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

arXiv: https://arxiv.org/abs/2305.16364 · HTML: https://mlsanity.com/paper/2305.16364/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport](https://mlsanity.com/paper/2106.12950/) (2021)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis](https://mlsanity.com/paper/1909.10660/) (2019)
- [Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation](https://mlsanity.com/paper/2107.05201/) (2021)
- [Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model](https://mlsanity.com/paper/1901.11493/) (2019)
- [A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets](https://mlsanity.com/paper/2108.05801/) (2021)
- [Factor Investing with a Deep Multi-Factor Model](https://mlsanity.com/paper/2210.12462/) (2022)

## Built on by (most cited first, 4 of 4)

- [QuantBench: Benchmarking AI Methods for Quantitative Investment](https://mlsanity.com/paper/2504.18600/) (2025)
- [Large Investment Model](https://mlsanity.com/paper/2408.10255/) (2024)
- [Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making](https://mlsanity.com/paper/2411.10496/) (2024)
- [Large-scale portfolio optimization with variational neural annealing](https://mlsanity.com/paper/2507.07159/) (2025)

## Abstract (arXiv)

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.