Annual reports act as a comprehensive communication tools between banks and their stakeholders informed by the audited financial statements, management discussions, risk disclosures, and strategic outlooks for transparency and decision-making. Analyzing banks’ annual reports can update their financial stability, bank value creation and potential exposure to risk. Researchers have illustrated the correlation between banks’ profitability, liquidity, and loan portfolio quality with disclosure and transparency ([20]), better stock returns with greater revelation and more fragility and systemic risk with lack of transparency. Beyond the determinant of banks’ performance, annual reports has emerged as insights into the market discipline. Risk disclosure in annual reports can forecast market efficiency and financial system stability ([23]) and facilitate regulators to take proactive actions before irreversible risks that destabilize economy appear ([4]). However, disclosure is not solely dependent on the intention and initiatives of banks, but also on the readability and quality of disclosures. Evidence from the UK banks during 1995 to 2010 shows the move from quantitative disclosure to rich qualitative narratives, signaling a shift towards perception-driven communication, a result of scaling transparency with public trust ([22]). When bank performance is weak, the readability of annual report de-clines, with less concise and foggier expressions to increase complexity and obscure vulnerabilities ([17]). Inadequate risk disclosures reduce investor confidence ([28]). Then it is not just what is unveiled in the annual report but how it is communicated that deserves equal analysis.

A variety of text analysis has been conducted aiming to uncover the real evaluation of transparency, compliance, and market signaling from banks’ annual reports. Content analysis is used to explore whether the text can be used to predict future fluctuation ([14]) and how risk disclosures are responsible for investor perception ([2]), though the appearance of terms is the main indicator. Readability indices using Flesch Reading Ease, Fog Index, and Flesch-Kincaid Grade Level are employed to assess the obscurity of financial reports based on standard readability metrics, assuming the positive relationship between language complexity and blurring with lower clarity ([26]), except that banking jargon may reduce readability from the first place and companies may purposefully improve readability without adding contents. Keywords are counted with the premise that priorities can be seen through measuring the frequency of specific terms ([15]) and potential opportunities and risks can be reflected ([14]), ignoring context-blind textural analysis without considering sentiment or tone may mislead the public and disguise risk un-covering. While these conventional text analysis methods advanced our understanding of the banks’ annual report, limitations accompanied.

The growing availability of large-scale quantitative methods featuring Python and statistical approaches provide a new perspective that offer greater scalability and depth. Hundreds or even thousands of annual reports across time and tertiary boundaries can be processed and analyzed with the aid of automated methods, making longitudinal and cross-sectional comparisons possible and easier ([15]). Some nuanced differences in tone can be measured to survey positivity, negativity, or neutrality of the text with use of sentiment dictionaries, as tone is of equal importance in shaping stakeholder perceptions as contents ([14]). The shortcomings of traditional methods can be addressed by integrating computational approaches. [1] uses Python to analyze the annual reports of Chinese listed companies from 2008 to 2021 and to examine the link between report readability and corporate performance. A significant correlation is found: firms with more readable, concise annual reports tend to be more profitable and have lower agency costs while poor-performing companies produce longer, more complex reports with frequent accounting jargon. [10] investigated how the positive tone of annual reports influences corporate green innovation among Shanghai and Shenzhen A-share listed Chinese companies from 2010 to 2022 with 34,830 observations. A positive effect on both the quantity and quality of a firm’s green innovation is found due to the easing of financial constraints and enhanced market attention. Such effect is particularly strong for companies facing high economic policy uncertainty and for non-heavy polluting firms.

Discourse analyzing through quantitative approaches has provided a new perspective for examining companies’ annual reports and certain correlations have been identified. But:

Whether such methods are applicable across boundaries? Does a similar correlation between positive tone with performance exist in banks’ annual report? Is it possible to generate a predicting model that is sustainable in terms of data, computational efficiency and energy?

With these questions, 8 banks from China and the US are investigated.

Compared with non-financial firms, whose reports typically center on operational risks, supply chain issues, and market competition, bank annual reports are highly “financialized”, using jargon related to complex financial products, asset management, and financial engineering, oriented toward financial market actors ([5]). However, it does not mean there is no sentiment in bank annual reports. While all annual reports have a promotional dimension, bank reports often employ a more explicit justificatory or self-appraisal discourse ([8]). Our panel data regression confirms this dynamic, demonstrating a significant positive association between the percentage of positive sentiment and EPS after controlling for unobserved bank and year effects.

Meanwhile, banks usually aim to construct an image of stability and trustworthiness, a crucial aspect of a confidence-based business model. This involves attributing successes to management foresight and ethical values while framing negative events as unavoidable consequences of external economic conditions. This justificatory strategy explains our finding that U.S. banks employ significantly more positive language when experiencing a decline in ROE. This strategic use of language is highly pronounced in the banking sector due to its inherent vulnerability to market sentiment and public perception, where management attempts to linguistically offset poor quantitative performance.

