Democratizing Index Tracking: Meta-Learning GNNs for Sparse Portfolio Selection

Have you ever wanted to invest in a US ETF or mutual fund, but found that many of the actively managed index trackers were expensive or out of reach due to regulations? I have recently developed a solution to this problem that allows small investors to create their sparse stock portfolios for tracking an index by proposing a novel population-based large-scale non-convex optimization method via a Deep Generative Model that learns to sample good portfolios.

I've compared this approach to the state-of-the-art evolutionary strategy (Fast CMA-ES) and found that it is more efficient at finding optimal index-tracking portfolios. The PyTorch implementations of both methods and the dataset are available on my Github repository for reproducibility and further improvement. Check out the repository to learn more about this new meta-learning approach for evolutionary optimization, or run your small index fund at home!