What question did this study set out to answer?

The study aims to improve asset return forecasting by combining ESG scores with sentiment analysis while examining their interactions.

March 6, 2026Open Access

Interpretable ESG–sentiment hybrid deep learning for asset return forecasting with quantified interactions and latency-aware deployment

Key Points

The study aims to improve asset return forecasting by combining ESG scores with sentiment analysis while examining their interactions.
Introduced a hybrid framework using Temporal Fusion Transformer and Support Vector Regression for forecasting.
Implemented gating mechanisms for emphasizing ESG features versus sentiment signals.
Applied a strict walk-forward protocol to evaluate the model on US large-cap technology equities and cryptocurrencies.
Conducted ablation studies to assess the impact of ESG and sentiment features on model performance.
Achieved a mean absolute error of 2.77e-3 and directional accuracy of 94.5% in forecasting log returns.
Demonstrated significant improvements over baseline machine learning models with p<0.01 in Fisher aggregation tests.
Found that ESG-sentiment interactions were regime-dependent, with ESG being more significant in calm markets and sentiment in turbulent times.

Abstract

Abstract Accurate forecasting of financial time series increasingly relies on alternative data such as environmental, social and governance (ESG) scores and news-based sentiment, yet the way these signals interact and when they actually improve forecasts is still poorly understood. We introduce an interpretable hybrid framework for asset return forecasting that combines a Temporal Fusion Transformer (TFT) with a lightweight Support Vector Regression (SVR) residual corrector and an explicit gated late fusion of ESG features with aspect-based financial sentiment (FinBERT-based ABSA). The gating mechanism learns when to emphasize sustainability versus sentiment signals, while SHAP interaction values and Friedman’s H quantify ESG–sentiment interactions across assets and regimes. A finance-grade, leak-proof walk-forward protocol (252 trading days train / 10 days test, within-fold scaling, ABSA items strictly before 16: 00 ET; ESG effective T+3; macro T+1, HAC-robust Diebold–Mariano tests) is applied to US large-cap technology equities, major global indices, and BTC/ETH over 2020–2024. Across n=5 independent seeds, the hybrid achieves aggregate mean absolute error of 2. 77 10^-3 and RMSE of 5. 18 10^-3 on next-day log returns, with directional accuracy 94. 5\%, IC 0. 39, and ICIR 0. 82, significantly outperforming tuned deep-learning and machine-learning baselines (HAC-robust per-asset Diebold–Mariano tests with BH-FDR q=0. 05 ; Fisher aggregation yields p<0. 01). Simple long-only, thresholded simulations indicate higher risk-adjusted performance and lower maximum drawdown under conservative transaction-cost assumptions. Ablation studies show that removing either ESG or sentiment features yields the largest degradations, and that the SVR corrector stabilizes errors under regime shifts. To directly address market-cycle sensitivity, we evaluate stability across event-defined stress windows (COVID-19 crash, 2022 tightening cycle, and 2023 banking stress) and volatility-defined regimes using terciles of 20-day realized volatility. We report regime-split forecasting and strategy metrics with block-bootstrap confidence intervals, HAC-robust Diebold–Mariano tests within each regime, and residual-stabilization diagnostics that quantify the SVR variance and skewness reduction under stress. ESG–sentiment interactions are statistically non-zero and regime-dependent, with sentiment gaining importance in turbulent periods and ESG in calmer markets. A latency-optimized variant that removes auxiliary BiLSTMs retains over 90\% of the accuracy gains while reducing inference time by approximately 55\% of the full model (i. e. , a reduction of about 45\%), supporting near-real-time deployment.

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