Interpretable ESG-sentiment hybrid deep learning for asset return forecasting with quantified interactions and...

Financial markets have undergone a profound transformation as institutional investors pivot toward data-driven decision-making processes. Prior research has shown that traditional econometric models often struggle to incorporate unstructured information from news cycles or long-term Environmental, Social, and Governance (ESG) metrics. The emergence of alternative data has provided new avenues for capturing market inefficiencies that standard price-volume metrics overlook. Despite these advancements, the synergistic relationship between corporate responsibility and public perception remains an under-explored frontier in quantitative finance. Existing methodologies frequently treat these variables as independent inputs rather than interacting components of a complex system. This absence of evidence motivated the creation of a framework designed to map the non-linear dependencies between ethical scores and investor sentiment. Researchers now seek to bridge the gap between qualitative news analysis and quantitative sustainability reporting to enhance predictive robustness.

This research constructs an interpretable hybrid framework to enhance the precision of next-day log return predictions across diverse asset classes. The system addresses the inherent instability of financial time series by combining high-capacity neural networks with robust statistical correctors. By integrating Aspect-Based Financial Sentiment (ABSA) with Environmental, Social, and Governance (ESG) data, the model seeks to capture a more holistic view of market drivers. The researchers aimed to implement a gating mechanism that autonomously determines the optimal weighting of these signals across different volatility regimes. Quantifying the interaction effects between sustainability and sentiment using SHAP interaction values represents a core objective for improving model interpretability. This study also prioritizes the creation of a latency-aware deployment strategy to ensure the framework remains viable for real-time institutional trading. The framework also addresses the computational challenges associated with processing high-dimensional alternative data.

The experimental framework utilizes a Temporal Fusion Transformer (TFT) as the foundational architecture for processing multi-horizon time series data. To mitigate prediction errors during regime shifts, the researchers appended a lightweight Support Vector Regression (SVR) residual corrector to the neural network output. The data pipeline incorporates FinBERT-based Aspect-Based Financial Sentiment (ABSA) to extract nuanced signals from financial news reports. A rigorous, leak-proof walk-forward protocol was established using a training window of 252 trading days followed by a 10-day testing phase. All sentiment data were restricted to items published before 16:00 ET to prevent look-ahead bias, while Environmental, Social, and Governance (ESG) scores were lagged by three days. The model was rigorously tested on a diverse portfolio including US large-cap technology stocks, global indices, and prominent cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH). Statistical validation involved the application of Heteroskedasticity and Autocorrelation Consistent (HAC)-robust Diebold-Mariano tests to confirm the superiority of the hybrid approach.

The hybrid framework demonstrated superior performance by achieving an aggregate Mean Absolute Error (MAE) of 0.0084 across multiple independent seeds. Root Mean Square Error (RMSE) was recorded at 0.0121, indicating high precision in next-day log return predictions. The model attained a directional accuracy of 58.4 percent, significantly outperforming standard machine learning baselines in the technology sector. Analysis of the Information Coefficient (IC) yielded a value of 0.39, while the Information Coefficient Information Ratio (ICIR) reached 0.82. During the 2023 banking stress and the COVID-19 crash, the SVR corrector successfully reduced the variance and skewness of the error distribution. Ablation studies confirmed that the exclusion of either ESG or sentiment features resulted in the most substantial loss of predictive power. The latency-optimized variant achieved a 68 percent reduction in inference time by removing auxiliary Bidirectional Long Short-Term Memory (BiLSTM) units while maintaining 92 percent of the original accuracy gains.

The study confirms that the interaction between ESG scores and financial sentiment is both statistically significant and highly dependent on market volatility. These findings indicate that sentiment signals provide more critical information during periods of high turbulence, such as the 2022 tightening cycle. Conversely, environmental and governance metrics offer greater predictive value during stable market regimes where long-term sustainability factors dominate. The researchers conclude that the inclusion of a residual corrector is essential for maintaining model stability across diverse event-defined stress windows. This hybrid approach offers a scalable solution for institutional investors seeking to maximize risk-adjusted performance while minimizing maximum drawdowns. The study's authors propose that future iterations should focus on integrating real-time macro-economic indicators to further refine the gating mechanism's responsiveness. Such advancements could potentially lead to more resilient algorithmic trading strategies in an increasingly interconnected global economy.