IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2404.00424.html
   My bibliography  Save this paper

Quantformer: from attention to profit with a quantitative transformer trading strategy

Author

Listed:
  • Zhaofeng Zhang
  • Banghao Chen
  • Shengxin Zhu
  • Nicolas Langren'e

Abstract

In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architecture based on transformers, to build investment factors. By transfer learning from sentiment analysis, quantformer not only exploits its original inherent advantages in capturing long-range dependencies and modeling complex data relationships, but is also able to solve tasks with numerical inputs and accurately forecast future returns over a given period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies. Notably, the model's innovative use of transformer-liked model to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.

Suggested Citation

  • Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "Quantformer: from attention to profit with a quantitative transformer trading strategy," Papers 2404.00424, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2404.00424
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2404.00424
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alec N. Kercheval & Yuan Zhang, 2015. "Modelling high-frequency limit order book dynamics with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1315-1329, August.
    2. Diamond, Peter A., 1971. "A model of price adjustment," Journal of Economic Theory, Elsevier, vol. 3(2), pages 156-168, June.
    3. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    4. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    5. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    6. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
    7. Yanhui Chen & Hanhui Zhao & Ziyu Li & Jinrong Lu, 2020. "A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from China," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.
    8. Haritha P H & Abdul Rishad, 2020. "An empirical examination of investor sentiment and stock market volatility: evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-15, December.
    9. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    Full references (including those not matched with items on IDEAS)

    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. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    2. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    3. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    4. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    5. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    6. Rian Dolphin & Barry Smyth & Ruihai Dong, 2024. "Contrastive Learning of Asset Embeddings from Financial Time Series," Papers 2407.18645, arXiv.org.
    7. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    8. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    9. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    10. Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
    11. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    12. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    13. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    14. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
    15. Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020. "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers 2010.01197, arXiv.org.
    16. Pushpendu Ghosh & Ariel Neufeld & Jajati Keshari Sahoo, 2020. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Papers 2004.10178, arXiv.org, revised Jun 2021.
    17. Firuz Kamalov, 2019. "Forecasting significant stock price changes using neural networks," Papers 1912.08791, arXiv.org.
    18. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
    19. Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    20. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.

    More about this item

    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:2404.00424. 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.