E2EAI: End-to-End Deep Learning Framework for Active Investing
Zikai Wei Bo Dai Dahua Lin
Active investing aims to construct a portfolio of assets that are believed to
be relatively profitable in the markets, with one popular method being to
construct a portfolio via factor-based strategies. In recent years, there have
been increasing efforts to apply deep learning to pursue "deep factors'' with
more active returns or promising pipelines for asset trends prediction.
However, the question of how to construct an active investment portfolio via an
end-to-end deep learning framework (E2E) is still open and rarely addressed in
existing works. In this paper, we are the first to propose an E2E that covers
almost the entire process of factor investing through factor selection, factor
combination, stock selection, and portfolio construction. Extensive experiments
on real stock market data demonstrate the effectiveness of our end-to-end deep
leaning framework in active investing.