In this paper, we consider the problem of optimization of a portfolio consisting of securities. An investor with an initial capital, is interested in constructing a portfolio of securities. If the prices of securities change, the investor shall decide on reallocation of the portfolio. At each moment of time, the prices…
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48 results for “dynamic portfolio”
Solves portfolio optimization with costs using numerical methods.
problem Dynamic portfolio optimization with transaction costs and constraints.
method Numerical dynamic programming techniques.
result Problems can now be solved tractably.
Dynamic portfolio strategy using generative model with attention mechanism.
problem Dynamic modeling of multivariate stock returns with tail-side properties.
method Dynamic generative factor model using Attention-GRU network for dynamic learning and forecasting.
result The proposed model leads to wiser investments with higher reward-risk ratios and lower tail risks.
Spectral portfolio theory links neural networks to wealth dynamics via SGD weight matrices.
problem Understanding wealth dynamics from neural network training.
method Direct identification of weight matrices as portfolio allocation matrices, linking SGD forces to portfolio dynamics.
result Spectral properties of SGD weight matrices transition between additive and multiplicative regimes, influencing wealth dynamics.
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
problem Optimizing portfolios with complex asset dynamics and common drivers.
method Model asset dynamics with PDEs, approximate sensitivities with neural networks, and use hierarchical clustering on sensitivity matrix for optimization.
result Achieves over-performance in portfolio optimization across various markets and datasets.
Dynamic risk factor model improves portfolio performance in high dimensions.
problem Dynamic portfolio allocation in high-dimensional financial markets.
method Time-varying sparsity on factor loadings, sequential learning of parameters and volatilities.
result Significant portfolio performance improvements and higher utility gains.
Dynamic Factor Model-Based Multiperiod Mean-Variance Portfolio Selection with Portfolio Constraintsq-fin.PM
The paper solves multi-period portfolio selection with constraints using a dynamic factor model.
problem Multi-period mean-variance portfolio selection with constraints.
method Dynamic factor model, dynamic programming, piecewise linear feedback policy.
result Optimal portfolio policies determined by two stochastic processes.
Investors can enhance their portfolios by strategically using LETFs, especially with dynamic strategies.
problem Unsuitability of passive or static approaches to LETFs leads to undesirable risk-return profiles.
method Demonstrated the effectiveness of simple dynamic strategies in exploiting favorable Omega ratio dynamics.
result Dynamic strategies can exploit the compounding effect of LETFs, improving risk-return profiles.
Investigates fund separations and stability for long-term optimal investments.
problem Optimizing long-term investments in an incomplete market with risky and safe assets.
method Analyzes three market models with different state variable processes to find optimal portfolios and prove convergence stability.
result Dynamic optimal portfolios converge to static portfolios over time, with vanishing sensitivities in the long run.
Dynamic risk constraints help limit risky behavior in financial portfolios.
problem Static risk measures fail to control tail-risk-seeking traders.
method Introduces dynamic risk constraints applied throughout the trading horizon.
result Dynamic risk constraints can effectively limit risky behavior in portfolios.
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 research combines DRL with BL model for better portfolio optimization.
problem Lack of dynamic correlation knowledge in DRL for optimal portfolio optimization.
method Hybrid model combining DRL and Black-Litterman model.
result DRL agent significantly outperforms other strategies in terms of return and risk.
Proposes ICC method for dynamic portfolio optimization.
problem Non-stationarity in market conditions makes traditional portfolio optimization ineffective.
method Inverse Covariance Clustering (ICC) to identify market states and integrate into dynamic optimization.
result ICC-PO generates portfolios with higher Sharpe Ratios and greater robustness.
Ensemble method for fast portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management from cash flow data.
method Regression trees for dynamic value process learning.
result Fast and accurate estimator with closed-form solution.
This paper considers portfolio construction in a dynamic setting. We specify a loss function comprised of utility and complexity components with an unknown tradeoff parameter. We develop a novel regret-based criterion for selecting the tradeoff parameter to construct optimal sparse portfolios over time.
The investment economy is a main characteristic of prosperous society. The investment portfolio management is a main financial problem, which has to be solved by the investment, commercial and central banks with the application of modern portfolio theory in the investment economy. We use the learning analytics together…
Optimizes dynamic investment portfolios with correlated jumps.
problem Maximizing expected terminal wealth in a multivariate Merton model with dependent jumps.
method Approximating CVaR with comonotonic bounds and maximizing expected terminal wealth.
result Improved optimization of dynamic investment portfolios.
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
problem Understanding dynamic connectedness in global supply chain infrastructure portfolios under various risk factors and extreme events.
method Time-varying parameter vector autoregression (TVP-VAR) model to study spillover and interconnectedness of risk factors.
result Risk shocks influence dynamic connectedness between portfolios and risk factors, and extreme events affect investment outcomes.
Study proposes DRL for investor-specific portfolio optimization considering asset volatility.
problem Dynamic allocation of funds balancing risk and return under market conditions.
method Volatility-guided Deep Reinforcement Learning (DRL) framework.
result Proposed DRL portfolios outperform baseline strategies.
We consider an investor facing a classical portfolio problem of optimal investment in a log-Brownian stock and a fixed-interest bond, but constrained to choose portfolio and consumption strategies that reduce a dynamic shortfall risk measure. For continuous- and discrete-time financial markets we investigate the loss i…
Unified framework combines views and optimization for better portfolio management.
problem Optimizing portfolio weights with dynamic adjustment based on volatility.
method Dynamic sliding window adjusting horizon, factor estimates, BL posterior returns, and weights over time.
result Outperforms dynamic mean-variance optimization without BL views, providing stronger downside risk control.
Commodity ETFs' portfolio optimization under heavy-tailed returns.
problem Optimizing commodity ETF portfolios under heavy-tailed return behavior.
method Passive buy-and-hold vs. rolling-window optimized portfolios.
result Improved risk-adjusted performance with minimum-risk and CVaR-based portfolios.
The study forecasts portfolio volatility using cointegrated asset dynamics.
problem Forecasting volatility in portfolios with high accuracy.
method Developed HVR/DVR ratios and used Vector Error Correction Model (VECM) to forecast volatility.
result VECM forecasts of portfolio volatility have lower MAPE than covariance-based forecasts.
The study analyzes ETFs' portfolio optimization and tail-risk management.
problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.
New formula for portfolio risk management using conditional PDEs.
problem Optimal diversification and risk management of portfolios.
method Closed-form formula for conditional probability, Gaussian copulas, conditional risk-neutral PDE.
result Dynamic monitoring of portfolio volatilities and weights from PDEs.
Paper explores two methods for optimal portfolio selection in financial markets.
problem Optimal portfolio selection for financial markets with jumps.
method Maximum principle and dynamic programming approach.
result Relationship between two methods and their adjoint processes.
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.
Unified framework for ESG-inclusive portfolio optimization and pricing.
problem Incorporating ESG ratings into dynamic asset pricing theory.
method Introducing ESG-valued return as a linear transformation of financial and ESG scores, preserving traditional risk aversion with an ESG affinity parameter.
result Developed a more complex portfolio optimization problem in a space governed by reward, risk, and ESG score.
RL accelerates portfolio optimization and option pricing by dynamically adjusting preconditioner sizes.
problem Large linear systems in portfolio optimization and option pricing lead to slow convergence.
method Reinforcement Learning (RL) dynamically adjusts block-preconditioner sizes to accelerate convergence.
result RL-driven solver significantly reduces computational cost and accelerates convergence.
Investigates optimal portfolio strategies in markets with latent side information.
problem Investment problem in markets with latent dependence structure and side information.
method Dynamic and constant portfolio strategies, analyzing log-optimal portfolio as benchmark.
result Optimal dynamic strategy growth rate asymptotically converges to constant strategy in stationary markets.
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
problem Dynamic portfolio optimization under non-stationary market conditions.
method Combines LSTM for forecasting and PPO for adaptive portfolio adjustments.
result Hybrid framework outperforms single-model and equal-weight approaches in various metrics.
Algorithm selects best model based on state, reducing costs.
problem Choosing the best model among many in different states of the world.
method Reinforcement learning algorithm to estimate optimal policy.
result Algorithm consistently selects optimal model based on covariates.