Since sentiment from banks’ annual reports provides valuable “soft information” that traditional financial metrics do not unveil, creating a statistical model that maps sentiment to concurrent bank performance is highly valuable. Academic evidence shows that the tone and sentiment expressed in annual reports can explain financial performance ([13]) and stock returns ([11]), improve risk assessment ([9]), and provide early warning signals for distress ([12]) and supervisory insights ([6]; [19]). Rather than strictly predicting future outcomes, mapping management’s implicit signals, risks, and confidence levels to same-year performance helps investors, regulators, and policymakers build robust associative early warning systems and reduce information asymmetry.

Neural network models are often used to obtain more accurate modeling. Yet, creating deep Convolutional Neural Network (CNN) models from thousands of annual reports often results in “black boxes” where the decision-making process is opaque, carries a higher risk of overfitting, and requires massive data preprocessing and computational power ([12]). If we shift from just “complexity” to “sustainability” in explanatory modeling of banks’ performance using sentiment, simpler neural networks with shallow architectures may outperform complex ones. In financial forecasting contexts, data samples are often relatively small, noisy, and volatile. Deeper models face a high risk of overfitting, while simpler NNs (Neural Networks) can generalize more reliably ([27]). A one-layer NN enjoys computational sustainability in which training is lightweight, requiring minimal processing time and no specialized hardware ([25]). This brings lower energy consumption, faster deployment, and greater scalability for practical use in financial institutions. A shallow NN also provides clearer transparency: the weights connecting predictors to the hidden layer can be directly examined, offering insights into the relationships between financial sentiment indicators and performance outcomes ([16]). A one-layer NN is more resilient to changing input patterns and easier to maintain over time, which is a crucial consideration in financial research where consistent and interpretable models are favored ([24]), especially in terms of institutional usability.

Therefore, building upon the explanatory relationships established in our panel regression, a feed-forward artificial neural network (ANN) was constructed using SPSS. The network comprised one input layer, a single hidden layer, and one output node. The input layer included one categorical factor variable (EPS_P_N, coded as positive or negative) and one continuous covariate (positive_percent). The hidden layer consisted of one processing unit with a hyperbolic tangent activation function, while the output layer applied a linear (identity) activation function (see Formula (10)). The model employed the sum of squares as the error function, and covariates were standardized prior to training (see Formula (11)). The parameter estimates indicate that the model captures both categorical and continuous influences (see Formula (12)).

Model construct:

Hidden layer with 1 unit:

where:

Hidden to output layer:

where:

The final model is:

where D N e g and D P o s are dummy variables for EPS_P_N category.

The model performance is quite robust, with a training relative error of 0.406 and a markedly lower testing relative error of 0.150, which demonstrates that the shallow architecture generalizes well to unseen data and avoids overfitting.

This study investigated the explanatory relationship between sentiment language in annual reports and key financial performance metrics—Return on Equity (ROE) and Earnings Per Share (EPS)—across eight major banks in China and the United States. By transitioning from traditional exploratory analysis to a rigorous Two-Way Fixed Effects Panel OLS regression, this research established a statistically significant positive association between the percentage of positive sentiment and EPS, effectively controlling for unobserved bank-specific characteristics and annual macroeconomic shocks. Crucially, a robustness check utilizing the finance-specific Loughran-McDonald lexicon confirmed that these linguistic patterns reflect genuine management tone rather than the misclassification of standard banking terminology.

A notable strategic nuance emerged when analyzing periods of financial underperformance. The analysis revealed that U.S. banks exhibit a significant tendency to employ more positive expressions when experiencing a decline in ROE. This highlights a justificatory discourse specific to the banking sector, where management leverages optimistic language to offset poor quantitative performance and maintain stakeholder confidence.

The established associative relationships present a compelling opportunity to develop statistical models based on linguistic features extracted from corporate disclosures. Leveraging these findings, this study constructed a deliberately shallow, one-layer feed-forward neural network. By incorporating EPS polarity and the percentage of positive sentiment as transparent inputs, the model achieved robust performance while avoiding the opaque “black box” limitations typical of deep learning architectures. Its lower risk of overfitting, reduced computational burden, and enhanced interpretability make it particularly well-suited for small-scale, structured financial data environments. Ultimately, this demonstrates that sustainability in financial modeling encompasses not only energy efficiency but also regulatory transparency and institutional usability.

Conceptualization, Hu.T.; methodology, Hu.T. and Li.J.; software, Hu.T. and Li.J.; validation, Hu.T. and Li.J.; formal analysis, Hu.T.; investigation, Hu.T.; resources, Li.J.; data curation, Hu.T. and Li.J.; writing original draft preparation, Hu.T. and Li.J.; writing, review and editing, Hu.T.; project administration, Hu.T.; funding acquisition, Hu.T. All authors have read and agreed to the published version of the manuscript.