Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
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.
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile.
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
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 $W$.
This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks.
Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk.