E2EAI: End-to-End Deep Learning Framework for Active Investing

ArXiv ID: 2305.16364 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: end-to-end deep learning, factor investing, portfolio construction, active investing, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper introduces novel, advanced mathematical architectures (gated attention blocks, directional linear estimators, graph neural networks) and a custom loss function, indicating high mathematical complexity. It is backed by extensive experiments on real-world stock market data and claims outperformance over baselines, providing sufficient empirical rigor for a research paper, though it lacks the raw data/code links or exhaustive statistical validation typical of immediate backtest readiness.

  flowchart TD
    A["Research Goal: Construct Portfolio via End-to-End Deep Learning"] --> B["Data Input: Historical Stock Market Data & Factors"]
    B --> C["Methodology: E2E Deep Learning Framework"]
    C --> D["Core Process: Factor Selection & Combination"]
    D --> E["Core Process: Stock Selection & Prediction"]
    E --> F["Core Process: Portfolio Construction"]
    F --> G["Outcome: Active Investment Portfolio"]
    G --> H["Key Finding: Demonstrated Effectiveness on Real Market Data"]