Portfolio Optimization
48 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.
Benchmarks
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Most implemented papers
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
Deep Learning for Portfolio Optimization
shilewenuw/deep-learning-portfolio-optimization
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We adopt deep learning models to directly optimise the portfolio Sharpe ratio.
Artificial Counselor System for Stock Investment
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
guaguakai/surrogate-optimization-learning
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NeurIPS 2020
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Bayesian Optimization of Risk Measures
saitcakmak/BoRisk
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NeurIPS 2020
We consider Bayesian optimization of objective functions of the form $\rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function and $\rho$ denotes either the VaR or CVaR risk measure, computed with respect to the randomness induced by the environmental random variable $W$.
Qlib: An AI-oriented Quantitative Investment Platform
microsoft/qlib
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Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
Portfolio Construction as Linearly Constrained Separable Optimization
Mean-variance portfolio optimization problems often involve separable nonconvex terms, including penalties on capital gains, integer share constraints, and minimum position and trade sizes.
Smart "Predict, then Optimize"
Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective.
Computation of optimal transport and related hedging problems via penalization and neural networks
stephaneckstein/transport-and-related
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This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks.
