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Classical financial models struggle with the non-stationary noise of modern, news-driven markets, posing a severe risk to institutional investments and civil economic welfare during systemic shocks. This paper proposes Adapted DeepLOB, a hybrid CNN-LSTM architecture designed to predict directional trends and mitigate risk in highly volatile technology assets. The model fuses microstructural price data, macroeconomic indicators (S&P 500, VIX), and NLP-quantified sentiment from financial news via FinBERT across a comprehensive historical period spanning from January 2010 to December 2023. Additonally, it integrates the Black-Scholes framework, using implied volatility to generate forward-looking risk metrics. Trained with a Focal Loss function to address market regime imbalance, the system establishes a robust defensive mechanism. Compared to a passive Buy & Hold baseline, the model consistently increases risk-adjusted returns (Sharpe and Sortino ratios) while drastically reducing the Maximum Drawdown. The empirical results validate this multidimensional Artificial Intelligence approach not merely as a speculative tool, but as a resilient computational framework for capital preservation and systemic risk mitigation.

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Hybrid CNN-LSTM Architecture for Financial Trend Prediction: Integrating NLP Sentiment and Derivative Models in High-Volatility Assets

  • 会议论文

  • 发表日期：

    2026年

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  • M. A. Parrilla,
  • F. Segovia,
  • A. Zorrilla,
  • C. Vázquez-García,
  • F. J. Martinez-Murcia,
  • J. M. Górriz

摘要

Classical financial models struggle with the non-stationary noise of modern, news-driven markets, posing a severe risk to institutional investments and civil economic welfare during systemic shocks. This paper proposes Adapted DeepLOB, a hybrid CNN-LSTM architecture designed to predict directional trends and mitigate risk in highly volatile technology assets. The model fuses microstructural price data, macroeconomic indicators (S&P 500, VIX), and NLP-quantified sentiment from financial news via FinBERT across a comprehensive historical period spanning from January 2010 to December 2023. Additonally, it integrates the Black-Scholes framework, using implied volatility to generate forward-looking risk metrics. Trained with a Focal Loss function to address market regime imbalance, the system establishes a robust defensive mechanism. Compared to a passive Buy & Hold baseline, the model consistently increases risk-adjusted returns (Sharpe and Sortino ratios) while drastically reducing the Maximum Drawdown. The empirical results validate this multidimensional Artificial Intelligence approach not merely as a speculative tool, but as a resilient computational framework for capital preservation and systemic risk mitigation.