Scholarlink

<p>Financial markets exhibit complex, non-linear dynamics characterized by high volatility and uncertainty, making accurate stock movement prediction a challenging task. This study introduces FusionLSTM-CNF, a hybrid deep learning framework that integrates multi-modal data fusion, Long Short-Term Memory (LSTM) networks, and confidence calibration for stock movement prediction under uncertainty. Our model leverages a late fusion architecture, combining the outputs of three parallel LSTM sub-models trained on technical indicators, textual sentiment from financial news, and cross-asset correlation signals. A confidence-aware neural fusion (CNF) layer adaptively reweights modality contributions based on learned uncertainty estimates. We validate our model across multiple financial indices including S&amp;P 500, NASDAQ, and FTSE 100. Experimental results show a 12.3% relative improvement in prediction accuracy over single-modal LSTM baselines and 23.7% reduction in prediction variance. Compared to recent state-of-the-art hybrid architectures, improvements are more modest (1.1–1.8% absolute accuracy gain) but statistically significant (Diebold-Mariano test, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p &lt; 0.05\)</EquationSource> </InlineEquation>). The framework provides calibrated confidence estimates (Expected Calibration Error: 0.031) that enable confidence-filtered trading strategies with improved risk-adjusted returns (Sharpe ratio: 0.267 for top-40% confidence trades versus 0.131 unfiltered). This work contributes to financial artificial intelligence by demonstrating the practical benefits of unifying uncertainty quantification with heterogeneous time-series fusion, providing practitioners with both predictions and confidence estimates for risk-aware decision-making.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

查看全文
  • Tian Wen Wang,
  • Zaffar Ahmed Shaikh,
  • Sook Lu Yong,
  • Hela Elmannai,
  • Lip Yee Por

摘要

Financial markets exhibit complex, non-linear dynamics characterized by high volatility and uncertainty, making accurate stock movement prediction a challenging task. This study introduces FusionLSTM-CNF, a hybrid deep learning framework that integrates multi-modal data fusion, Long Short-Term Memory (LSTM) networks, and confidence calibration for stock movement prediction under uncertainty. Our model leverages a late fusion architecture, combining the outputs of three parallel LSTM sub-models trained on technical indicators, textual sentiment from financial news, and cross-asset correlation signals. A confidence-aware neural fusion (CNF) layer adaptively reweights modality contributions based on learned uncertainty estimates. We validate our model across multiple financial indices including S&P 500, NASDAQ, and FTSE 100. Experimental results show a 12.3% relative improvement in prediction accuracy over single-modal LSTM baselines and 23.7% reduction in prediction variance. Compared to recent state-of-the-art hybrid architectures, improvements are more modest (1.1–1.8% absolute accuracy gain) but statistically significant (Diebold-Mariano test, \(p < 0.05\) ). The framework provides calibrated confidence estimates (Expected Calibration Error: 0.031) that enable confidence-filtered trading strategies with improved risk-adjusted returns (Sharpe ratio: 0.267 for top-40% confidence trades versus 0.131 unfiltered). This work contributes to financial artificial intelligence by demonstrating the practical benefits of unifying uncertainty quantification with heterogeneous time-series fusion, providing practitioners with both predictions and confidence estimates for risk-aware decision-making.