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 with no code
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning
In this paper, we propose a novel approach to handle allocation constraints based on a decomposition of the constraint action space into a set of unconstrained allocation problems.
Quantum computing approach to realistic ESG-friendly stock portfolios
We introduce a utility function incorporating ESG ratings to balance risk, return, and ESG-friendliness, and discuss implications for ESG-aware investors.
Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients
To make these forecasts actionable, these directional forecasts are integrated to a portfolio optimization framework using expected returns conditional on the market forecast as an estimate for the return vector.
Portfolio management using graph centralities: Review and comparison
We investigate an application of network centrality measures to portfolio optimization, by generalizing the method in [Pozzi, Di Matteo and Aste, \emph{Spread of risks across financial markets: better to invest in the peripheries}, Scientific Reports 3:1665, 2013], that however had significant limitations with respect to the state of the art in network theory.
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective.
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing.
Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
This research paper delves into the application of Deep Reinforcement Learning (DRL) in asset-class agnostic portfolio optimization, integrating industry-grade methodologies with quantitative finance.
Finding Near-Optimal Portfolios With Quality-Diversity
In this paper, we present a novel approach for finding a diverse set of such portfolios based on quality-diversity (QD) optimization.
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization
However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns.
Portfolio Optimization under Transaction Costs with Recursive Preferences
The Merton investment-consumption problem is fundamental, both in the field of finance, and in stochastic control.
