In contrast to sample estimates of the expected returns and covariance matrices, we employ Bayesian parameter estimation that determines the posterior distribution of parameters based on observed data.
We use hybrid quantum optimizers to solve the portfolio selection problem which may offer a potential computational advantage for solving NP-hard problems.
The posterior distribution of parameters allow for the propagation of uncertainties into the construction of the efficient frontier that aids the search for statistically robust portfolios and avoids numerically unstable solutions.
Posterior distributions allow performance metrics to be tracked with confidence intervals into the future.
Uncertainty analysis constructs the posterior distributions of optimal stock allocations that is crucial for assessing robustness of the portfolio.
Currently we are developing an app for mobile devices and desktop systems. Here we focus on efficient workflows and a quality user experience.
Utilise our premium algorithms by calling our API. Bayesian optimisation and quantum selection algorithms will be available for the integration in your system.