Portfolio Optimization
37 papers with code • 0 benchmarks • 0 datasets
Portfolio management is the task of obtaining higher excess returns through the flexible allocation of asset weights. In reality, common examples are stock selection and the Enhanced Index Fund (EIF). The general solution of portfolio management is to score the potential of assets, buy assets with upside potential and increase their weighting, and sell assets that are likely to fall or are relatively weak. A large number of strategies have been proposed for portfolio management.
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Latest papers
Stochastic Control Barrier Functions for Economics
Numerical simulations are used to demonstrate the effectiveness of using traditional control solutions in tandem with CBFs and stochastic CBFs to solve such problems in the presence of state constraints.
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
In the field of portfolio management using reinforcement learn- ing, existing approaches have mainly focused on cash-only trading, overlooking the potential benefits and risks of margin trading.
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces.
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification
The size reduction is based on predictions from machine learning techniques and the solution to a linear programming problem.
A Simple Method for Predicting Covariance Matrices of Financial Returns
We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix.
Online Portfolio Management via Deep Reinforcement Learning with High-Frequency Data
In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.
Metaheuristic Approach to Solve Portfolio Selection Problem
In this paper, a heuristic method based on TabuSearch and TokenRing Search is being used in order to solve the Portfolio Optimization Problem.
Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
We introduce a new Langevin dynamics based algorithm, called e-TH$\varepsilon$O POULA, to solve optimization problems with discontinuous stochastic gradients which naturally appear in real-world applications such as quantile estimation, vector quantization, CVaR minimization, and regularized optimization problems involving ReLU neural networks.
Markov Decision Processes under Model Uncertainty
We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting.
Distributionally Robust End-to-End Portfolio Construction
Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk.