E2EAI: End-to-End Deep Learning Framework for Active Investing
This addresses the challenge of active investing for financial practitioners by proposing a novel end-to-end framework, though it appears incremental in applying deep learning to a known pipeline.
The paper tackles the problem of constructing an active investment portfolio using an end-to-end deep learning framework, achieving effectiveness as demonstrated through extensive experiments on real stock market data.
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.