The classical dynamic programming-based optimal stochastic control methods fail to cope with nonseparable dynamic optimization problems as the principle of optimality no longer applies in such situations. Among these notorious nonseparable problems, the dynamic mean-variance portfolio selection formulation had posted a…
Paper solves dynamic portfolio selection using generative models.
problem Dynamic mean-variance portfolio selection problem in a model-free manner.
method Adaptive training and sampling methods for diffusion models, quantification bounds using adapted Wasserstein metric.
result Proposes a policy gradient algorithm that outperforms baselines on real data.
Optimizes portfolios with constraints and stochastic factors, deriving explicit solutions.
problem Optimizing expected utility in an incomplete market with stochastic factors and convex constraints.
method Fundamental duality results and HJB PDE, derived condition for exponential affine solutions.
result Explicit expressions for optimal allocations and Riccati ODE solutions in specific markets.
Study quantifies model risk in dynamic portfolio selection using KL divergence.
problem Model risk in financial portfolio selection under uncertainty.
method Defined model risk as KL divergence loss, solved nonlinear equations for optimal robust strategy.
result Optimal robust strategy can be obtained semi-analytically in worst case scenario.
Framework uses RL with dynamic embedding to outperform benchmarks in volatile markets.
problem Challenges in high-dimensional, non-stationary, and noisy market information.
method Dynamic embedding of market information using generative autoencoders and online meta-learning in a reinforcement learning framework.
result Framework outperforms common portfolio benchmarks and PTO approach during market stress.
Study introduces AMVP and AMRR for dynamic portfolio optimization in volatile markets.
problem Optimizing portfolios in volatile and nonstationary financial markets.
method Adaptive Minimum-Variance Portfolio (AMVP) framework with ARFIMA-FIGARCH processes and non-Gaussian innovations.
result Demonstrated superior performance in risk reduction and portfolio stability during market breaks.
Paper uses DRL to optimize portfolios, balancing risk and return.
problem Optimizing portfolios under market uncertainty and risk constraints.
method Integrates Sharpe ratio-based reward with risk control mechanisms, uses PPO for adaptive asset allocation.
result DRL agent stabilizes volatility but sacrifices risk-adjusted returns.
New method optimizes portfolios by dynamically integrating ESG constraints.
problem Static ESG scores mismatch sequential portfolio decisions.
method MACF-X, a family of adapters that learns ESG costs from multimodal evidence.
result Reduces tail ESG budget pressure while maintaining financial performance.
Graphical models improve portfolio optimization for financial time series.
problem Optimizing portfolios with time-varying covariance patterns.
method Various graphical models (PCA-KMeans, autoencoders, dynamic clustering, structural learning) to capture covariance matrix patterns.
result Graphical models outperform baseline methods in generating steady returns with low risk.
Onflow optimizes portfolio allocation with gradient flows, robust to transaction fees.
problem Optimizing portfolio allocation with transaction costs.
method Gradient flow reinforcement learning method for dynamic asset allocation.
result Onflow outperforms benchmarks in high transaction cost regimes.
Investor selects portfolios based on news attention in a hidden Markov model.
problem Mean-variance portfolio selection in a dynamic attention context.
method Closed-loop equilibrium strategies via extended HJB equation and Markov chain approximation.
result Equilibrium strategies found through iterative algorithm and numerical examples.
A new model optimizes portfolios by accounting for dynamic market conditions.
problem Static models fail to capture asymmetry, heavy tails, and time-varying dependencies.
method Semiparametric dynamic copula model integrating non-parametric copulas and parametric marginals.
result Dynamic market conditions improve portfolio performance and risk management.
The paper introduces a dynamic MVP model using high-frequency financial data.
problem Capturing the dynamics of minimum variance portfolio weights in financial markets.
method Imposes autoregressive structure on MVP processes and uses CLIME and LASSO for estimation.
result Proposes DR-MVP model with established asymptotic properties.
Study proposes adaptive RL for dynamic portfolio optimization.
problem Traditional portfolio optimization models fail to adapt to regime shifts.
method Regime-aware reinforcement learning framework with hybrid observations and constrained reward functions.
result Transformer PPO achieves highest risk-adjusted returns, while LSTM variants offer a good balance.
Deep RL optimizes dynamic portfolio weights in China's stock market.
problem Traditional portfolio optimization methods struggle with dynamic asset weight adjustments.
method Developed a deep reinforcement learning framework with novel reward functions and random sampling.
result Model outperforms traditional methods in portfolio optimization and risk mitigation.