IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2208.09968.html
   My bibliography  Save this paper

Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity

Author

Listed:
  • Daniel Poh
  • Stephen Roberts
  • Stefan Zohren

Abstract

Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies have been applied successfully to data-rich settings involving mature assets with long histories, deploying them on instruments with limited samples generally produce over-fitted models with degraded performance. In this paper, we introduce Fused Encoder Networks -- a novel and hybrid parameter-sharing transfer ranking model. The model fuses information extracted using an encoder-attention module operated on a source dataset with a similar but separate module focused on a smaller target dataset of interest. This mitigates the issue of models with poor generalisability that are a consequence of training on scarce target data. Additionally, the self-attention mechanism enables interactions among instruments to be accounted for, not just at the loss level during model training, but also at inference time. Focusing on momentum applied to the top ten cryptocurrencies by market capitalisation as a demonstrative use-case, the Fused Encoder Networks outperforms the reference benchmarks on most performance measures, delivering a three-fold boost in the Sharpe ratio over classical momentum as well as an improvement of approximately 50% against the best benchmark model without transaction costs. It continues outperforming baselines even after accounting for the high transaction costs associated with trading cryptocurrencies.

Suggested Citation

  • Daniel Poh & Stephen Roberts & Stefan Zohren, 2022. "Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity," Papers 2208.09968, arXiv.org, revised Feb 2023.
  • Handle: RePEc:arx:papers:2208.09968
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2208.09968
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
    2. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    3. Tzouvanas, Panagiotis & Kizys, Renatas & Tsend-Ayush, Bayasgalan, 2020. "Momentum trading in cryptocurrencies: Short-term returns and diversification benefits," Economics Letters, Elsevier, vol. 191(C).
    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. Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
    2. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Migliavacca, Milena & Goodell, John W. & Paltrinieri, Andrea, 2023. "A bibliometric review of portfolio diversification literature," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    6. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    7. Andreas Renard Widarto & Harjum Muharam & Sugeng Wahyudi & Irene Rini Demi Pangestuti, 2022. "ASEAN-5 and Crypto Hedge Fund: Dynamic Portfolio Approach," SAGE Open, , vol. 12(2), pages 21582440221, April.
    8. Massimiliano MARCELLINO & Michael PFARRHOFER, 2024. "Bayesian nonparametric methods for macroeconomic forecasting," BAFFI CAREFIN Working Papers 24224, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    9. 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.
    10. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    11. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    12. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
    13. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    14. Zehra Eksi & Daniel Schreitl, 2022. "Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model," Mathematics, MDPI, vol. 10(1), pages 1-13, January.
    15. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    16. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    17. Xiao, Xiang & Hua, Xia & Qin, Kexin, 2024. "A self-attention based cross-sectional return forecasting model with evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 62(PA).
    18. Kaya, Orçun & Mostowfi, Mehdi, 2022. "Low-volatility strategies for highly liquid cryptocurrencies," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Adam Baybutt, 2024. "Dynamic Latent-Factor Model with High-Dimensional Asset Characteristics," Papers 2405.15721, arXiv.org.
    20. Qihui Chen, 2022. "A Unified Framework for Estimation of High-dimensional Conditional Factor Models," Papers 2209.00391, arXiv.org.

    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:2208.09968. 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.