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Shui, X., Zheng, H., Xia, X., Yang, F., Wang, W., & Yu, Y. (2022). Diffraction Model-Informed Neural Network for Unsupervised Layer-Based Computer-Generated Holography. Optics Express, 30, Article 44814.
https://doi.org/10.1364/oe.474137

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• TITLE: More Positive Sentiment, Less Earnings? A Sentiment-Based Explanatory Modeling in Banking Earnings—A Case Study of Banks from China and the US

  AUTHORS: Ting Hu, Jun Li

  KEYWORDS: Sentiment Analysis, Bank Performance, ROE, EPS, Shallow Models

  JOURNAL NAME: Modern Economy, Vol.17 No.9, September 18, 2026

  ABSTRACT: This paper explores simpler models for a more sustainable approach to financial risk prediction once correlations between sentiment and performance have been established. Empirical evidence from an investigation of annual reports of eight banks from China and the United States using Python demonstrates significant relationships existed in the language and banks’ key performance metrics. Across both countries, positive word counts correlated negatively with ROE, while negative word counts correlated positively with EPS. But divergences emerged in sentiment proportion: Chinese banks showed a positive correlation with ROE that was absent in U.S. banks. Correlations between positive language and ROE and ROE difference appeared mainly in years when ROE and EPS declined from the previous year in all banks. A country-specific nuance was also identified where U.S. banks employed more positive expressions when experiencing a significant decline in ROE, a pattern not statistically significant in Chinese banks. A one-layer neural network explanatory model with a linear equation was then composed, illustrating that simpler shallow models with small data set, supported by empirical discourse analysis, may represent a more viable long-term strategy for sentiment-based bank performance evaluation. Sustainability can not only be framed in environmental terms but also in regulatory acceptance and institutional usability.