  This paper proposes a novel meta-learning approach to optimize a robust
portfolio ensemble. The method uses a deep generative model to generate diverse
and high-quality sub-portfolios combined to form the ensemble portfolio. The
generative model consists of a convolutional layer, a stateful LSTM module, and
a dense network. During training, the model takes a randomly sampled batch of
Gaussian noise and outputs a population of solutions, which are then evaluated
using the objective function of the problem. The weights of the model are
updated using a gradient-based optimizer. The convolutional layer transforms
the noise into a desired distribution in latent space, while the LSTM module
adds dependence between generations. The dense network decodes the population
of solutions. The proposed method balances maximizing the performance of the
sub-portfolios with minimizing their maximum correlation, resulting in a robust
ensemble portfolio against systematic shocks. The approach was effective in
experiments where stochastic rewards were present. Moreover, the results (Fig.
1) demonstrated that the ensemble portfolio obtained by taking the average of
the generated sub-portfolio weights was robust and generalized well. The
proposed method can be applied to problems where diversity is desired among
co-optimized solutions for a robust ensemble. The source-codes and the dataset
are in the supplementary material.
