperformance.
Markowitz simplified portfolio returns assuming constant trade volumes.
problem Understanding portfolio returns and variance in markets with variable trade volumes.
method Investor observes market trades, models portfolio as single security, derives portfolio return and variance.
result Markowitz's equation for portfolio returns and variance is a simplified approximation of real markets with constant trade volumes.
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
This study compares Markowitz and Single-Index models for Malaysian stocks.
problem Optimizing portfolio selection for Malaysian stocks using different models.
method Applied Markowitz and Single-Index models to 10-year historical data of 10 stocks and a risk-free asset.
result Comparison of minimum variance and maximum Sharpe portfolios for both models under various constraints.
Empirical study finds robust optimization can improve portfolio performance in Indian markets.
problem Comparing robust optimization to Markowitz model for portfolio performance.
method Three robust optimization models (box, ellipsoidal, separable uncertainty sets) tested on Indian market data.
result Robust optimization can be a viable alternative to Markowitz model in real market setups.
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
problem Traditional Markowitz optimization limits to a single time scale, ignoring variance across different frequencies.
method Introduces multifrequency optimization allowing specification of target Hurst exponents across multiple time scales.
result Effective risk management strategy that aligns with investor preferences at various time scales.
HybridCGAN improves portfolio analysis by balancing trend prediction and market uncertainty.
problem Markowitz framework's overemphasis on market uncertainty and trend prediction.
method A hybrid approach combining deep generative models to balance trend prediction and market uncertainty.
result HybridCGAN leads to better portfolio allocation compared to existing methods.
Improved portfolio optimization using machine learning and hierarchical clustering.
problem Suboptimal out-of-sample performance and unrealistic allocations in the Markowitz Model.
method Refined Markowitz Model with hierarchical clustering-based approach.
result Enhanced portfolio performance on a risk-adjusted basis.
Signed network models reduce portfolio risk by considering negative edges in financial markets.
problem Tackles portfolio optimization in financial markets by exploiting negative edges in network representations.
method Proposes a discrete optimization scheme to reduce asset selection, building time series of signed networks from asset returns.
result Empirical results show that signed network portfolios perform similarly to classical mean-variance optimization and equally weighted benchmarks.
Robustifies Markowitz portfolios to reduce transaction costs and improve performance.
problem Markowitz portfolios are unreliable due to estimation errors and extreme weights.
method Projected gradient descent and robust statistics for stable weights and costs.
result Robustified Markowitz portfolios have lower turnover and maintain or improve performance.
ACGAN improves portfolio allocation by learning trends and uncertainty.
problem Markowitz framework's overemphasis on market uncertainty.
method Autoencoding CGAN (ACGAN) that learns trends and uncertainty.
result ACGAN leads to better portfolio allocation and more accurate series.
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…
Unified market-based description of returns and variances of trades.
problem Market-based variance of trades and market portfolio.
method Unified market-based approach to describe returns and variances of trades and market portfolio.
result Market-based variance accounts for random volumes of trades and differs from Markowitz's portfolio variance.
Bayesian method improves portfolio selection by updating expected returns.
problem Optimizing portfolio selection with unknown expected returns.
method Bayesian filtering and dynamic programming for learning posterior distribution.
result Explicit optimal strategy computed for Gaussian prior, quantifying learning impact.
This paper compares modern portfolio theories and applies them to real-world portfolio selection.
problem Balancing risk and return in financial investments.
method Introduction of Markowitz's MPT and Fernholz's SPT, application of four models (Markowitz, Constant Correlation, Single Index, Multi-Factor), and use of Portfolio Algorithm and time series models for prediction.
result Comparison and evaluation of portfolio performance and risk management strategies.
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…
Naive investors make riskier choices than optimal strategies in continuous-time finance.
problem Continuous-time Markowitz portfolio selection with naive reoptimization.
method Analytical derivation of naive policies from discretely naive policies.
result Naive policies are always riskier and less efficient than equilibrium policies.
Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.
problem Optimal portfolio selection in markets with different borrowing and saving rates.
method Hamilton-Jacobi-Bellman equation, partial differential equation, verification argument.
result Existence and smoothness of the value function, identification of trading regions and strategies.
This review deals with several microscopic (``agent-based'') models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation a…
The paper develops models for asset returns based on market conditions and uses them to construct a trading policy.
problem Developing a robust trading strategy based on market conditions.
method The authors create stratified models of asset return mean and covariance, fit these models using Laplacian regularization, and combine them with a Markowitz optimization method.
result The trading policy performs well out of sample and can be scaled to larger problems.
PAGAN uses GANs to model market uncertainty for better portfolio optimization.
problem High market efficiency makes traditional prediction models ineffective.
method Generative Adversarial Networks (GANs) to model market uncertainty.
result PAGAN optimizes portfolios by minimizing risk and maximizing returns.
This paper shows Markowitz-style strategies are inefficient when considering drawdown risk.
problem Inefficiency of Markowitz-style investment strategies in recursive betting scenarios.
method Use of drawdown as risk metric, time-varying linear feedback block K(k) called the drawdown modulator.
result Classical Markowitz-style strategies are inefficient when considering drawdown risk.
Bayesian Black-Litterman model updated to use market data for latent variables.
problem Subjective investor views in BL model.
method Treating (q,Ω) as latent variables and learning them from market data. result Improved Sharpe ratios by 50% and reduced turnover by 55%.
New algorithm optimizes adaptive return level for Markowitz portfolios.
problem Finding an optimal return level for Markowitz portfolios when investor's risk appetite is unknown.
method Krasnoselskii-Mann Proximity Algorithm based on proximity operator and momentum technique.
result Significant improvements over state-of-the-art methods in portfolio optimization.
