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Quantitative Finance > Statistical Finance

arXiv:2502.15757 (q-fin)
[Submitted on 12 Feb 2025 (v1), last revised 7 May 2025 (this
version, v3)]

Title:TLOB: A Novel Transformer Model with Dual Attention for Price
Trend Prediction with Limit Order Book Data

Authors:Leonardo Berti, Gjergji Kasneci
View a PDF of the paper titled TLOB: A Novel Transformer Model with
Dual Attention for Price Trend Prediction with Limit Order Book Data,
by Leonardo Berti and 1 other authors
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    Abstract:Price Trend Prediction (PTP) based on Limit Order Book
    (LOB) data is a fundamental challenge in financial markets.
    Despite advances in deep learning, existing models fail to
    generalize across different market conditions and assets.
    Surprisingly, by adapting a simple MLP-based architecture to LOB,
    we show that we surpass SoTA performance; thus, challenging the
    necessity of complex architectures. Unlike past work that shows
    robustness issues, we propose TLOB, a transformer-based model
    that uses a dual attention mechanism to capture spatial and
    temporal dependencies in LOB data. This allows it to adaptively
    focus on the market microstructure, making it particularly
    effective for longer-horizon predictions and volatile market
    conditions. We also introduce a new labeling method that improves
    on previous ones, removing the horizon bias. We evaluate TLOB's
    effectiveness across four horizons, using the established FI-2010
    benchmark, a NASDAQ and a Bitcoin dataset. TLOB outperforms SoTA
    methods in every dataset and horizon. Additionally, we
    empirically show how stock price predictability has declined over
    time, -6.68 in F1-score, highlighting the growing market
    efficiency. Predictability must be considered in relation to
    transaction costs, so we experimented with defining trends using
    an average spread, reflecting the primary transaction cost. The
    resulting performance deterioration underscores the complexity of
    translating trend classification into profitable trading
    strategies. We argue that our work provides new insights into the
    evolving landscape of stock price trend prediction and sets a
    strong foundation for future advancements in financial AI. We
    release the code at this https URL.

          Statistical Finance (q-fin.ST); Artificial Intelligence
Subjects: (cs.AI); Machine Learning (cs.LG); Trading and Market
          Microstructure (q-fin.TR)
Cite as:  arXiv:2502.15757 [q-fin.ST]
          (or arXiv:2502.15757v3 [q-fin.ST] for this version)
          https://doi.org/10.48550/arXiv.2502.15757
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Leonardo Berti [view email]
[v1] Wed, 12 Feb 2025 12:41:10 UTC (14,857 KB)
[v2] Thu, 27 Feb 2025 13:59:09 UTC (14,858 KB)
[v3] Wed, 7 May 2025 21:14:14 UTC (13,842 KB)
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    View a PDF of the paper titled TLOB: A Novel Transformer Model
    with Dual Attention for Price Trend Prediction with Limit Order
    Book Data, by Leonardo Berti and 1 other authors
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