IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2305.16364.html
   My bibliography  Save this paper

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

Author

Listed:
  • Zikai Wei
  • Bo Dai
  • Dahua Lin

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.

Suggested Citation

  • Zikai Wei & Bo Dai & Dahua Lin, 2023. "E2EAI: End-to-End Deep Learning Framework for Active Investing," Papers 2305.16364, arXiv.org.
    • Handle: RePEc:arx:papers:2305.16364
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2305.16364
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eugene F Fama & Kenneth R French & Andrew KarolyiEditor, 2020. "Comparing Cross-Section and Time-Series Factor Models," Review of Finance, European Finance Association, vol. 33(5), pages 1891-1926.
    2. Ayse Sinem Uysal & Xiaoyue Li & John M. Mulvey, 2021. "End-to-End Risk Budgeting Portfolio Optimization with Neural Networks," Papers 2107.04636, arXiv.org.
    3. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    4. Zikai Wei & Bo Dai & Dahua Lin, 2022. "Factor Investing with a Deep Multi-Factor Model," Papers 2210.12462, arXiv.org.
    5. Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    6. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport," Papers 2106.12950, arXiv.org, revised Jun 2021.
    7. Kei Nakagawa & Tomoki Ito & Masaya Abe & Kiyoshi Izumi, 2019. "Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model," Papers 1901.11493, arXiv.org.
    8. Eugene F Fama & Kenneth R French, 2020. "Comparing Cross-Section and Time-Series Factor Models," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1891-1926.
    9. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers 2107.05201, arXiv.org, revised Oct 2021.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jian Guo & Heung-Yeung Shum, 2024. "Large Investment Model," Papers 2408.10255, arXiv.org, revised Aug 2024.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zikai Wei & Bo Dai & Dahua Lin, 2022. "Factor Investing with a Deep Multi-Factor Model," Papers 2210.12462, arXiv.org.
    2. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
    3. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    4. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    5. Allen, David, 2022. "Asset Pricing Tests, Endogeneity issues and Fama-French factors," MPRA Paper 113610, University Library of Munich, Germany.
    6. Damir Filipovic & Paul Schneider, 2024. "Fundamental properties of linear factor models," Papers 2409.02521, arXiv.org, revised Oct 2024.
    7. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    8. Han, Han & Zhao, Xueqing & Wang, Zhibin, 2023. "The effect of stock pledge on corporate fraudulence: Evidence from China," Finance Research Letters, Elsevier, vol. 57(C).
    9. James W. Kolari & Jianhua Z. Huang & Wei Liu & Huiling Liao, 2022. "Further Tests of the ZCAPM Asset Pricing Model," JRFM, MDPI, vol. 15(3), pages 1-23, March.
    10. Skočir, Matevž & Lončarski, Igor, 2024. "On the importance of asset pricing factors in the relative valuation," Research in International Business and Finance, Elsevier, vol. 70(PB).
    11. Zikai Wei & Anyi Rao & Bo Dai & Dahua Lin, 2023. "HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE," Papers 2306.02848, arXiv.org.
    12. Lifan Zhao & Shuming Kong & Yanyan Shen, 2023. "DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting," Papers 2306.09862, arXiv.org, revised Apr 2024.
    13. Wei Liu & James W. Kolari, 2022. "Multifactor Market Indexes," JRFM, MDPI, vol. 15(4), pages 1-26, March.
    14. Karolyi, G. Andrew & Wu, Ying, 2022. "Understanding the pricing of currency risk in global equity markets," Journal of Multinational Financial Management, Elsevier, vol. 63(C).
    15. Penman, Stephen & Zhu, Julie, 2022. "An accounting-based asset pricing model and a fundamental factor," Journal of Accounting and Economics, Elsevier, vol. 73(2).
    16. Nina Ryan & Xinfeng Ruan & Jin E. Zhang & Jing A. Zhang, 2021. "Choosing Factors for the Vietnamese Stock Market," JRFM, MDPI, vol. 14(3), pages 1-23, February.
    17. Chen, Shan & Liu, Xujun & Li, Tao, 2023. "Does the investment-profitability correlation affect the factor premiums? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    18. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
    19. Kolari, James W. & Huang, Jianhua Z. & Butt, Hilal Anwar & Liao, Huiling, 2022. "International tests of the ZCAPM asset pricing model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    20. Hung-Wen Lin & Jing-Bo Huang & Kun-Ben Lin & Shu-Heng Chen, 2022. "The competitions of time-varying and constant loadings in asset pricing models: empirical evidence and agent-based simulations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 577-612, April.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2305.16364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.