Network-based strategy for optimal cryptocurrency portfolios identified.
problem Challenges in predicting cryptocurrency prices in a volatile market.
method Network methods to identify decorrelated cryptocurrencies, Markowitz Portfolio Theory.
result Network-based portfolios outperform benchmarks with high expected returns.
This paper optimizes portfolio selection for multivariate affine and quadratic Volterra models with rough volatilities.
problem Optimizing portfolio selection for multivariate models with rough volatilities and stochastic correlations.
method Investigates continuous-time Markowitz mean-variance problem for multivariate affine and quadratic Volterra models using Riccati backward stochastic differential equations (BSDEs).
result Derives explicit solutions for BSDEs in affine Volterra models and new analytic formulae for quadratic models.
New model uses interval-valued CVaR for better risk assessment in finance.
problem Measuring tail risk in rapidly changing financial markets.
method Employing random intervals to describe asset returns and using ICVaR as a risk measure.
result Optimal portfolio selection models show better risk assessment in real data.
Investigates stock correlations during market crises, finds nonlinear dependencies increase, and optimizes portfolios.
problem Investigating stock correlations during market crises.
method Pearson correlation and mutual information based complex networks, surrogate data for nonlinear dependencies, Markowitz mean variance portfolio optimization.
result Nonlinear dependencies increase during financial market crises, not reducing to linear correlations.
The paper explores Markowitz geometry in economics.
problem Mean-variance theory of efficient portfolios.
method Detailed treatment of Markowitz geometry.
result Solid mathematical foundation for portfolio theory.
It has been understood that the "local" existence of the Markowitz' optimal portfolio or the solution to the local-risk minimization problem is guaranteed by some specific mathematical structures on the underlying assets price processes known in the literature as "{\it Structure Conditions}". In this paper, we consider…
This paper bridges Markowitz planning and deep reinforcement learning for portfolio optimization.
problem Combining Markowitz planning and deep reinforcement learning for portfolio optimization.
method Mapping market conditions to actions using deep reinforcement learning, casting portfolio allocation as a continuous control problem.
result Deep reinforcement learning techniques can provide new insights for portfolio allocation.
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…
Improved Markowitz method handles uncertainty in return forecasts.
problem Uncertainty in return statistics forecasts.
method Convex optimization with practical constraints.
result Handles uncertainty gracefully and efficiently.
This paper optimizes Iran's stock portfolio using neural networks and genetic algorithms.
problem Optimizing capital allocation in Iran's stock market with low risk and high return.
method Markowitz Mean-Variance-Skewness model with neural network prediction of stock returns and risks.
result Designing 8 different portfolios for various risk tolerance levels.
Study improves portfolio risk estimation methods using robust covariance and CVaR constraints.
problem Improving portfolio risk estimation in the presence of financial data noise and extreme market conditions.
method Exploration of robust covariance estimators, application of CVaR constraints, use of K-means clustering in optimization.
result Robust covariance estimators can outperform market-weighted benchmarks, especially during bull 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…
Market-based portfolio variance measures risks using trade data.
problem Measuring portfolio risks using traditional methods ignores trade volume randomness.
method Uses time series of trades with securities and portfolio to assess variance.
result Portfolio variance can be decomposed into securities' contributions, accounting for trade volume randomness.
Investigates mean-variance portfolio selection in non-Markovian markets.
problem Continuous-time Markowitz mean-variance portfolio selection in fake stationary affine Volterra models.
method Stochastic factor solution to a Riccati BSDE, deriving explicit solutions as multi-dimensional Riccati-Volterra equations.
result Analytical closed-form expressions for optimal portfolio policies and mean-variance efficient frontier.
Quantum computer helps optimize stock portfolios.
problem Finding the best mix of stocks for optimal risk and return.
method Classical and quantum approaches to portfolio optimization.
result Quantum computer improves portfolio selection.
This study evaluates different portfolio designs for Indian stocks.
problem Optimizing portfolio weights for risk and return in volatile stock markets.
method Three portfolio design approaches: risk minimization, risk optimization, and equal weighting. Historical data from 2017-2022 used.
result Equal-weight portfolios outperformed other designs in most sectors.
This paper compares three portfolio designs for Indian stocks.
problem Designing an optimum portfolio that balances return and risk.
method Three approaches: minimum risk, optimum risk, and Eigen portfolios.
result Optimum risk portfolios and Eigen portfolios identified for each sector.
The paper proposes a new portfolio allocation method combining RMT and machine learning.
problem Optimal allocation instability in high-dimensional portfolios.
method Combines Random Matrix Theory covariance estimators with Nested Clustered Optimization.
result The modified NCO algorithm achieves stable allocations without risky short positions.
A new portfolio optimization method using the Sherman-Morrison identity.
problem Portfolio optimization with covariance and variance.
method Sherman-Morrison identity applied to replace covariance with second moment matrix.
result Sherman-Morrison-Markowitz portfolio solves standard portfolio optimization problems.
Unified framework for portfolio theory combining utility and risk.
problem Balancing utility and risk in investment portfolios.
method General convex curve in utility-risk space, with specific cases like standard deviation and identity mapping.
result Unified understanding of various portfolio theories including Markowitz, Sharpe, Rockafellar, and Lintner.
A system predicts stock prices and recommends investment portions.
problem Optimizing stock investment decisions based on predicted prices and risk tolerance.
method Support Vector Regression for price prediction, Markowitz portfolio theory and fuzzy logic for investment recommendations.
result Experimental results on NYSE show the system's effectiveness.
Bayesian Markowitz portfolio problem shows entropy regularization is ineffective.
problem Entropy regularization in Bayesian Markowitz portfolio optimization.
method Combines continuous-time Bayesian filtering with stochastic policy optimization.
result Entropy regularization does not accelerate learning of unknown drift.